RMCP Vol. 14 Num. 1 (2023) : January-March (English Version)

Edición Bilingüe
Bilingual Edition
ISSN: 2448-6698
Revista Mexicana de Ciencias Pecuarias Rev. Mex. Cienc. Pecu. Vol. 14 Núm. 1, pp. 1-259, ENERO-MARZO-2023
Rev. Mex. Cienc. Pecu. Vol. 14 Núm. 1, pp. 1-259, ENERO-MARZO-2023

REVISTA MEXICANA DE CIENCIAS PECUARIAS Volumen 14 Numero 1, Enero-Marzo
2023. Es una publicación trimestral de acceso abierto, revisada por pares y arbitrada, editada
por el Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias (INIFAP).
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Derecho de Autor (INDAUTOR).
Responsable de la última actualización de este número: Arturo García Fraustro, Campo
Experimental Mocochá, Km. 25 Antigua Carretera Mérida–Motul, Mocochá, Yuc. C.P. 97454.
http://cienciaspecuarias. inifap.gob.mx, la presente publicación tuvo su última actualización
en diciembre de 2022.
Macho semental de venado cola blanca
manejado en cautiverio en la Facultad de
Ciencias Biológicas de Córdova, Ver.
Fotografía: Ricardo Serna Lagunes y Norma DIRECTORIO
Mora Collado
FUNDADOR
John A. Pino
EDITOR EN JEFE EDITORES ADJUNTOS
Arturo García Fraustro Oscar L. Rodríguez Rivera
Alfonso Arias Medina
EDITORES POR DISCIPLINA
Dra. Yolanda Beatriz Moguel Ordóñez, INIFAP, México Dr. Alejandro Plascencia Jorquera, Universidad Autónoma de
Dr. Ramón Molina Barrios, Instituto Tecnológico de Sonora, Baja California, México
Dr. Alfonso Juventino Chay Canul, Universidad Autónoma de Dr. Juan Ku Vera, Universidad Autónoma de Yucatán, México
Tabasco, México Dr. Ricardo Basurto Gutiérrez, INIFAP, México
Dra. Maria Cristina Schneider, Universidad de Georgetown, Dr. Luis Corona Gochi, Facultad de Medicina Veterinaria y
Estados Unidos Zootecnia, UNAM, México
Dr. Feliciano Milian Suazo, Universidad Autónoma de Dr. Juan Manuel Pinos Rodríguez, Facultad de Medicina
Querétaro, México Veterinaria y Zootecnia, Universidad Veracruzana, México
Dr. Javier F. Enríquez Quiroz, INIFAP, México Dr. Carlos López Coello, Facultad de Medicina Veterinaria y
Dra. Martha Hortencia Martín Rivera, Universidad de Sonora Zootecnia, UNAM, México
URN, México Dr. Arturo Francisco Castellanos Ruelas, Facultad de
Dr. Fernando Arturo Ibarra Flores, Universidad de Sonora Química. UADY
URN, México Dra. Guillermina Ávila Ramírez, UNAM, México
Dr. James A. Pfister, USDA, Estados Unidos Dr. Emmanuel Camuus, CIRAD, Francia.
Dr. Eduardo Daniel Bolaños Aguilar, INIFAP, México Dr. Héctor Jiménez Severiano, INIFAP., México
Dr. Sergio Iván Román-Ponce, INIFAP, México Dr. Juan Hebert Hernández Medrano, UNAM, México
Dr. Jesús Fernández Martín, INIA, España Dr. Adrian Guzmán Sánchez, Universidad Autónoma
Dr. Maurcio A. Elzo, Universidad de Florida Metropolitana-Xochimilco, México
Dr. Sergio D. Rodríguez Camarillo, INIFAP, México Dr. Eugenio Villagómez Amezcua Manjarrez, INIFAP, CENID
Dra. Nydia Edith Reyes Rodríguez, Universidad Autónoma del Salud Animal e Inocuidad, México
Estado de Hidalgo, México Dr. José Juan Hernández Ledezma, Consultor privado
Dra. Maria Salud Rubio Lozano, Facultad de Medicina Dr. Fernando Cervantes Escoto, Universidad Autónoma
Veterinaria y Zootecnia, UNAM, México Chapingo, México
Dra. Elizabeth Loza-Rubio, INIFAP, México Dr. Adolfo Guadalupe Álvarez Macías, Universidad Autónoma
Dr. Juan Carlos Saiz Calahorra, Instituto Nacional de Metropolitana Xochimilco, México
Investigaciones Agrícolas, España Dr. Alfredo Cesín Vargas, UNAM, México
Dr. José Armando Partida de la Peña, INIFAP, México Dra. Marisela Leal Hernández, INIFAP, México
Dr. José Luis Romano Muñoz, INIFAP, México Dr. Efrén Ramírez Bribiesca, Colegio de Postgraduados,
Dr. Jorge Alberto López García, INIFAP, México México
TIPOGRAFÍA Y FORMATO: Oscar L. Rodríguez Rivera
Indizada en el “Journal Citation Report” Science Edition del ISI . Inscrita en el Sistema de Clasificación de Revistas Científicas y
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I
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II
REVISTA MEXICANA DE CIENCIAS PECUARIAS
REV. MEX. CIENC. PECU. VOL. 14 No. 1 ENERO-MARZO-2023
CONTENIDO
Contents
ARTÍCULOS
Articles
Pág.
Identification of candidate genes and SNPs related to cattle temperament using a

GWAS analysis coupled with an interacting network analysis
Identificación de genes candidatos y SNP relacionados con el temperamento del ganado
utilizando un análisis GWAS junto con un análisis de redes interactuantes
Francisco Alejandro Paredes-Sánchez, Ana María Sifuentes-Rincón, Edgar Eduardo
Lara-Ramírez, Eduardo Casas, Felipe Alonso Rodríguez-Almeida, Elsa Verónica
Herrera-Mayorga, Ronald D. Randel ...........................................................................................1
Efecto de la consanguinidad y selección sobre los componentes de un índice

productivo en ratones bajo apareamiento estrecho
Effect of consanguinity and selection on the components of a productive index, in mice under
close mating
Dulce Janet Hernández López, Raúl Ulloa Arvizu, Carlos Gustavo Vázquez Peláez, Graciela
Guadalupe Tapia Pérez .....................................................................................…………............23
Variabilidad genética en biomasa aérea y sus componentes en alfalfa bajo riego y

sequía
Genetic variability in aerial biomass and its components in alfalfa under irrigation and drought
Milton Javier Luna-Guerrero, Cándido López-Castañeda ..............…………………………………………….39
Estimación de masa de forraje en una pradera mixta por aprendizaje automatizado,

datos del manejo de la pradera y meteorológicos satelitales
Estimation of forage mass in a mixed pasture by machine learning, pasture management and
satellite meteorological data
Aurelio Guevara-Escobar, Mónica Cervantes-Jiménez, Vicente Lemus-Ramírez, Adolfo
Kunio Yabuta-Osorio, José Guadalupe García-Muñiz ……………………………………………………………….61
Thymol and carvacrol determination in a swine feed organic matrix using Headspace
SPME-GC-MS
Determinación de timol y carvacrol en una matriz orgánica de alimento para cerdo utilizando
Headspace SPME-GC-MS
Fernando Jonathan Lona-Ramírez, Nancy Lizeth Hernández-López, Guillermo González-Alatorre,
Teresa del Carmen Flores-Flores, Rosalba Patiño-Herrera, José Francisco Louvier-Hernández …….78
III
Cambios en el recuento de cuatro grupos bacterianos durante la maduración del
Queso de Prensa (Costeño) de Cuajinicuilapa, México
Changes in the count of four bacterial groups during the ripening of Prensa (Costeño) Cheese
from Cuajinicuilapa, Mexico
José Alberto Mendoza-Cuevas, Armando Santos-Moreno, Beatriz Teresa Rosas-Barbosa, Ma.
Carmen Ybarra-Moncada, Emmanuel Flores-Girón, Diana Guerra-Ramírez ......………………………..…94
Detección molecular de un fragmento del virus de lengua azul en borregos de

diferentes regiones de México
Molecular detection of a fragment of bluetongue virus in sheep from different regions of
Mexico
Edith Rojas Anaya, Fernando Cerón-Téllez, Luis Adrián Yáñez-Garza, José Luis
Gutiérrez-Hernández, Rosa Elena Sarmiento-Salas, Elizabeth Loza-Rubio ...........................……110
Insulin-like growth factor 1 (IGF-1) concentrations in synovial fluid of sound and

osteoarthritic horses, and its correlation with proinflammatory cytokines IL-6 and TNF
Concentraciones del factor de crecimiento similar a la insulina 1 (IGF-1) en el líquido sinovial de
caballos sanos y osteoartríticos, y su correlación con las citoquinas proinflamatorias IL-6 y TNFα
Fernando García-Lacy F., Sara Teresa Méndez-Cruz, Horacio Reyes-Vivas, Victor Manuel Dávila-
Borja, Jose Alejandro Barrera-Morales, Gabriel Gutiérrez-Ospina, Margarita Gómez-Chavarín,
Francisco José Trigo-Tavera .......................................................…………………………………………122
Uso de células estromales mesenquimales derivadas de la gelatina de Wharton para

el tratamiento de uveítis recurrente equina: estudio piloto
Use of Wharton's jelly-derived mesenchymal stromal cells for the treatment of equine recurrent
uveitis: a pilot study
María Masri-Daba, Montserrat Erandi Camacho-Flores, Ninnet Gómez-Romero, Francisco Javier
Basurto Alcántara .................................................................................………………………………137
Escala de la producción y eficiencia técnica de la ganadería bovina para carne en

Puebla, México
Scale of production and technical efficiency of beef cattle farming in Puebla, Mexico
José Luis Jaramillo Villanueva, Lissette Abigail Rojas Juárez, Samuel Vargas López ……………….…154
Regresión cuantil para predicción de caracteres complejos en bovinos Suizo

Europeo usando marcadores SNP y pedigrí
Quantile regression for prediction of complex traits in Braunvieh cattle using SNP markers
and pedigree
Jonathan Emanuel Valerio-Hernández, Paulino Pérez-Rodríguez, Agustín Ruíz-Flores ……………….172
Análisis de crecimiento estacional de una pradera de trébol blanco ( Trifolium repens L)

Seasonal growth analysis of a white clover meadow ( Trifolium repens L.)
Edgar Hernández Moreno, Joel Ventura Ríos, Claudia Yanet Wilson García, María de los Ángeles
Maldonado Peralta, Juan de Dios Guerrero Rodríguez, Graciela Munguía Ameca,
Adelaido Rafael Rojas García....................................................................................…………….190
IV
REVISIONES DE LITERATURA
Reviews
Aspects related to the importance of using predictive models in sheep production.

Review
Aspectos relacionados con la importancia del uso de modelos predictivos en la producción
ovina. Revisión
Antonio Leandro Chaves Gurgel, Gelson dos Santos Difante, Luís Carlos Vinhas Ítavo,
João Virgínio Emerenciano Neto, Camila Celeste Brandão Ferreira Ítavo, Patrick Bezerra
Fernandes, Carolina Marques Costa, Francisca Fernanda da Silva Roberto, Alfonso Juventino
Chay-Canul ..........................................................................................................................204
NOTAS DE INVESTIGACIÓN
Techcnical notes
Preferencia de ocho plantas por Odocoileus virginianus en cautiverio

Preference for eight plants among captive white-tailed deer Odocoileus virginianus in
Veracruz, Mexico
Hannia Yaret Cueyactle-Cano, Ricardo Serna-Lagunes, Norma Mora-Collado, Pedro
Zetina-Córdoba, Gerardo Benjamín Torres-Cantú .....................................………………...............228
Rendimiento y valor nutricional de brásicas forrajeras en comparación con forrajes

tradicionales
Yield and nutritional value of forage brassicas compared to traditional forages
David Guadalupe Reta Sánchez, Juan Isidro Sánchez Duarte, Esmeralda Ochoa Martínez,
Ana Isabel González Cifuentes, Arturo Reyes González, Karla Rodríguez Hernández …….............237
Genetic characterization of bovine viral diarrhea virus 1b isolated from mucosal

disease
Caracterización del virus de la diarrea viral bovino subtipo 1b aislado de un caso de la
enfermedad de las mucosas
Roberto Navarro-López, Juan Diego Perez-de la Rosa, Marisol Karina Rocha-Martínez,
Marcela Villarreal-Silva, Mario Solís-Hernández, Eric Rojas-Torres, Ninnet Gómez-Romero ...........248
V
Actualización: marzo, 2020
NOTAS AL AUTOR
La Revista Mexicana de Ciencias Pecuarias se edita 6. Los manuscritos de las tres categorías de trabajos que
completa en dos idiomas (español e inglés) y publica tres se publican en la Rev. Mex. Cienc. Pecu. deberán
categorías de trabajos: Artículos científicos, Notas de contener los componentes que a continuación se
investigación y Revisiones bibliográficas. indican, empezando cada uno de ellos en página
aparte.
Los autores interesados en publicar en esta revista
deberán ajustarse a los lineamientos que más adelante se Página del título
indican, los cuales en términos generales, están de Resumen en español
acuerdo con los elaborados por el Comité Internacional de Resumen en inglés
Editores de Revistas Médicas (CIERM) Bol Oficina Sanit Texto
Panam 1989;107:422-437. Agradecimientos y conflicto de interés
Literatura citada
1. Sólo se aceptarán trabajos inéditos. No se admitirán
si están basados en pruebas de rutina, ni datos
7. Página del Título. Solamente debe contener el título
experimentales sin estudio estadístico cuando éste
del trabajo, que debe ser conciso pero informativo; así
sea indispensable. Tampoco se aceptarán trabajos
como el título traducido al idioma inglés. En el
que previamente hayan sido publicados condensados
manuscrito no es necesaria información como
o in extenso en Memorias o Simposio de Reuniones o
nombres de autores, departamentos, instituciones,
Congresos (a excepción de Resúmenes).
direcciones de correspondencia, etc., ya que estos
2. Todos los trabajos estarán sujetos a revisión de un datos tendrán que ser registrados durante el proceso
Comité Científico Editorial, conformado por Pares de de captura de la solicitud en la plataforma del OJS
la Disciplina en cuestión, quienes desconocerán el (http://ciencias pecuarias.inifap.gob.mx).
nombre e Institución de los autores proponentes. El
8. Resumen en español. En la segunda página se debe
Editor notificará al autor la fecha de recepción de su
incluir un resumen que no pase de 250 palabras. En
trabajo.
él se indicarán los propósitos del estudio o
3. El manuscrito deberá someterse a través del portal de investigación; los procedimientos básicos y la
la Revista en la dirección electrónica: metodología empleada; los resultados más
http://cienciaspecuarias.inifap.gob.mx, consultando importantes encontrados, y de ser posible, su
el “Instructivo para envío de artículos en la página de significación estadística y las conclusiones principales.
la Revista Mexicana de Ciencias Pecuarias”. Para su A continuación del resumen, en punto y aparte,
elaboración se utilizará el procesador de Microsoft agregue debidamente rotuladas, de 3 a 8 palabras o
Word, con letra Times New Roman a 12 puntos, a frases cortas clave que ayuden a los indizadores a
doble espacio. Asimismo se deberán llenar los clasificar el trabajo, las cuales se publicarán junto con
formatos de postulación, carta de originalidad y no el resumen.
duplicidad y disponibles en el propio sitio oficial de la
9. Resumen en inglés. Anotar el título del trabajo en
revista.
inglés y a continuación redactar el “abstract” con las
4. Por ser una revista con arbitraje, y para facilitar el mismas instrucciones que se señalaron para el
trabajo de los revisores, todos los renglones de cada resumen en español. Al final en punto y aparte, se
página deben estar numerados; asimismo cada deberán escribir las correspondientes palabras clave
página debe estar numerada, inclusive cuadros, (“key words”).
ilustraciones y gráficas.
10. Texto. Las tres categorías de trabajos que se publican
5. Los artículos tendrán una extensión máxima de 20 en la Rev. Mex. Cienc. Pecu. consisten en lo
cuartillas a doble espacio, sin incluir páginas de Título, siguiente:
y cuadros o figuras (los cuales no deberán exceder de
a) Artículos científicos. Deben ser informes de trabajos
ocho y ser incluidos en el texto). Las Notas de
originales derivados de resultados parciales o finales
investigación tendrán una extensión máxima de 15
de investigaciones. El texto del Artículo científico se
cuartillas y 6 cuadros o figuras. Las Revisiones
divide en secciones que llevan estos
bibliográficas una extensión máxima de 30 cuartillas y
encabezamientos:
5 cuadros.
VI
Introducción referencias, aunque pueden insertarse en el texto
Materiales y Métodos (entre paréntesis).
Resultados
Reglas básicas para la Literatura citada
Discusión
Conclusiones e implicaciones Nombre de los autores, con mayúsculas sólo las
Literatura citada iniciales, empezando por el apellido paterno, luego
iniciales del materno y nombre(s). En caso de
En los artículos largos puede ser necesario agregar apellidos compuestos se debe poner un guión entre
subtítulos dentro de estas divisiones a fin de hacer ambos, ejemplo: Elías-Calles E. Entre las iniciales de
más claro el contenido, sobre todo en las secciones de un autor no se debe poner ningún signo de
Resultados y de Discusión, las cuales también pueden puntuación, ni separación; después de cada autor sólo
presentarse como una sola sección. se debe poner una coma, incluso después del
b) Notas de investigación. Consisten en penúltimo; después del último autor se debe poner un
modificaciones a técnicas, informes de casos clínicos punto.
de interés especial, preliminares de trabajos o El título del trabajo se debe escribir completo (en su
investigaciones limitadas, descripción de nuevas idioma original) luego el título abreviado de la revista
variedades de pastos; así como resultados de donde se publicó, sin ningún signo de puntuación;
investigación que a juicio de los editores deban así ser inmediatamente después el año de la publicación,
publicados. El texto contendrá la misma información luego el número del volumen, seguido del número
del método experimental señalado en el inciso a), (entre paréntesis) de la revista y finalmente el número
pero su redacción será corrida del principio al final del de páginas (esto en caso de artículo ordinario de
trabajo; esto no quiere decir que sólo se supriman los revista).
subtítulos, sino que se redacte en forma continua y
coherente. Puede incluir en la lista de referencias, los artículos
aceptados aunque todavía no se publiquen; indique la
c) Revisiones bibliográficas. Consisten en el
revista y agregue “en prensa” (entre corchetes).
tratamiento y exposición de un tema o tópico de
relevante actualidad e importancia; su finalidad es la En el caso de libros de un solo autor (o más de uno,
de resumir, analizar y discutir, así como poner a pero todos responsables del contenido total del libro),
disposición del lector información ya publicada sobre después del o los nombres, se debe indicar el título
un tema específico. El texto se divide en: del libro, el número de la edición, el país, la casa
Introducción, y las secciones que correspondan al editorial y el año.
desarrollo del tema en cuestión.
Cuando se trate del capítulo de un libro de varios
11. Agradecimientos y conflicto de interés. Siempre autores, se debe poner el nombre del autor del
que corresponda, se deben especificar las capítulo, luego el título del capítulo, después el
colaboraciones que necesitan ser reconocidas, tales
nombre de los editores y el título del libro, seguido del
como a) la ayuda técnica recibida; b) el
país, la casa editorial, año y las páginas que abarca el
agradecimiento por el apoyo financiero y material,
capítulo.
especificando la índole del mismo; c) las relaciones
financieras que pudieran suscitar un conflicto de En el caso de tesis, se debe indicar el nombre del
intereses. Las personas que colaboraron pueden ser autor, el título del trabajo, luego entre corchetes el
citadas por su nombre, añadiendo su función o tipo de grado (licenciatura, maestría, doctorado), luego el
colaboración; por ejemplo: “asesor científico”, nombre de la ciudad, estado y en su caso país,
“revisión crítica de la propuesta para el estudio”, seguidamente el nombre de la Universidad (no el de
“recolección de datos”, etc. Siempre que corresponda, la escuela), y finalmente el año.
los autores deberán mencionar si existe algún
conflicto de interés. Emplee el estilo de los ejemplos que aparecen a
continuación, los cuales están parcialmente basados
12. Literatura citada. Numere las referencias en el formato que la Biblioteca Nacional de Medicina
consecutivamente en el orden en que se mencionan de los Estados Unidos usa en el Index Medicus.
por primera vez en el texto. Las referencias en el
texto, en los cuadros y en las ilustraciones se deben
identificar mediante números arábigos entre Revistas
paréntesis, sin señalar el año de la referencia. Evite
hasta donde sea posible, el tener que mencionar en el Artículo ordinario, con volumen y número. (Incluya el
texto el nombre de los autores de las referencias. nombre de todos los autores cuando sean seis o
Procure abstenerse de utilizar los resúmenes como menos; si son siete o más, anote sólo el nombre de
referencias; las “observaciones inéditas” y las los seis primeros y agregue “et al.”).
“comunicaciones personales” no deben usarse como
VII
I) Basurto GR, Garza FJD. Efecto de la inclusión de grasa XI) Olea PR, Cuarón IJA, Ruiz LFJ, Villagómez AE.
o proteína de escape ruminal en el comportamiento Concentración de insulina plasmática en cerdas
de toretes Brahman en engorda. Téc Pecu Méx alimentadas con melaza en la dieta durante la
1998;36(1):35-48. inducción de estro lactacional [resumen]. Reunión
nacional de investigación pecuaria. Querétaro, Qro.
Sólo número sin indicar volumen.
1998:13.
II) Stephano HA, Gay GM, Ramírez TC. Encephalomielitis,
XII) Cunningham EP. Genetic diversity in domestic
reproductive failure and corneal opacity (blue eye) in
animals: strategies for conservation and
pigs associated with a paramyxovirus infection. Vet
development. In: Miller RH et al. editors. Proc XX
Rec 1988;(122):6-10.
Beltsville Symposium: Biotechnology’s role in
III) Chupin D, Schuh H. Survey of present status ofthe use genetic improvement of farm animals. USDA.
of artificial insemination in developing countries. 1996:13.
World Anim Rev 1993;(74-75):26-35.
Tesis.
No se indica el autor.
XIII) Alvarez MJA. Inmunidad humoral en la anaplasmosis
IV) Cancer in South Africa [editorial]. S Afr Med J y babesiosis bovinas en becerros mantenidos en una
1994;84:15. zona endémica [tesis maestría]. México, DF:
Universidad Nacional Autónoma de México; 1989.
Suplemento de revista.
XIV) Cairns RB. Infrared spectroscopic studies of solid
V) Hall JB, Staigmiller RB, Short RE, Bellows RA, Bartlett oxigen [doctoral thesis]. Berkeley, California, USA:
SE. Body composition at puberty in beef heifers as University of California; 1965.
influenced by nutrition and breed [abstract]. J Anim
Sci 1998;71(Suppl 1):205. Organización como autor.
Organización, como autor. XV) NRC. National Research Council. The nutrient
requirements of beef cattle. 6th ed. Washington,
VI) The Cardiac Society of Australia and New Zealand. DC, USA: National Academy Press; 1984.
Clinical exercise stress testing. Safety and performance
guidelines. Med J Aust 1996;(164):282-284. XVI) SAGAR. Secretaría de Agricultura, Ganadería y
Desarrollo Rural. Curso de actualización técnica para
En proceso de publicación. la aprobación de médicos veterinarios zootecnistas
responsables de establecimientos destinados al
VII) Scifres CJ, Kothmann MM. Differential grazing use of
sacrificio de animales. México. 1996.
herbicide treated area by cattle. J Range Manage [in
press] 2000. XVII) AOAC. Oficial methods of analysis. 15th ed.
Arlington, VA, USA: Association of Official Analytical
Chemists. 1990.
Libros y otras monografías
XVIII) SAS. SAS/STAT User’s Guide (Release 6.03). Cary
Autor total. NC, USA: SAS Inst. Inc. 1988.
VIII) Steel RGD, Torrie JH. Principles and procedures of XIX) SAS. SAS User´s Guide: Statistics (version 5 ed.).
statistics: A biometrical approach. 2nd ed. New Cary NC, USA: SAS Inst. Inc. 1985.
York, USA: McGraw-Hill Book Co.; 1980.
Publicaciones electrónicas
Autor de capítulo.
XX) Jun Y, Ellis M. Effect of group size and feeder type
IX) Roberts SJ. Equine abortion. In: Faulkner LLC editor. on growth performance and feeding patterns in
Abortion diseases of cattle. 1rst ed. Springfield,
growing pigs. J Anim Sci 2001;79:803-813.
Illinois, USA: Thomas Books; 1968:158-179.
http://jas.fass.org/cgi/reprint/79/4/803.pdf.
Accessed Jul 30, 2003.
Memorias de reuniones.
XXI) Villalobos GC, González VE, Ortega SJA. Técnicas
X) Loeza LR, Angeles MAA, Cisneros GF. Alimentación
para estimar la degradación de proteína y materia
de cerdos. En: Zúñiga GJL, Cruz BJA editores.
orgánica en el rumen y su importancia en rumiantes
Tercera reunión anual del centro de investigaciones
forestales y agropecuarias del estado de Veracruz. en pastoreo. Téc Pecu Méx 2000;38(2): 119-134.
Veracruz. 1990:51-56. http://www.tecnicapecuaria.org/trabajos/20021217
5725.pdf. Consultado 30 Ago, 2003.
VIII
XXII) Sanh MV, Wiktorsson H, Ly LV. Effect of feeding ha hectárea (s)
level on milk production, body weight change, feed h hora (s)
conversion and postpartum oestrus of crossbred i.m. intramuscular (mente)
lactating cows in tropical conditions. Livest Prod Sci i.v. intravenosa (mente)
2002;27(2-3):331-338. http://www.sciencedirect. J joule (s)
com/science/journal/03016226. Accessed Sep 12, kg kilogramo (s)
2003.
km kilómetro (s)
13. Cuadros, Gráficas e Ilustraciones. Es preferible L litro (s)
que sean pocos, concisos, contando con los datos log logaritmo decimal
necesarios para que sean autosuficientes, que se Mcal megacaloría (s)
entiendan por sí mismos sin necesidad de leer el texto. MJ megajoule (s)
Para las notas al pie se deberán utilizar los símbolos
m metro (s)
convencionales.
msnm metros sobre el nivel del mar
14 Versión final. Es el documento en el cual los autores µg microgramo (s)
ya integraron las correcciones y modificaciones µl microlitro (s)
indicadas por el Comité Revisor. Los trabajos deberán
µm micrómetro (s)(micra(s))
ser elaborados con Microsoft Word. Las fotografías e
imágenes deberán estar en formato jpg (o mg miligramo (s)
compatible) con al menos 300 dpi de resolución. ml mililitro (s)
Tanto las fotografías, imágenes, gráficas, cuadros o mm milímetro (s)
tablas deberán incluirse en el mismo archivo del texto. min minuto (s)
Los cuadros no deberán contener ninguna línea ng nanogramo (s)Pprobabilidad (estadística)
vertical, y las horizontales solamente las que delimitan p página
los encabezados de columna, y la línea al final del PC proteína cruda
cuadro.
PCR reacción en cadena de la polimerasa
15. Una vez recibida la versión final, ésta se mandará para pp páginas
su traducción al idioma inglés o español, según ppm partes por millón
corresponda. Si los autores lo consideran conveniente % por ciento (con número)
podrán enviar su manuscrito final en ambos idiomas.
rpm revoluciones por minuto
16. Tesis. Se publicarán como Artículo o Nota de seg segundo (s)
Investigación, siempre y cuando se ajusten a las t tonelada (s)
normas de esta revista. TND total de nutrientes digestibles
17. Los trabajos no aceptados para su publicación se UA unidad animal
regresarán al autor, con un anexo en el que se UI unidades internacionales
explicarán los motivos por los que se rechaza o las vs versus
modificaciones que deberán hacerse para ser xg gravedades
reevaluados.
Cualquier otra abreviatura se pondrá entre paréntesis
18. Abreviaturas de uso frecuente: inmediatamente después de la(s) palabra(s)
cal caloría (s) completa(s).
cm centímetro (s) 19. Los nombres científicos y otras locuciones latinas se
°C grado centígrado (s) deben escribir en cursivas.
DL50 dosis letal 50%
g gramo (s)
IX
Updated: March, 2020
INSTRUCTIONS FOR AUTHORS
Revista Mexicana de Ciencias Pecuarias is a scientific

journal published in a bilingual format (Spanish and
English) which carries three types of papers: Research
Articles, Technical Notes, and Reviews. Authors interested Title page
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X
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Journals
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references should be identified by means of Arabic followed by et al.)
numbers. Avoid, whenever possible, mentioning in the
text the name of the authors. Abstain from using I) Basurto GR, Garza FJD. Efecto de la inclusión de grasa
abstracts as references. Also, “unpublished o proteína de escape ruminal en el comportamiento
observations” and “personal communications” should de toretes Brahman en engorda. Téc Pecu Méx
not be used as references, although they can be 1998;36(1):35-48.
inserted in the text (inside brackets).
Issue with no volume
Key rules for references
II) Stephano HA, Gay GM, Ramírez TC. Encephalomielitis,
a. The names of the authors should be quoted reproductive failure and corneal opacity (blue eye) in
beginning with the last name spelt with initial capitals, pigs associated with a paramyxovirus infection. Vet
followed by the initials of the first and middle name(s). Rec 1988;(122):6-10.
In the presence of compound last names, add a dash
between both, i.e. Elias-Calles E. Do not use any III) Chupin D, Schuh H. Survey of present status of the
punctuation sign, nor separation between the initials use of artificial insemination in developing countries.
of an author; separate each author with a comma, World Anim Rev 1993;(74-75):26-35.
even after the last but one.
No author given
b. The title of the paper should be written in full,
followed by the abbreviated title of the journal without IV) Cancer in South Africa [editorial]. S Afr Med J
any punctuation sign; then the year of the publication, 1994;84:15.
after that the number of the volume, followed by the
number (in brackets) of the journal and finally the Journal supplement
number of pages (this in the event of ordinary article).
V) Hall JB, Staigmiller RB, Short RE, Bellows RA, Bartlett
c. Accepted articles, even if still not published, can SE. Body composition at puberty in beef heifers as
be included in the list of references, as long as the influenced by nutrition and breed [abstract]. J Anim
journal is specified and followed by “in press” (in Sci 1998;71(Suppl 1):205.
brackets).
d. In the case of a single author’s book (or more
than one, but all responsible for the book’s contents),
the title of the book should be indicated after the
XI
Organization, as author Organization as author
VI) The Cardiac Society of Australia and New Zealand. XV) NRC. National Research Council. The nutrient
Clinical exercise stress testing. Safety and requirements of beef cattle. 6th ed. Washington,
performance guidelines. Med J Aust 1996;(164):282- DC, USA: National Academy Press; 1984.
284. XVI) SAGAR. Secretaría de Agricultura, Ganadería y
In press Desarrollo Rural. Curso de actualización técnica para
la aprobación de médicos veterinarios zootecnistas
VII) Scifres CJ, Kothmann MM. Differential grazing use of responsables de establecimientos destinados al
herbicide-treated area by cattle. J Range Manage [in sacrificio de animales. México. 1996.
press] 2000.
XVII) AOAC. Official methods of analysis. 15th ed.
Books and other monographs Arlington, VA, USA: Association of Official Analytical
Chemists. 1990.
Author(s)
XVIII) SAS. SAS/STAT User’s Guide (Release 6.03). Cary
VIII) Steel RGD, Torrie JH. Principles and procedures of NC, USA: SAS Inst. Inc. 1988.
statistics: A biometrical approach. 2nd ed. New
York, USA: McGraw-Hill Book Co.; 1980. XIX) SAS. SAS User´s Guide: Statistics (version 5 ed.).
Cary NC, USA: SAS Inst. Inc. 1985.
Chapter in a book
Electronic publications
IX) Roberts SJ. Equine abortion. In: Faulkner LLC editor.
Abortion diseases of cattle. 1rst ed. Springfield, XX) Jun Y, Ellis M. Effect of group size and feeder type
Illinois, USA: Thomas Books; 1968:158-179. on growth performance and feeding patterns in
growing pigs. J Anim Sci 2001;79:803-813.
http://jas.fass.org/cgi/reprint/79/4/803.pdf.
Conference paper
Accesed Jul 30, 2003.
X) Loeza LR, Angeles MAA, Cisneros GF. Alimentación
XXI) Villalobos GC, González VE, Ortega SJA. Técnicas
de cerdos. En: Zúñiga GJL, Cruz BJA editores.
para estimar la degradación de proteína y materia
Tercera reunión anual del centro de investigaciones
forestales y agropecuarias del estado de Veracruz. orgánica en el rumen y su importancia en rumiantes
Veracruz. 1990:51-56. en pastoreo. Téc Pecu Méx 2000;38(2): 119-134.
http://www.tecnicapecuaria.org/trabajos/20021217
XI) Olea PR, Cuarón IJA, Ruiz LFJ, Villagómez AE. 5725.pdf. Consultado 30 Jul, 2003.
Concentración de insulina plasmática en cerdas
alimentadas con melaza en la dieta durante la XXII) Sanh MV, Wiktorsson H, Ly LV. Effect of feeding
inducción de estro lactacional [resumen]. Reunión level on milk production, body weight change, feed
nacional de investigación pecuaria. Querétaro, Qro. conversion and postpartum oestrus of crossbred
1998:13. lactating cows in tropical conditions. Livest Prod Sci
2002;27(2-3):331-338.
XII) Cunningham EP. Genetic diversity in domestic
animals: strategies for conservation and http://www.sciencedirect.com/science/journal/030
development. In: Miller RH et al. editors. Proc XX 16226. Accesed Sep 12, 2003.
Beltsville Symposium: Biotechnology’s role in 12. Tables, Graphics and Illustrations. It is preferable
genetic improvement of farm animals. USDA.
that they should be few, brief and having the
1996:13.
necessary data so they could be understood without
reading the text. Explanatory material should be
Thesis
placed in footnotes, using conventional symbols.
XIII) Alvarez MJA. Inmunidad humoral en la anaplasmosis
y babesiosis bovinas en becerros mantenidos en una 13. Final version. This is the document in which the
zona endémica [tesis maestría]. México, DF: authors have already integrated the corrections and
Universidad Nacional Autónoma de México; 1989. modifications indicated by the Review Committee. The
works will have to be elaborated with Microsoft Word.
XIV) Cairns RB. Infrared spectroscopic studies of solid Photographs and images must be in jpg (or
oxigen [doctoral thesis]. Berkeley, California, USA:
compatible) format with at least 300 dpi resolution.
University of California; 1965.
Photographs, images, graphs, charts or tables must
be included in the same text file. The boxes should
not contain any vertical lines, and the horizontal ones
only those that delimit the column headings, and the
line at the end of the box.
XII
14. Once accepted, the final version will be translated into MJ mega joule (s)
Spanish or English, although authors should feel free m meter (s)
to send the final version in both languages. No µl micro liter (s)
charges will be made for style or translation services. µm micro meter (s)
15. Thesis will be published as a Research Article or as a mg milligram (s)
Technical Note, according to these guidelines. ml milliliter (s)
mm millimeter (s)
16. Manuscripts not accepted for publication will be min minute (s)
returned to the author together with a note explaining ng nanogram (s)
the cause for rejection, or suggesting changes which
P probability (statistic)
should be made for re-assessment.
p page
CP crude protein
PCR polymerase chain reaction
17. List of abbreviations:
pp pages
cal calorie (s) ppm parts per million
cm centimeter (s) % percent (with number)
°C degree Celsius rpm revolutions per minute
DL50 lethal dose 50% sec second (s)
g gram (s) t metric ton (s)
ha hectare (s) TDN total digestible nutrients
h hour (s) AU animal unit
i.m. intramuscular (..ly) IU international units
i.v. intravenous (..ly) vs versus
J joule (s) xg gravidity
kg kilogram (s)
The full term for which an abbreviation stands should
km kilometer (s) precede its first use in the text.
L liter (s)
log decimal logarithm 18. Scientific names and other Latin terms should be
written in italics.
Mcal mega calorie (s)
XIII
https://doi.org/10.22319/rmcp.v14i1.6077
Article
Identification of candidate genes and SNPs related to cattle temperament

using a GWAS analysis coupled with an interacting network analysis
Francisco Alejandro Paredes-Sánchez a
Ana María Sifuentes-Rincón b*
Edgar Eduardo Lara-Ramírez c
Eduardo Casas d
Felipe Alonso Rodríguez-Almeida e
Elsa Verónica Herrera-Mayorga f
Ronald D. Randel g
a
Universidad Autónoma de Tamaulipas, IA-UAMM. Mante, México.
b
Instituto Politécnico Nacional. Centro de Biotecnología Genómica. Laboratorio de
Biotecnología Animal, Blvd. Del Maestro esq. Elías Piña. Col. Narciso Mendoza s/n. Cd.
Reynosa, Tam. México.
c
Instituto Mexicano del Seguro Social, Unidad de Investigación Biomédica de Zacatecas,
Zacatecas, México.
d
United States Department of Agriculture. National Animal Disease Center, Iowa, USA.
e
Universidad Autónoma de Chihuahua. Facultad de Zootecnia y Ecología, Chihuahua,
México.
f
Universidad Autónoma de Tamaulipas. IBI-UAMM, Mante, México.
g
Texas A&M University. AgriLife Research. Texas, USA.
*Corresponding author: asifuentes@ipn.mx
1
Rev Mex Cienc Pecu 2023;14(1):1-22
Abstract:
The objective of this study was to identify in Angus and Brangus breed animals with
extreme temperament, measured as exit velocity, genomic regions and candidate genes
associated with bovine temperament. The population was genotyped with the Genomic
Profiler HD 150K chip and after the genome-wide association analysis, the SNPs
rs133956611 (P=2.65 E-06) and rs81144933 (P=9.58 E-06) were associated with
temperament. The mapping analysis of the regions close to the SNP rs81144933 identified
the SNCA (alpha-synuclein) and MMRN1 (multimerin-1) genes at 222.8 and 435.9 Kb
downstream respectively, while for the rs133956611 loci the gene GPRIN3 (GPRIN
family-member-3) was identified at 245.7 Kb upstream, all three genes are located on the
BTA6 chromosome. The analysis of SNCA protein-protein interactions allowed the
identification of the genes APP (β-amyloid precursor protein), PARK7 (parkinsonism-
associated-deglycase), UCHL1 (ubiquitin-C-terminal-hydrolase-L1), PARK2 (parkin-RBR-
E3-ubiquitin-protein-ligase), and genes of the SLC family as candidates to be associated
with bovine temperament. All these candidate genes and their interacting were
resequenced, which allowed the discovery of new SNPs in the SNCA and APP genes. Of
these, the SNPs located in introns 5, 8 and 11 of the APP gene affect splicing site motifs.
These results indicate that SNCA and its interacting genes are candidates to be related to
bovine temperament.
Key words: Beef cattle, Behaviour, BTA6, Candidate genes, Temperament.
Received: 18/10/2021
Accepted: 16/08/2022
Introduction
Temperament is an economically relevant trait that impacts animal welfare and traits related
to productivity. Bovine temperament is considered to be the most important trait of an
animal's personality and comprises a wide range of behaviors, from docility to fear and
nervousness or a lack of response, attempts to escape, and aggressive behavior, in which
various parameters such as general locomotor activity and reactivity to stress are observable.
Temperament is affected by age, experience, sex, handling, maternal effects, environmental
factors, genetics, species and breed(1,2). To date, several genomic approaches attempted to
2
identify genomic regions and genes in which underlying single nucleotide polymorphisms
(SNPs) are associated with temperament, a complex phenotypical trait.
Quantitative trait locus (QTL) mapping uncovered the first evidence of genomic regions
associated with behavioral traits in dairy breeds(3,4). The detection of QTLs in the genome
led to the proposal of candidate genes under the genomic region encompassed by the QTL,
which could potentially be responsible for the differences in trait expression. The
identification of candidate genes based on their function and possible involvement in bovine
temperament has been a strategy for the search for SNPs. Garza-Brenner et al(5) selected a
group of 19 genes that participate in the dopamine and serotonin pathway, and through a
protein-protein interaction (PPI) analysis, they identified four new interacting candidate
genes (POMC, NPY, SLC18A2, and FOSFBJ), of which POMC, SLC18A2 and DRD3,
HTR2A (selected based on their function) revealed SNPs associated with Exit Velocity (EV)
and Pen Score (PS), which are measurements of bovine temperament in a population of
Charolais cattle. The same group found that the variations in these genes (DRD3, HTR2A,
and POMC) had an effect on bovine growth (birth weight) in a population of Charolais cattle,
showing that the identified variations not only had an effect on bovine temperament but also
on live weight traits(6). Similarly, with the objective of evaluating the potential relationship
of two of these SNPs in the DRD3 and HTR2A genes with bovine temperament and growth
characteristics, and feed efficiency, a population of Angus, Brangus, and Charolais cattle
with temperament assessments was analysed; the results indicated that there was no
association with EV and PS, but the SNP in the HTR2A gene was associated with feed
efficiency in Brangus cattle(7).
Genome-wide association studies (GWAS), based on high-throughput single nucleotide

polymorphism (SNP) genotyping technologies, are a relatively recent approach applied to
genetic studies of cattle temperament and have allowed the identification of different groups
of candidate genes. Lindholm-Perry et al(8) analysed a population of the Angus, Hereford,
Simmental, Limousin, Charolais, Gelbvieh, and Red Angus breeds to identify genomic
regions and genes associated with flight speed (FS); they determined chromosomal regions
on BTA 9 and 17 associated and identified within them three genes GRIA2, GLRB, and QKI
associated with nearby SNPs. Valente et al(9) evaluated a Nellore population using EV to
assess their temperament. The NCKAP5, PARK2, DOCK1, ANTXR1, CPE, and GUCY1A2
genes were detected as potential candidates for the trait of interest. Finally, Dos Santos et
al(10) used a Guzerat population in which reactivity was measured as an indicator of
temperament. The genes POU1F1, DRD3, VWA3A, ZBTB20, EPHA6, SNRPF, and NTN4
were proposed as candidate genes responsible for expression of the trait.
In a related context, exome-specific resequencing of specific regions using next-generation

sequencing (NGS) technologies has become a powerful technique that allows the
identification of SNPs. This method can efficiently capture all variation in the regions of
3
interest. The potential effects can be assessed in an association study, which provides an
effective tool to find SNPs affecting a determined trait(11). However, due to differences in
temperament phenotyping in previous studies, (i.e., each study uses different techniques to
assess bovine temperament, pen score, exit velocity, reactivity, which evaluate different
aspects of bovine temperament), it is not possible to link information for those genes
identified as candidates, or to find a representative biological process, protein-protein
interactions between these genes, or a biological path in which these genes converge to
visualize how the set of genes explains bovine temperament. Thus, genomic information
often remains isolated and needs to be integrated. Hence, the objective was to identify
genomic regions and candidate genes associated with temperament in beef cattle through the
integration of a GWAS strategy, protein-protein interaction analysis, and SNPs obtained by
specific exome resequencing.
Material and methods
Description of animals and biological sample sources
Data and hair samples were obtained from the biobank located at the Animal Biotechnology
Laboratory CBG-IPN and were from a cattle population (n= 104) of young Angus (AN,
n=63) and Brangus (BR, n=41) bulls, with an average age and body weight of 273 ± 38 d and
272 ± 38 kg, respectively, analysed during two centralized feed efficiency performance tests
based on residual feed intake (RFI) in northern Mexico. Data recording and animal
management have been previously described by Garza-Brenner et al(7). Briefly, animals were
fed in a feedlot for a period of 70 d with a pre-trial adaptation period of 20 d, weighed at the
beginning and at the end of the test with intervals of 14 d in which the bovine temperament
measurements were made.
From the population, a GWAS was performed using a selective genotyping approach
following the strategy of the tails of the phenotypic distribution of bovine temperament
measured by exit velocity (EV) because it facilitates the detection of phenotypic differences
between alleles(12). Selective genotyping was achieved by selecting a group of the calmest
(n=17; 10-AN and 7-BR) and most temperamental animals (n=17; 9-AN and 8-BR) based on
EV values of study population. Temperament was assessed by EV measurements after a
stimulus from hair sampling in a chute by measuring the rate of travel over 1.83 m (6 ft) with
an infrared sensor (FarmTek Inc., North Wylie, TX, USA). The velocity was calculated as
EV= distance (m)/time (s)(13,14). It was defined the contrasting temperament groups based on
4
animals’ EV measurements. Animals with EV measurements ≤1.9 m/s were classified as

calm, and those with EV scores ≥2.4 m/s were classified as temperamental(14,15).
To identify informative SNPs in candidate genes, 91 animals were used. A total of 91 animals
were selected as a SNP discovery population: 18 (9 docile; 9 temperamental) of the Angus
breed, 68 (44 docile; 24 temperamental) of the Brahman breed, and 5 (2 docile; 3
temperamental) of Charolais breed. From hair samples and ear notches, DNA extraction was
performed using the GenElute™ extraction kit (Sigma, St. Louis, Missouri, United States).
GWAS analysis and gene discovery
Thirty-four (34) animals were genotyped using the GeneSeek Genomic Profiler HD 150K
chip (Neogen, Lincoln, NE). Association analysis and identification of genomic regions
associated with bovine temperament were performed with PLINK 1.9 software(16). Quality
control of the genotypes was performed to identify animals with no assigned genotype or
with a low genotyping rate (MIND >0.1). Allele frequency was also evaluated, and those
SNPs with lower thresholds (MAF <0.01) were eliminated. Significance threshold was set at
P < 3 × 10−5. A Manhattan plot was constructed using qqman: an R package for visualization
of GWAS results(17). Positions of significant SNPs were identified using the bovine Bos
taurus genome (UMD 3.1.1) and Map Viewer software available at the National Center for
Biotechnology Information (NCBI). Genes closest to the significant SNPs (within ~350 kb)
were also identified with Map Viewer.
Pathway analysis and protein-protein interactions
For the identification of gene pathways, Gene Ontology (GO) term enrichment and protein-
protein interaction (PPI) network analysis were performed in the Ensembl genome
browser(18), Gene Ontology database(19), and STRING database(20), respectively.
Candidate genes resequencing
With the objective of identifying SNPs in the coding regions and of the SNCA gene and its
interacting genes, identified through the protein-protein interaction analysis (PPI), these
genes were resequenced in the SNP discovery population. As part of the sequencing strategy,
5
besides the exons, non-coding regions (140 bp before and after each gene-exon) were also
analysed. Thus, a customized panel was designed using the Design Studio software
(https://designstudio.illumina.com) (Illumina, San Diego, CA, United States) for the
AmpliSeq DNA Gene Assay, in which the coding regions and the boundaries of the APP,
PARK7, SLC6A2, SNCA, UCHL1, PARK2, SLC18A2, and POMC genes were included, using
the Bos taurus UMD 3.1.1 genome as a reference.
DNA quantification was performed in all steps using the Qubit dsDNA HS Assay kit on the
Qubit 3.0 fluorometer (Thermo Scientific, Massachusetts, United States). The libraries were
prepared using the reference guide for custom panels AmpliSeq (Document #
1000000036408 v04) of Illumina, following the instructions for 2 pools and for 49–96 pairs
of primers per pool. The quality and quantification of the libraries were carried out using the
Bioanalyzer 2100 equipment (Agilent, California, United States) with the Agilent DNA 1000
kit. Sequencing (paired-end; read length 126 bp) was performed with the MiniSeq ™
Sequencing System.
Bioinformatics analysis of sequencing data
Sequence reads generated by the MiniSeq™ Sequencing System were aligned with the
reference genome UMD 3.1.1 of Bos taurus using the Burrows-Wheeler aligner (BWA-
MEM) v0.(21). The reads were processed using Picard v1.135
(http://broadinstitute.github.io/picard) and cleaned by marking and removing duplicate reads
to generate BAM files. Variations were identified using the genomic variant call format
(GVCF) workflow with HaplotypeCaller(22). SNPs were generated in VCF files and filtered
using the following criteria: variant confidence normalized by depth (QD) <2.0, mapping
quality (MQ) <40.0, strand bias (FS) >60.0, HaplotypeScore >13.0, MQRankSum <−12.5,
and ReadPosRank-Sum <−8.0(23).
Prediction of the effect of non-coding SNPs on splice sites
To study the effect of the 58 SNPs identified in the non-coding sequences from the exome-
specific sequencing of the SNCA and APP genes, the online ESE finder3.0 web interface
(http://krainer01.cshl.edu/cgi-bin/tools/ESE3) was used(24); the SNCA sequences
NC_037333.1 and APP: NC_037328.1 were used as input, introducing them intron by intron
(<5000 bp) without and with mutations, according to the location of the SNPs. This process
allowed to determine if the SNPs were part of a donor (5´) or acceptor (3´) splice site motif;
6
the programme assigns a score to the input sequence according to the loss of the consensus
sequence, so that scores above a default threshold value (donor: 6.67; acceptor: 6.632) are
predicted to act as a splice site, allowing the analysis of whether the SNPs affect splice sites
motifs.
Results
GWAS analysis and candidate gene identification coupled to protein-

protein interaction analysis
Figure 1 depicts a Manhattan plot with the results from the GWAS analysis of SNPs
evaluated for their association with temperament in Brangus and Angus cattle. Rs133956611
and rs81144933 were associated with a docile temperament (Table 1). The genes SNCA
(alpha-synuclein; GenID 282857) and MMRN1 (multimerin 1; GenID 516574) are located
approximately 222.8 and 435.9 Kb upstream respectively, from rs81144933; while the
GPRIN3 (GPRIN family member 3; GenID 517995) gene was identified 245.7 Kb
downstream of rs133956611.
Figure 1: Manhattan plot of the -log10 (p-values) for the genome-wide association with
exit velocity
7
Table 1: SNPs associated with bovine temperament in Angus and Brangus cattle
CHR rs ID Position Frecuency P-value

pb T D
6 rs133956611 36,676,986 0.14 0.67 9.2 E-06
6 rs81144933 36,655,249 0.20 0.70 3.48 E-05
T= temperamental; D= docile.
The horizontal line corresponds to a significant threshold of P=3× 10−5 using the identified
genes, we proceeded to perform a PPI analysis by querying the STRING(20) database. For
MMRN1, the PPI analysis showed interactions with genes such as F5 and VWF, involved in
the coagulation process (Figure 2), in the Gene Ontology (GO) database, MMRN1 is
annotated with the term GO:0007596, named blood coagulation. For GPRIN3, the search
engine showed interactions between the phosphorylation process encoded by the LOC790121
and OR6N1 genes with proteins that are mainly involved in cytoskeletal assembly and
neurotransmission modulation (Figure 3). The GO database showed that this gene was
annotated with the term GO:0031175, biological process named neuron projection
development, progression of a neuron projection from its formation to the mature structure.
Figure 2: Protein-protein interactions reported for bovine MMRN1 in the STRING

database
8
Figure 3: Protein-protein interactions reported for bovine GPRIN3 in the STRING

database
Finally, SNCA protein, some GO terms identified (GO:0045920, GO:004241 and

GO:0014059) were found to be involved in the regulation, synthesis, and secretion of
dopamine. Interestingly, the SNCA gene was associated with the terms associated with
behavior, including those related to “flight behaviour” and animal responses (through
jumping, standing or walking) to internal and external stimuli (terms GO:0007610,
GO:0007629 GO:0007628 GO:0007630, respectively).
The PPI analysis indicated that SNCA interacts with APP (β-amyloid precursor protein),
PARK7 (parkinsonism associated deglycase), and UCHL1 (ubiquitin C-terminal hydrolase
L1) proteins (P= 5.88e-06) involved in adult locomotory behaviour. In addition, the term
GO:0008344 reveals strong interactions of SNCA with genes belonging to a neurotransmitter
transporter family (SLC6A) in the network (Figure 4).
9
Figure 4: Protein-protein interactions reported for bovine SNCA in the STRING database
Red nodes annotated with the GO:0008344 term, adult locomotory behavior (p-value 5.88E-06). Green nodes
annotated with the GO:0043005 term, neuron projection (p-value 0.000966). Blue nodes annotated with the
05012 KEGG pathway ID, Parkinson s disease (p-value 6.49E-11).
Based on their reported functional role, GPRIN3 and, particularly, SNCA genes could be
considered as candidate genes associated with cattle temperament, the MMRN1 gene analysis
indicate no obvious implications for this trait, however its identification could be important
for further analysis.
Genetic variation in candidate genes
According to the PPI analysis results, it was inferred that the APP, PARK7, SLC6A2, UCHL1,
PARK2, SLC18A2, and POMC genes were candidates associated with bovine temperament
(Table 2). They were resequenced to discover genetic variation to potentially explain cattle
temperament. Fifty-eight (58) SNPs were found in the non-coding regions of the SNCA and
APP genes. Three SNPs were identified in introns 2 and 3 of the SNCA gene, and 55 SNPs
were identified in introns 1, 5, 8, 11, 13, 14, and 17 of the APP gene (Table 3). Fifteen of the
58 SNPs were unique to the Angus breed, 1 in the SNCA gene and the remaining in the APP
gene. The remaining SNPs (n= 43) were informative (polymorphic) in the Brahman and
Charolais breeds, as opposed to the Angus breed in which they were uninformative
(monomorphic). The allelic frequencies and distribution pattern of the SNPs varied according
to the breed.
10
Table 2: Biological functions and processes associated with interacting SNCA genes
Gene Description
No information in cattle. In humans, protects dopaminergic neurons against
PARK7 oxidative damage and degeneration; indirectly inhibits aggregation of α-
synuclein(25); thus, mutations in this gene have been demonstrated to cause
Parkinson’s disease(26).
No information in cattle. In humans, controls the action of norepinephrine that
SLC6A2 support arousal, mood, attention, and reactions to stress; thus, it has been
associated with temperamental personality dimensions (novelty seeking, harm
avoidance, reward dependence, and persistence)(27).
No information in cattle. In humans, it is abundantly expressed in neurons and
UCHL1 interacts with APP, and SNPs in this gene have been implicated in the
neurodegenerative disorders Parkinson’s disease and Alzheimer’s disease(28).
In cattle, it has been associated with temperament (flight speed)(9) and in
PARK2 humans in the functions of dopaminergic neurons due to the mutations in this
gene associated with Parkinson’s disease(29).
In cattle, it has been associated with temperament (Pen Score) (Garza-
(5)
SLC18A2 Brenner et al . It participates in the transport of dopamine, preventing its
accumulation and dopaminergic neuron death; therefore, it is a risk factor for
Parkinson’s disease(30).
In cattle, it has been associated with temperament (Pen Score)(5). POMC is the
precursor for corticotropic hormone (ACTH), which increases the expression
POMC of brain-derived neurotrophic factor (BDNF) responsible for neuron
proliferation, differentiation, and survival; thus, it has been implicated in
Parkinson’s disease (31).
From the 58 SNP´s identified in the non-coding regions of SNCA and APP genes, three SNPs
were part of a splice site motif according to established thresholds (donor: 6.67; acceptor:
6.632), as shown in Table 4; the identified SNPs were located in introns 5, 8, and 11. All the
splice site motifs were of the acceptor type, that is, they were located at the 3´ end. The SNP
g. 9770593 (C/T) did not add or abolish any splice site motif, but only increased the score
value, while the SNPs g. 9806689 (G/T) and g. 9845821 (C/G) added and abolished the splice
site motifs, respectively.
11
Discussion
Genomic studies aimed at the exploration of cattle temperament are still scarce, mainly due
to the biological complexity of the system, differences in the temperament measurement
(objective/subjective), and differences between the studied cattle breeds. In this work, was
used the GWAs as an exploratory tool to find candidate genes associated with EV, contrasting
by temperament a pool of Angus and Brangus animals. GWAS allowed to identify a genomic
region on BTA6 that harbours three candidate genes associated with EV [SNCA (Gen ID
282857), MMRN1 (Gen ID 516574), and GPRIN3 (Gene ID: 517995)]. For these genes, Chen
et al(32) reported an elevated expression of GPRIN3 in the human brain, and information from
UniProtKB(33) indicates that the GPRIN3 protein may be involved in neurite outgrowth.
However, the literature data (regarding function and interacting genes) strongly supports the
bovine SNCA gene as a novel candidate associated with cattle temperament(9,34).
The SNCA gene is a highly conserved protein that is abundant in the brain of humans and
other species like rats, mice, and monkeys(35); it is found in neurons, especially in presynaptic
terminals(36). The molecular function of SNCA is quite ambiguous, and based on its structure,
physical properties, and interacting partners, several hypotheses regarding its normal
function in humans have been proposed. For example, it is thought to be involved in the
regulation of dopamine release and transport(34). Consequently, in humans it plays an
important role in neurodegenerative disorders. According to Giasson et al(37), aggregates of
SNCA protein in humans cause brain lesions that are characteristic of neurodegenerative
synucleinopathies. The SNCA gene is associated, in the Kyoto Encyclopedia of Genes and
Genomes (KEGG)(38), with biological pathways of neurodegenerative diseases such as
Alzheimer's disease (ko05010) and Parkinson's disease (ko05012). Both diseases are
important brain disorders in humans. Parkinson's disease is characterized by symptoms
related to locomotion (involuntary tremor, muscle stiffness, and postural instability), as well
as depression and psychosis, and it involves the progressive loss of dopaminergic neurons,
with the main feature presenting as the appearance of inclusion bodies called Lewy bodies,
the main component of which is SNCA(37).
Although the pathological alterations linked to those human diseases cannot be extrapolated
to this study model, this biological link provides some evidence to support the findings
because the understanding of the relationship between genotype and phenotype in humans
was derived from model animals with mutations in orthologous genes. Large animal species,
such as dog, pig, sheep and cattle, have been some of the most important model animals,
mainly because they are more similar to humans than mice (similar size, genetics, and
physiology). Thus, discoveries in humans can serve as a reference to infer effects on bovine
temperament(39).
12
Connecting gene networks to explain cattle temperament
Despite scarce attempts to identify genes and genomic regions underlying the genetic
architecture of temperament, until now there have been no reports connecting the gene
networks associated with this complex trait.
Protein-protein interaction analysis of the SNCA gene allowed to identify and analyse six
additional genes, of which two gene members of the SLC family (SLC18A2 and SLC6A4)
have already been identified by Garza-Brenner et al(5) as interacting genes in a protein-protein
network based on dopamine- and serotonin-related genes. These authors also found a SNP
located in the SLC18A2 gene that causes a change in the amino acid sequence from alanine
to threonine, with significant effects on temperament as measured by pen scores. In addition,
the PPI analysis included genes in the PARK family (PARK2 and PARK7), which encode
ubiquitin ligase proteins, including parkin RBR E3. The gene PARK2 was identified by
Valente et al(9) as a candidate gene associated with temperament in Nellore cattle; the authors
used EV as a test to evaluate bovine temperament. Multiple studies have used the GWAS
strategy to identify genes that are linked to bovine temperament phenotypes(8-10), but in none
of these cases has it been possible to establish interactions between the identified genes, and
the information from each study seems to be isolated and independent, preventing the
clarification of the genetic architecture of temperament from the information available to
date. In addition, the set of candidate genes does not seem to be associated with a
representative biological process that suggests participation in temperament. The
identification of SNCA in this work allows to connect the results of Valente et al(9) and Garza-
Brenner et al(5), showing that the genes identified through different strategies (GWAS and
protein-protein interaction network analysis) present an important connection. According to
these results, it was explored the genetic variation in these genes in cattle with an emphasis
on their coding sequences, and the results revealed a high conservation of the exonic
sequences in all seven analysed genes. In humans, a low genetic variation has been reported
between genes such as SNCA and UCHL1(40).
Interestingly, and according to previous reports, a high genetic variation was found in the in
the non-coding regions of the bovine SNCA and APP genes.
The exact function of the amyloid beta (A4) precursor protein (APP) gene is unknown, but it
has been associated with meat softness in pigs(41), can participate in the formation of neurons,
and is known for its participation in Alzheimer's disease(42). Because patients with
Alzheimer's disease show the presence and accumulation of both SNCA and APP proteins, it
has been proposed that they may be related in some way. Roberts et al(43) have shown that
SNCA overexpression increases APP levels, and certain mutations in SNCA increase the
13
processing of APP, so the discovery of mutations in the coding regions of these genes could
have a functional impact on them and therefore on bovine temperament.
It has been documented that approximately 21% of bovine genes are alternatively spliced(44).
In silico analyis identified three APP-SNP´s with the potential to have a functional effect in
the pre-mRNA splicing process and, therefore, the expression of bovine temperament. As far
as known, no different isoforms of the bovine APP gene have been reported, but splice site
motifs in bovine genes have been reported to be highly conserved relative to humans(44). The
human and bovine genes for APP are orthologs, having the same number of amino acids
(770) and an identical amino acidic sequence. In humans, 8 different isoforms of the APP
gene have been identified due to the alternative splicing in exons 7, 8, and 15, which
terminates APP gene expression in neurons, resulting in the implication of a fundamental
role in Alzheimer's disease(45). Here there was identified 3 SNPs that affect, add, and abolish
splice site motifs in the APP gene, in introns 5, 8, and 11, so they could probably affect the
final product and have an effect on the expression of bovine temperament.
In the present study, it was used the contrasting phenotype strategy to perform an exploratory
GWAS analysis to identify candidate genes for temperament in cattle, and even with the
small sample size limitation, the results showing a connection between SNCA and
temperament are consistent with larger GWAS studies. Additionally, the coupling of these
result with a PPI analysis allowed to establish connections between different genes that were
previously identified within the association to the locomotor system. Fine mapping of the
candidate genes predicted that the GWAS and PPI genes confirmed the existence of SNPs
with the potential to affect bovine temperament. The present study provides valuable
information that contributes to the -still scarce- efforts to describe the cattle temperament
genetic architecture, and shows that an analytic strategy is appropriate for application in
studies with a limited sample size, especially in countries where phenotyping for this
complex trait is limited.
Conclusions and implications
A BTA6 genomic region (36,655,249-36,676,986 bp) neighboring the SNCA gene was
associated with temperament trait in Angus and Brangus breeds. Six genes, linked to SNCA,
were identified as being potentially associated with temperament. From those, the APP gene
harboured three SNPs with a potential effect on the pre-mRNA splicing process and
expression of bovine temperament.
14
Acknowledgements
This investigation was funded by research grants supported by CONACYT and IPN (project
294826, SIP 20171674) and partial financial support by CONARGEN, A.C. to support the
feeding performance tests. The authors would also like to acknowledge the different herd
owners and the technical staff from the Palomas complex UGRCH, who collected and
provided the data and samples used in this study. The mention of trade name, proprietary
products, or specified equipment does not constitute a guarantee or warranty by the USDA
and does not imply approval for the exclusion of other products that may be suitable. The
USDA is an Equal Opportunity Employer.
Literature cited:
1. Buchenauer D. Genetics of behaviour in cattle. In: Fries R Ruvinsky A, editors. The

genetics of cattle. CABI Publishing, Wallingford;1999:365–390.
2. Mormède P. Molecular genetics of behaviour: research strategies and perspectives for

animal production. Livest Prod Sci 2005;93(1):15–21.
3. Schmutz SM, Stookey JM, Winkelman-Sim DC, Waltz CS, Plante Y, Buchanan FC. A
QTL study of cattle behavioural traits in embryo transfer families. J Hered 2001;
92(3):290–292.
4. Hiendleder S, Thomsen H, Reinsch N, Bennewitz J, Leyhe-Horn B, Looft C, et al.

Mapping of QTL for body conformation and behavior in cattle. J Hered 2003;
94(6):496–506.
5. Garza-Brenner E, Sifuentes-Rincón AM, Randel RD, Paredes-Sánchez FA, Parra-

Bracamonte GM, Arellano-Vera W, et al. Association of SNPs in dopamine- and
serotonin-pathway genes and their interacting genes with temperament traits in
Charolais cows. J Appl Genetics 2016;58(3):363–371.
6. Garza-Brenner E, Sifuentes-Rincón AM, Rodríguez-Almeida FA, Randel RD, Parra-

Bracamonte GM, Arellano-Vera W. Influence of temperament-related genes on live
weight traits of Charolais cows. R Bras Zootec 2020:49:e20180121.
7. Garza-Brenner E, Sifuentes-Rincón AM, Rodríguez Almeida FA, Randel RD, Parra-

Bracamonte GM, Arellano Vera W. Influence of genetic markers on the feeding
behavior of yearling bulls. Rev Colomb Cienc Pecu 2019;32(1):14-20.
15
8. Lindholm-Perry AK, Kuehn LA, Freetly HC, Snelling WM. Genetic markers that
influence feed efficiency phenotypes also affect cattle temperament as measured by
flight speed. Anim Genet 2014;46(1):60-64.
9. Valente TS, Baldi F, SantÁnna AC, Albuquerque LG, Da Costa MJRP. Genome-wide
association study between single nucletide polymorphismos and flight speed in Nellore
cattle. PLoS One 2016;11(6):1-18.
10. Dos Santos FC, Campolina PMG, De Souza PA, Ávila PMF, Ventura RV, Rosse IC, et
al. Identification of candidate genes for reactivity in Guzerat (Bos indicus) cattle: a
Genome-Wide Association Study. Plos One 2017;12(1):1-15.
11. Jiang L, Liu X, Yang J, Wang H, Jiang J, Liu L, et al. Targeted resequencing of GWAS
loci reveals novel genetic variants for milk production traits. BMC Genomics
2014;15(1):1105.
12. Kurz JP, Yang Z, Weiss RB, Wilson DJ, Rood KA, Liu GE, Wang Z. A genome-wide
association study for mastitis resistance in phenotypically well-characterized Holstein
dairy cattle using a selective genotyping approach. Immunogenetics 2019;71(1):35-47.
13. Burrow HM. Measurements of temperament and their relationships with performance
traits of beef cattle. Anim Breed 1997;65(7):477–495.
14. Curley KOJr, Paschal JC, Welsh THJr, Randel RD. Exit velocity as a measure of cattle
temperament is repeatable and associated with serum concentration of cortisol in
Brahman bulls. J Anim Sci 2006;84(11):3100-3103.
15. Burrow HM. Effect of intensive handling of zebu crossbred weaner calves on
temperament. Proc Assoc Advmt Anim Breed Genet 1991;9:208-211.
16. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D. PLINK: a tool
set for whole-genome association and population-based linkage analyses. Am J Hum
Genet 2007;8(3):559-575.
17. Turner SD. qqman: an R package for visualizing GWAS results using Q-Q and
manhattan plots. bioRxiv 2014;3(25):005165.
18. Zerbino DR, Achuthan P, Akanni W, Amode MR, Barrell D, Bhai J. Ensembl 2018.
Nucleic Acids Res 2018;46(D1):754–761.
19. Mi H, Huang X, Muruganujan A, Tang H, Mills C, Kang D, Thomas PD. PANTHER

version 14: more genomes, a new PANTHER GO-slim and improvements in enrichment
analysis tools. Nucleic Acids Res 2019;47(D1):D419-D426.
16
20. Szklarczyk D, Morris JH, Cook H, Kuhn M, Wyder S, Simonovic M, et al. The STRING
database in 2017: quality-controlled protein-protein association networks, made broadly
accessible. Nucleic Acids Res 2017;45(D1):362-368.
21. Li H, Durbin R. Fast and accurate short read alignment with Burrows–Wheeler
transform. Bioinformatics 2009;25(14):1754–1760.
22. Van der Auwera GA, Carneiro MO, Hartl C, Poplin R, Del Angel G, LevyMoonshine
A, et al. From FastQ data to high-confidence variant calls: the genome analysis toolkit
best practices pipeline. Curr Protoc Bioinf 2013;43(1110):11101–111033.
23. Choi JW, Liao X, Stothard P, Chung WH, Jeon HJ, Miller SP, et al. Whole-genome
analyses of Korean native and Holstein cattle breeds by massively parallel sequencing.
PLoS One 2014;9(7):e101127.
24. Cartegni L, Wang J, Zhu Z, Zhang MQ, Krainer AR. ESEfinder: A web resource to
identify exonic splicing enhancers. Nucleic Acid Res 2003;31(13):3568-3571.
25. Dolgacheva LP, Berezhnov AV, Fedotova EI, et al. Role of DJ-1 in the mechanism of
pathogenesis of Parkinson's disease. J Bioenerg Biomembr 2019;51(3):175–188.
26. He L, Lin S, Pan H, Shen R, Wang M, Liu Z, et al. Lack of association between DJ-1
gene promoter polymorphism and the risk of parkinson’s disease. Front Aging Neurosci
2019;11(24):1-11.
27. Narita SI, Kazuhiko N, Kenta N, Maki Y, Eiji O, Nobuyo I. Analysis of association
between norepinephrine transporter gene polymorphisms and personality traits of NEO-
FFI in a Japanese population. Psychiatry Investig 2015;12(3):381-387.
28. Rydning SL, Backe PH, Sousa MML, Iqbal Z, Øye AM, Sheng Y, et al. Novel UCHL1
mutations reveal new insights into ubiquitin processing. Hum Mol Genet
2017;26(6):1031-1040.
29. Bakhit YH, Ibrahim MO, Amin M, Mirghani YA, Hassan MA. In silico analysis of SNPs
in PARK2 and PINK1 genes that potentially cause autosomal recessive Parkinson
disease. Adv Bioinformatics 2016;9313746:1-5.
30. Brighina L, Riva C, Bertola F, Saracchi E, Fermi S, Goldwurm S, et al. Analysis of

vesicular monoamine transporter 2 polymorphisms in Parkinson's disease. Neurobiol
Aging 2013;34(6):1712.e9-13.
17
31. Shadrina M, Nikopensius T, Slominsky P, Illarioshkin S, Bagyeva G, Markova E, et al.

Association study of sporadic Parkinson's disease genetic risk factors in patients from
Russia by APEX technology. Neurosci Lett 2006;405(3):212-6.
32. Chen LT, Gilman AG, Kozasa T. A Candidate target for G protein action in brain. J Biol
Chem 1999;274(38):26931–26938.
33. Chen C, Huang H, Wu CH. Protein bioinformatics databases and resources. Methods
Mol Biol 2017;1558:3-39.
34. Siddiqui IJ, Pervaiz N, Abbasi AA. The Parkinson disease gene SNCA: Evolutionary
and structural insights with pathological implication. Sci Rep 2016;6(1):24475.
35. Deng H, Yuan L. Genetic variants and animal models in SNCA and Parkinson disease.
Ageing Res Rev 2014;15:161-76.
36. Campêlo CLDC, Silva RH. Genetic variants in SNCA and the risk of sporadic
Parkinson’s disease and clinical outcomes: A review. Parkinsons Dis 2017;4318416:1-
11.
37. Giasson BI, Duda JE, Murray IVJ, Chen Q, Souza JM, Hurtig HI, et al. Oxidative
damage linked to neurodegeneration by selective alpha-synuclein nitration in
synucleinopathy lesions. Science 2000;290(5493):985-989.
38. Kanehisa M, Furumichi M, Tanabe M, Sato Y, Morishima K. KEGG: New perspectives

on genomes, pathways, diseases and drugs. Nucleic Acids Res 2017;45(D1):353-361.
39. Pinnapureddy AR, Stayner C, McEwan J, Baddeley O, Forman J, Eccles MR. Large
animal models of rare genetic disorders: sheep as phenotypically relevant models of
human genetic disease. Orphanet J Rare Dis 2015;10:107-115.
40. Dong N, Zhang X, Liu Q. Identification of therapeutic targets for Parkinson's disease
via bioinformatics analysis. Mol Med Rep 2017;15(2):731-735.
41. Lobjois V, Liaubet L, San Cristobal M, Glénisson J, Fève K, Rallières J. A muscle

transcriptome analysis identifies positional candidate genes for a complex trait in pig.
Anim Genet 2008;39(2):147-162.
42. Masters CL, Simms G, Weinman NA, Multhaup G, McDonald BL, Beyreuther K.
Amyloid plaque core protein in Alzheimer disease and Down syndrome. Proc Natl Acad
Sci USA 1985;82(12):4245-4249.
18
43. Roberts HL, Schneider BL, Brown DR. α-Synuclein increases β-amyloid secretion by
promoting β-/γ-secretase processing of APP. PLoS One 2017;12(2):e0171925.
44. Chacko E, Ranganathan S. Genome-wide analysis of alternative splicing in cow:

Implications in bovine as a model for human diseases. BMC Genomics 2009;10(3):S11.
45. Sandbrink R, Masters CL, Beyreuther K. APP gene family. Alternative splicing
generates functionally related isoforms. Ann N Y Acad Sci 1996;777:281-287.
19
Table 3: SNPs identified by specific exome sequencing in each population of APP, PARK7, SLC6A2, SNCA, UCHL1, PARK2,
SLC18A2, and POMC genes
Position Frecuency
Gene Region Alleles
Angus Brahman Charolais
(bp)
Ref Alt Ref Alt Ref Alt Ref Alt
36297353 Intron 3 G A 0.9924 0.0076 1.0 0.0
SNCA 36297374 Intron 3 A G 0.8500 0.1500 1.0 0.0
36297422 ¥ Intron 2 T A 0.5000 0.5000
9674371 Intron 1 T C 0.9717 0.0283 1.0 0.0
9674423 Intron 1 A C 0.9403 0.0597 1.0 0.0
9674429 Intron 1 T A 0.9478 0.0522 1.0 0.0
9674430 ¥ Intron 1 T A 0.9722 0.0278
9674431* Intron 1 A T 0.9706 0.0294 0.9925 0.0075 1.0 0.0
9674437 Intron 1 T C 1.0000 0.0 0.9000 0.1000
9674448 Intron 1 T C 0.9921 0.0079 1.0 0.0
9674451 Intron 1 A G 0.9921 0.0079 0.9000 0.1000
9674455* Intron 1 G A 0.6071 0.3929 0.0093 0.9907 0.5000 0.5000
9770586* Intron 5 A G/T 0.6944 0.3056/0.0 0.8772 0.0395/0.0833 0.8000 0.2000/0.0
9770593 Intron 5 C T 0.3507 0.6493 1.0 0.0
9770633 Intron 5 G A 0.5373 0.4627 1.0 0.0
APP 9803985* Intron 8 C T 0.9722 0.0278 0.0944 0.9056 1.0 0.0
9803991* Intron 8 A G 0.9722 0.0278 0.0909 0.9091 1.0 0.0
9806624* Intron 8 A G 0.9167 0.0833 0.0574 0.9426 0.8000 0.2000
9806672 Intron 8 T C 0.9769 0.0231 0.8000 0.2000
9806689 Intron 8 G T 0.9851 0.0149 1.0 0.0
9845631 Intron 11 C A 1.0000 0.0000 0.8000 0.2000
9845821 Intron 11 C G 0.7177 0.2823 1.0 0.0
9845862 ¥ Intron 11 G T 0.8750 0.1250
9845934 Intron 11 G A 0.9844 0.0156 1.0 0.0
9845944 Intron 11 G A 0.8750 0.1250 1.0 0.0
9845966 Intron 11 G A 0.9692 0.0308 1.0 0.0
9845980 Intron 11 A G 0.8056 0.1944 1.0 0.0
9863873* Intron 13 T C 0.9722 0.0278 0.0522 0.9478 1.0 0.0
9863960 Intron 13 T C 0.0818 0.9182 1.0 0.0
20
9863974¥ Intron 13 T C 0.6666 0.3333

9863983¥ Intron 13 T C 0.0 1.0
9863984¥ Intron 13 G T 0.0 1.0
9866489 Intron 13 G A 0.8433 0.1567 1.0 0.0
9866528 Intron 13 A G 0.9925 0.0075 1.0 0.0
9866542¥ Intron 13 A G 0.9722 0.02
9866545 Intron 13 C T 0.8433 0.1567 1.0 0.0
9866552¥ Intron 13 T C 0.9118 0.08
9866569 Intron 13 C T 0.8624 0.1376 1.0 0.0
9879860 Intron 13 T C 0.7881 0.2119 1.0 0.0
9880018 Intron 13 C A 0.5694 0.4306 1.0 0.0
9880025 Intron 13 G T 0.7787 0.2213 1.0 0.0
9889605¥ Intron 14 C G 0.6250 0.3750
9889627 Intron 14 G A 0.9462 0.0538 1.0 0.0
9889677* Intron 14 G T 0.0 1.0 0.9925 0.0075 0.0 1.0
9889687¥ Intron 14 T C 0.4167 0.5833
9891054¥ Intron 14 C T 0.5833 0.4167
9891056¥ Intron 14 T C 0.5000 0.5000
9891124¥ Intron 14 G T 0.4000 0.6000
9891130¥ Intron 14 A G 0.4063 0.5938
9891155¥ Intron 14 T G 0.4063 0.5938
9918483 Intron 17 C T 0.9841 0.0159 1.0 0.0
9918506* Intron 17 A G 0.1389 0.8611 0.9250 0.0750 0.2000 0.8000
9918508 Intron 17 C T 0.9655 0.0345 1.0 0.0
9918512 Intron 17 C T 0.9914 0.0086 1.0 0.0
9931517 Intron 17 C G 0.9924 0.0076 1.0 0.0
9931524 Intron 17 C G 0.9924 0.0076 1.0 0.0
9931525 Intron 17 T G 0.9924 0.0076 1.0 0.0
9931529 Intron 17 C T 1.0000 0.0000 0.9000 0.1000
* Variations present in the three populations. ¥ Specific variations in the Angus population.
21
Table 4: The ESE finder results for non-coding SNPs identified in the SNCA and APP genes
Donor/
Gene Position Intron SNP Site Sequence Score
acceptor
C WT CTCTCCCCTCGTCAGTGCTGTAGTTCAGGT acceptor 6.74720
9770593 5
T M CTTTCCCCTCGTCAGTGCTGTAGTTCAGGT acceptor 7.11480 ↑
G WT ------ ------ ------
APP 9806689 8
T M CTTTGGATTTGCCAGGCACACTCACCTCCA acceptor 6.81380 ↑
C WT CTCCTTCCACAACAGAAGGCGCTATTTTAA acceptor 6.71530
9845821 11
G M ------ ------ ------
The SNP nucleotide is highlighted in bold in the sequence. WT: wild type. M: sequence with non-coding SNP. ↑ indicates an increased score compared with the
wild type sequence.
22
Article
Effect of consanguinity and selection on the components of a

productive index, in mice under close mating
Dulce Janet Hernández López a
Raúl Ulloa Arvizu a
Carlos Gustavo Vázquez Peláez a
Graciela Guadalupe Tapia Pérez a*
a
Universidad Nacional Autónoma de México, Departamento de Genética y
Bioestadística de la Facultad de Medicina Veterinaria y Zootecnia. Ciudad de México,
México.
*Corresponding author: tapiadoctora@gmail.com
Abstract:
In order to examine the influence of inbreeding depression on some productive

characteristics of the laboratory mouse, 871 records were reanalyzed, which were from
20 generations in a line with narrow inbred crossing with selection for a productive index
(WOFW) comparing with a line without selection, with inbred crossing (n= 135).
Inbreeding coefficients (F) were calculated for each generation. In all the components of
the index (reproductive life, fertile postpartum estruses and litter size), the two lines were
compared, in the 15 available generations of the non-selected one, by the least squares
method, grouping every five generations. The selected one was analyzed in the 20
generations for intergenerational differences with the same method. Inbreeding
depression was estimated in all generations with a linear regression of consanguinity
(expressed in 10 %) in all components. A significant difference (P<0.01) was observed
between lines in the variables analyzed. The fertile postpartum estruses of the selected
line remained constant, there was a decrease of 0.331 in the non-selected one (P<0.01).
The productive index remained stable (increased 0.071) in the selected one, in the non-
selected one it decreased (0.39) until disappearing (G15). Inbreeding depression impacted
the reproductive life of both, decreased 4.741 d in the selected one vs 7.718 d in the non-
selected one (P<0.01). In the non-selected one, it affected mortality at weaning and
23
estrous cycle, the selection to the index counteracted that impact, probably due to the
selection of genes that favor the gonadal development of mice.
Key words: Mice, Selection, Reproductive life, Number of estruses, Inbreeding

depression.
Introduction
At present many genetically different mouse lines have been developed, which have
particular research purposes. The inbred lines were the prototype of the genetically
standardized lines, which allowed developing experiments eliminating the variability of
genetic origin. Although genomics provides laboratories with the necessary tools to
produce mice with the characteristics that research demands, when a characteristic has
been fixed, a rigorous selection and directed mating process is needed to maintain the
viability of the line, which usually leads to inbreeding depression(1,2).
The genetic basis of this phenomenon is related to three hypotheses, namely, partial
dominance (greater expression of deleterious recessive alleles), overdominance
(superiority of heterozygotes over both types of homozygotes) and epistasis (greater
probability of genetic combinations favorable to heterozygotes)(3).
Breeders of purebred domestic animals use inbreeding to fix desirable genetic traits
within a population or to try to eliminate deleterious traits, inbreeding depression can
affect the economic income of breeders(4). Studies in mice, by offering a greater number
of generations in less time, help to understand inbreeding depression, in populations
where it is sought to select some characteristic.
In the mouse, a 7.2 % reduction in litter size was observed for every 10 % increase in
consanguinity, under consecutive mating of complete siblings without selection(2) and,
with the same increase in consanguinity in crosses between half-siblings without
selection, the decrease was 6.22 % in litter size(5).
Depression was less severe in lines under directed selection than in lines without
selection(6), this was observed when selecting for litter size in mice, finding that the
reduction in the reproductive ability was significantly lower in inbred lines under
selection, compared to that of inbred lines without selection; this is explained because
24
thanks to selection, there is an increase in genes related to better reproductive ability,

which counteracts the inbreeding depression that causes the reduction of this ability(7). It
has been seen that the behavior of an inbred line selected for litter size is similar to that
of a non-inbred line, selected for the same trait. In one study, consanguinity allowed
exceeding the limit of selection for large litter size, when in the selected line an inbred
crossing was allowed(8).
The number of weaned offspring per female per week (WOFW) is a productive index that
is measured during and at the end of the reproductive life in each pair of mice. It is used
in the founding colonies of some laboratory animal companies(9,10). Although it is
recommended to select mice from families with higher WOFW to maintain laboratory
lines, by narrow inbred crossing with fixed characteristics(11), in the literature there is little
information on the effect of this selection in inbred mice on the variables included in it.
Therefore, the objective of this study was to evaluate the effect of inbreeding on the
components of a productive index, in the animal model of laboratory mouse, during 20
generations of selection with narrow inbred crossing, as well as to evaluate whether the
selection can be affected in its progress, by the effect of inbreeding depression, in the
characteristics that constitute it.
The present work is a retrospective, cross-sectional, comparative and observational study.

Eight hundred seventy-one records of a bioterium were reanalyzed, which were taken for
five years in mice with continuous selection and narrow inbred cross (brother with sister),
where there were five lines selected for a productive index: (WOFW) in 20 generations
(n= 871). The data, collected between 1989-1994, had been analyzed with the aim of
obtaining realized heritability, for the productive index, a detailed description can be seen
in Tapia-Pérez(12).
For this study, a line of the same contemporary strain was added, which was from the
same bioterium (n= 135), with narrow inbred crossing without selection until generation
15; after this generation the pairs ceased to be fertile.
The animals were housed in shoebox-type polycarbonate cages, which offer an area of
375 cm, with Cambridge-type stainless steel lid and Kraft-type rigid polyester filter; food
was provided ad libitum, drinking water filtered by reverse osmosis acidified at a pH of
2.5. The air was filtered, and a temperature of 18 to 26 °C was maintained. The
identification of the animals was individual, first, by means of notches in the ears, and the
records by means of cards in each cage. These cards were then summarized in record
folders called REA (Reproductive Efficiency Analysis), from which the WOFW index
25
was calculated every three generations, or when divergent lines were detected (this could
occur in the fourth generation in each selected line), to obtain and select the subline with
the highest average, which also had offspring of the third parturition, with at least two
females and two males for breeding. It should be noted that those pairs who were sterile
(no gestation was recorded), infertile (gestation was recorded, but not parturition) or who
cannibalized their offspring had a WOFW value of zero, which was included to obtain
the average since they are considered the result of inbreeding depression. As for the non-
selected line, the management was similar; this line was the only one that remained active
of the five that started at the same time of the selected ones, the other four were lost in
the second generation. Reproductive management in both lines was under an intensive
monogamous method, that is, a male with a female were placed in the same cage and
remained together throughout their reproductive life (165 ± 3.6 d). Mating began when
the animals reached sexual maturity (8-10 wk).
The pairs selected for reproduction were formed randomly, a female and a male full
siblings, from the third parturition of their parents, both in the selection generation (3 or
4) and in the previous ones. An average of eight pairs per line was maintained in each
generation.
Description of variables
The variables that were analyzed were:

RL: reproductive life, measured as the total days in reproduction.
FPPE: total number of fertile postpartum estruses in RL. A fertile postpartum estrus is
considered when the female has a parturition in the first estrus, within 35 or less days
from the previous one (since a gestation period of 21 d is assumed, with an implantation
within 5 d, if this occurs out when the mother is still lactating a previous litter, it can occur
in 14 d maximum, thus: 21 + 14 =35)(9).
OBOR: total number of offspring born in RL.
OWEA: total number of offspring weaned in RL.
𝑂𝐵𝑂𝑅
OBPP: offspring born per parturition = 𝑃𝐴𝑅𝑇𝑈𝑅𝐼𝑇𝐼𝑂𝑁𝑆(PARTURITIONS: number of total
parturitions in RL).
𝑂𝑊𝐸𝐴
OWPP: offspring weaned per parturition = .𝑃𝐴𝑅𝑇𝑈𝑅𝐼𝑇𝐼𝑂𝑁𝑆
WOFW: (Productive Efficiency Index) is the number of weaned offspring per female per
𝑂𝑊𝐸𝐴
week = ×7.
𝑅𝐿
𝑂𝐵𝑃𝑃−𝑂𝑊𝑃𝑃
Percentage of mortality at weaning = 𝑋100.
𝑂𝐵𝑃𝑃
26
Statistical analysis
For the statistical analysis, the selected line corresponds to the set of the five lines with
selection for WOFW divided into: line selected for 15 generations (S15G) n= 733, the
same line selected in the 20 generations (S20G) n= 871 and non-selected line, which
remained until generation 15 (NS15G) n= 135, since in this one, there were five pairs, of
which only one reached the third parturition and the offspring were not enough to make
the crosses.
The information of five generations in each line was grouped, for all the variables, so that
the data analyzed at each level correspond to those of five successive generations. They
were grouped in this way to observe the result of the selection in the fifth generation,
because, as explained, the selection was made at least every three generations or when
divergent lines were detected, which could occur in up to four generations. Level 1
contains the sum of generations from 1 to 5; level 2, from 6 to 10 and level 3, from 11 to
15 of S15G and NS15G, while level 4 only corresponds to S20G, in generations 16 to 20.
Normality tests of the variables mentioned were performed by the Kolmogorov-Smirnov

method.
The general linear model used to compare S15G and NS15G was (Model 1):
𝑌𝑖𝑗𝑘 = 𝜇 + 𝑠𝑖 + 𝑔𝑗 + (𝑠𝑔)𝑖𝑗 + 𝜀𝑖𝑗𝑘
Where
𝒀𝒊𝒋𝒌 is the sum of five successive generations of pairs, for each quantitative variable;
𝒔𝒊 is the effect of the i-th selection group (i=1,2);
𝒈𝒋 is the effect of the generation grouped every five generations (j=1,2,3);
(𝒔𝒈)𝒊𝒋 is the effect of the interaction between the selection group and the grouped
generation;
𝜺𝒊𝒋𝒌 the random error (𝜀~𝑁0,𝜎𝜀2 ).
The analysis model for S20G (Model 2) only included the effect of the grouped generation
gi, (i=1,2,3,4):
𝑌𝑖𝑗 = 𝜇 + 𝑔𝑖 + 𝜀𝑖𝑗
Where
𝒀𝒊𝒋 is the sum of 5 successive generations of pairs, for each quantitative variable, in the
i-th generation;
𝜺𝒊𝒋 the random error (𝜀~𝑁0,𝜎𝜀2 ).
27
Both models were analyzed by the least squares method. The coefficient of consanguinity
was calculated for each animal, with the Pedigree Viewer© Computer Program,
developed by Brian Kinghorn(13), which uses the method developed by Wright (1922)(14).
The mean inbreeding depression (𝛽̂1) and its standard error (s.e.), of WOFW were
estimated by the least squares method with the coefficients of consanguinity (Fi) of each
generation, in units of 10 % for lines S15 and NS15 (i= 1,2,3, ...,15) and S20 (i= 1,2,3,
..., 20), with the following simple linear regression model. (Model 3)
̂0 + ̂
𝑌̂ 𝑖 = 𝛽 𝛽1 𝐹𝑖 + 𝑒𝑖
Where
̂ i is the average of each component of the index in the i-th generation;
𝒀
̂ 𝟎 is the estimate of the intercept;
𝜷
̂
𝜷𝟏 is the mean inbreeding depression, Fi is the coefficient of consanguinity (10 %);
𝒆𝒊 is the random error (𝑒𝑖 ~𝑁µ,𝜎𝑒2 ).
Since the increase in inbreeding was considered in units of 10 %, inbreeding depressions

were related to this measure, which was chosen to be able to compare the results of this
study with other mouse articles where it is calculated in that way.
The models were analyzed with the statistical package, IBM SPSS Version 22(15), the
percentages of mortality at weaning, of S15G and NS15G, in each of the three generations
grouped, were analyzed by the Chi-square test, with the online MedCalc® program(16).
The P value ≤0.05 was considered as significant and P≤0.01 as highly significant.
Results
Linear models
Model 1
A highly significant effect of selection group (si) (P<0.01) was observed in all variables.
Regarding the interaction (sg)ij, in the variables FPPE, OBOR, OWEA and WOFW, it
was highly significant (P<0.01), in RL and OWPP, the effect of the interaction was
significant (P<0.05), which did not happen in the OBPP variable (P>0.05) (Table 1).
28
Table 1: Least squares means (M) and standard errors (SE) of the interaction (sg)ij, in
five grouped generations, for S15G and NS15G
LINE GG F(%) FPPE RL OBOR OWEA OBPP OWPP WOFW
S15GA 1-5 50 M 1.74a 165.2a 20.35a 17.77a 4.77a 4.16a 0.66a
(n=733) SE 1.58 3.52 8.16 7.65 1.59 0.54 0.029
a b a a a a
6 - 10 82.6 M 2.18 129.5 19.01 16.09 4.62 3.92 0.598a
SE 1.62 3.44 10.33 10.29 2. 06 2.27 0.029
11 - 15 93.9 M 2.1a 146.2a 21.06a 19.31a 5.01a 4.60a 0.687a
SE 1.5 3.32 9.78 9.61 1.92 2.00 0.028
NS15GB 1 - 5 50 M 2.65a 146.7a 17.16a 13.63a 3.76a 2.96a 0.59a
(n=135) SE 1.63 6.64 7.52 7.164 1.26 1.49 0.055
6 - 10 82.6 M 1.32b 99.67b 7.76b 4.64b 3.04a 1.45b 0.12b
SE 1.41 7.74 4.37 4.85 1.45 1.70 0.064
11 -15 93.9 M 0.3c 99.75b 7.85b 6.15b 3.87a 2.88a 0.20b
SE 0.66 8.36 5.26 5.59 1.57 2.10 0.069
P(SG) <0.01 0.047 <0.01 0.01 0.482 0.048 <0.01
GG= grouped generations. F= consanguinity obtained in 5 generations. FPPE= number of fertile
postpartum estruses. RL= reproductive life of the pair in days. OBOR= total number of offspring born in
RL. OWEA= total number of offspring weaned in RL. OBPP= offspring born per parturition. OWPP=
weaned offspring per parturition. WOFW= number of weaned offspring per female per week.
A,B
Different literals denote highly significant differences between the selection groups s i (P<0.01).
P(SG) is the significance calculated by the model,
abc
Different literals denote significant intergenerational differences (P<0.05), within line.
The means of the FPPEs of S15G increased 0.44 estruses and remained (P>0.05), while
the means of NS15G decreased 2.4 estruses on average, from 1 to 5 until generations 11
to 15 (P<0.05) (Table 1 and Figure 1). Reproductive life (RL) decreased in S15G, almost
36 d (P<0.05) in generations 6 to 10, then recovered, although not at the level of the first
five generations, while in NS15G it remained 47 d lower than in the first five generations
(P<0.05). Both the number of offspring born and weaned, in the total reproductive life of
the pairs, the lowest values were observed in generations 6 to 10 in both lines; however,
only NS15G showed significant differences (P<0.05) with a decrease of 9.4 and 9
offspring, respectively. Both OBPP and OWPP were obtained as an average of all
parturitions in the reproductive life of the female, grouped every five generations and
were lower in NS15G, the lowest peak was observed between generations 6 to 10 in both,
but it was only significant in NS15G in OWPP with a decrease of 0.7 weaned offspring
(P<0.05) (Table 1).
29
Figure 1: Means and standard deviations of the number of fertile postpartum estruses
(FPPE) with consanguinity grouped every five generations (1 to 5, 6 to 10 and 11 to 15)
S15G and NS15G are the lines with selection and without selection for WOFW in the 15 generations of
the latter.
The index (WOFW) remained stable through all accumulated generations (P>0.05) in
S15G, while NS15G has an abrupt drop from generations 1-5 to 6-10 (-0.47 offspring)
(P<0.05), with a slight recovery in the following five grouped generations (0.08
offspring) (P<0.05); then it is lost due to high mortality (Table 1 and Figure 2).
Figure 2: Means and standard deviations of the number of weaned offspring per female
per week (productive efficiency index) (WOFW) with consanguinity grouped every five
generations (1 to 5, 6 to 10 and 11 to 15)
the latter.
30
Model 2
When S20G was analyzed, the lowest peak was observed in generations 6 to 10 (P<0.05),
in almost all components, while FPPE and the WOFW index showed no significant
intergenerational changes (P>0. 05) (Table 2).
Table 2: Least square means (M) and standard errors (SE) of five generations grouped,
for S20G.
GG F(%) STA FPPE RL OBOR OWEA OBPP OWPP WOFW
1 to 5 50 M 1.59a 165.2a 18.64a 16.29a 4.77a 4.17a 0.616a

SE 0.12 3.58 0.81 0.76 0.14 0.14 0.031
6 to 10 82.6 M 1.79a 130.0b 15.26b 12.92b 4.63a 3.92b 0.598a
SE 0.12 3.49 0.78 0.74 0.14 0.15 0.030
11 to 15 93.9 M 1.78a 146.7c 17.64ab 16.17a 5.01a 4.59a 0.687a
SE 0.11 3.38 0.76 0.71 0.13 0.14 0.029
16 to 20 97.4 M 1.80a 133.9bc 17.26ab 15.17ab 5.18b 4.49ab 0.675a
SE 0.11 3.39 0.76 0.72 0.14 0.14 0.029
GG= grouped generations. F= consanguinity grouped into 5 generations. STA= statistic. FPPE= number of
fertile postpartum estruses. RL= reproductive life of the pair in days. OBOR= total number of offspring
born. OWEA= total number of offspring weaned in RL. OBPP= offspring born per parturition. OWPP=
offspring weaned per parturition. WOFW= number of weaned offspring per female per week.
abc Different literals denote significant intergenerational differences (P<0.05).
Model 3. Inbreeding depression
There was a highly significant effect (P<0.01) of inbreeding depression in S15G, in RL,
OBPP and OWPP, while in OBOR, OWEA, FPPE and WOFW it was not significant
(P>0.05); in NS15G, the characteristics FPPE, RL, OBOR, OWEA and WOFW showed
a highly significant effect (P<0.01) of inbreeding depression, however, in OBPP and
OWPP, it was not significant (P>0.05) (Table 3).
31
Table 3: Mean non-standardized regression coefficients (𝛽̂1) and their standard error
(SE), of the WOFW components and of the index itself, in NS15G, S15G and S20G, on
the coefficient of consanguinity
Line Statistic FPPE RL OBOR OWE OBPP OWP WOFW
A P
S15G ̂1
𝛽 0.052 4.741 -0.454 -0.332 -0.466 -0.578 -0.001
(n=733) SE 0.030 1.276 0.256 0.256 0.084 0.086 0.007
NS15G ̂1
𝛽 0.331 7.718 -1.705 -1.325 0.028 -0.010 -0.062
(n=135) SE 0.066 2.507 0.341 0.326 0.087 0.109 0.015
S20G ̂1
𝛽 0.047 -4.76 -0.361 -0.268 0.02 0.026 -0.001
(n=871) SE 0.026 1.09 0.213 0.210 0.036 0.036 0.007
S15G= line with 15 generations of selection. NS15= line without selection for 15 generations. S20G=
same line with selection for 20 generations. FPPE: number of fertile postpartum estruses. RL=
reproductive life in days. OBOR= offspring born in the total reproductive life. OWEA= offspring weaned
in the total reproductive life. OBPP= offspring born per parturition. OWPP= offspring weaned per
parturition. WOFW= number of weaned offspring per female per week.
Regression coefficients in bold were highly significant (P<0.01).
Reproductive life decreased in both lines for every 10 % of consanguinity, NS15G 7.718
days vs. 4.741 in S15G (P<0.01). In OBOR and OWEA, there was an effect of inbreeding
depression only in NS15G (-1.705 and -1.325 offspring, respectively) (P<0.01). OBPP
and OWPP were obtained as an average of the parturitions in the RL of each pair, in these
variables, the inbreeding depression only affected S15G (-0.466 and -0.578 offspring,
respectively) (P<0.01), while in NS15G there was an apparent stability (P>0.05), because
the average number of parturitions in the accumulated generations was decreasing (4.2,
1.7 and 1.1), in the accumulated generations 1 to 5, 6 to 10 and 11 to 15 respectively,
while in the selected one, they remained almost unchanged (3.9, 3.8 and 3.6). The WOFW
index did not show inbreeding depression in S15G (P>0.05), the opposite occurred in
NS15G (P<0.01). On the other hand, S20G showed highly significant inbreeding
depression of -4.76 d in RL for every 10 % increase in consanguinity (P<0.01) (Table 3).
32
Mortality at weaning
The percentage of mortality per parturition at weaning, with respect to OBPP, had a
similar behavior in both lines, with a maximum peak in generations 6 to 10, however, the
non-selected line maintained a higher mortality than the selected one (52.30 %), having
a difference with the selected one of 36.8 (P<0.01) (Figure 3). Survival per parturition
(100 - % mortality per parturition) then declines, in the generations from 6 to 10, 31 % in
the non-selected line (78.47 - 47.7 %) and 2.37 % in the selected line (87.22 - 84.85 %),
remaining higher in the line with selection.
Figure 3: Mortality per parturition at weaning, in percentage with respect to OBPP,

with consanguinity grouped into 5 generations
the latter.
Discussion
In the literature, there are few selection papers on long-term fertility and its effect on
inbreeding depression in mice; in a study of selection of litter size at the first parturition
that began in 1972, avoiding crosses between complete siblings, half-siblings or cousins,
after 124 generations of selection, a consanguinity of 0.64 was found in one of its lines,
which led it to greater inbreeding depression (-0.39), with a lower number of live
offspring at the first parturition, for every 10 % of consanguinity(17). The results of the
present work coincide with that, when the average of the offspring born alive per
parturition (OBPP) was obtained in the line with selection, the effect of inbreeding
depression was -0.466, a little higher than that, because in that work only the first
parturition was measured. The number of offspring born per parturition, in the NS15G
group, showed a non-significant effect of consanguinity (P>0.05) (Table 3), which
33
seemed contrary to what was expected; the explanation is that the number of parturitions
in the reproductive life of the pairs was decreasing as the generations increased and when
averaging the size of the litter at parturition and weaning (of the whole RL of each pair)
with these, it seems that it would have remained constant (𝛽̂1= 0.028). Another difference
is that the consanguinity in that study is lower, although the number of selection
generations is 124, during that time (1972 to 2007), there were several changes in the
direction of selection in that work, but it was tried to avoid inbred crosses(17). No work
with selection for litter size of the entire reproductive life with inbred cross was found in
the literature, however, in a study with selection for an index combining litter size with
birth weight, but with open crosses (not inbred) for 150 generations, litter sizes of 17.6
offspring born and 20.2 offspring on average(18) in the reproductive life of a pair were
obtained, very similar to the average OBOR at the beginning of this study. The decrease
in this average in the following generations in the present work is most likely due to
inbreeding depression, which was -1.705 offspring for every 10 % of consanguinity
(P<0.01) in the non-selected line. It should be noted that in the OBOR and OWEA
characteristics, originally used to obtain the WOFW (in all the RL of each pair), there
was a highly significant effect (P<0.01) of inbreeding depression in NS15G , which does
not occur in S15G (Tables 1, 2 ,3). In that study, an increase in testosterone levels was
observed in males, while in females there was an elevation of progesterone in one of its
lines; these showed a higher number of oocytes per cycle, but a greater loss of embryos,
and a decrease in reproductive life, compared to the line without selection. These results
coincide with the present work since a decrease in reproductive life was also obtained in
S1G and S20G (Tables 1, 2).
In the present study, it was revealed that the number of FPPE has a constant decrease over
the generations studied in NS15G, with a decrease of 0.331 fertile postpartum estruses
for every 10 % increase in consanguinity (P<0.01), compared to S15G which remains
almost constant (P>0.05) (Table 3); this behavior is also observed in WOFW (the
productive index), with a marked decrease in NS15G of generations 1 - 5 to 6 - 10, with
a slight recovery in the last five; inbreeding depression was -0.062 (P<0.01) weaned
offspring per female per week for every 10 % increase in consanguinity, vs. -0.001
(P>0.05) in S15G. One study showed that a deletion of Kiss1r in the neurons of the GnRH
axis interrupts the signal of kisspeptin, the protein that induces the secretion of GnRH
(gonadotropin-releasing hormone); this results in infertility due to hypogonadism,
probably, consanguinity had a negative effect on this mechanism through the interruption
of this signal(19) in S15G mice; in the present work, the effects on males were not
measured. Something very similar was found in pigs under selection for prolificacy, when
this was done with family indices, since an increase in inbreeding depression of three
times more than expected without selection and a decrease in the response to selection
were seen(20).
RL was affected by consanguinity both in S15G, with a decrease of 4.741 d, and in

NS15G, it decreased almost three more days (7.718 d) (P<0.01 in both) (Table 3), in a
study where there was selection for longer life in mice, it was found that the increase was
34
related to mutations, which increased the levels of growth hormone in the GH/IGF-1
axis(21), which could be related to a decrease in the life time of the mice, as happened in
the lines selected for an index involving litter size and birth weight, without
consanguinity(18) in the present work, it decreased in both lines since the selection
objective was different.
Postpartum mortality is higher in NS15G, from the first five generations of narrow inbred
crossing, compared to S15G, a result similar to that obtained by Sallah(5), both in the
selected line and the non-selected line, with mating between half-siblings, where the
highest mortality occurred in the intermediate generations. Charlesworth(22) explains this
under two hypotheses: 1) dominance, where inbreeding increases the frequency of
individuals that express the effects of deleterious mutations or 2) overdominance, where
the homozygous would have a lower aptitude due to lack of alleles, with heterozygous
advantage that they would maintain by balancing selection at intermediate frequencies in
the heterozygous.
As a result of the above, the number of offspring at weaning per parturition in NS15G
falls significantly in generations 6 to 10 (1.51 offspring), with a recovery in the following
accumulated generations, while in the number of offspring at birth per parturition, it
remains in all generations (P<0.05) (Table 1) (Figure 3). A recent study(23) revealed that,
in litters with less than four offspring at birth, in non-selected mice, there is a higher
mortality at weaning, and in the present study, the NS15G line showed less than four
offspring at birth on average in the first five generations.
The result in these characteristics was presumable, since, on the one hand, a high
inbreeding depression can be expected when performing a narrow inbred cross (brother,
sister) and, on the other hand, due to the low heritability (0.024 to 0.063) of the productive
index(12), only limited reproduction progress can be expected; a similar result was
obtained in litter size with a consanguinity of 0.61 in eight generations of selection with
consanguinity in crosses between half-siblings(5).
These results lead to reflect on whether in a selection program in domestic animals, even
avoiding crosses between siblings, in generations later, it could occur between relatives,
and this induces inbreeding, with the counterproductive effects that were seen here.
In a Holstein cattle improvement program, it was found that with a 1 % increase in

consanguinity, milk production in 305 d decreased by 36.3 kg on average, in cows aged
4 to 5 yr, and 2.42 kg of fat(24). Recently, the implementation of genomic selection was
evaluated in the loss of genetic diversity in Holstein and Jersey cattle in North America,
due to consanguinity; their results showed an increase in inbreeding from 1.19 to 2.06 %
per generation, over a period of 10 yr in Holstein cattle, and warned about the need to
implement measures to avoid inbreeding in this type of programs(25). In the Holstein
population of Mexico, it was found that, with levels less than 5 % of consanguinity, no
effect was detected in fat or milk protein, however, when inbreeding increased to more
35
than 5 %, a decrease in milk production of 260 kg per lactation was found, in addition to
a loss in fat production of 11 kg and 10 kg in protein with respect to the average of groups
with less than 5 %(26).
As the results of NS15G show for a productive index (number of offspring per female per
week), inbreeding depression affected its different components, especially in the
reproductive characteristics, which could be regulated to a large extent by their
simultaneous selection, probably due to a maintenance of genes that favored gonadal
development in females and males. The work is relevant because the selection of a
productive index in mice in its different components had not been analyzed, in an integral
way, in addition to showing that, in a selection program with simultaneous consanguinity,
the fixation of desirable alleles in the maintenance of reproductive cycles and the survival
of the offspring is favored.
Literature cited:
1. Silver LM. Mouse genetics: Concepts and applications. 1st ed. Oxford, United UKA:
Oxford University; 2001.
2. Falconer DS, Mackay TFC. Introduction to quantitative genetics. 4th ed. Harlow, UKA:
Longman; 1996.
3. Curik I, Sölkner J, Stipic N. The influence of selection and epistasis on inbreeding

depression estimates. J Anim Breed Genet 2001;118:247-262.
https://doi.org/10.1046/j.1439-0388.2001.00284.x.
4. Leroy G. Inbreeding depression in livestock species: review and meta-analysis. Anim

Genet 2014;45 (5):618-628.
5. Sallah BI, Seeland G. Einfluss von Inzucht und selektion auf die Fruchtbarkeit und das
Wachstum der Maus. Arch. Tierz. Dummerstorf 2001;44(6): 671– 676.
6. Bohren BB. Designing artificial selection experiments for specific objectives. Genetics
1975; 80(1):205-220.
7. De la Fuente LF, San Primitivo F. Selection for large and small litter size of the first
three litters in mice. Gênet Sêl Evo 1985;(17):251-264.
8. Eklund J, Bradford GE. Genetic analysis of a strain of mice plateaued for litter size.
Genetics 1977;(85):529-542.
9. Festing FWM. Inbred strains in biomedical research. 1ra ed. London, UKA: Palgrave;
1979.
36
10. Hubrecht R, Kirkwood J. Handbook on care and management of laboratory animals.

8a ed. London, UKA: UFAW; 2010.
11. Benavides FJ, Guénet JL. Manual de genética de roedores de laboratorio: Principios
básicos y aplicaciones. 1ra ed. Madrid, España: Universidad de Alcalá; 2003.
12. Tapia-Pérez G. Respuesta a la selección para el número de crías destetadas por semana
en líneas congénicas y singénicas de ratones de laboratorio [tesis maestría]. México,
CDMX: Universidad Nacional Autónoma de México; 1995.
13. Kinghorn B, Kinghorn S. Pedigree Viewer, Ver 5.0. The University of New England
2010. https://bkinghor.une.edu.au/pedigree.htm. Accessed Jan 10, 2021.
14. Wright S. Coefficients of inbreeding and relationship. The American Naturalist 1992;
(56):330-338.
15. IBM SPSS Statistics for Windows, Ver 22.0. Armonk, New York: IBM Corp. 2013.
16. MedCalc Software Ltd. Comparison of proportions calculator, Ver 20.022.

https://www.medcalc.org/calc/comparison_of_proportions.php. Accessed Dec 22,
2021.
17. Hinrichs D, Meuwissen THE, Odegard J, Holt M, Vangen O, Woolliams JA. Analysis
of inbreeding depression in the first litter size of mice in a long-term selection
experiment with respect to the age of the inbreeding. Heredity 2007;(99):81-88.
https://doi.org/10.1038/sj.hdy.6800968.
18. Langhammer M, Michaelis M, Hoeflich A, Sobczak A, Schoen J, Weitzel JM. High-

fertility phenotypes: two outbred mouse models exhibit substantially different
molecular and physiological strategies warranting improved fertility. Reproduction
2014;147(4):427-133. https://doi.org/10.1530/REP-13-0425.
19. Novaira HJ, Momodou LS, Hoffman G, Koo Y, Ko C, Wolfe A, Radovik S. Disrupted
kisspeptin signaling in GnRH neurons leads to hypogonadotrophic hypogonadism.
Molecular Endocrinology 2014; 28 (2): 225–238. https://doi.org/10.1210/me.2013-
1319.
20. Toro M, Silio L, Rodrigañez J, Dobao M. Inbreeding and family index selection for
prolificacy in pigs. Anim Sci 1988; 46(1): 79-85.
https://doi:10.1017/S0003356100003135.
21. Junnila RK, List EO, Berryman DE, Murrey JW, Kopchick JJ. The GH/IGF-1 axis in
ageing and longevity. Nat Rev Endocrinol 2013;9(6):366-376. doi:
10.1038/nrendo.2013.67.
22. Charlesworth D, Willis J. The genetics of inbreeding depression. Nat Rev Genet 2009;
(10):783–796.
37
23. Morello GM, Hultgren J, Capas-Peneda S, Wiltshire M, Thomas A, Wardle-Jones H,

et al. High laboratory mouse pre-weaning mortality associated with litter overlap,
advanced dam age, small and large litters. PloS one 2020;15(8):e0236290.
https://doi.org/10.1371/journal.pone.0236290.
24. Doekes HP, Veerkamp RF, Bijma P, De Jong G, Hiemstra SJ, Windig JJ. Inbreeding
depression due to recent and ancient inbreeding in Dutch Holstein–Friesian dairy
cattle. Genet Sel Evol 2019;51(54). https://doi.org/10.1186/s12711-019-0497-z.
25. Makanjuola BO, Miglior F, Abdalla EA, Maltecca C, Schenkel FS, Baes CF. Effect
of genomic selection on rate of inbreeding and coancestry and effective population
size of Holstein and Jersey cattle populations. J Dairy Sci 2020;103(6):5183-5199.
https://doi.org/10.3168/jds.2019-18013.
26. García-Ruíz A, Martínez-Marín GJ, Cortes-Hernández J, Ruíz-López FJ. Niveles de

consanguinidad y sus efectos sobre la expresión fenotípica en ganado Holstein. Rev
Mex Cienc Pecu 2021;12(4):996-1007. https://doi.org/10.22319/rmcp.v12i4.5681.
38
Article
Genetic variability in aerial biomass and its components in alfalfa under

irrigation and drought
Milton Javier Luna-Guerrero a
Cándido López-Castañeda a*
a
Colegio de Postgraduados. Postgrado en Recursos Genéticos y Productividad. Carretera
México-Texcoco km. 36.5, Montecillo, Texcoco, Estado de México, México.
*Corresponding author: clc@colpos.mx
Abstract:
Drought decreases the yield of aerial biomass (BM) and its components, and the quality of
forage in alfalfa. The genetic variation in BM and its components was studied in 10 varieties
of alfalfa under irrigation (I) and drought (D) in a greenhouse. A randomized complete block
experimental design was used, with four repetitions in I and four in D. The experimental unit
was an individual plant in a PVC pipe. Sowing was carried out on March 15, 2017, and
transplanting in the pipes, 20 days after sowing. The fertilization dose 60-140-00 was applied
at 44, 240 and 420 dat (days after transplanting). D reduced (P≤0.01) BM, leaf dry matter
yield (LDMY), number of stems (NS) and radiation use efficiency (RUE). The plants in D
did not recover their productive capacity after experiencing the water deficit, even after the
recovery irrigation. D also decreased (P≤0.01) the phenotypic variance for BM and its
components; the additive variance was greater (P≤0.01) than the dominance variance for all
traits in I and D. The BM, L:S ratio, plant height (PH), NS and RUE had higher (P≤0.01)
heritability in I and D. The Genex, Atlixco, Júpiter and Milenia varieties were the most
productive (P≤0.01) in D and could be used for forage production in water-scarce areas or as
parental lines for forage yield improvement in selection programs.
Key words: Greenhouse, Heritability, Principal component analysis, Variance components.
39
Introduction
In Mexico, alfalfa (Medicago sativa L.) for forage is grown mainly under irrigation
conditions and consumes large volumes of water. In regions with irrigation systems, a plant
canopy of alfalfa can consume an amount of water of 10 mm day-1 at its peak of maximum
development(1). In these growing conditions, the fall in the amount of precipitation over long
periods of time decreases the water storage capacity in the subsoil and, therefore, the
availability of irrigation. Likewise, when drought extends, the scarcity of water for irrigation
is more severe and alfalfa crops may experience some degree of water stress, which can be
reflected in a significant decrease in yield and forage quality(2).
In the near future, the water resource will be less available for the production of alfalfa forage,
due to the occurrence of frequent periods of drought, climate change and greater demands
caused by the increase in the human population(3). One way to meet the demand in alfalfa
forage production will be through the obtaining of new varieties with drought tolerance, high
capacity of osmotic adjustment and gas exchange, high water use efficiency (e.g., more dry
matter per unit of transpired or evapotranspirated water) and productive capacity(3). Alfalfa
is considered a drought-resistant species, but its aerial biomass yield can fluctuate
considerably under water deficit conditions; under these conditions, alfalfa has some
agronomic advantages compared to other annual crops, as it has a root system that allows it
to explore deeper soil layers to absorb water and tolerate drought to a greater degree; in
addition to reducing the stomatal conductance and minimizing the transpiration rate(4).
The most common reaction to a soil water deficit is the increase in the ratio of dry weight of
root biomass/dry weight of aerial biomass, as a result of a greater reduction in the growth of
aerial organs than in the growth of roots under drought. The increase in the root/aerial part
ratio implies greater increases in root density with respect to aerial biomass, which is
consequently reflected in a better capacity to maintain the water status of the plant under a
given evapotranspiratory demand(5). Drought also reduces the yield of aerial biomass and its
components, relative rates of growth, transpiration and elongation of the stem, chlorophyll
content, relative water content, and dry weight and diameter of the root(6), and concentration
of crude protein and water-soluble carbohydrates(7).
40
On the other hand, drought-resistant alfalfa varieties exhibit high concentration of water-
soluble carbohydrates in storage organs under conditions of severe water stress. This situation
is combined with a water conservation strategy that implies less evapotranspiration in the
initial phases of drought stress, due to a limited development of the root system that results
in more available moisture, for its use under severe conditions of water stress (8). Biomass
accumulation rates in plant roots and aerial organs were higher in 2-yr-old grasslands and
aerial biomass accumulation was higher and maintained the best soil moisture conditions in
4-yr-old grasslands, once the crop reached the maximum development of the root system and
cover of the soil surface(9). Drought-tolerant germplasm shows a lower degree of wilting
under initial conditions of water deficit, more plants with the green plant canopy under severe
water stress conditions and more stems per plant under stress conditions or favorable
moisture conditions(3). Despite the existence of a wide genetic variability in morphological
and physiological traits associated with drought resistance, it is difficult to achieve the
combination of adaptive traits to specific environments in the same variety with wide
adaptation to environments vulnerable to drought(8).
The genetic improvement of drought resistance and the yield of aerial biomass and its
components requires special attention to traits with high heritability, general combining
ability, additive genetic effects, maternal genetic effects, low genotype*environment
interaction and ease of selection. In the analysis of the genetic variation of a population of
the same species, additive genetic variance is the most important because it is the main
determinant of the genetic properties observable in the population and of the response to
selection(10). The additive variance is the only one that can be estimated directly from the
observations made in the population and can be used in the estimation of heritability, which
represents the reliability of the phenotypic value as an indication of the reproductive value,
which determines its influence on the next generation(10). The similarity observed in the
heritability values, for the traits measured in the plant under irrigation and drought, can be
used as an indication of the effectiveness in the selection of new progenies, regardless of the
selection environment(10). Broad-sense heritability (H2) measures the contribution of the
genotype to the total phenotypic variance (𝜎𝑝2 ); theoretically, it can vary in a range from zero,
when there is no genetic variation present, to 1, when all the observed variation is genotypic
in origin(11).
Selection for drought resistance can be achieved by increasing water use efficiency, drought
severity index, mean productivity, harmonic mean, geometric mean, stress tolerance index,
modified stress tolerance index, superiority index and abiotic tolerance index in water deficit
conditions(12). Selection for morphological components of aerial biomass yield can be
achieved by including the number of secondary stems and crown diameter per plant in the
selection criteria(13). Other components of aerial biomass yield with moderate to high
heritability that could be successfully used in selection to increase yield are absolute growth
41
rate, radiation use efficiency, number of stems, L:S ratio and plant height, in addition to the
presence of maternal genetic effects favorable to aerial biomass yield(14). The selection of
new varieties with drought resistance and high yield of aerial biomass and its components
can be achieved by identifying the genetic traits with greater heritability and contribution to
the productivity of the genotype. The objective of the present research was to study the
genetic variability in the production of aerial biomass and its components, in commercial
varieties of alfalfa under irrigation and drought in greenhouse conditions.
An experiment was carried out under irrigation and drought conditions in a greenhouse with
a metal structure and transparent glass without whitewashing, and with a mechanical
ventilation system in the College of Postgraduates, Montecillo, Texcoco, State of Mexico
(19° 29’ N, 98° 53’ W and altitude of 2,250 masl) in the 2017-2019 period. The locality is
characterized by having a subhumid temperate climate with long cool summer (Cb (wo) (w)
(i´)g), average annual rainfall of 637 mm and winter rainfall of less than 5 %; average annual
temperature with fluctuations from 12 to 18 °C and thermal oscillation between 5 and 7 °C(15).
The genetic material used included the following commercial varieties of alfalfa: San Miguel,
Oaxaca, Atlixco, Aragón, Victoria, Genex, Júpiter, Milenia, San Isidro and Cuf 101, with
germination percentage greater than 95 %. A randomized complete block experimental
design was used, with four repetitions and two soil moisture treatments (irrigation and
drought). The experimental unit was an individual plant transplanted in a cylindrical
polyethylene bag inside a PVC pipe 1 m high and 4” in diameter, to favor the expression of
the genetic potential of the morphological characteristics of the variety. The sowing was
carried out on March 15, 2017, by placing five seeds of each variety in individual cells of
seedbed boxes. At 20 days after sowing (das), the most vigorous seedling of each cell was
selected and transplanted individually into the PVC pipes. The PVC pipes were filled with
dry soil of sandy-loamy texture, bulk density of 1.12 T m-3 and pH of 7.3; 18.8 and 0.22 %
of organic matter and total nitrogen; 176.3 mg kg-1 and 2,420 mg kg-1 of phosphorus and
potassium; 54.6 Cmol(+) kg-1 and 0.53 dS m-1 of cation exchange capacity and electrical
conductivity; and 52 and 38.2 % of field capacity (FC) and permanent wilting percentage
(PWP) (Central University Laboratory, Chapingo Autonomous University, Chapingo,
Mexico, 2016). The fertilization dose 60-140-00 was applied at 44 days after transplantation
(dat), using urea and calcium triple superphosphate as sources of nitrogen and phosphorus,
diluted in the irrigation water; a second and third fertilization was done at 240 and 420 dat
with the same dose of fertilizer. Two treatments of soil moisture were used: irrigation, where
the soil water content remained close to FC from the date of transplantation (20 das) to 406
dat (I1) and from 406 dat until the end of the experiment (798 dat) (I2), and drought, where
42
the application of water to plants was suspended in a first period for 61 d [345 to 406 dat;
March to May 2018; (D1)] and a second period for 68 days [620-688 dat; November 2018 to
February 2019; (D2)]. Recovery irrigation (RI) was applied to the plants at the end of the
treatments of D1 (406 dat, RI1) and D2 (688 dat, RI2).
Cuts were made in the aerial part of the plant every 5 wk in the autumn-winter period and
every four weeks in the spring-summer period, at a height of 5 cm above ground level. In
each cut, the plant height (PH, cm) was measured from the soil surface to the last leaf exposed
on the highest stem with a ruler graduated to 5 mm; in addition, the total number of stems
(NS) was counted and the leaf:stem ratio (L:S) was determined in a subsample of four
secondary stems, by dividing the leaf dry weight (LDW) by the stem dry weight (SDW),
obtained after a drying period of 48 h at a temperature of 65 °C (L:S = LDW/SDW). The
total dry matter yield (TDMY, g) or aerial biomass (BM) was calculated by adding the dry
weight of leaves and secondary stems of the subsample used to determine the L:S ratio, and
the dry weight of the leaves and secondary stems of the remaining sample of the plant. The
leaf dry matter yield (LDMY, g) was represented by the dry weight of leaves. The radiation
use efficiency (RUE, g d DM MJ-1) was calculated by dividing the TDMY by the solar
radiation accumulated daily (data obtained from the meteorological station of the Chapingo
Autonomous University) during the period between subsequent cuts(16). The maximum and
minimum air temperature in the greenhouse was recorded daily with a maximum and
minimum mercury column thermometer, Taylor brand model 5458P, placed next to the plants
at a height of 2 m above floor level. The maximum temperature during the study ranged from
19 to 40 °C and the minimum from -4 to 15 °C, with an average of 32 and 8.5 °C. The water
content in the soil was determined by the gravimetric method every third day with a Tor-Rey
electronic balance, PCR Series model. In irrigation, the water content of the soil was kept
close to FC, by adding water in each weighing during the experiment, while in drought, the
plants were treated in the same way as in irrigation, except in the periods in which the
application of water was suspended [345 to 406 (D1) and 620-688 (D2) dat] and only the
decrease in soil weight in each PVC pipe (data not shown) was recorded.
The phenotypic variance (σ2𝑝 ) and its components were estimated for the variables measured
in all the cuts in irrigation (I1 and I2) and drought (D1 and D2), under the following statistical
model(17,18):
Yijk = µ + DCi + R(DC)ij + Gk + G*DCik + Eijk
Where,
Yijk is the value of the response variable;

μ is the overall mean;
43
DCi is the effect of the date of cut;

R(DC)ij is the effect of repetitions within the date of cut;
Gk is the effect of genotypes;
G*DCik is the effect of the interaction between genotypes and dates of cut;
Eijk is the experimental error.
Estimates of phenotypic variance and its components were made under the assumption of
Hardy-Weinberg equilibrium, linkage equilibrium and absence of epistasis(17,19). The values
of phenotypic variance (σ2𝑝 ) and its components, and heritability (h2) were obtained from the
values of the expectations of the mean squares of the analysis of phenotypic variance and its
components as follows:
2
σ2𝑝 = σ𝐴2 + σ2e + σ𝑔∗𝑑𝑐
Where, σ𝐴2 is the additive variance (σ2A = (M1 – M2)/r*d), σ2e is the environmental variance
2 2
(σ2e = M3) and σ𝑔∗𝑑𝑐 is the variance of the interaction of genotypes*dates of cut (σ𝑔∗𝑑𝑐 = (M2
– M3)/r); M1, M2 and M3 represent the expectations of the mean squares, d represents the date
of cut and r represents the number of repetitions(17).
Narrow-sense heritability (h2) was calculated according to the following equation:

h2 = (σ𝐴2 ) / (σ2𝑝 ). Where, σ𝐴2 is the additive variance and σ2𝑝 is the phenotypic variance.
The dominance variance (𝜎𝐷2 ) was estimated(17) by using the additive variance (𝜎𝐴 ) between
half-sib families(20):
3 1
𝜎𝐺2 = 4 𝜎𝐴2 + 𝜎𝐷2 and 𝜎𝐴2 = 4 𝜎𝐺2
Where, 𝜎𝐺2 is the genetic variance and the value of 𝜎𝐷2 is obtained as follows(20):
1
𝜎𝐷2 = 𝜎𝐴2
4
Narrow-sense heritability (h2) was calculated under the assumption that the varieties used are
a random and representative sample of the genetic variability of alfalfa and considering that
this is an allogamous species(17). Thus, the component of variance obtained from the
mathematical expectation of the mean square of the factor of varieties is an estimator of the
additive variance(21).
The data obtained were analyzed with the GLM(22) procedure, version for Windows 10, with
a completely randomized design in factorial arrangement. The means of soil moisture
44
treatments, genotypes and genotypes within soil moisture treatments were compared with the
honest minimum significant difference (HMSD, P<0.05) according to the following model:
Yij = µ + Ti + Gj + T*Gjj + Eij
Where,
Yij is the value of the response variable;

μ is the overall mean;
Ti represents soil moisture treatments;
Gj represents genotypes;
T*Gjj represents the interaction between soil moisture treatments and genotypes;
Eij is the experimental error(23).
Results and discussion
The soil moisture treatments were different (P≤0.01) in total dry matter yield and leaf dry
matter yield in the cuts made between 406 and 798 dat; differences (P≤0.01) in the L:S ratio
at 406, 434, 462, 490 and 686 dat; differences (P≤0.01) in plant height at 406, 434, 462, 686,
742, 770 and 798 dat; and differences (P≤0.01) in number of stems and radiation use
efficiency between 406 and 798 dat (Table 1). The varieties showed differences (P≤0.01) in
total dry matter yield, L:S ratio, plant height and radiation use efficiency in all cuts made
between 112 and 798 dat; differences (P≤0.01) in leaf dry matter yield and number of stems
in all cuts, except for cuts made at 245, 406, 434, 553 and 588, and 140 dat. The interaction
of soil moisture treatments*varieties showed differences (P≤0.01) in total dry matter yield at
112, 140, 210, 406 and 746 dat and differences (P≤0.05) at 175, 315, 434 and 770 dat;
differences (P≤0.01) in leaf dry matter yield at 112, 140 and 210 dat, and differences
(P≤0.05) at 175, 742 and 770 dat; differences (P≤0.01) in the L:S ratio at 112, 140, 175, 210,
245, 280, 315, 406, 434, 490, 686, 770 and 798 dat, differences (P≤0.05) at 588 dat;
differences (P≤0.01) in plant height at 112, 245, 280, 490, 742 and 798 dat, and differences
(P≤0.05) at 112, 210, 315 and 406 dat; differences (P≤0.01) in number of stems at 175, 315
and 434 dat, and differences (P≤0.05) at 140, 245, 462, 518 and 686 dat; and differences
(P≤0.01) in radiation use efficiency at 140, 210, and 742 dat, and differences (P≤0.05) at
112, 175, 315, 434, and 770 dat.
The comparison of the total dry matter yield and its components in irrigation vs. drought
showed that the water deficit of the soil in D1 and D2 reduced (P≤0.01) the total dry matter
yield and leaf dry matter yield, number of stems and radiation use efficiency from 406 to 798
45
dat; plants under drought did not recover their productive capacity after experiencing the
water deficit in D1 and D2 with respect to plants under irrigation (I1 and I2), even after
recovery irrigations (RI1 and RI2) (Figure 1). The L:S ratio in plants under drought was higher
(P≤0.01) than in irrigation (I1 and I2), and these differences between irrigation and drought
were more noticeable during the application of drought (D1 and D2). The plant height in D1
and D2 was lower (P≤0.01) than in irrigation (I1 and I2) and subsequently recovered its growth
capacity with respect to its behavior in irrigation. The survival of alfalfa through periods of
water deficit in field conditions depends on the length and intensity of the drought, the
genotype, the type of soil (water capacity of the soil and depth of the root system) and the
environment (salinity and temperature); its survival to short periods (2-3 weeks) without
irrigation is reflected in its high recovery capacity when receiving irrigation again and
producing normal yields in subsequent years(24). The greater recovery capacity of alfalfa
when receiving water after experiencing periods of water deficit(24) may be due to the fact
that plants that grow in field conditions have greater access to moisture and nutrients in the
soil profile, unlike plants that grow in greenhouse conditions in pots or PVC pipes, where
plant roots grow in an environment limited in soil volume, moisture and nutrients; this is
reflected in a reduction in the accumulation of aerial biomass due to a decrease in stomatal
conductance, transpiration and assimilation(3). The high values in the L:S ratio in drought
could be due to a lower partition of assimilates to the stem with respect to the leaf; plants
subjected to water stress show some morphological changes in response to water deficit, by
reducing the loss or increasing the absorption of water to maintain the water status of the
tissue(25). Plant height was the only morphological characteristic that showed recovery
capacity after water application (RI1 and RI2), reaching values similar to those observed in
plants under irrigation; soil water deficit affects different morphological characteristics of
plants, such as plant height, stem diameter, number, size and area of leaves, dry matter
production, assimilate partitioning, flower and fruit production, and physiological
maturity(25).
46
Figure 1: Yield of total dry matter (a) and leaf dry matter (b), leaf:stem ratio (c), plant
height (d), number of stems (e) and radiation use efficiency (f) in 18 cuts in irrigation
(R1=I1 and R2=I2) and drought (S1=D1 and S2=D2), average of 10 varieties of alfalfa
Montecillo, Texcoco, State of Mexico [RR1=Recovery irrigation in I1 (RI1); RR2=Recovery irrigation in I2

(RI1); *(P≤0.05); **(P≤0.01); ns (not significant)].
On the other hand, in irrigation (I1 and I2), a wide variability (P≤0.01) was observed between
genotypes for total dry matter yield (Figures 2a and 3a), L:S ratio (Figures 2c and 3c), plant
height (Figures 2d and 3d) and radiation use efficiency (Figures 2f and 3f) in all cuts in I1
(112 to 434 dat) and I2 (462 to 798 dat). The Genex, Atlixco, Júpiter, Oaxaca, San Miguel
and Milenia varieties produced more (P≤0.01) total dry matter yield than the other varieties
in all cuts in I1 (Figure 2a), and only the Genex, Atlixco, Júpiter and Milenia varieties showed
47
high (P≤0.01) total dry matter yield in I2 (Figure 3a). The high total dry matter yield in the
Genex, Atlixco, Júpiter, Oaxaca, San Miguel and Milenia varieties (Figure 2a) was
accompanied by high (P≤0.01) leaf dry matter yield (Figure 2b), plant height (Figure 2d),
number of stems (Figure 2e) and radiation use efficiency (Figure 2f) in I1. The high (P≤0.01)
total dry matter yield of the Genex, Atlixco, Júpiter and Milenia varieties (Figure 3a) was
also accompanied by high (P≤0.01) leaf dry matter yield (Figure 3b), plant height (Figure
3d), number of stems (Figure 3e) and radiation use efficiency (Figure 3f) in I2. The Victoria,
Aragón and San Isidro (Figure 2c), and Aragón and San Isidro (Figure 3c) varieties showed
a higher (P≤0.01) L:S ratio than the other varieties in I1 and I2. In a study with 11 alfalfa
cultivars under greenhouse irrigation conditions, it was determined that BCB, ALF and AFR
varieties showed higher yields of total dry matter, root dry matter, stem elongation rate,
relative water content and root diameter than the other alfalfa varieties(6). The varieties F
1412-02, F 1535-03, Roxana and F 2007-08, and F 1414-02, F 1711-05, F 1715-05 and F
2010-08 stood out from a group of 74 genotypes under greenhouse irrigation conditions,
producing higher total dry matter yield, plant height and number of stems than the rest of the
varieties(4).
48
height (d), number of stems (e) and radiation use efficiency (f) in nine cuts in irrigation (I1),
for 10 varieties of alfalfa
R1= Irrigation in the cutting period from 112 to 406 dat (I1).
49
height (d), number of stems (e) and radiation use efficiency (f) in nine cuts in irrigation (I2),
R2= Irrigation in the cutting period from 462 to 798 dat (I 2).
In drought, a wide variability (P≤0.01) was also observed between genotypes for total dry
matter yield (Figures 4a and 5a), L:S ratio (Figures 4c and 5c), plant height (Figures 4d and
5d) and radiation use efficiency (Figures 4f and 5f) in all cuts in D1 (112 to 406 dat) and D2
(462 to 798 dat). The Genex, Atlixco, Júpiter, Oaxaca, San Miguel and Milenia varieties
produced higher (P≤0.01) total dry matter yield than the other varieties in all cuts in D1
(Figure 4a), and only the Genex, Atlixco, Júpiter and Milenia varieties showed high (P≤0.01)
total dry matter yield in D2 (Figure 5a). The high total dry matter yield of the Atlixco, Júpiter,
50
Oaxaca, San Miguel and Milenia varieties (Figure 4a) was accompanied by higher (P≤0.01)
leaf dry matter yield (Figure 4b), plant height (Figure 4d), number of stems (Figure 4e) and
radiation use efficiency (Figure 4f) in I1. In I2, the highest (P≤0.01) total dry matter yield of
the Genex, Atlixco, Júpiter and Milenia varieties (Figure 5a) was also accompanied by high
(P≤0.01) leaf dry matter yield (Figure 5b), plant height (Figure 5d), number of stems (Figure
5e) and radiation use efficiency (Figure 5f). The Milenia, Victoria, Cuf-101, Aragón and San
Isidro (Figure 4c), and Victoria, Aragón and San Isidro (Figure 5c) varieties showed a higher
(P≤0.01) L:S ratio than the other varieties in I1 and I2. Other studies in different varieties of
alfalfa under greenhouse drought detected genotypes that reduce less stem elongation,
relative growth rate and aerial biomass with respect to irrigation, in addition to maintaining
greater root growth capacity, relative water content, chlorophyll content and water use
efficiency(6). The Gold Queen variety produced higher yield of dry matter and water-soluble
carbohydrates and was more drought-resistant than the Suntory variety under field
conditions; drought decreased crude protein content and increased fiber fraction in response
to water deficiency in the two alfalfa varieties(7). The Amerist (USA), Sardi10 and Siriver
(Australia), and Melissa (France) genotypes showed greater drought tolerance than other
alfalfa varieties, because they produced thinner leaves, accumulated more proline and
potassium, and maintained greater efficiency in the use of water in conditions of water
deficiencies(26). The Aragon and San Isidro varieties consistently showed high average values
for the L:S ratio in irrigation and drought; this morphological characteristic of the plant is
highly appreciated as an estimator of forage quality and can be used to improve yield, and
dry matter quality in lines, half-sib families or clones in large populations, considering its
high values of narrow-sense heritability (h2=0.75)(27).
51
height (d), number of stems (e) and radiation use efficiency (f) in nine cuts in drought (D1),
S1= Drought in the cutting period from 112 to 406 dat (D 1).
52
height (d), number of stems (e) and radiation use efficiency (f) in nine cuts in drought (D2),
S2= Drought in the cutting period from 462 to 798 dat (D 2).
The phenotypic variance for total dry matter yield and leaf dry matter yield, L:S ratio, plant
height, number of stems and radiation use efficiency in irrigation (I1 and I2) was higher
(P≤0.05) than in drought (D1 and D2). The phenotypic variance for the total dry matter yield
and its components was greater (P≤0.05) than the other components of variance in irrigation
and drought. However, environmental variance contributed more (P≤0.05) to phenotypic
variance than genetic variance in both irrigation and drought. The additive genetic variance
was greater (P≤0.05) than the dominance genetic variance for all traits measured in plants in
53
irrigation and drought. The variance of the interaction was lower than the phenotypic,
environmental and additive genetic variances, for all the traits measured in the plants in
irrigation and drought (Table 2). In autotetraploid alfalfa, similar results were obtained when
estimating the components of variance; the dominance variance was much lower than the
additive variance for the yield of dry matter and its components(28). The additive variance
was significantly greater than zero and the genetic variance for dry matter yield was mainly
additive in an F1 population of alfalfa under controlled growth conditions(29). Heritability (h2)
was low for leaf dry matter yield to moderate for total dry matter yield, L:S ratio, plant height,
number of stems, and radiation use efficiency in irrigation and drought (Table 2). These
heritability values are similar to those obtained for aerial biomass and plant height in annual
alfalfa (Medicago sativa subsp. falcata) under field conditions(28) and could be useful in
improving the yield of alfalfa dry matter with the support of genomic selection(27).
Table 2: Estimated genetic parameters for total dry matter yield (TDMY) and leaf dry
matter yield (LDMY), leaf:stem ratio (L:S), plant height (PH), number of stems (NS) and
radiation use efficiency (RUE) in irrigation (I1 and I2), and drought (D1 and D2), average of
10 varieties of alfalfa
Genetic parameters TDMY LDMY L:S PH NS RUE
Irrigation I1 and I2
86.4 0.021
Phenotypic variance (𝜎𝑝2 ) 3.6 (0.7) 0.5 (0.1) 0.01 (0.001) 16.0 (1.6)
(7.6) (0.001)
Genotypic variance (𝜎𝑔2 )
0.005 31.6
additive (𝜎𝐴2 ) 1.2 (0.4) 0.1 (0.05 4.5 (0.8) 0.01 (0.001)
(0.0003) (1.2)
dominance (𝜎𝐷2 ) 0.3 0.02 0.001 7.9 1.1 0.002
2
interaction (𝜎𝑔∗𝑑𝑐 ) 0.7 0.06 0.002 12.3 2.5 0.002
0.004 42.6
Environmental variance (𝜎𝑒2 ) 1.7 (0.4) 0.4 (0.08) 9.0 (1.6) 0.01 (0.001)
(0.0008) (6.9)
0.3 0.4
Heritability (h2) 0.2 (0.04) 0.4 (0.04) 0.3 (0.04) 0.4 (0.04)
(0.04) (0.03)
Drought D1 and D2
61.6 0.015
Phenotypic variance (𝜎𝑝2 ) 1.5 (0.2) 0.2 (0.03) 0.01 (0.001) 11.5 (0.8)
(5.8) (0.007)
Genotypic variance (𝜎𝑔2 )
0.5 0.04 0.004 20.4 0.046
additive (𝜎𝐴2 ) 4.1 (0.3)
(0.02) (0.004) (0.0003) (2.0) (0.005)
dominance (𝜎𝐷2 ) 0.1 0.01 0.001 5.1 1.0 0.001
2
interaction (𝜎𝑔∗𝑑𝑐 ) 0.2 0.04 0.004 13.7 1.8 0.002
0.001 27.5
Environmental variance (𝜎𝑒2 ) 0.8 (0.2) 0.1 (0.03) 5.5 (0.8) 0.008 (0.2)
(0.0003) (4.9)
0.3 0.3
Heritability (h2) 0.2 (0.04) 0.4 (0.03) 0.4 (0.04) 0.3 (0.04)
(0.04) (0.04)
54
The analysis of principal components (PC1 and PC2) identified two components that explain
the largest proportion of the total variation (75.8 %) shown in the experiment. PC1 explained
56.2% of the variation and had a positive correlation with total dry matter yield (r= 0.52),
leaf dry matter yield (0.50), number of stems (r= 0.42), radiation use efficiency (r= 0.40) and
plant height (r= 0.34), and negative correlation with L:S ratio (r= -0.19). PC2 explained only
19.6 % of the observed variability and had a positive correlation with the L:S ratio (r= 0.78)
and leaf dry matter yield (r=0.31), and negative correlation with plant height (r= -0.49)
(Figure 6). Additionally, total dry matter yield was positively related to the number of stems
and leaf dry matter yield, and negatively related to plant height; plant height was negatively
related to L:S ratio. The variability observed for yield of dry matter and its components in
the present study was similar to that observed in a group of 27 populations and cultivars of
alfalfa under field conditions, where PC1 contributed 58.2 % of the total variability and
showed positive association with dry and green matter yield, vigor, growth habit,
regeneration of the plant and width of the central leaflet(30). Other results in irrigated and
rainfed alfalfa in the field showed a PC1 with 54.3 % of the total variability and positive
association with the diameter of lateral roots and number of lateral or branched roots(31). It is
interesting to note the similarity in the values observed for PC1 and the variability between
genotypes in these studies, and the traits of the plant that had the greatest positive association
with this component, especially with dry matter yield.
Figure 6: Biplot plane of dry matter yield vs. total dry matter yield (RMST), leaf dry matter
yield (RMSH), L:S ratio (H:T), number of stems (NT), plant height (AP) and radiation use
efficiency (EUR) in irrigation (I1 and I2) and drought (D1 and D2), on average of 10
varieties of alfalfa in greenhouse conditions
55
The drought decreased the total dry matter yield and its components, and plants under soil
water deficit conditions did not recover their productive capacity after experiencing the water
deficiencies of the soil, even after recovery irrigation. In contrast, the L:S ratio was higher in
plants in drought than in irrigation and plant height was the only component of yield that
regained its growth capacity after recovery irrigation. Soil water deficit also reduced
phenotypic variance for total dry matter yield and its components; environmental variance
was greater than genetic variance in irrigation and drought. Additive variance was greater
than dominance variance for all traits measured in irrigation and drought. Total dry matter
yield, L:S ratio, plant height, number of stems, and radiation use efficiency had higher
heritability in irrigation and drought. Leaf dry matter yield, number of stems, radiation use
efficiency and plant height were positively related to total dry matter yield. The most
productive varieties could be used for forage production in water-scarce areas and/or as
parental lines for forage yield improvement in selection programs. Future research work on
this topic requires confirmation under field conditions.
Literature cited:
1. Guitjens JC. Alfalfa. In: Stewart BA, Nielsen DR, editors. Irrigation of agricultural crops.
American Society of Agronomy, Inc. Madison, Wisconsin USA; Monograph Number
30 in the series of Agronomy; 1990:537-596.
2. Lauriault L, Marsalis M, Contreras-Govea F, Angadi S. Managing alfalfa during drought.

Cooperative Extension Service, College of Agricultural and Environmental Sciences,
New Mexico State University. Las Cruces, New Mexico; 2009 (Circular 646):4.
3. Luna-Guerrero MJ, López-Castañeda C, Quero-Carrillo AR, Herrera-Haro JG, Ortega-

Cerrilla ME, Martínez-Hernández PA. Water relations and gas exchange in lucerne
under drought conditions. Rev Mex Cienc Agríc 2020; Special Publication Number
24:81-92.
4. Petcu E, Schitea M, Drăgan L, Bǎbeanu N. Physiological response of several alfalfa

genotypes to drought stress. Rom Agric Res 2019;36:107-118.
5. Blum A. Crop responses to drought and the interpretation of adaptation. Plant Grow Reg
1996;20:135–148.
6. Anower MR, Boe A, Auger D, Mott IW, Peel MD, Xu L, Kanchupati P, Wu Y.

Comparative drought response in eleven diverse alfalfa accessions. J Agron Crop Sci
2017;203:1-13.
56
7. Liu Y, Wu Q, Ge G, Han G, Jia Y. Influence of drought stress on alfalfa yields and

nutritional composition. BMC Plant Biology 2018;18(13):1-9. doi:10.1186/s12870-017-
1226-9.
8. Annicchiarico P, Pecetti L, Tava A. Physiological and morphological traits associated with

adaptation of lucerne (Medicago sativa) to severely drought-stressed and to irrigated
environments. Ann Appl Biol 2013; 162:27–40. doi:10.1111/j.1744-
7348.2012.00576.x.
9. Huang Z, Liu Y, Cui Z, Fang Y, He H, Liu BR, Wu GL. Soil water storage deficit of alfalfa
(Medicago sativa) grasslands along ages in arid area (China). Field Crop Res
2018;221:1-6.
10. Falconer DS. Introducción a la genética cuantitativa. México: Cía. Editorial Continental,
SA de CV: 1984.
11. Hill J, Becker HC, Tigerstedt PMA. Quantitative and ecological aspects of plant breeding.
London: Chapman & Hall; 1998.
12. Bellague D, Hammedi-Bouzina MM, Abdelguerfi A. Measuring the performance of

perennial alfalfa with drought tolerance indices. Chil J Agric Res 2016;76(3):273-284.
doi:10.4067/ S0718-58392016000300003.
13. Márquez-Ortiz JJ, Lamb JFS, Johnson LD, Barnes DK, Stucker RE. Heritability of crown
traits in alfalfa. Crop Sci 1999;39:38-43.
14. Luna-Guerrero MJ, López-Castañeda C, Hernández-Garay A. Genetic improvement of

aerial alfalfa biomass and its components: half-sib family selection. Rev Mex Cienc
Pecu 2020;11(4):1126-1141. doi.org/10.22319/rmcp.v11i4.5344.
15. García E. Modificaciones al sistema de clasificación climática de Köppen. Serie Libros

Núm. 6, Instituto de Geografía, UNAM. México, DF; 2004.
16. Luna-Guerrero MJ, López-Castañeda C, Hernández-Garay A, Martínez-Hernández PA,

Ortega-Cerrilla ME. Evaluación del rendimiento de materia seca y sus componentes en
germoplasma de alfalfa (Medicago sativa L.). Rev Mex Cienc Pecu 2018;9(3):486-505.
doi.org/10.22319/rmcp.v9i3.4440.
17. Molina-Galán JD. Introducción a la genética de poblaciones y cuantitativa (algunas

implicaciones en genotecnia). México, DF: AGT Editor, SA; 1992.
18. Márquez-Sánchez F, Sahagún-Castellanos J. Estimation of genetic variances with

maternal half-sib families. Maydica 1994;39(3):197-201.
57
19. Melendres-Martínez JI, Valdivia-Bernal R, Lemus-Flores C, Medina-Torres R, García-

López M, Ortiz-Caton M, et al. Estimación de parámetros genéticos de maíz bajo
mejoramiento por selección recíproca recurrente. Rev Mex Cienc Agríc 2018;9(7):1327-
1337.
20. Galicia-Juárez M. Varianza genética y mapeo molecular de rendimiento y calidad

nutricional en familias de medios hermanos en Medicago sativa [tesis maestría].
Texcoco, México: Colegio de Postgraduados; 2012.
21. Hill J, Becker HC, Tigerstedt PMA. Quantitative and ecological aspects of plant breeding.
London: Chapman & Hall; 1998.
22. SAS (Statistical Analysis System), Version 9.4 para Windows. SAS Institute Inc., Cary,
NC, USA; 2012.
23. Hinkelmann K, Kempthorne O. Design and analysis of experiments. Volume 1:

Introduction to experimental design. USA: A John Wiley and Sons, Inc; 2008.
24. Orloff S, Putnam D, Bali K. Drought strategies for alfalfa. Agriculture and Natural
Resources, UC, USA. Publication 8522; 2015:1-9 (http://anrcatalog.ucanr.edu/).
25. Anjum SA, Ashraf U, Zohaib A, Tanveer M, Naeem M, Ali I, Tabassum T, Nazir U.
Growth and developmental responses of crop plants under drought stress: a review.
Zemdirbyste-Agriculture 2017;104(3):267-276. doi:10.13080/z-a.2017.104.034.
26. Benabderrahim MA, Hamza H, Haddad M, Ferchichi A. Assessing the drought tolerance
variability in Mediterranean alfalfa (Medicago sativa L.) genotypes under arid
conditions. Plant Biosystems 2015; 149 (2):395-403.
doi.org/10.1080/11263504.2013.850121.
27. Annicchiarico P. Alfalfa forage yield and leaf/stem ratio: narrow-sense heritability,
genetic correlation, and parent selection procedures. Euphytica 2015;205(2):409–420.
doi:10.1007/s10681-015 1399-y.
28. Riday H, Brummer EC. Narrow sense heritability and additive genetic correlations in
alfalfa subsp. falcata. J Iowa Academy Sci 2007;114(1-4):28-34.
29. Bowley SR, Christie BR. Inheritance of dry matter yield in a heterozygous population of
alfalfa. Can J Plant Sci 1981;61:313-318.
30. Tucak M, Popović S, Ćupić T, Šimić G, Gantner R, Meglić V. Evaluation of alfalfa

germplasm collection by multivariate analysis based on phenotypic traits. Rom Agric
Res 2009;26:47-52.
58
31. Odorizzi A, Basigalup D, Arolfo V, Balzarini M. Análisis de la variabilidad de caracteres

de raíz en poblaciones de alfalfa (Medicago sativa L.) con alto número de raíces
laterales. AgriSci 2008;25(2):65-73.
59
Table 1: Factors of variation, degrees of freedom (DF) and significance of total dry matter yield (TDMY) and leaf dry matter yield
(LDMY), leaf:stem ratio (L:S), plant height (PH), number of stems (NS) and radiation use efficiency (RUE) in irrigation (I1)
and drought (D1) (112-434 dat), and in I2 and D2 (462-798 dat)
Characteristic DF 112 140 175 210 245 280 315 406 434 462 490 518 553 588 686 742 770 798
TDMY (g DM plant-1)
A 1 ns ns ns ns ns ns ns ** ** ** ** ** ** ** ** ** ** **
B 9 ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** **
A*B 9 ** ** * ** ns ns * ** * ns ns ns ns ns ns ** * ns
LDMY (g DM plant-1)
B 9 ** ** ** ** ns ** ** ns ns ** * ** ns ns * ** ** **
A*B 9 ** ** * ** ns ns ns ns ns ns ns ns ns ns ns * * ns
L:S ratio
A 1 ns ns ns ns ns ns ns ** ** ** ** ns ns ns ** ns ns ns
B 9 ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** **
A*B 9 ** ** ** ** ** ** ** ** ** ns ** ns ns * ** ns ** **
PH (cm)
A 1 ns ns ns ns ns ns ns ** ** ** ns ns ns ns ** ** ** **
B 9 ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** **
A*B 9 ** * ns * ** ** * * ns ns ** ns ns ns ns ** ns **
NS
B 9 ** ns ** ** ** ** ** * ** ** ** ** ** ** ** ** ** **
A*B 9 ns * ** ns * ns ** ns ** * ns * ns ns * ns ns ns
RUE (g DM MJ-1)
B 9 ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** **
A*B 9 * ** * ** ns ns * ns * ns ns ns ns ns ns ** * ns
A=Soil moisture treatments (Irrigation=I1 and I2, and Drought=D1 and D2); B=Genotypes; A*B Interaction of soil moisture treatments*genotypes; *(P≤0.05);
**(P≤0.01); ns (not significant). D1 (345-406 dat) and D2 (620-688 dat).
60
Article
Estimation of forage mass in a mixed pasture by machine learning,

pasture management and satellite meteorological data
Aurelio Guevara-Escobar a
Mónica Cervantes-Jiménez a*
Vicente Lemus-Ramírez b
Adolfo Kunio Yabuta-Osorio b
José Guadalupe García-Muñiz c
a
Universidad Autónoma de Querétaro. Facultad de Ciencias Naturales. 76230 Juriquilla,
Santiago de Querétaro, Querétaro, México.
b
Universidad Nacional Autónoma de México. Facultad de Medicina Veterinaria y
Zootecnia, Centro de Enseñanza, Investigación y Extensión en Producción Animal en
Altiplano CEIEPAA. Querétaro, México.
c
Universidad Autónoma Chapingo. Departamento de Zootecnia, Posgrado en Producción
Animal. Estado de México, México.
* Corresponding author: monica.cervantes@uaq.mx
Abstract:
Measuring forage mass (FM) in the pasture, prior to grazing, is critical to determining the
daily allocation of forage in pastoral animal production systems. FM is estimated by
cutting forage in known areas, using allometric equations, or with the use of remote
sensors (RS); however, the accuracy and practicality of the different methods for
estimating FM is variable. The objective was to obtain predictive models using
environmental and pasture management variables to predict FM. Regression models were
fitted to estimate FM based on variables of pasture management (PM) or measurements
obtained by RS, such as reflectance, air temperature, and rainfall. A mixed pasture grazed
by beef cattle was studied for three years. With 80 % of data, models were built by
ordinary least squares (OLS) or by machine learning (ML) algorithms. The remaining
20 % of the data was used to validate the models using the coefficient of determination
61
and average bias between estimated and observed values. The base model of study was
the relationship between pasture height before grazing and FM, this model was fitted
using OLS; the r2 was 0.43. When models that included PM variables were fitted, the r2
was 0.45 for OLS and 0.63 for ML. When fitting models with PM and RS variables, the
r2 was 0.71 for OLS and 0.96 for ML. ML-fitted model ensembles reduced the bias of FM
estimates of the examined pasture. Overall, ML models better represented the relationship
between pasture height before grazing and FM than OLS models, when fitted with pasture
management variables and RS information. ML models can be used as a tool for daily
decision-making in pastoral production systems.
Key words: Alfalfa, Forage, Rain, Lucerne, Temperature, Remote sensors.
Introduction
Animal production using grazed pastures depends on the rate of accumulation of forage
mass (FM), as well as on the timely allocation of an adequate stocking rate to take
advantage of the FM; other important aspects are nutritional quality and seasonality in
the rate of accumulation of FM. Cost-effective management of a pasture through direct
grazing involves, among other things, implementing grazing management without
compromising vegetation cover regrowth, as well as accurately knowing the FM in the
pasture before and after grazing(1). Traditionally, FM is measured directly with forage
cuts in quadrants of known area, distributed in a spatially representative manner and in a
sufficient number that represents the variability of the vegetation cover in the pasture(2,3).
The cutting of quadrants is laborious and therefore methods and devices have been
developed for the indirect estimation of FM(4-6). Pasture canopy height, measured with a
sward stick, is useful to represent the FM, although the relationship may be different
depending on the botanical composition, density of the pasture canopy and season of the
year(7-9). The height of the compressed forage measured with a rising plate meter estimates
the FM considering the density of the canopy and is a very common practice at the farm
level in countries such as New Zealand(2). The relationship between canopy height and
FM in ryegrass and white clover pastures is well known and routinely applied in New
Zealand(10); for pastures with other forage species such as alfalfa, more research is needed
to determine the relationship between canopy height and FM(8).
Remote sensing (RS) by orbital satellites measures spectral reflectance, the proportion of
incident energy reflected by the Earth’s surface at different wavelengths; these
measurements have been associated with vegetation activity processes(11). With RS
information, it is also possible to estimate environmental variables such as temperature,
62
rainfall, and others(12). The wide availability and free access of RS products is an
opportunity to explore crop dynamics and establish relationships with productive
parameters, such as FM. The time series available for different RS products allow
retrospective studies to be made, which is valuable for evaluating pasture management
practices and regional grassland studies. However, the spatial scale of measurement is
coarse in some RS sensors and is an important disadvantage in studies such as the one
described in this research.
Recently, a variety of machine learning (ML) algorithms have been incorporated into
regression analysis and they are an alternative to ordinary least squares (OLS) regression.
Photosynthesis in ecosystems, named gross primary productivity and net primary
productivity (when discounting losses by respiration), has been modeled with empirical
or mechanistic approaches, from OLS models to those that simulate ecophysiological
processes at the global level based on RS(13). Net primary productivity includes
photosynthetic partitioning into aerial and root biomass and therefore does not reflect the
FM available for grazing. Lang et al(14) estimated arid grassland production using
measurements from rainfall RS sensors, spectral reflectance obtained from the Landsat 7
satellite and random forest; a ML algorithm. Using Neural Networks, another ML-type
algorithm, Chen et al(15) related the spectral reflectance measured by the Sentinel-2
satellite and FM on dairy farms of Tasmania in Australia. In these studies, the coefficient
of determination (r2) in different models was between 0.6 and 0.7. Conceptually, it is
important to incorporate humidity conditions, in the short or medium term, to explain the
carrying capacity of the grassland(16), since water is the main limiting resource of plants
in arid and semi-arid environments. The conditions of water availability for plants can be
represented by the precipitation (P) that occurred, water available in the soil or vapor
deficit in the atmosphere. However, to explain the FM, not only the P occurred in the
period of accumulation of the FM (month in which the FM was measured) is important,
but also the humidity conditions that occurred in previous months.
In the present work, the relationship between FM and pasture height was examined as a
baseline to compare other models that used meteorological variables obtained by RS or
in conjunction with variables representative of pasture management (PM) conditions;
such as the grazing and rest periods of the grazed area or the pasture height itself. In
particular, the usefulness of models to predict FM based on previous rainfall and
temperature conditions in different time windows was explored; for example, the P
accumulated in the previous month, in two months or three months before the
measurement of the FM. The objective was to obtain a predictive model of FM that could
be incorporated into grazing planning. For this purpose, three years of measurements on
a mixed of alfalfa-grass pasture grazed by beef cattle, were used.
63
Site
The study was carried out at the Centro de Enseñanza, Investigación y Extensión en
Producción Animal en el Altiplano, run by Facultad de Medicina Veterinaria y Zootecnia
from the Universidad Nacional Autónoma de México. The site is located at 20° 36’ 13.88”
N, 99° 55’ 02.91” W and altitude of 1,913 m asl. The climate is extreme dry Ganges type
without dry spells, BS1 0w(e)g, according to the historical climatological records (1951
to 2006) of climatological station 22025; the closest to the site, where the annual averages
of precipitation and temperature are 458 mm and 23.5 °C(17).
The pasture was established in 2004 with a mixture of 50 % alfalfa (Medicago sativa) and
grasses such as orchard grass (Dactylis glomerata), tall fescue (Festuca arundinaceae)
and perennial ryegrass (Lolium perenne). The grazing area was 19 ha divided into 16
paddocks of equal size and delimited through mobile electric fence. The pasture was
irrigated with a center-pivot sprinkler system; however, there were no records of the
irrigation sheet or calendar. The grazing mob was made up of 88 dams of the Limousin
breed and their calves. The grazing time in each division was established based on: the
estimation of FM, proximate chemical analysis of FM samples, and the dry matter (DM)
allowance for the mob in each turn. Reproductive management was mainly with artificial
insemination and year round calving.
Data
From 2008 to 2010, 399 FM observations were obtained prior to grazing of the allocated
grazing area. Each FM observation corresponded to the beginning of a grazing cycle of
the mob. The observations were considered experimental units, and each consisted of
eight random measurements obtained with the modified quadrat technique; to protect the
alfalfa regrowth the pasture samples were cut to 10 cm height in an area of 0.25 m2(18).
Forage samples were dehydrated in a forced-air oven for 48 h to determine the DM
content and the data was expressed in kg DM ha-1. In each grazing cycle, the following
were recorded: the height of the pasture (H_pasture), the date of grazing (Day_grazing
and Month_grazing), grazing time (G_time), resting time of the grazed area from the
previous grazing (R_time), month of the beginning of growth in the previous grazing
cycle (Month_beg_grow) and the average monthly pasture accumulation rate of DM
(PAR, kg DM ha-1 d-1). These variables were collectively referred to as pasture
management (PM) variables.
Using the Application for Extracting and Exploring Analysis Ready Samples of the Land
Processes Distributed Active Archive Center of the National Aeronautics and Space
Administration (NASA), the MCD43A4 version 6(19) product was requested. The
MCD43A4 product is generated from measurements made by Moderate-Resolution
64
Imaging Spectroradiometer (MODIS) sensors at a spatial resolution of 500 m2. This

product consists of seven reflectance bands adjusted by the Bidirectional Reflectance
Distribution Function and produced daily, which are a moving average of the contiguous
16 days measurements. Data from eight contiguous pixels corresponding to the polygon
of coordinates: 99.93 W, 20.60 N to 99.92 W, 20.61 N, were downloaded. The radiation
spectrum (nm) covered by bands one to seven is (b1-b7): 620-670, 841-876, 459-479,
545-565, 1230-1250, 1628-1652 and 2105-2155. Rainfall data were from the 3IMERG
version 6 product of the Global Precipitation Measurement Mission of NASA obtained
through the Giovanni portal (https://giovanni.gsfc.nasa.gov/giovanni). The P data (mm)
was the monthly accumulated for the coordinate 99.92 W, 20.60 N; the spatial resolution
of 3IMERG is 10 km2. Through the Giovanni portal, the MODIS MOD11A2 version 6
product of daily surface temperature during the day (LST_d) and night (LST_n) was also
obtained.
For MODIS, good quality was determined according to the quality data accompanying
the respective products. In the R(20) language, a code was generated to find the
measurement dates of the MCD43A4 closest to the measurement date of the FM. Using
Qgis v3.16.4(21) and a satellite image from Google Maps(22) as a guiding template, a vector
layer corresponding to the area of irrigation by central pivot was determined; the circle
comprised different area of the sampled pixels of the MCD43A4. For each reflectance
band, the average corresponding to the vector was obtained using the extract function of
the raster package.
Variable generation
The reflectance in the bands b2 and b1 is associated with the ability of vegetation to
absorb photosynthetically active light and there are different indices to represent this
activity of the vegetation. The normalized vegetation index (NDVI) and the enhanced
vegetation index (EVI) were calculated using the spectral bands of the MCD43A4
product:
𝑏2−𝑏1
𝑁𝐷𝑉𝐼 = 𝑏2+𝑏1 1)
(𝑏2−𝑏1)
𝐸𝑉𝐼 = 2.5 (𝑏2+2.4𝑏1+1) 2)
With the time series of P, the following variables were calculated: the P accumulated in
the previous month (P_lag_1), the P accumulated in the previous two months (P_lag_2)
and so on until the P accumulated in six previous months: (P_lag_3, P_lag_4, P_lag_5
and P_lag_6). For LST_d and LST_n, the average of the previous month (LST_x_avg_1),
of the previous two months (LST_x_avg_2) or of the previous three months
(LST_x_avg_3), where x represents the indicative d or n, for day or night, was calculated.
These variables represented the prevailing environment before measuring FM.
65
Modeling
The baseline model for comparison was the linear regression between FM and H_pasture.
Four modeling scenarios according to the type of algorithm were explored: ML or OLS
and the type of variables available for modeling: using only explanatory variables of RS
origin (ML_RS and OLS_RS) or RS variables and those of PM (ML_RS_PM and
OLS_RS_PM). The models were trained with 80 % of observations chosen randomly and
20 % were reserved for evaluation. Model evaluation is a black box concept about the
relevance of the result of the model(23). The statistical procedures were carried out in the
R language, the name of the packages is indicated where relevant. An orthogonal
regression (major axis regression) model was fitted between observed values and
predicted values using the smatr 3 package, since observed FM values are measured with
error(24). The following were calculated: coefficient of determination (r2), root mean
square error (RMSE), the Akaike (AIC) and Bayesian (BIC) information criteria,
deviance, and bias. These quantitative indicators, as well as graphical evaluation, are
techniques commonly used to evaluate mathematical models for predictive purposes(25).
In the case of OLS, the variance inflation value (VIF) was used to identify
multicollinearity using the stepAIC and vif(26) functions; 10.0 was the maximum allowed
value of VIF to retain variables in the OLS multiple regression model. The significance
level was set at 0.05 for parametric analyses and residual analysis of the OLS regression.
The ML model was generated with the h2o.automl function of the H2O(27) package, it
produces a set of models with different algorithm realizations: deep learning (DL),
feedforward artificial neural network (NN), general linear models (GLMs), gradient-
boosting machine (GBM), extreme gradient-boosting (XGBoost), default distributed
random forest (DRF) and extremely randomized trees (XRT). Each individual model can
be used to predict the response, but also to generate two types of model ensemble: one is
from all the algorithms used in the generated models, and the second type of ensemble
only considers the best models of each class or family of algorithms; both types of
ensembles generally produce better predictions than individual models(23).
The h2o.automl function was run twenty times with the following parameters: a)
max_runtime_secs = 500, the maximum runtime before training a final ensemble of
models, b) nfolds = 15, number of folds for cross-evaluation (k-folds), c) seed = a random
integer value with value between 1 and 50; each of the runs used a randomly chosen seed
value, d) nthreads = 50, the number of available processing threads, e) max_mem_size =
100GB, the available RAM in Gigabytes. The approximate runtime was 50 min on an
equipment with dual Xeon 2680 v4 processor with 14 cores and double thread each and
128 GB of RAM.
With the h2o.explain function, the importance of the variables in the individual ML
models and dependence figures was obtained(27). Deviance was used as a goodness-of-fit
statistic to rank the generated models. Machine learning has two elements for supervised
66
learning: training loss and regularization. The training task attempts to find the best
parameters for the model while minimizing the training loss function; this function could
be the mean square error or others. The regularization term controls the complexity of the
model, helping to reduce overfitting. Overfitting becomes apparent when the model
performs accurately during training, but accuracy decreases during the evaluation of the
model. A good model needs extensive fitting of parameters by running the algorithm
several times to explore the effect on regularization and accuracy of cross-validation(28).
In this research, the function of training loss was the deviance, which is a likelihood
generalization of the sum of squares of the error; lower or negative values indicate a better
performance of the model(29).
The average of FM of the pasture was 2,134 kg DM ha-1 with a seasonal pattern of lower
production in winter and higher production in summer (Figure 1a). FM was different
among the three years 2,121, 1,770 and 2,392 kg ha-1 for 2008 to 2010 (P<0.05). The
rainfall was 636, 382 and 552 mm, respectively. The greatest amount of rainfall was from
July to September; for 2010, February was atypical with 151 mm (Figure 1b) and possibly
positively impacting the FM from March in that year. The rainfall recorded by the IMERG
product in 2008 and 2010 was higher than that recorded by the climatological station
closest to the study site; this rainfall estimate was considered accurate because this
product has shown good agreement with terrestrial precipitation records(30). The seasonal
behavior of the FM suggested an important effect of rainfall, even in the case of this
irrigated pasture. April and May were the months with the highest average LST_d (Figure
1c). The difference between LST_d and LST_n was greater from April to May (28.5 and
27.3 °C) and lower in July to September (17.3, 16.4 and 15.6 °C); which indicates the
site’s extreme characteristic of the climate during the spring. These environmental
conditions were also reflected in seasonal changes in pasture management on rest days,
forage height, and PAR (Figure 2).
Figure 1: Environmental variables and production of a mixed alfalfa-grass mixed

pasture grazed by beef cattle: a) forage mass (FM), b) rainfall (P) and c) diurnal (●) and
nocturnal (○) surface temperature (LST)
67
Figure 2: Management of a mixed alfalfa-grass mixed pasture grazed by beef cattle

during 2008 (●), 2009 (○) and 2010 (■): a) Rest days before grazing, b) rate of
accumulation of forage (PAR) of the period, c) pasture height
In MA regression, the intercept was numerically close to 0 in the ML_RS_PM scenario

and its slope was equal to 1, a model with slope equal to 1 and intercept equal to 0
indicates good fit. The lower value of the RMSE, AIC, BIC and deviance suggested a
better representation of the FM with the ML_RS_PM scenario (Table 1). Regarding
deviance analysis, the comparison between two or more models will be valid if they fit
the same data set, this requirement was not met because the predicted values of FM were
inherently different for each model generated. The difference of deviances is distributed
approximately as X2 with degrees of freedom equal to the difference in the number of
parameters between the models(14), with this difference being 0 for the case of simple
linear regression models used to represent the relationship between estimated and
predicted values in each modeling scenario. For these two reasons, deviance analysis was
not possible; therefore, the selection of the best model was based solely on the numerical
value of the goodness-of-fit measures. The worst model was the simple regression
between FM and H_pasture, not only according to the goodness-of-fit means but also in
the graphical representation of the estimated vs. observed values (Figure 3).
68
Table 1: Goodness-of-fit measures between observed and estimated FM values

resulting from modeling scenarios using algorithms of ordinary least squares (OLS) or
machine learning (ML) in combination with explanatory variables related to pasture
management (PM) alone or in conjunction with remote sensing variables (PM_RS)
OLS_height OLS_RS OLS_RS_PM ML_RS ML_RS_PM
r2 0.40 0.49 0.67 0.70 0.97
RMSE 361.0 341.0 269.0 259.0 78.0
AIC 734.0 724.0 686.0 691.0 542.0
BIC 738.0 728.0 690.0 695.0 546.0
Deviance 8079684.0 6874194.0 4377078.0 4003784.0 363954.0
Bias -3.4 47.1 16.5 -35.1 -1.3
CI 2.5 % -95.9 -39.2 -52.7 -43.5 -21.2
CI 97.5 % 89.0 133.5 85.7 96.4 18.6
MA intercept -1799.0 -2044.0 -594.0 -735.0 27.0
CI 2.5 % -3386.0 -3395.0 -1137.0 -1257.0 62.0
CI 97.5 % -831.0 -1162.0 -162.0 -316.0 112.0
MA slope 1.9 2.0 1.3 1.4 1.0
CI 2.5 % 1.4 1.6 1.1 1.2 0.9
CI 97.5 % 2.6 2.7 1.6 1.6 1.0
r2= coefficient of determination; RMSE= root mean square error; AIC= Akaike information criterion;
BIC= Bayesian information criterion; MA= major axis regression; CI= confidence interval.
Figure 3: Evaluation between observed and estimated values of FM using algorithms of

ordinary least squares (OLS) or machine learning (ML)
a) OLS, predictor variable forage height; b) OLS_RS scenario; c) OLS_RS_PM scenario; d) ML_RS
scenario; e) ML_RS_PM scenario. Coefficient of determination (r2), root mean square error (RMSE), bias
and its 95 % confidence interval (CI=IC).
69
The PAR and H_pasture variables of PM were the most important (Table 2), both in the
ML and OLS models; the variable R_time was much less important (Table 2). The most
important RS variables were: LST_n, P, P_lag_3 or P_lag_5, LST_d_avg_3 or
LST_n_avg_3; indicating the relevance of the environmental conditions of precipitation
and temperature not only of the current month, but of the conditions preceding the
measurement of the FM. Reflectance (b1 – b7) and vegetation indices were incorporated
into ML models, but the stepwise procedure did not choose them for OLS. Compared
with PAR and H_pasture, reflectance variables were of low importance in the RS_PM
scenarios of ML. Spectral reflectance bands were more important than EVI and NDVI;
this finding coincides with the FM study for mixed pastures of temperate climate(15).
Although the prediction of fresh biomass in Brachiaria pastures based on the NDVI with
r2= 0.73(31) was considered adequate.
Table 2: Important variables included in the scenarios using two possible algorithms:
ordinary least squares (OLS) or machine learning (ML) and two types of explanatory
variable: only remote sensors (RS) or pasture management variables and RS (RS_PM)
Machine learning (ML) Ordinary least squares (OLS)
Remote sensors
Remote (RS)_Pasture
sensors (RS) management
Variable (PM) RS RS_PM
LST_d_avg_3 0.081 0.023 0.036 0.027
LST_n_avg_3 0.064 0.017
LST_d 0.036 0.036
LST_n 0.161 0.007 0.060
b1 0.027 0.008
b2 0.034 0.014
b3 0.028 0.003
b4 0.033 0.004
b5 0.044 0.008
b6 0.048 0.010
b7 0.096 0.008
P 0.058 0.008 0.048
P_lag_3 0.099 0.023
P_lag_5 0.270
NDVI 0.001
EVI 0.018 0.001
H_pasture 0.303 0.231
Month_beg_grow 0.006 0.020
R_time 0.101 0.072
PAR 0.417 0.368
For ML models the sum of importance is 1, for OLS models the sum of importance is equal to the r 2.
70
The partial dependence that existed between the prediction of FM and the value of some
of the most important variables in some ML models is shown in Figure 4, in the ML_RS
scenario and in Figure 5 for the ML_RS_PM scenario. The ensembles of ML models had
lower deviance compared to some ML algorithms in the two scenarios and were therefore
considered better representations of the FM. The partial dependence figures indicate how
the explanatory variable influences the predictions of one of the models or ensembles,
after standardizing the effect of other variables. For linear regression models (such as the
GLM model obtained by ML), the figure is a straight line with slope equal to the
parameter of the model(32). FM depended directly and proportionally on the variables
PAR, H_pasture and R_time in different models even for a GLM model (pink line), but
for variables P_lag_3 and LST_d, the dependence differed between the GLM model and
ML models, particularly the DL-type model (dark green line) which was the best
individual ML model (Figure 5). The interpretation of the figures is improved with the
frequency histogram of the observations, depending on the value of the variable. Where
there was less frequency of data, it was interpreted that dependence was not supported by
sufficient evidence. An example of this situation was the dependence of LST_n in Figure
4, where the DL-type model has an abrupt ascent, but the last two class intervals of the
histogram have few observations.
Figure 4: Partial dependence of FM and: A) monthly average of nocturnal surface

temperature (LST_n), B) precipitation accumulated in the previous three months
(P_lag_3), C) reflectance band b7 of the MODIS MCD43A4 product, D) monthly
average of the diurnal surface temperature in the previous three months (LST_d_avg_3)
The gray bars are the data frequency according to class intervals of the variable. Only models of lower
deviance (value in parentheses) obtained by machine learning in the scenario using only variables
measured with remote sensors (ML_RS) are shown.
71
Figure 5: Partial dependence of FM and: A) rate of accumulation of forage (PAR), B)

forage height (H_pasture), C) pasture rest days (R_time), D) monthly average of the
diurnal surface temperature (LST_d)
The gray bars are the data frequency according to class intervals of the variable. Only models of lower
deviance (value in parentheses) obtained by machine learning in the scenario using variables measured
with remote sensors and of pasture management (ML_RS_PM) are shown.
The ML_RS_PM scenario included the PAR variable, and this could be a limitation for
the practical application of the model. To clarify this aspect, an ML model was built
without this variable and using the same training data, resulting in an r2 of 0.76, RMSE
of 232.2 and bias of –35.6 (CI –94.4 to 23.1), being better than that obtained in the
ML_RS scenario (data not shown). This result has two aspects of importance: other
variables available for modeling can replace a variable identified as the most important
and second, it is possible to incur into a local optimal solution, even when the ML
algorithm explored a solution space with different optimization parameters. A possible
alternative would be to increase the number of times the h2o.automl function is run and
increase the value of the max_runtime_secs constant.
Despite the coarse spatial resolution of the MODIS and GPM remote sensors (250 m2 and
10 km2), the FM was adequately estimated in the ML_RS scenario (Figure 3d), the r2=
0.70 of this model was within the range recently reported in the literature for ML models
that estimate biomass with RS data(14,15) or gross primary productivity(33). A model based
on RS data is only attractive for the management of large grasslands. When RS variables
72
were used in combination with pasture management variables that are easy to measure
(H_pasture) or record (R_time and Month_beg_grow), the estimate was very good
(Figure 3e); the r2= 0.97 was similar to the r2= 0.96 of biomass estimated from 30 m
spatial resolution RS data(5). The prediction of the forage mass obtained with forage
height measurements improved when pasture management variables and local
meteorological data were incorporated into an ML algorithm of random forest (r2= 0.82),
this approach was judged practical for producers, albeit the cost of meteorological
instruments(2).
Despite being an irrigated pasture, the previous short-term rain was important information
for OLS and ML models. In a recent study, it was identified that the spatial-temporal
variation of gross primary productivity was not only explained by reflectance bands of
the MODIS MCD43A4 but was also related to the vapor pressure deficit (33). Similar to
the result of these authors, here it was useful to include other reflectance bands besides
b1, b2 and vegetation indices such as NDVI and EVI. From a practical point of view, the
model of the ML_RS_PM scenario was considered very feasible to implement as it used
routine measurements of the management of the pasture and NASA’s remote sensor data
which are publicly accessible.
Animal production under grazing is sustainable when feed consumption that meets
nutrient needs is ensured. In grazing management, this depends on adjusting the stoking
rate according to the phenological stage of the plant, to the FM before and after grazing
and to the forage that is decided to leave as residual pasture mass. For beef cattle, adequate
FM before grazing can be set at 2,500 kg ha-1 and FM after grazing around 1,200 kg
ha-1(10); although these thresholds will depend on the reproductive and physiological stage
of the animal, the season of year and different pasture management strategies for feed
rationing, phenological control or balance in botanical composition(34). For these reasons,
it is important that the predictive model of FM fits well at the extremes of its range and
with the exception of the ML_RS_PM scenario, there was an overestimation of the FM
when it was less than approximately 1,500 kg ha-1 (Figure 3).
Pasture mass is spatially variable given by differences in soil moisture and fertility, dung
deposition, alterations in the plant community by selective grazing and other factors.
Forage quadrant cuttings are limited to represent and capture this spatial variability in
pastures and therefore the statistical method of sampling is important. Sensors on board
unmanned aerial vehicles or drones are an alternative to capture variability in vegetation
reflectance on the spatial scale of centimeters, but the cost of multispectral equipment,
data processing and operational limitation to cover the territory (35), in addition to the need
for a calibration function for forage mass, must be considered.
73
The prediction of FM had lower bias with ML models than with OLS models, especially
when remote sensors and pasture management variables were incorporated in the models.
ML ensembles had lower deviance compared to some of the individual ML models. The
use of RS variables predicted FM similarly to the relationship between H_pasture and
FM, although the ML model had lower bias. The models explored would have to be tested
in other pasture conditions in order to have a spatial application, be able to represent
ecosystems and to value the environmental service of carbon capture. At the local farm
scale, these models could be applicable for everyday use in farm feed budgeting or
retrospective evaluation of farm pasture management. In these cases, the results presented
here are promising.
Acknowledgements
The study was the product of the support for sabbatical leave of the first author by the
Autonomous University of Querétaro.
Literature cited:
1. Sheath GW, Hay RJM, Giles KH. Managing pastures for grazing animals. In: Nicol,
AM, editor. Livestock feeding on pasture. NZ Soc Anim Prod occasional
publication. 1987;65–74.
2. Murphy D, O’Brien B, Hennessy D, Hurley M, Murphy M. Evaluation of the

precision of the rising plate meter for measuring compressed sward height on
heterogeneous grassland swards. Precis Agric 2021;22(3):922–946.
3. Radcliffe J. Cutting techniques for pasture yields on hill country. Proc NZ Grassland
Association. 1971;33:91–104.
4. Jáuregui JM, Delbino FG, Bonvini MIB, Berhongaray G. Determining yield of

forage crops using the Canopeo mobile phone app. J NZ Grassl 2019;41–46.
5. Marsett RC, Qi J, Heilman P, Biedenbender SH, Watson MC, Amer S, et al. Remote
sensing for grassland management in the arid southwest. Rangel Ecol Manag
2006;59(5):530–540.
6. O’Donovan M, Dillon P, Rath M, Stakelum G. A comparison of four methods of

herbage mass estimation. Ir J Agric Food Res 2002;17–27.
7. Mills A, Smith M, Moot DJ. Relationships between dry matter yield and height of
rotationally grazed dryland lucerne. J NZ Grassl 2016;(78):185–196.
74
8. Moot DJ, Yang X, Ta HT, Brown HE, Teixeira EI, Sim RE, et al. Simplified methods
for on-farm prediction of yield potential of grazed lucerne crops in New Zealand. NZ
J Agric Res 2021;65(4-5)1–19.
9. Robertson S. Mass to height relationships in annual pastures and prediction of sheep

growth rates. Anim Prod Sci 2014;54(9):1305–1310.
10. Nicol AM, Nicoll GB. Pastures for beef cattle. In: Nicol, AM. editor. Feeding
livestock on pasture. Society of Animal Production. Lincoln, New Zealand.
1987;119–131.
11. Zhang Y, Ye A. Would the obtainable gross primary productivity (GPP) products
stand up? A critical assessment of 45 global GPP products. Sci Total Environ
2021;783:146965.
12. Jiao W, Wang L, McCabe MF. Multi-sensor remote sensing for drought
characterization: current status, opportunities and a roadmap for the future. Remote
Sens Environ 2021;256:112313.
13. Anav A, Friedlingstein P, Beer C, Ciais P, Harper A, Jones C, et al. Spatiotemporal

patterns of terrestrial gross primary production: A review. Rev Geophys
2015;53(3):785–818.
14. Lang M, Mahyou H, Tychon B. Estimation of rangeland production in the arid

oriental region (Morocco) combining remote sensing vegetation and rainfall indices:
challenges and lessons learned. Remote Sens 2021;13(11):2093.
15. Chen Y, Guerschman J, Shendryk Y, Henry D, Harrison MT. Estimating pasture

biomass using sentinel-2 imagery and machine learning. Remote Sens
2021;13(4):603.
16. Hacker R, Smith W. An evaluation of the DDH/100 mm stocking rate index and an
alternative approach to stocking rate estimation. Rangel J 2007;29(2):139–148.
17. CICESE C de IC y de ES de E. Base de datos climatológica nacional (CLICOM).

[Internet]. Tequisquiapan, Querétaro; 2021. Estación 22025: Consultada 6 Mar,
2021. http://clicom-mex.cicese.mx/.
18. Hodgson J. Grazing management. Science into practice. Longman Group UK Ltd.
1990.
19. Schaaf C, Wang Z. MCD43A4 MODIS/Terra+ Aqua BRDF/Albedo Nadir BRDF

Adjusted Ref Daily L3 Global 500 m V006. NASA EOSDIS Land Processes DAAC.
2015.
75
20. R Development Core Team. R: A language and environment for statistical

computing. R Foundation for Statistical Computing. 2009. https://www.r-project.org
21. QGIS.org. QGIS Geographic Information System. QGIS Association. 2021.

http://www.qgis.org
22. Google maps. Mapa satelital. México; 2021.
23. Pasquel D, Roux S, Richetti J, Cammarano D, Tisseyre B, Taylor JA. A review of

methods to evaluate crop model performance at multiple and changing spatial scales.
Precis Agric 2022;23:1489–1513.
24. Warton DI, Duursma RA, Falster DS, Taskinen S. smatr 3-an R package for
estimation and inference about allometric lines. Methods Ecol Evol 2012;3(2):257–
259.
25. Tedeschi LO. Assessment of the adequacy of mathematical models. Agric Syst
2006;89(2):225–247.
26. Hall P, Gill N, Kurka M, Phan W, Bartz A. Machine learning interpretability with
H2O driverless AI. Bartz A. Editor. California, U.S.: H2O.ai Inc.; 2019.
27. LeDell E, Poirier S. H2o automl: Scalable automatic machine learning. In 2020.
https://www.automl.org/wp-ontent/uploads/2020/07/AutoML_2020_paper_61.pdf.
28. Mitchell R, Frank E. Accelerating the XGBoost algorithm using GPU computing.
PeerJ Comput Sci 2017;3:e127.
29. McElreath R. Statistical rethinking: A bayesian course with examples in R and

STAN. Boca Raton, FL. U.S.: CRC Press; 2020.
30. Wang J, Petersen WA, Wolff DB. Validation of satellite-based precipitation products
from TRMM to GPM. Remote Sens 2021;13(9):1745.
31. Bretas IL, Valente DS, Silva FF, Chizzotti ML, Paulino MF, D’Áurea AP, et al.
Prediction of aboveground biomass and dry‐matter content in Brachiaria pastures
by combining meteorological data and satellite imagery. Grass Forage Sci
2021;76(3):340–352.
32. Friedman JH. Greedy function approximation: a gradient boosting machine. Ann Stat
2001;1189–1232.
33. Joiner J, Yoshida Y. Satellite-based reflectances capture large fraction of variability

in global gross primary production (GPP) at weekly time scales. Agric For Meteorol
2020;291:108092.
76
34. Griffiths W, Dodd M, Kuhn-Sherlock B, Chapman D. Management options to

recover perennial ryegrass populations and productivity in run-out pastures. NZGA:
Research and Practice Series. 2021;17.
35. Ahmad A, Ordonez J, Cartujo P, Martos V. Remotely piloted aircPARt (RPA) in

agriculture: A pursuit of sustainability. Agronomy 2021;11(1):7.
77
Article
Thymol and carvacrol determination in a swine feed organic matrix using

Headspace SPME-GC-MS
Fernando Jonathan Lona-Ramírez a
Nancy Lizeth Hernández-López a
Guillermo González-Alatorre a
Teresa del Carmen Flores-Flores a
Rosalba Patiño-Herrera a
José Francisco Louvier-Hernández a*
a
Tecnológico Nacional de México en Celaya. Departamento de Ingeniería Química.
Guanajuato, México.
*Corresponding author: francisco.louvier@itcelaya.edu.mx
Abstract:
In recent years, oregano essential oil has been used as an animal food additive due to its
antifungal and antibacterial properties as well as s synthetic antibiotic substitute. It is
desirable to develop fast and effective thymol and carvacrol quantification method in a swine
feed organic matrix. In this work, a performance comparison between the Soxhlet solvent
extraction technique using petroleum ether and ethyl acetate and the head space-solid phase
microextraction (HS-SPME) technique is made. A 24 design of experiments is performed for
defining HS-SPME parameters: equilibrium temperature of 40 ºC, extraction temperature of
40 ºC, ionic strength of 0.57 M, and extraction time of 40 min. The HS-SPME method is
more efficient for extracting thymol and carvacrol extraction from an organic matrix. Limits
of detection and quantification values using Soxhlet extraction with ethyl acetate were 3.7
and 12.5 μL-1 for thymol and 1.4 and 4.7 μg L-1 for carvacrol, respectively; while LOD and
LOQ for HS-SPME were 0.9 and 3.1 μg L-1 for thymol and 0.6 and 1.9 μg L-1 for carvacrol,
78
respectively. The head space-solid phase microextraction method has the potential for quality
control in the industry for active compounds present in oregano’s essential oil as an additive
into an organic matrix.
Key words: Essential oil, Thymol, Carvacrol, Oregano, Origanum, HS-SPME-GC-MS.
Introduction
People use herbal spices as food flavor enhancers and medicinal aids since antiquity(1),
mainly for their biological activity. Oregano is one of the most important herbs, which is the
common name from a wide variety of plant genera and species worldwide, but usually
referred to as Origanum in the Lamiaceae (Labiatae) family(2) or Lippia graveolens in the
Verbenaceae family. Oregano’s essential oil (OEO) has been used as a food additive due to
its antimicrobial activity attributed to its high monoterpenes content such as thymol and
carvacrol, the latter generally recognized as a safe food additive(3-6). Due to the banning of
antibiotics by the European Commission, OEO has received increased attention from the
poultry and swine industry for improving natural defenses and strengthening animal
organisms with favorable results(7-10). OEO can be incorporated into the swine feed by mixing
the oil and the organic matrix. However, a confident quantification method is required for
quality control.
Thymol and carvacrol(11) show antibacterial(4,12), antioxidant(13), and fungicide activity(3,4)

and are two of the main components of the OEO. Thus, they can serve as markers for
quantification. The quality control method begins with a solvent extraction of the volatile
compounds from the feed matrix; however, organic solvents are neither environmentally
friendly nor acceptable for food processing. Some other extraction technologies, such as
supercritical carbon dioxide extraction, require high-cost equipment and high-pressure
operational conditions(14,15). Thus, it is desirable to develop quick and effective thymol and
carvacrol quantification method inside a swine feed organic matrix. In this paper propose the
Head Space Solid Phase Micro Extraction (HS-SPME) technique along with the gas
chromatography-mass spectroscopy (GC-MS) method since HS-SPME is an effective, non-
expensive, and environmentally friendly technique for the detection and quantification of
volatile compounds(16,17).
79
As is known, this technique has not been used for thymol and carvacrol detection in an
organic matrix added with OEO, but only to quantify these active compounds in pure
oil(18-20). This work, compare a solvent extraction technique using petroleum ether or ethyl
acetate in a Soxhlet extractor(21) with an HS-SPME technique from the swine feed flour
matrix to quantify the thymol and carvacrol of the extract using a GC-MS system. It was used
nitrosopiperidine (NPIP) as an internal standard for absorbing signal variations due to the
extraction method and the equipment itself(22) and calculate the limit of detection (LOD) and
the limit of quantification (LOQ) for assessing the extraction technique’s effectiveness.
Finally, it was performed a design of experiments using R(23) and RStudio(24) software.
Reagents
Thymol (100.0%), carvacrol (99.9%), nitrosopiperidine (99.9%), and sodium chloride were
obtained from Sigma Aldrich (St. Louis, USA). Analytical grade (ACS) petroleum ether and
ethyl acetate were obtained from Fermont (Monterrey, México). Tridistilled water from
MERCK was used in HS-SPME experiments. The carrier gas used for GC-MS was ultra-
high purity (grade 5.0) helium from Praxair. Polyacrylate (PA) fibers for SPME were
obtained from Sigma Aldrich. A local industry provided the swine feed flour added with
OEO.
Chromatographic method
An Agilent gas chromatograph model 7890A coupled with a mass spectrometer model 5975C
with a positive pole ion, single quadrupole with electron impact ionization (EI) source were
used for detection and identification. An HP-INNOWax capillary column (30 m, 0.25 mm
ID, and 0.5 μm thickness polyethylene glycol film; Alltech) was used for compound
separation. Transfer line temperature was set at 250 ºC and GC injector port at 260 °C on
splitless mode. The oven temperature was initially set at 60 °C for 3 min, then raised to 250
ºC at a rate of 20 °C per minute and kept there for 3 min. MS was programmed both on scan
and SIM mode, with a solvent delay time of 8 min. Scan mode was set from 20 to 300 m/z
while SIM mode was set to 114 m/z (characteristic ions of nitrosopiperidine) for the time
interval of 8 to 11.5 min and immediately shifted to follow the 135 and 150 m/z signals
(characteristic ion of thymol and carvacrol) until the end of the analysis.
Sample preparation
A local industry provided the swine feed flour samples added with oregano’s essential oil
during the manufacturing process. The samples were stored in hermetic plastic bags until
80
used. Two different techniques for extracting thymol and carvacrol were used in this work:
(i) solvent extraction using a Soxhlet distillation apparatus, and (ii) HS-SPME using a PA
fiber. Three different solvents were used: ethyl acetate and petroleum ether for Soxhlet
extraction and deionized water for the HS-SPME technique. 5.0 mg L-1 of NPIP was added
to every sample as an internal standard.
Soxhlet solvent extraction
A sample of 10 g of swine feed was put into a Soxhlet extraction apparatus with 130 mL of
solvent (either ethyl acetate or petroleum ether). The mixture was heated until five cycles
were completed, then an aliquot of this extract was stored for analysis. A new fresh solvent
was immediately added to perform another five cycles using the same sample, and another
extract aliquot was taken. A third distillation step with more fresh solvent was made, and a
third aliquot was taken. Thus, each sample (10 g of swine feed) was subjected to extraction
three times using two different solvents.
Moreover, it was used two quantification methods using (a) an external standard calibration
curve and (b) a standard addition method. The calibration curve for the external standard is
made using known concentrations for thymol and carvacrol (2, 4, 6, 8, and 10 mg L-1). For
the standard addition method, 0.5 mL of extract aliquot was mixed with 0.5 mL of solvent
with different thymol and carvacrol concentrations (2, 4, 6, 8, and 10 mg L-1). In all cases,
the sample amount injected into the GC was 1.0 μL.
Head space-solid phase microextraction
The HS-SPME technique involves some parameters such as equilibrium time (teq),
equilibrium temperature (Teq), extraction time (text), extraction temperature, (Text), and ionic
strength (I). The equilibrium time and temperature are the time and temperature at which the
sample is left to reach equilibrium between the solid phase (swine feed matrix) and the vial’s
headspace. The extraction time and extraction temperature correspond to the time and
temperature at which the microfiber is in contact with the headspace adsorbing volatile
compounds. Ionic strength is a measure of the concentration of ions in a solution and modifies
the equilibrium of the system. It is necessary to determine the effect of these parameters on
the signal obtained in GC-MS. To evaluate this effect, it was added thymol and carvacrol
standards in water (along with 5 mg L-1 of NPIP as the internal standard) to form a 10 mg
L-1 solution.
For thymol and carvacrol quantification in swine feed, a sample of 0.5 g powder swine feed
was added to 15 mL glass vials with PTFE/Silicone septum with the required NaCl content
and 10 mL of water with different thymol and carvacrol concentrations to perform the
81
standard addition technique analysis. External standard calibration curves were not possible
to perform in the HS-SPME due to the interactions between volatile compounds of the
powder swine feed matrix in the gas phase and the fiber during the extraction. The relative
area between thymol or carvacrol and the added standard (NPIP) was calculated and used as
the response variable to evaluate the performance of the extraction method.
Design of experiments
A 24 factorial analysis was performed to evaluate the effect of the equilibrium temperature
(40–50 ºC), extraction temperature (40–50 ºC), extraction time (20–40 min), and ionic
strength (0.57–2.28 mole L-1 of NaCl). Equilibrium time is fixed at a sufficiently long time
to assure equilibrium (Table 1).
Table 1: Level values of the factors for the design of the experiment
Factor Low level -1 High level +1
Equilibrium temperature, Teq (ºC) 40 50
Extraction temperature, Text (ºC) 40 50
Extraction time, text (min) 20 40
Ionic strength, I (mole L-1) 0.57 2.28
A 24 factorial design of experiments with a single replicate consists of 16 experimental runs.

The analysis of variances of the complete model (main factors and all possible interaction
combinations) gives no residuals, Fo, and P-values since the degree of freedom of the error
is equal to zero and there is no estimate of the internal error. So, the negligible three- and
four-order interactions are used to estimate error. Moreover, after evaluating ANOVA of
main effects and two-factor interactions, the significant factors are defined, and another
ANOVA analysis is performed taking in account only the factors that are significant. A
regression model is then evaluated, and residuals and contour plots are plotted using R-studio.
Results
Thymol and carvacrol mass spectrum identification
A sample of 0.5 g of powder swine feed was put in a vial with 10 mL of water and NPIP as
the internal standard. An HS-SPME process was performed to identify the presence of thymol
and carvacrol, as shown in Figure 1. Spectra from scan mode were analyzed with the
NUST/EPA/NIH mass spectral library for confirmation with a 90 % concordance between
the experimental and theoretical spectrum. Retention times of internal standard, thymol, and
82
carvacrol were 10.3, 12.3, and 12.5 min, respectively. The retention time is obtained by
following their respective characteristic ion: 144 for NPIP, 135 for thymol, and 150 for
carvacrol. It is important to note that the HP-Innowax column was appropriate for good
separation between thymol and carvacrol due to its isomeric nature.
Figure 1: Chromatograms of powder swine feed using ethyl acetate and HS-SPME
Calibration curves
For comparison purposes, three different methodologies for thymol and carvacrol
quantification were performed: (a) Soxhlet extraction using organic solvents and calibration
with an external standard, (b) Soxhlet extraction using organic solvents and calibration by
standard addition, and (c) HS-SPME with water as solvent and calibration using standard
addition. The use of an external standard and standard addition is intended for sensibility
comparison.
Figure 1 shows the comparison of SIM chromatograms using HS-SPME and Soxhlet
extraction with ethyl acetate solvent. For the HS-SPME technique, the sensibility increases
almost nine times when compared with the solvent extraction technique, even using less
sample quantity during the micro-extraction process, which proves the effectiveness d
advantage of the HS-SPME methodology.
The obtained signal [relative area = (thymol or carvacrol area) / (internal standard area)] and
its relative standard deviation (RSD) for all the experiments of the factorial design is shown
in Figure 2. There are seven experiments with an RSD <15 % for both analytes, but only two
with an RSD <5.5 %, experiments #1 and #12. A high signal is desirable, so it was identified
four experiments with a high relative area. Experiments #4, #12, and #16 show a combination
83
of high signal (relative area) with low dispersion (RSD) values. All these experiments were
performed at a salt content of 0.57 M and extraction time of 40 min, but extraction
temperature and equilibrium temperature of 40 and 50 ºC. It is interesting to note that ionic
strength (salt content) and extraction time are the factors in common, and they are significant
factors as will be seen later.
Figure 2: Relative area and dispersions (relative standard deviation) for thymol and
carvacrol for each experiment of the factorial design for improving process parameters
In Figure 3 it can be observed the calibration curves for (a) the use of thymol and carvacrol
as external standards for the Soxhlet extraction technique; (b) the use of NPIP as added
standard for the Soxhlet extraction technique; and (c) the use of NPIP as added standard for
HS-SPME technique. It is not possible to use external standards for the SPME technique. It
is noteworthy that the signal of the relative area is in the order of tens for carvacrol either
with external standard or addition standard, while the signal of relative area for thymol is in
the order of units for external and addition standards. But for HS-SPME the signal of the
relative area is in the order of hundreds for both thymol and carvacrol, which again confirms
the increased sensibility of one order of magnitude (two orders for thymol) of this technique.
84
Figure 3: Soxhlet extraction external standard calibration curves (a), Soxhlet extraction
with standard addition curves (b), and HS-SPME with standard addition curves (c), for
carvacrol and thymol quantification
It should be noted that relative area values (y-axis) are greater for the HS-SPME technique compared to the
Soxhlet extraction technique. Soxhlet extraction using ethyl acetate and HS-SPME using water as solvents.
85
Discussion
Design of experiments
Table 2 shows the analysis of variance for main effects and two-factor interactions for
carvacrol and thymol quantification, and it is possible to observe that only extraction time
and salt content as well as the interaction between them are significant for both analytes.
Table 3 shows the analysis of variance considering only ionic strength, extraction time, and
ionic strength–extraction time interaction factors for carvacrol and thymol. Considering only
the significant factors and interactions, the regression model for the carvacrol HS-SPME
extraction is:
𝐶𝑎𝑟𝑣𝑎𝑐𝑟𝑜𝑙𝑅𝑒𝑙𝐴𝑟𝑒𝑎 = 13.9782 + 9.0768 𝐼 + 1.3297 𝑡𝑒𝑥𝑡 − 0.6242 𝐼 × 𝑡𝑒𝑥𝑡
With an R2 of 0.8558, meaning that 85.6 % of the data variability is explained by the model
with a randomly distributed residuals plot (not shown). A contour plot in Figure 4, shows
that when extraction time is at a high level, there is a strong negative effect of salt content,
meaning that the relative area of carvacrol is higher when salt content is lower; moreover,
when extraction time is at a low level, there is a still negative effect of salt content but weaker
than at the high level of extraction time. Considering only the significant factors and
interactions, the regression model for the thymol HS-SPME extraction is:
𝑇ℎ𝑦𝑚𝑜𝑙𝑅𝑒𝑙𝐴𝑟𝑒𝑎 = 9.5790 + 5.5841 𝐼 + 0.8329 𝑡𝑒𝑥𝑡 − 0.4008 𝐼 × 𝑡𝑒𝑥𝑡
With an R2 of 0.8401, meaning that 84 % of the data variability is explained by the model
with a randomly distributed residuals plot (not shown). A contour plot in Figure 5, shows
that when extraction time is at a high level, there is a strong negative effect of salt content,
meaning that the relative area of thymol is higher when salt content is lower; moreover, when
extraction time is at a low level, there is a still negative effect of salt content but weaker than
at the high level of extraction time. This is the same for thymol and carvacrol, the only
difference is that carvacrol shows a 1.5 times higher relative area signal than thymol.
Table 2: ANOVA of main effects and two-factor interactions for the design of the
experiment
Carvacrol Thymol
Sum of Mean Sum of Mean
DF F0 P-value DF F0 P-value
squares squared squares squared
Teq (ºC) 1 0.6 0.6 0.016 0.90550 1 0.2 0.2 0.011 0.92026
Text (ºC) 1 1.4 1.4 0.038 0.85218 1 0.7 0.7 0.042 0.84494
86
I (M) 1 1089.2 1089.2 29.208 0.00293 1 484.9 484.9 30.822 0.00261

text (min) 1 310.1 310.1 8.315 0.03444 1 109.7 109.7 6.974 0.04593
Teq x Text 1 39.0 39.0 1.046 0.35339 1 28.3 28.3 1.798 0.23760
Teq x I 1 13.7 13.7 0.368 0.57070 1 6.6 6.6 0.416 0.54715
Teq x text 1 3.6 3.6 0.096 0.76966 1 0.1 0.1 0.006 0.94100
Text x I 1 2.8 2.8 0.075 0.79541 1 1.4 1.4 0.089 0.77805
Text x text 1 65.0 65.0 1.742 0.24407 1 33.1 33.1 2.105 0.20652
text x I 1 455.8 455.8 12.222 0.01736 1 187.9 187.9 11.941 0.01813
Residuals 5 186.5 37.3 5 78.7 15.7
DF= degrees of freedom.
Table 3 shows the analysis of variance evaluated only with the significant factors of the DOE,
i.e., ionic strength, extraction time, and ionic strength-extraction time interaction factors.
Table 3: ANOVA of the significant factors for the for the design of the experiment
Carvacrol Thymol
Sum of Mean Sum of Mean
DF F0 P-value DF F0 P-value
squares squared squares squared
I (M) 1 1089.2 1089.2 41.83 <0.001 1 484.9 484.9 39.065 <0.001
text (min) 1 310.1 310.1 11.91 0.00480 1 109.7 109.7 8.839 0.01163
text x I 1 455.8 455.8 17.50 0.00127 1 187.9 187.9 15.134 0.00215
Residuals 12 312.5 26.0 12 148.9 12.4
DF= degrees of freedom.
For choosing the best operational parameters, should be always select a high extraction time
and low salt content. Since the other two factors are not significant, it can work at any equi-
librium temperature and extraction temperature; so, was decided to work at low equilibrium
and extraction temperatures for economics.
Table 4: Table of effects for the design of the experiment

Carvacrol Thymol
Effect T-value P-value Effect T-value P-value
Ionic strength, M -16.502 -6.467 <0.001 -11.0102 -6.250 <0.001
Extraction time, min 8.804 3.451 0.00480 5.2372 2.973 0.01163
text x I -10.674 -4.183 0.00127 -6.853 -3.890 0.00215
87
For both analytes, an increase in salt content (or ionic strength) results in a decrement in the
relative area, probably because the solution is close to saturation. Also, a high extraction time
(text) benefits the relative area signal which is in good agreement with the concept that
extraction increases as extraction time increments.
Table 5 shows thymol and carvacrol content in the powder swine feed when comparing the
two extraction techniques and the solvents used. The total thymol and carvacrol content for
each solvent is calculated by adding the measured quantity of each of the three consecutive
extractions. Regarding Soxhlet solvent extraction, petroleum ether was not efficient in
extracting thymol and carvacrol from the powder swine feed, as indicated by the low
quantification obtained of both components, but especially for thymol. Petroleum ether
extracted 52 to 55 % less thymol and 19 to 22 % less carvacrol than ethyl acetate.
Interestingly, there is a selective extraction capability of both solvents for thymol over
carvacrol. Again, the HS-SPME technique shows an improved extraction capacity for both
thymol and carvacrol, and there are extracted with no selectivity.
Table 5: Comparison of the total thymol and carvacrol content in powder swine feed
measured by different extraction techniques
Technique Soxhlet extraction HS-SPME
Standard Standard addi-

Standard addition External standard
method tion
Solvent Ethyl acetate Petroleum ether Ethyl acetate Petroleum ether water
Analyte Thy- Car- Thy- Car- Thy- Car- Thy- Car- Thy- Car-
mol vacrol mol vacrol mol vacrol mol vacrol mol vacrol
First ex-
tract, 4.25 0.67 1.99 0.48 4.03 0.58 1.92 0.40 - -
mg L-1
Second ex-
tract, 0.63 0.16 0.33 0.18 0.79 0.15 0.24 0.16 - -
mg L-1
Total con-
tent, 4.88 0.82 2.32 0.67 4.82 0.73 2.16 0.56 3.25 4.17
mg L-1
Content in
swine feed, 63.45 10.71 30.22 8.66 62.60 9.43 28.12 7.30 65.00 83.40
mg kg-1
The calibration method also shows some differences. Calibration with an external standard
shows a concentration value that is 9 % less on average than that using standard addition
88
calibration; the standard addition method has an improved uncertainty, but it is an expensive
method since the standard must be added to each sample.
Figure 4: Contour plot for ionic strength and extraction time for carvacrol extraction using
HS-SPME
The HS-SPME technique shows the highest concentration for thymol and carvacrol. The total
thymol content agrees with the total thymol content obtained by Soxhlet extraction with ethyl
acetate of about 63-65 mg/kg; however, carvacrol’s total content is very different. Carvacrol
quantification by HS-SPME has a value of 83.40 mg/kg, while quantification using Soxhlet
extraction with ethyl acetate is 10.71 mg/kg, which is eight times lower than the HS-SPME
result. It might be related to the steric behavior of thymol and carvacrol (stereoisomers) and
interactions with the fiber material (polyacrylate).
Figure 5: Contour plot for ionic strength and extraction time for thymol extraction using
HS-SPME
89
Method validation
Limits of detection (LOD) and quantification (LOQ) were estimated to evaluate the
performance of the extraction methods and were calculated using the baseline noise and the
signal, defined as three times the relation signal/noise for LOD and ten times for the LOQ
(16). Figure 3 shows the calibration curves for each component (thymol and carvacrol) for
the three different solvents used. LOD and LOQ values using Soxhlet extraction with ethyl
acetate were 3.7 and 12.5 μL-1 for thymol and 1.4 and 4.7 μg L-1 for carvacrol, respectively.
The HS-SPME technique gives better results for both substances since LOD, and LOQ values
were 0.9 and 3.1 μg L-1 for thymol and 0.6 and 1.9 μg L-1 for carvacrol, respectively. The
linearity of data was estimated via the linear correlation coefficient, where the lowest value
found was 0.9892.
Oregano’s essential oil is positively identified in the swine feed powder using characteristic
volatile compounds thymol and carvacrol using two different extraction methods: Soxhlet
and HS-SPME. Among the organic solvents for Soxhlet extraction, petroleum ether was not
suitable since it only extracted about 50 and 10 % of the total thymol and carvacrol content,
respectively (relative to HS-SPME quantification). Furthermore, regarding the use of ethyl
acetate in Soxhlet extraction, this solvent was able to extract all the thymol, but not the
carvacrol, showing some sort of selectivity. For the HS-SPME technique, a 24-factorial
design of experiments was performed to evaluate process parameters and obtain the highest
possible S/N ratio. The proper conditions are equilibrium temperature (Teq) of 40 ºC,
extraction temperature (Text) of 40 ºC, ionic strength (I) of 0.57 M, and extraction time (text)
of 40 min. HS-SPME showed a nine-times better extraction performance compared to
Soxhlet extraction, even with smaller sample amounts, with a limit of detection and
quantification of 0.9 and 3.1 μg L-1 for thymol, and 0.6 and 1.9 μg L-1 for carvacrol,
respectively. The results show that the HS-SPME method is more efficient for thymol and
carvacrol extraction from an organic matrix and has the potential for a quality-control
technique in the food industry to quantify the active compounds of oregano’s essential oil
when used as an additive to an organic matrix such as swine feed.
Acknowledgments
To CONACYT (The National Council of Science and Technology) for the financial support
awarded to doctoral student Fernando Jonathan Lona Ramírez (grant Number: 344837) and
Tecnológico Nacional de México (TecNM) for research grant number 5267.14-P to carry out
this study. To Alimentos Aicansa SA for providing swine feed samples with OEO as an
additive.
90
Conflict of interest
The authors declare no conflict of interest.
Literature cited:
1. Sánchez-Ruíz JF, Tejeda-Rosales ME, Sánchez-Tejeda JF, Sánchez-Tejeda MG.
Pharmacy, medicine and herbolary in the Florentine Codex. Rev Mex Cienc Farm
2012;43(3):55-66.
2. Kintzios SE. Oregano. In: Peter KV editor. Handbook of herbs and spices Vol. 2.
Cambridge, UK: Woodhead; 2004:215-229.
3. Ahmad A, Khan A, Akhtar F, Yousuf S, Xess I, Khan LA, Manzoor N. Fungicidal

activity of thymol and carvacrol by disrupting ergosterol biosynthesis and membrane
integrity against Candida. Eur J Clin Microbiol Infect Dis 2011;30(1):41-50.
4. Liolios CC, Gortzi O, Lalas S, Tsaknis J, Chinou I. Liposomal incorporation of carvacrol

and thymol isolated from the essential oil of Origanum dictamnus L. and in vitro
antimicrobial activity. Food Chem 2009;112(1):77-83.
5. Marchese A, Orhan IE., Daglia M, Barbieri R, Di Lorenzo A, Nabavi SF, Gortzi O, Izadi
M, Nabavi SM. Antibacterial and antifungal activities of thymol: A brief review of the
literature. Food Chem 2016;210:402-414.
6. Pesavento G, Calonico C, Bilia AR, Barnabei M, Calesini F, Addona R, et al.

Antibacterial activity of Oregano, Rosma-rinus and Thymus essential oils against
Staphylococcus aureus and Listeria monocytogenes in beef meatballs. Food Control
2015;54:188-199.
7. Bozkurt M, Bintaş E, Kırkan Ş, Akşit H, Küçükyılmaz K, Erbaş G, et al. Comparative

evaluation of dietary supplementation with mannan oligosaccharide and oregano
essential oil in forced molted and fully fed laying hens between 82 and 106 weeks of
age. Poult Sci 2016;95(11):2576-2591.
8. Franciosini MP, Casagrande-Proietti P, Forte C, Beghelli D, Acuti G, Zanichelli D, et

al. Effects of oregano (Origanum vulgare L.) and rosemary (Rosmarinus officinalis L.)
aqueous extracts on broiler performance, immune function and intestinal microbial pop-
ulation. J Appl Anim Res 2016;44(1):474-479.
9. Scocco P, Forte C, Franciosini MP, Mercati F, Casagrande-Proietti P, Dall’Aglio C, et

al. Gut complex carbohydrates and intestinal microflora in broiler chickens fed with
oregano (Origanum vulgare L.) aqueous extract and vitamin E. J Anim Physiol Anim
Nutr (Berl) 2017;101(4):676-684.
91
10. Zeng Z, Zhang S, Wang H, Piao X. Essential oil and aromatic plants as feed additives
in non-ruminant nutrition: a review. J Anim Sci Biotechnol 2015;6:7.
11. Russo M, Galletti GC, Bocchini P, Carnacini A. Essential oil chemical composition of
wild populations of italian oregano spice (Origanum vulgare ssp. hirtum (Link)
Ietswaart): A preliminary evaluation of their use in chemotaxonomy by cluster analysis.
1. Inflorescences. J Agric Food Chem 1998;46(9):3741-3746.
12. de Oliveira Nóbrega R, de Castro Teixeira, AP, Araújo de Oliveira W, de Oliveira Lima
E, Oliveira Lima I. Investigation of the antifungal activity of carvacrol against strains of
Cryptococcus neoformans. Pharm Biol 2016;54(11):2591-2596.
13. Safaei-Ghomi J, Ebrahimabadi AH, Djafari-Bidgoli Z, Batooli H. GC/MS analysis and

in vitro antioxidant activity of essential oil and methanol extracts of Thymus
caramanicus Jalas and its main constituent carvacrol. Food Chem 2009;115(4):1524-
1528.
14. Díaz-Maroto MC, Pérez-Coello MS, Cabezudo MD. Supercritical carbon dioxide
extraction of volatiles from spices: Comparison with simultaneous distillation–
extraction. J Chromatogr A 2001;947(1):23-29.
15. Hossain MB, Barry-Ryan C, Martin-Diana AB, Brunton NP. Optimisation of

accelerated solvent extraction of antioxidant compounds from rosemary (Rosmarinus
officinalis L.), marjoram (Origanum majorana L.) and oregano (Origanum vulgare L.)
using response surface methodology. Food Chem 2011;126(1):339-346.
16. Lona-Ramirez FJ, Gonzalez-Alatorre G, Rico-Ramírez V, Perez-Perez, MCI, Castrejón-

González EO. Gas chromatography/mass spectrometry for the determination of nitrosa-
mines in red wine. Food Chem 2016;196:1131-1136.
17. Méndez-Pérez D, González Alatorre G, Botello Álvarez E, Escamilla Silva E, Alvarado

JFJ. Solid-phase microextraction of N-nitrosodimethylamine in beer. Food Chem 2008;
107(3):1348-1352.
18. Adams A, Kruma Z, Verhé R, De Kimpe N, Kreicbergs V. Volatile profiles of rapeseed

oil flavored with basil, oregano, and thyme as a function of flavoring conditions. J Am
Oil Chem Soc 2011;88(2):201-212.
19. Karami-Osboo R, Miri R, Asadollahi M, Jassbi AR. Comparison between head-space

spme and hydrodistillation-gc-ms of the volatiles of Thymus daenensis. J Essent Oil Bear
Pl 2015;18(4):925-930.
92
20. Stashenko EE, Martínez JR. Sampling volatile compounds from natural products with
headspace/solid-phase micro-extraction. J Biochem Biophys Methods 2007;70(2):235-
242.
21. Pothier J, Galand N, El Ouali M, Viel C. Comparison of planar chromatographic

methods (TLC, OPLC, AMD) applied to essential oils of wild thyme and seven chemo-
types of thyme. II Farmaco 2001;56(5-7):505-511.
22. Pawliszyn J. Solid phase microextraction: Theory and practice. New York, USA: Wiley-
VCH; 1997.
23. R Core Team. R: A language and environment for statistical computing. Vienna,
Austria: R Foundation for Statistical Computing, 2021. http://www.R-project.org/
24. RStudio Team. RStudio: Integrated Development Environment for R. Boston,

Massachusetts, USA: RStudio. PBC; 2020. http://www.rstudio.com/
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Article
Changes in the count of four bacterial groups during the ripening of

Prensa (Costeño) Cheese from Cuajinicuilapa, Mexico
José Alberto Mendoza-Cuevas a
Armando Santos-Moreno a
Beatriz Teresa Rosas-Barbosa b
Ma. Carmen Ybarra-Moncada a
Emmanuel Flores-Girón a
Diana Guerra-Ramírez c*
a
Universidad Autónoma Chapingo. Departamento de Ingeniería Agroindustrial. Carretera
México-Texcoco km 38.5, Texcoco, Estado de México. México.
b
Universidad de Guadalajara. Centro Universitario de Ciencias Biológicas y Agropecuarias,
Zapopan, Jal. México.
c
Universidad Autónoma Chapingo. Departamento de Preparatoria Agrícola. Texcoco,
Estado de México. México.
* Corresponding author: guerrard@correo.chapingo.mx
Abstract:
Prensa Cheese, also called Costeño, is made in an artisanal way from raw cow’s milk in the
Costa Chica region of the state of Guerrero. In order to know the characteristics of Mexican
artisanal cheeses, the objective of this research was to analyze the changes in the count of
aerobic mesophilic bacteria (AMB), total coliform (TCs) microorganisms, lactic acid bacteria
(LAB) and coagulase-positive staphylococci (CPS), during the ripening (5, 30, 60 and 90 d)
of Prensa cheeses, made by four different cheese factories (A, B, C and D) of Cuajinicuilapa,
Guerrero, Mexico. A portion (25 g) of each cheese sample was homogenized with peptone
94
diluent (225 mL) and dilutions from 10-1 to 10-6 were prepared with which 3MTM PetrifilmTM
plates were sown. After incubating under different conditions, depending on the type of
microorganism, AMB, TCs, LAB and CPS counts were made. The results showed that as the
ripening time of the Prensa Cheese progressed, the microbial load decreased: AMB from 4
to 2, TCs from 6 to 3, LAB from 6 to 2 and CPS from 5 to 2 log10 CFU g-1. The changes in
the counts of the bacterial groups studied can be attributed to the physicochemical and
microbiological transitions typical of cheese maturation and to the characteristics of the
microbiota present in each of the cheese factories. The results of this research provide
elements for the microbial characterization of Mexican artisanal cheeses.
Key words: Lactic acid bacteria, Aerobic mesophilic bacteria, Coagulase-positive

staphylococci, Raw milk, Microbiota, Total coliform microorganisms, Artisanal cheeses.
Introduction
Around the world, cheese, in addition to being a rich source of nutrients, is an essential food
used in the local gastronomy of different societies(1,2). Currently, around 1,833 varieties of
cheese located in 74 countries are known(3); this diversity is determined by the technological
processes used for its preparation, such as the origin of milk, fat-protein ratio, types of
cultures and coagulating agents; the shape, size of the cheese and the maturation
conditions(4,5,6).
Cheese ripening consists of its storage, under certain conditions of temperature and moisture,
for a period of time that can range from 3 to 7 d, up to 2 yr(5,7). The maturation process, in
addition to providing sensory characteristics, is a method of conservation(5,6,8). In this stage,
biotic and abiotic changes that have a direct impact on the microbiota present in the cheese
occur(5,7,9).
Most artisanal cheeses are made from raw milk, with spontaneous fermentation, non-
technified preparation processes and varied maturation times(5,7,10).
In Mexico there are about 40 artisanal cheeses(7), among them are matured cheeses such as
Cotija from the Sierra JalMich, Añejo cheese from Zacazonapan, Maduro cheese from
Veracruz, Chihuahua cheese and artisanal cheese from the Ojos Negros region of Baja
95
California, of which some aspects of their microbiology have been published(11-14). Prensa
cheese (PC) is made with unpasteurized cow’s milk, commercial liquid rennet and salt; it
goes through a pressing stage whose duration varies at the discretion of the manufacturer
from 1 to 3 days, then it is left to mature for periods of up to three months. The cheese thus
obtained has color variations between white and yellow(15). It is generally rectangular or
circular in shape, its consistency is firm, and its weight is 1 to 14 kg per piece (Figure 1)(15).
Figure 1: Prensa cheese in rectangular and cylindrical shapes
PC has been produced for more than 100 years in southwestern Mexico, mainly in the Costa
Chica region of the state of Guerrero in the municipalities of Cuajinicuilapa and Ometepec,
as well as in the municipality of Pinotepa Nacional, on the coast of the state of Oaxaca (15).
According to INEGI(16), the climate of the Costa Chica region is warm subhumid, and its
temperatures range from 22 to 28 °C.
Studies are currently being carried out to identify the characteristics of artisanal cheeses from
Mexico(7,15), the objective of this research was to analyze the changes that occur in the count
of aerobic mesophilic bacteria, total coliform microorganisms, lactic acid bacteria and
coagulase-positive staphylococci, during the ripening (5, 30, 60 and 90 d) of prensa cheeses
made by four cheese factories (A, B, C and D) of Cuajinicuilapa, Guerrero, Mexico.
Cheese samples
Samples of PC made in an artisanal way in the municipality of Cuajinicuilapa, Guerrero,

Mexico (16°28′18′ N, 99°24′55′ W), were analyzed in July 2018. Based on a targeted
96
sampling, four cheese factories were selected as sampling units, which will henceforth be
named A, B, C and D. Four freshly made cheeses, weighing 1 kg, were purchased from each
cheese factory. The samples were moved to the municipality of San Marcos, Guerrero
(16°47′46′ N, 99°23′05′ W) to a space with characteristics similar to those of the cheese
factories of Cuajinicuilapa. In this place, the samples of the cheeses from the cheese factories
A, B, C and D (four of each cheese factory) were left to mature for 5, 30, 60 and 90 d. After
the ripening time, each batch was transferred in polyethylene bags, inside coolers with
refrigerant, to the laboratory. The samples were kept in refrigeration at 4 °C until analysis.
The maximum ripening time was 90 d because after that period the flavors intensify, and
local consumers avoid it because they prefer softer flavors.
Sample preparation
Each of the cheese samples (25 g) was mixed with 225 mL of peptone diluent, the mixture
was homogenized for 2 min (VWR® symphony D S41 Vortex, VWR International) and
dilutions from 10-1 to 10-6 were made by transferring 1 mL of the sample to vials containing
9 mL of peptone diluent(17).
Microorganism count
The following culture media (3M PetrifilmTM plates) were used: aerobe count (AC No. of
catalog 6400), coliform count (TC No. of catalog 6410), lactic acid bacteria (No. of catalog
6461) and staph express for coagulase-positive staphylococci (No. of catalog 6493); 1 mL of
the corresponding dilution was placed in each of the plates(18-21).
All counts were done in duplicate. For AMB, dilutions 10-3 and 10-4 were sown and the
medium was incubated at 35 ± 2 °C for 48 ± 3 h(18). TC microorganisms were studied based
on dilutions 10-2 and 10-3, being incubated at 35 ± 1 °C for 24 ± 2 h(19). The determination of
LAB was made by inoculating the media with dilutions from 10-3 to 10-6 and incubating at
35 ± 2 °C for 48 ± 3 h(20). The CPS study was conducted from dilutions 10-2 to 10-4 and an
incubation at 37 ± 1 °C for 24 ± 3 h(21).
Once the incubation time was completed, the growth was reviewed and the plates containing
between 15 and 300 colonies were counted, the mean of the two repetitions was obtained and
this average was multiplied by the inverse of the dilution with which the plate was
inoculated(22). The result of the count was reported as log10 of the number of colony-forming
units per gram (log10 CFU g-1)(23).
97
The statistical analysis was based on a design with repeated means and completely random
distribution of treatments, tested over time, whose probabilistic model corresponds to:
𝑌𝑖𝑗𝑘 = 𝜇 + 𝛼𝑖 + 𝛾𝑘 + (𝛼𝛾)𝑖𝑘 + 𝑒𝑖𝑗𝑘̇ (24)
Where:
𝝁 + 𝒂𝒊 + 𝒚𝒌 + (𝒂𝒚)𝒊𝒌 is the mean of treatment i at time k, which contains the effects of
treatment, time, and the time × treatment interaction;
𝒆𝒊𝒋𝒌 is the random error associated with the measurement at time k on j assigned to treatment
i.
The effect of the treatments (cheese factories A, B, C and D) was evaluated through the
ripening time (5, 30, 60 and 90 d) with four repetitions, generating 64 experimental units,
each consisting of 25 g of cheese.
The response variables evaluated were: total count of aerobic mesophilic bacteria (AMB),
total coliforms (TCs), lactic acid bacteria (LAB) and coagulase-positive staphylococci (CPS).
The data were analyzed using a mixed model(24,25) whose random effect corresponds to the
maturation time and the fixed effect to the cheese factories. The Tukey-Kramer method
(P<0.05) was applied to identify the effect of the treatments. Analyses were performed in the
SAS package version 9.1 (SAS Institute, Inc., Cary, NC, USA).
During the time of ripening, a decrease in the count of the different microorganisms was
observed. The highest counts of all microbial groups were reached at 5 d of ripenig while the
minimum values occurred at 90 d (Table 1).
98
Table 1: Count of bacterial groups, log10 CFU g-1, during the ripening of Prensa cheese made in four cheese factories (A, B, C and D)
of Cuajinicuilapa, Guerrero, Mexico
Bacterial group Time Cheese factories

(days) A B C D
Aerobic mesophilic 5 6.3143 ±0.2973 Cc 4.6802 ±0. 2973 Ba 5.8948 ±0.2973 Cb 5.9077 ±0.2973 Cb
bacteria 30 5.1156 ±0.2973 Bb 4.3884 ±0. 2973 Ba 4.4981 ±0.2973 Ba 5.1043 ±0.2973 Bb
(log10 CFU g-1) 60 4.0021 ±0.2973 Ab 3.1505 ±0. 2973 Aa 4.4047±0.2973 Bbc 4.6703±0.2973 ABc
90 4.3178 ±0.2973 Ab 2.5638 ±0. 2973 Aa 2.4005 ±0.2973 Aa 4.3597 ±0.2973 Ab
Total coliforms 5 4.1093 ±0.1002 Cb 2.1505 ±0.1002 Aa 2.4203 ±0.1002 Aa 4.7211 ±0.1002 Db
(log10 CFU g-1) 30 3.7726 ±0.0541 Cc 2.8838 ±0.0541 Ba 3.7916 ±0.0541 Cc 3.0878 ±0.0541 Cb
60 2.3138 ±0.2854 Bb 2.3763 ±0.2854 ABb 2.2698 ±0.2854 Ab 1.5753 ±0.2854 Ba
90 <1 ±0.0440 Aa 2.3451 ±0.0440 Ab 2.0753 ±0.0440 Ab <1 ±0.0440 Aa
Lactic acid bacteria 5 6.5457 ±0.0651 Dc 5.9454 ±0.0651 Cb 5.5084 ±0.0651 Ba 6.7366 ±0.0651 Cc
(log10 CFU g-1) 30 5.8336 ±0.0651 Cb 5.8231 ±0.0651 Cb 5.1193 ±0.0651 Ba 6.1241 ±0.0651 Cb
60 3.5524 ±0.0651 Ba 3.7918 ±0.0651 Ba 3.1945 ±0.0651 Aa 5.0914 ±0.0651 Bb
90 <1 ±0.0651 Aa <1 ±0.0651 Aa 3.2258 ±0.0651 Ab 4.2394 ±0.0651 Ac
Coagulase-positive 5 5.6562 ±0.0540 Cb 3.7456 ±0.0540 Ba 5.6918 ±0.0540 Cb 5.8746 ±0.0540 Cb

staphylococci 30 3.6276 ±0.3811 Bb 2.2500 ±0.3811 Aa 3.7271 ±0.3811 Bb 3.8389 ±0.3811 Bb
(log10 CFU g-1) 60 2.6945 ±0.0438 Aa 2.7143 ±0.0438 Aa 2.7311 ±0.0438 Aa 2.8063 ±0.0438 Aa
90 2.5951 ±0.0524 Aa 2.4203 ±0.0524 Aa 2.6945 ±0.0524 Aa 2.5951 ±0.0524 Aa
Means with lowercase letter in rows and means with uppercase letter in columns, followed by different letter, indicate statistical significance (Tukey, P<0.05).
99
Aerobic mesophilic bacteria
In the AMB counts, there were significant differences (P<0.05) between the cheese factories
(Table 1). The gradual decrease in AMB from day 5 to 90 was close to 2 log10 CFU g-1, for
cheese factories A, B and D; while for cheese factory C, it was 3.49 log10 CFU g-1.
Most of the AMB values found in the PC are within the range of 4 to 9 logarithms CFU g-1,
reported for cheeses made from raw milk and matured for 60 or more days (26). Since the
maturation process of cheeses involves the multiplication of the microorganisms present,
AMB concentrations of 4 to 9 logarithms (104 to 109 CFU g-1) are expected in this type of
products, without this implying a deterioration of the food or suggesting that non-sanitary
conditions occurred during its preparation or storage(26,27,28). In the region of Ojos Negros in
the state of Baja California, based on a study that included matured cheeses from 22 cheese
factories, made with raw milk, it is reported that AMB were found in a range of 4.6 to 7.2
log10 CFU g-1(14).
In studies on the ripening of artisanal Cotija cheese, salted and matured at temperatures of
14 °C to 32 °C, Chombo(11) reports the following variations in AMB: 8.3, 7.0, 3.5 and 4.7
log10 CFU g-1, on d 8, 30, 60 and 90, while Magallón(29) found 5.3 and 1.8 log10 on d 30 and
90, respectively. The counts found in the PC at 30 and 90 d are very close to those reported
for Cotija cheese that is ripened in temperature ranges similar to those of PC.
The decrease in AMB was common in the cheeses from the four cheese factories (Table 1),
reflecting a certain homogeneity in the preparation processes and the ripening conditions of
the cheeses. The statistical difference (P<0.05) between cheese factories suggests
quantitative or qualitative variations in the microbiota of cheese generated by milk and the
microenvironments of each cheese factory. It should be noted that, between d 5 and 60, it is
observed that the AMB of cheeses from cheese factory A descend 2.31 log10 CFU g-1,
however, between d 60 and 90 they remain unchanged; while the AMB of cheeses from
cheese factory C, from d 60 to 90, show a reduction of 2 log10 CFU g-1.
The development of AMB in cheeses from cheese factory A shows the ability of bacteria to
adapt and survive, while the decrease in AMB in the cheeses from cheese factory C exhibits
a loss of viability with the release of enzymes that contribute to the generation of flavors and
textures(5). This suggests that it is appropriate to study the relationship of the organoleptic
characteristics of prensa cheese, between cheese factories and between different
concentrations of AMB over maturation time, as well as the relationship of AMB with the
shelf life of cheese at room temperature. On the other hand, AMB studies are useful as an
initial stage in the search for starter cultures from artisanal cheeses. Variations in salt
concentration and moisture may have influenced the survival of AMB. The results suggest
100
that, in cheeses from cheese factories A and D, there are bacteria adapted for long-term
survival. While in cheeses from cheese factories B and C, bacteria of short survival may be
present, and therefore can contribute more quickly to the production of tastes, smells and
textures (pleasant or unpleasant)(5). Another possibility is that the reduction of AMB in
cheeses from cheese factories B and C is due to the presence of substances with antimicrobial
action, caused by the metabolism of microorganisms, or by the biochemical changes that
occur in the cheese, derived from the proteolysis of casein to give rise to peptides with
antimicrobial activity(5,30).
Total coliform bacteria
Total coliform counts in the cheeses showed significant differences (P<0.05) between cheese
factories during the ripening period (Table 1). The highest counts were found on d 5 (4.72
log10 CFU g-1) and the lowest on d 90 (<1 log10). Cheese, being a solid sample, hinders a
direct count, so it is necessary to make an initial dilution that leads to the minimum detection
level being 10 CFU g-1, so the absence of growth was reported as < 1 log10.
Two different dynamics were observed, cheeses from cheese factories A and D had the
highest initial TC loads, which decreased at 30 d of maturation to reach 3.72 to 3.10 log10
CFU g-1, respectively (Table 1). In cheeses from cheese factories B and C, the TCs showed
initial levels of 2 logarithms, which rose during the first month and remained with slight
variations to coincide on d 90 with values very close to each other.
The dynamics of TCs in cheeses from cheese factories A and D have been reported in semi-
hard cheeses and are characterized by a progressive decrease in coliforms as ripened
progresses(31), which is attributed to the decrease in pH due to the fermentation of lactose(32).
In ripened cheeses such as Cheddar, coliforms die at a rate of 0.3 log10 CFU g-1 per week and
in Gouda cheese at 0.7 log10 per week(33). Therefore, the dynamics observed in cheeses from
cheese factories B and C is atypical, because between d 5 to 30, there is an increase of 0.73
and 1.37 log10, respectively, followed on d 60 to 90 by very small decreases, 0.03 and 0.19
log10, respectively (Table 1). This suggests that there are common aspects between the two
cheese factories that favor the selection of bacteria that persist during ripening, such as water
and milk quality, personnel or variations in the preparation or cleaning processes.
The accepted levels of total coliforms in matured cheeses are less than 100 CFU/g (< 2 log10
CFU g-1)(34), values that were reached between d 60 and 90 (Table 1) in cheese factories A
and D. According to Metz(32), cheeses made with good quality raw milk, under hygienic
sanitary conditions, applying good manufacturing practices and properly ripened, will have
101
low levels of total coliform bacteria, fecal coliforms, enterococci, Enterobacteriaceae and
Escherichia coli.
The activity of coliform organisms in cheeses can adversely affect their sensory
characteristics(28,35). However, it has been observed that certain genera of coliforms
contribute positively to the texture and sensory characteristics of the product; in addition to
the fact that some strains of Hafnia contribute to the accumulation of aromas, and generation
of flavors(32,35,36). The persistence of coliforms suggests the possibility of the participation of
these microorganisms in the organoleptic characteristics of cheeses from cheese factories B
and C, an aspect that has not been addressed in studies of Mexican cheeses.
Lactic acid bacteria
During the ripening period, the LAB counts of the cheeses from the cheese factories studied
showed significant differences (P<0.05) (Table 1). This microbial group had the highest
counts of the entire study. On d 5 the counts ranged between 5.50 and 6.73 log10 CFU g-1,
these values decreased from day 30. From day 5 to 60, the reductions were 2.99, 2.15, 2.31
and 1.77 log10, for cheeses from cheese factories A, B, C and D, respectively.
During the ripening of the Spanish artisanal cheeses Casar de Cáceres, Afuega’l Pitu and
Cabrales, decreases in lactococcal counts of 2 to 3 log10 CFU g-1 were reported between d 0
and 60, while in “La Serena” cheese there was only a reduction of less than one logarithm,
this is attributed to the fact that this cheese had a low salt content during the first weeks of
ripening(5). This suggests that the reductions observed in the PC from d 5 to 60 are consistent
with what happens with homofermentative lactic acid bacteria in cheeses made in an artisanal
way with native microbiota(5). The concentrations of LAB found in the PC from d 60 to 90
are lower than those reported in ripened cheeses from Europe, which have values of 7 to 9
log10 CFU g-1(5,37,38).
With differences in the type of cattle and geographical areas, Cotija cheese and PC share
temperatures, preparation and ripening processes. In Cotija cheese, small increases and
decreases have been found in LAB counts; one study reports 2.6 log10 CFU g-1 on d 30 with
an increase to 2.9 log10 on d 90(29), another study indicates 5.9 log10 on d 60, which decreases
to 5.0 log10 on d 90(11). The above data suggest that, in both PC and Cotija cheese, LAB
counts tend to be lower than in other ripened cheeses; this could be explained by the
temperatures at which they are ripened, which favors a greater loss of moisture that generates
values of water activity and moisture/salt ratio that are inhibitory for LAB(5).
102
Coagulase-positive staphylococci
The CPS counts of the cheeses from the cheese factories studied showed significant
differences (P<0.05) during the ripening period (Table 1). From d 5 to 60, a continuous
decrease in CPS was observed in cheeses from cheese factories A, C and D, followed by a
stabilization from d 60 to 90. In cheese factory B, there was a decrease from d 5 to 30
followed by an increase from d 30 to 60 and a stabilization from d 60 to 90 (Figure 2). The
death rates of CPS (average decrease log10 CFU g-1 divided between week of ripening)(39) in
cheeses from cheese factories A and C were 0.30 log10 and 0.23 and 0.31 log10 in cheese
factories B and D, respectively.
Figure 2: Antagonism and change in lactic acid bacteria (LAB) concentration with respect
to coagulase-positive staphylococci (CPS)
For cheeses made from raw milk, the accepted limits of coagulase-positive staphylococci are
104 to 105 CFU g-1, which is equivalent to 4-5 log10 CFU g-1(40), limits that were exceeded on
day 5 in cheese factories A, C and D but that were reached again on day 30 and remained
until day 90 (Table 1). CPS counts greater than 4 log10 show the need to apply corrective
measures in the hygiene of the processes of milk collection, cheese making and the selection
of raw materials(40). Values of 105 CFU g-1 or higher lead to study the presence of
staphylococcal toxin in cheeses(40), since being thermostable, it can persist even when
staphylococci have died. Concentrations of 106 CFUs g-1 are usually needed to produce
enough toxin (one nanogram per gram of cheese) to cause a disease outbreak(5).
Reductions of Staphyloccocus aureus of 1 to 3 logarithms have been reported in different

cheeses(41,42,43), figures that coincide with reductions in CPS during PC ripening.
103
The death rate of S. aureus in Manchego cheese made with raw milk from d 1 to 60 is 0.404
log10 CFU g-1(39), the death rates of CPS in cheese factories A, C and D (0.49, 0.50 and 0.52)
were close to this value, suggesting that the decrease was as expected for this type of cheese
(matured Prensa paste cheeses). No factors that explain the lower death rate observed in
cheese factory B (0.36) were identified.
The development and survival of S. aureus are affected by factors such as: physicochemical
changes that occurred during the ripening process, secondary metabolites generated by LAB,
as well as the composition of the product, storage period and temperature(44,45).
Staphyloccocus aureus is inhibited by LAB through nutrient competition, production of lactic
acid, hydrogen peroxide and production of antimicrobial substances(46), which may explain
the decrease in CPS in the first 30 d of ripening, a period in which LAB levels were higher
(Figure 2). Between d 30 and 60, storage conditions at room temperature could increase
moisture loss, which changes the moisture/salt ratio, being inhibitory for LAB(5), this favors
S. aureus, which could explain its slight increase in cheese factory B and the suspension of
its decrease in cheese factories A, C and D.
Prensa cheese is a cheese made in an artisanal way, ripened in warm subhumid climate with
the participation of native lactic acid bacteria, whose concentrations are lower than those
reported for European cheeses, but similar to those reported in Cotija cheese. Statistical
differences in microbial counts at different times show the changes that occur as cheese
matures. Meanwhile, the statistical differences between the cheese factories suggest the
existence of microbiomes specific to each cheese factory, which could be able to generate
variants of PC among different artisanal producers, even when they have similar production
processes. The changes in the counts of the bacterial groups studied can be attributed to
physicochemical changes and successions in the bacterial populations typical of the
maturation of the cheese and to the characteristics of the microbiota present in each of the
cheese factories. It is convenient to explore whether the shelf life of this cheese extends
beyond 90 days. The finding of coliforms that persist during ripening shows the need to
investigate whether this is an exceptional case, and bacteria from this group contribute to the
pleasant characteristics of the cheese or are related to its deterioration. The data on reduction
and survival of coagulase-positive staphylococci generated in this research can serve as a
reference to initiate and evaluate improvement programs in this type of cheese factories.
Although this research included four cheese factories in the main PC-producing municipality,
the information generated can serve as a reference for the characterization of this artisanal
cheese.
104
Acknowledgements and conflicts of interest
This research work was possible thanks to the scholarship granted by the National Council
of Science and Technology for the master’s studies of the first author. We also appreciate the
comments made on the manuscript by Doctors Angélica Luis Juan Morales and Ricardo
Alaniz de la O. None of the authors has any conflict of interest with respect to this
publication.
Literature cited:
1. Cidón CD, Canut E. Quesos españoles, ases en la mesa, comodines en la cocina. 1a ed.
Madrid, ESP: Everest; 2003.
2. Yescas C, Santacruz J. Quesos mexicanos. 1a ed. México, Distrito Federal: Larousse;

2013.
3. Cheese.com specialty cheeses. Alphabetical list, find over 1833 specialty cheeses from 74
countries in the world´s greatest cheese resource. https://www.cheese.com/alphabetical/.
Accessed Jan 26, 2021.
4. Kongo JM, Malcata FX. Cheese: Types of cheeses - soft. In: Caballero B, Toldrá F, Pinglas
PM, editors. Encyclopedia of food and health. 1rst ed. Oxford, GB: Academic Press;
2015:768–773.
5. Fox PF, Guinee TP, Cogan TM, McSweeney PLH. Fundamentals of cheese science. 2nd
ed. New York, USA: Springer; 2017.
6. Santiago-López L, Aguilar-Toalá JE, Hernández-Mendoza A, Vallejo-Cordoba B, Liceaga

AM, González-Córdova AF. Invited review: Bioactive compunds produced during
cheese ripening and health effects associated with aged cheese consumption. J Dairy Sci
2018;101(5):3742–3757.
7. Villegas GA, Santos MA, Cervantes EF. Los quesos mexicanos tradicionales. 1a ed.
Texcoco, MEX: Universidad Autónoma Chapingo; 2016.
8. Alejo-Martínez K, Ortiz-Hernández M, Recino-Metelin BR, González-Cortés N, Jiménez-

Vera R. Tiempo de maduración y perfil microbiológico del queso de poro artesanal.
ReIbCi 2015;2(5):15–24.
9. Sánchez-Valdés JJ, Colín-Navarro V, López-González F, Avilés-Nova F, Castelán-Ortega

OA, Estrada-Flores JG. Diagnóstico de la calidad sanitaria en las queserías artesanales
del municipio de Zacazonapan, Estado de México. Salud Publ Mex 2016;58(4):461–
467.
105
10. Vasek O, Cardozo M, Fusco AJ. Producción artesanal de quesos. Sistema de

transformación agroalimentario en la región correntina (Argentina). IV Congreso
internacional de la red SIAL. Mar del Plata, provincia de Buenos Aires.2006:1–32.
11. Chombo-Morales P, Kirchmayr M, Gschaedler A, Lugo-Cervantes E, Villanueva-

Rodríguez S. Effects of controlling ripening conditions on the dynamics of the native
microbial population of Mexican artisanal Cotija cheese assessed by PCR-DGGE.
LWT-Food Sci Technol 2016;65:1153–1161.
12. Ramírez-Rivera EJ, Ramón-Canul LG, Torres-Hernández G, Herrera-Corredor JA,

Juárez-Barrientos JM, Rodríguez-Miranda J, et al. Tipificación de quesos madurados de
cabra producidos en la zona montañosa central del estado de Veracruz, México.
Agrociencia 2018;52(1):15–34.
13. Sánchez-Gamboa C, Hicks-Pérez L, Gutiérrez-Méndez N, Heredia N, García S, Nevárez-

Moorillón GV. Microbiological changes during ripening of Chihuahua cheese
manufactured with raw milk and its seasonal variations. Foods 2018; 7(9):153.https://
www.mdpi.com/2304-8158/7/9/153. Accessed Jan 30, 2021.
14. Silva-Paz LE, Medina-Basulto G. E., López-Valencia G, Montaño-Gómez MF, Villa-

Angulo R, et al. Caracterización de la leche y queso artesanal de la región de Ojos
Negros, Baja California, México. Rev Mex Cienc Pecu 2020;11(2):553-564.
https://cienciaspecuarias.inifap.gob.mx/index.php/Pecuarias/article/view/5084.
Consultado 30 Ene, 2021.
15. Sandoval-Alarcón F. Caracterización y análisis de la productividad del queso de prensa

de la Costa Chica de Guerrero y Oaxaca [tesis maestría]. Texcoco, Estado de México:
Universidad Autónoma Chapingo; 2016.
16. INEGI. Instituto Nacional de Estadística y Geografía. Guerrero, Clima. Recuperado

de:http://www.cuentame.inegi.org.mx/monografias/informacion/gro/territorio/clima.as
px?tema=me&e=12. Consultado 02 Sep, 2020.
17. Romero-Castillo PA, Leyva-Ruelas G, Cruz-Castillo JG, Santos-Moreno A. Evaluación

de la calidad sanitaria de quesos crema tropical mexicano de la región de Tonalá,
Chiapas. Rev Mex Ing Quím 2009;8(1):111–119.
18. 3M México. Placas Petrifilm™ para el recuento de aerobios AC, guía de interpretación.
Ciudad de México, México: 3M. 2017.
19. 3M México. Placas Petrifilm™ para el recuento coliformes, guía de interpretación.

Ciudad de México, México: 3M. 2017.
106
20. 3M México. Placas para el recuento de bacterias ácido lácticas 3M® Petrifilm®, guía de
interpretación. Ciudad de México, México: 3M. 2017.
21. 3M México. Placas Petrifilm™ Staph Express para recuento de Staphylococcus aureus,
guía de interpretación. México DF, México: 3M. 2009.
22. Camacho A, Giles M, Ortegón A, Palao M, Serrano B, Velázquez O. Técnicas para el

análisis microbiológico de alimentos, cuenta en placa de bacterias. 2a ed. México, DF,
México: Facultad de Química.; 2009.
http://depa.fquim.unam.mx/amyd/archivero/TecnicBasicas-Cuenta-en-placa_6527.pdf.
Consultado 2 Sep, 2020.
23. FAO, WHO. Statistical aspects of microbiological criteria related to foods, a risk
managers guide. 1st ed. Rome, ITA: Food and Agriculture Organization of the United
Nations; 2016.
24. Little RC, Milliken GA, Stroup WW, Wolfinger RD, Schabenberger O. SAS® for mixed
models. 2nd ed. Cary, North Carolina, USA: SAS Institute Inc; 2006.
25. Stroup WW, Milliken GA, Claassen EA, Wolfinger RD. SAS® for mixed models:
Introduction and basic applications. 3rd ed. Cary, North Carolina, USA: SAS Institute
Inc; 2018.
26. Brooks JC, Martinez B, Stratton J, Bianchini A, Kroksrom R, Hutkins R. Survey of raw
milk cheeses for microbiological quality and prevalence of foodborne pathogens. Food
Microbiol 2012;31(2):154–158.
27. ICMSF. International Commission on Microbiological Specifications for Foods.

Microorganismos de los alimentos 1. Técnicas de análisis microbiológico. 2da ed.
Zaragoza, ESP: Acribia; 2000.
28. Fernández-Escartín E. Microbiología e inocuidad de los alimentos. 2da ed. Querétaro,

MEX: Universidad Autónoma de Querétaro; 2008.
29. Flores-Magallón R, Oliva-Hernández AA, Narváez-Zapata AA. Characterization of

microbial traits involved with the elaboration of the Cotija cheese. Food Sci Biotechnol
2011;20(4):997–1003.
30. López-Expósito I, Miralles B, Amigo L, Hernández-Ledesma B. Health effects of cheese

components with a focus on bioactive peptides. In: Frias J, Martinez-Villaluenga C,
Peñas E, editors. Fermented foods in health and disease prevention. 1st ed. London, UK:
Academy Press; 2017:239–273.
31. Asperger H, Brandl E. The significance of coliforms as indicator organisms in various

types of cheese. Antonie van Leeuwenhoek 1983;48:635–639.
107
32. Metz M, Sheehan J, Feng PCH. Use of indicator bacteria for monitoring sanitary quality
of raw milk cheeses – A literature review. Food Microbiol 2020;85(2020): 103283.
https://www.sciencedirect.com/science/article/abs/pii/S0740002018311213?via%3Dih
ub. Accesed Jan 30, 2021.
33. Fox PF, Guinee TP, Cogan TM, McSweeney PLH. Fundamentals of cheeses science. 1st
ed. Gaithersburg, Maryland, USA: Aspen Publisher 2000.
34.NACMCF. National Advisory Committe on Microbiological Criteria for Foods.

NACMCF-Report-Process-Control-061015 (1) response to questions posed by the
Department of Defense regarding microbiological criteria as indicators of process
control or insanitary conditions, Washington DC, USA: United States Department of
Agriculture; 2015. https://www.fsis.usda.gov/sites/default/files/media_file/2020-
07/NACMCF-Report-Process-Control-061015.pdf . Accesed Aug 07, 2021.
35. Martin NH, Trmčić A, Hsieh TH, Boor KJ, Wiedmann, M. The evolving role of coliforms
as indicators of unhygienic processing conditions in dairy foods. Front Microbiol
2016;7:1549. https://www.frontiersin.org/articles/10.3389/fmicb.2016.01549/full.
Accessed Aug 07, 2021.
36. Trmčić A, Chauhan K, Kent DJ, Ralyea RD, Martin NH, Boor KJ, et al. Coliform
detection in cheese is associated with specific cheese characteristics but no association
was found with pathogen detection. J Dairy Sci 2016;99(8):6105–6120.
37. Khalid NM, Marth EH. Lactobacili – their enzymes and role in ripening and spoil age of
cheese- A review. J Dairy Sci 1990;73(10):2669–2684.
38. Tavaria FK, Reis PJM, Malcata FX. Effect of dairy farm and milk refrigeration on
microbiological and microstructural characteristics of matured Serra da Estrella cheese.
Int Dairy J 2006;16(8):895–902.
39. Nuñez M, Bautista L, Medina M, Gaya P. Staphylococcus aureus, thermostable nuclease

and staphylococcal enterotoxins in raw ewes' milk Manchego cheese. J Appl Bacteriol
1988;65(1):29–34.
40. Commission Regulation (EC) No. 2073/2005 of 15 November 2005 on microbiological

criteria of foodstuffs L338. Official Journal of the European Union 2005 L338: 1–26.
https://eur-lex.europa.eu/legal-content/EN/ALL/?uri=CELEX%3A32005R2073
Accessed Sep 2, 2020.
41. Kirdar SS, Yurdakul O, Kalit S, Kalit M. Microbiological changes throughout ripening
of Keş cheese. J Cent Eur Agric 2018;19(1):61–71.
108
42. Cardoso VM, Dias RS, Soares BM, Clementino LA, Araújo CP, Rosa CA. The influence
of ripening period length and season on the microbiological parameters of a traditional
Brazilian cheese. Braz J Microbiol 2013;44(3):743–749.
43. Çolaklar M, Taban BM, Aytaç SA, Barbaros H, Gürsoy A, Akçelik N. Application of
bacteriocin-like inhibitory substances (BLIS)- Producing probiotic strain of
Lactobacillus plantarum in control of Staphylococcus aureus in White-Brined cheese
production. J AgrSci 2019;25(2019):401–408.
44. Bellio A, Astegiano S, Traversa A, Bianchi DM, Gallina S, Vitale N, et al. Behaviour of
Listeria monocytogenes and Staphylococcus aureus in sliced, vacuum-packaged raw
milk cheese stored at two different temperatures and time periods. Int Dairy J 2016;
57:15–19.
45. Stecchini MA, Sarais I, de Bertoldi M. The influence of Lactobacillus plantarum culture
inoculation on the fate Staphylococcus aureus and Salmonella typhimurium in Montasio
cheese. Int J Food Microbiol 1991;14(2):99–109.
46. Haines WC, Harmon LG. Effect of selected lactic acid bacteria on growth of
Staphylococcus aureus and production of enterotoxin. Appl Microbiol 1973;25(3):436–
441.
109
Article
Molecular detection of a fragment of bluetongue virus in sheep from

different regions of Mexico
Edith Rojas Anaya a
Fernando Cerón-Téllez b
Luis Adrián Yáñez-Garza c
José Luis Gutiérrez-Hernández b
Rosa Elena Sarmiento-Salas c
Elizabeth Loza-Rubio b*
a
Instituto Nacional de Investigaciones Forestales Agrícolas y Pecuarias (INIFAP). Centro
Nacional de Recursos Genéticos. México.
b
INIFAP. Centro Nacional de Investigación Disciplinaria en Salud Animal e Inocuidad
(CENID-SAI), Campus Ciudad de México. Carretera México-Toluca Km 15.5, Colonia Palo
Alto. 05110. Alcaldía Cuajimalpa de Morelos. Ciudad de México. México.
c
Universidad Nacional Autónoma de México, FMVZ. México.
*Corresponding author: eli_rubio33@hotmail.com; loza.elizabeth@inifap.gob.mx
Abstract:
Bluetongue disease (BTD) affects various species of wild and domestic ruminants. In
Mexico, the disease, caused by the bluetongue virus (BTV) is still regarded as exotic, despite
the fact that antibodies have been detected on several occasions. The objective was to
establish molecular techniques using a synthetic gene, including the genes NS1 and NS3 as
positive controls for the diagnosis of BTV in samples of sheep from different regions of the
country. A total of 320 total whole blood samples were obtained from sheep. The samples
obtained were evaluated by end-point RT-PCR and real-time RT-PCR, the conditions having
110
been established by the work group. Twelve sheep samples were found to be positive for the
detection of NS1; these samples were sequenced, and a fragment of 101 base pairs was
obtained. Upon alignment, were obtained identities with sequences reported in GenBank with
NS1 fragments ranging from 89 % (p= 1e-12) to 98 % (p= 4e-13), corresponding to serotypes
10, 11 and 12. From these samples, two positive sheep samples were obtained using real-
time PCR (RT-PCR): one from Chiapas (Chiapas breed), and the other, from Tamaulipas
(Suffolk breed). The results of the RT-PCR were corroborated by CPA-SENASICA. This
work provides evidence, for the first time in Mexico, of the importance of using a synthetic
gene as a positive control to perform BSL-2 detection in official laboratories, which in a
health emergency is of utmost importance.
Key words: Bluetongue disease, Bluetongue virus, Diagnosis, NS1 and NS3 genes, Sheep,
Synthetic gene.
Introduction
Bluetongue virus (BTV) belongs to the genus Orbivirus and the family Reoviridae, and
causes bluetongue disease (BTD) affecting both domestic and wild ruminants(1). The virus
has a negative-sense double-stranded RNA (dsRNA) genome consisting of 10 segments(2). It
is a non-enveloped virus with an icosahedral capsid, with a diameter of approximately 90
nm. The genome codes for the structural proteins that make up the external and internal
capsid or core (VP1 - VP7), and the four non-structural proteins, called non-structural (NS)
that are involved in the replication, maturation, or exit of the virion from infected cells).
Nonstructural genes are highly conserved across the genus(3,4). The NS1 gene encodes for a
protein of the same name, which is expressed in the largest quantity during BTV replication
and is the most abundant cytoplasmic protein. On the other hand, the NS3 gene encodes for
the NS3 protein that acts as a viroporin, which is related to cell lysis(3,4). Because of the above,
the two genes have been used as targets in screening assays for the identification of BTV(5).
The virus is transmitted by the bite of mosquitoes of the genus Culicoides spp; therefore, the
occurrence of the disease is associated with the spread of this vector, although other vectors
such as ticks have been reported(6); it is known that the virus can remain viable throughout
the life of the vector.
111
Currently, 28 different BTV serotypes have been described worldwide(7), and the virus is
distributed in practically all countries where cattle and sheep are raised. Bluetongue disease
(BTD) can occur both subclinically and clinically, especially in sheep, since in cattle it is
mostly asymptomatic. In countries where the disease is endemic, it causes severe economic
losses to producers(8). The name "blue tongue" was given to this disease by Africans who
observed cyanosis on the tongue of some animals; however, this sign is not observed in all
infected animals, as the signs vary between species and depend on the strain. Lesions such
as hyperemia and edema of the lips and face, oral erosions and ulcers, and the typical cyanosis
of the tongue are due to infection of the endothelial cells that allow increased cell
permeability(9).
The World Animal Health Organization (OIE)(10) classifies bluetongue disease as a notifiable
disease; therefore, timely diagnosis is important. The degree of severity of the disease
depends on the serotype, the virus strain, and the species, age and immune status of the
animal, with sheep and white-tailed deer being the most affected(11); in sheep, the incubation
period of BTV is six to eight days; on the other hand, cattle rarely show clinical signs, but
maintain a prolonged viraemia(12). Deer can also be infected by a closely related orbivirus
responsible for epizootic hemorrhagic disease(13). BTD is not contagious and is only
transmitted by Culicoides insects; its distribution is therefore associated with the prevalence
of the vector. Up to five serotypes have been identified in North America; however, seven
serotypes have been reported only in the United States(14). The occurrence of the virus in the
Americas is mainly associated with the presence of two vector species, C. sonorensis and C.
insignis(15). In Mexico, although the disease is considered exotic, in the 1980s, positive
serology to the virus was reported in both sheep and cattle in different regions of the
country(16,17). On the other hand, in 2015, the detection of a viral genome fragment in three
Culicoides species (C. variipennis, C. sonorensis, and C. occidentalis) was published(18). The
VP2 gene is used to define the 28 BTV serotypes described so far(7).
Finally, in February 2021, the Secretary of Agricultural Development, Fisheries and

Aquaculture (SEDPA) of Oaxaca in the Municipality of San Pedro Mixtepec notified the
CPA of oral lesions in sheep. The CPA detected two bluetongue virus-positive samples using
the RT-PCR technique and the notification was made in the CPA's AVISE Newsletter(19).
BTD is notifiable to the OIE, mainly because new outbreaks lead to movement and trade
restrictions, resulting in severe economic losses. However, active surveillance is
implemented worldwide to detect BTV infection through different tests such as virus
isolation or other screening or serological tests(11). The detection method par excellence is
the isolation of the virus in permissive cell cultures, for subsequent genetic analysis of the
virus to determine the serotype present in the sample of the affected animal. In virus endemic
areas, vector control is recommended to prevent the spread of the virus, in addition to
vaccination programs. Live attenuated vaccines have been used in the United States and
112
Europe. The objective of this work was to establish molecular techniques using a synthetic
gene as a positive control that included the NS1 and NS3 genes, in order to subsequently
evaluate sheep samples from different regions of the country.
Samples
A convenience sampling of apparently healthy sheep was carried out, obtaining 3 ml of whole
blood with anticoagulant (heparin) from 320 individuals from five states of the country
(Chiapas, Coahuila, Estado de México, Morelos, and Tamaulipas). Samples were obtained
during the summer of 2016 to 2018. Sampling was performed on males and breeding females
between one and five years of age. Table 1 describes the total number of samples analyzed.
Table 1: Blood samples obtained from sheep in five Mexican states analyzed for molecular
detection of bluetongue virus by RT-PCR
State Species Sex Breed Total
H Criollo 20
Chiapas Ovinos M Criollo 5
S/D S/D 66
Cruza 70
Ovinos H Dorper 37
Suffolk 13
Coahuila
Blackbelly 1
Ovinos H Criollo 29
Pelibuey 1
Morelos Ovinos S/D S/D 62
Pelibuey 8
Tamaulipas Ovinos H Dorset 4
Suffolk 4
Total 320
F= female; M= male; N/D= no data.
113
Synthetic gene
Since BTD is considered exotic in Mexico, a synthetic gene was designed to be used as a
positive control in order to avoid using the inactivated virus or its genetic material in a BSL2
laboratory. For this purpose, two fragments of the viral genome were inserted into the pUC57
vector (GeneScript, USA) —one corresponding to the NS1 gene (354 bp), and the other,
corresponding to the NS3 gene (300 bp)—, based on the BTV-11 sequences reported in
GenBank (KF986511 and KM580467, respectively). For use as a positive control in the
molecular assays, the plasmid concentration was set to 100 ng/µl.
Genetic material extraction
Viral RNA was extracted from 250 l of the blood sample using the Trizol LS® reagent
(Ambion, USA), following the manufacturer's instructions with some modifications to the
protocol. The RNA obtained was stored at -70 °C until use.
Molecular assays
Constitutive gene. In order to verify the quality of the RNA thus obtained, a fragment of the
constitutive GAPDH gene was amplified by RT-PCR using the primers and conditions
reported by González-Arto M, et al(20). Complementary DNA synthesized from viral RNA
with the M-MLV Reverse Transcriptase kit was used as a template (Invitrogen, USA).
Detection of a fragment of gene NS3. The RNA extracted from the samples served as a
template for the detection of a fragment of the NS3 gene of orbiviruses using a pair of primers
and the probe recommended in the OIE Manual10. RT-PCR was carried out according to a
one-step amplification protocol established in our laboratory, utilizing the iTaq Universal
Probe One-Step Kit (Bio-Rad, USA).
Detection of a fragment of gene NS1. In order to corroborate the presence of the BTV genome
in real-time PCR positive samples, a protocol was established for the detection of a fragment
of the NS1 gene using primers described in the OIE Manual(10). The iProof HF Master Mix
Kit was utilized for this purpose (Bio-Rad, USA). The products for the amplification of
114
positives to this protocol were purified in agarose gels and sequenced according to the Sanger
method at the IBT-UNAM Synthesis and Sequencing Unit.
Sequencing. Sequencing results were analyzed with NCBI's BLAST tool. The obtained
sequences were compared with 29 sequences reported in the Gene Bank of bluetongue virus
and with sequence AM745001.1 for the epizootic hemorrhagic fever virus as an outgroup.
The alignment was performed using the “Multiple alignment program for amino acid or
nucleotide sequences” (MAFFT version 7, AIST). A phylogenetic analysis was performed
using Bayesian methods (Markov Chain Monte Carlo) and the alignment was carried out
with Mesquite in MrBayes software (Open source).
Results
As an assay to evaluate the quality of the genetic material, the amplification of a fragment of
approximately 400 bp of the ovine GAPDH gene was carried out as previously above. All
samples used for detection of a viral genomic fragment were positive for GAPDH
amplification by RT-PCR, which indicates that the genetic material was intact and in good
condition for use in RT-PCR assays (Figure 1).
Figure 1: RT-PCR for amplification of the sheep’s constitutive GAPDH gene
Lane 1, 50bp fragment size marker (Low Mass ladder); Lanes 2-7, sheep samples. Amplification products
were run on a 1.5% agarose gel.
115
As for the assay for the detection of a fragment of the NS1 gene by RT-PCR endpoint, the
identification of this gene was obtained in 12 samples of sheep blood from the states of
Chiapas, Coahuila, and Tamaulipas. In order to harmonize the methods, it was decided to use
primers suggested by the OIE, as described above. The analysis of the sequences obtained
showed in the alignment an identity with sequences reported in GenBank with the NS1
fragment from 89 % (p= 1e-12) to 98 % (p= 4e-13), corresponding to serotypes 10, 11 and
12.
Figure 2 shows the results of the phylogenetic inference, depicting the clustering of the
samples primarily with serotypes 10 and 11. The positive results were corroborated by real-
time PCR in two of the samples, one from Chiapas and the other from Tamaulipas; as
mentioned above, RT-PCR uses the gene NS3.
Figure 2: Phylogenetic inference of bluetongue virus NS1-positive sheep samples
The dendogram was obtained using the alignment of 100 bp of the NS1 gene from the sequences of
the positive samples in this study and 30 sequences obtained from GenBank belonging to
serogroups 10, 11, and 12. Sheep samples positive by end-point RT-PCR are identified as follows:
OT= Tamaulipas sheep: OT1, OT2, OT3, OT4, OT5. OC= Chiapas sheep: OC1, OC2, OC3,
OCF10, OCF39, OC5, OC4. The breeds are indicated by color: Creole , Pelibuey , Suffolk
, and Dorper . The samples were taken between 2016 and 2017. The sequence of the epizootic
hemorrhagic fever virus (AM745001) was used as outgroup, indicated by .
The positive result of RT-PCR detection was corroborated by the SENASICA – CPA
laboratory and notified to SIVE. In this laboratory, the detection of a fragment of gene NS3
116
also serves as a positive control, using viral RNA obtained from the supernatant of cell
cultures infected with the virus that are subsequently inactivated (CPA personal
communication). With respect to the tests performed in collaboration with the official
laboratory to corroborate the results of the animals that tested positive, the synthetic control
proposed in this work showed that it could be used without problem in any BSL2 laboratory
in order to perform virus detection in official BSL2 laboratories.
Discussion
As mentioned above, BTD in different hosts can be subclinical, and detection of the causative
agent in sheep populations can be complex(21). Therefore, for this study, clinically healthy
animals were considered for sampling, where the status of viraemia in animals and signs may
or may not have been observed, depending on the viral load or subtypes involved. In addition,
sampling was carried out in those Federal Entities of the country located in areas where the
vector transmitting the virus is present in a climate suitable for its development(22), from
sheep that were close to cattle farms, as bovines can be a healthy carrier of the virus.
Regarding the diagnosis of the causative agent of BTD, the recommended method is by virus
isolation in a cell culture or in embryonated eggs(23). However, different versions of RT-PCR
have been developed that can be used to detect BTV, specifically the Orbivirus serogroup,
and to determine the BTV serotype. These molecular approaches are much faster than
traditional virological and immunological methods, which can take up to four weeks to
provide information on serogroups and serotypes. Currently, there are targeted assays mainly
for VP1, NS1, NS2, VP6, and NS3 proteins. None of these proteins is related to virus
serotyping, and they are strongly conserved among BTV serotypes, while some, such as NS3,
have a higher degree of conservation among orbiviruses. Therefore, these assays lack the
potential to classify isolates(24).
In addition, each technique offers a range of virus or genome detection; for instance, Bonneau
et al(25) report that the RT-PCR assay is capable of detecting the genome within a period
ranging from 3 to 122 days. Therefore, it is important to make the recommendation to carry
out sampling and surveillance campaigns not only on ruminants but also on the potential
vectors that are reported as transmitters of the virus.
BTD is considered exotic in Mexico; however, this status should be reconsidered taking into
account the various notifications made since the 1980s to the present date in different regions
of the country; this would allow to assess the presence of the virus in different hosts and
vectors using a variety of methods. In 1981, Moorhead et al(26) determined the presence of
117
antibodies by immunoprecipitation in sheep slaughtered at the slaughterhouse, finding 8.5 %

positivity in serum. Subsequently, Vilchis et al (1986)(27), using immunodiffusion,
demonstrated 27.4 % seropositivity in the animals sampled. Stott et al(28) reported
seropositivity of 6 %, 35 % and 60 % in three independent studies on cattle from different
states of the country. The most recent scientific publication by Lozano-Rendón JA and his
work group(18) proved a 14.4 % molecular detection of the NS1 gene of BTV in Culicoides
vectors in the state of Nuevo León.
As for the results presented in this research, a 3.75 % positivity rate was detected in the
samples of clinically healthy sheep using the same NS1 gene as Lozano-Rendón et al(15).
However, this study was conducted on the vector, where the probability of demonstrating the
presence of the virus is greater than in sheep, where the viraemia time is shorter. This NS1
gene, as already mentioned, is one of the most conserved among the different BTV
serotypes(29). The results of the detection of a fragment of the viral genome in sheep samples
in this study are consistent with those reported this year by the CPA(19).
On the other hand, the detection rate is similar to that described in older reports using
ruminant samples. The results reported in the present work, as well as those presented by
other authors, show the need to change the status of the disease, as well as to implement virus
surveillance systems in both the vectors and the main hosts of the virus, whether these be
domestic or wild animals.
A synthetic positive control is presented herein as an alternative to viral RNA, which can
only be utilized in the BSL3 laboratory of the country's official agencies. The use of such
synthetic positive control would enlarge the network of laboratories capable of implementing
the viral detection technique to determine the real status of the disease in the country.
Acknowledgments
The authors are grateful to Roberto Navarro López, MSc; to Marcela Villarreal Silva, PhD;
to Mariana García Plata, MSc, and to Martín García Osorio, DVM, for their collaboration in
corroborating the results in the CPA-SENASICA Laboratory. This research was financed by
INIFAP project No. 12583634008 and the validated form No. 914545716.
118
Literature cited:
1. Qing-Long G, Wang Q, Yang XY, Li DL, Zhao B, Ge GY, et al. Seroprevalence and
risk factors of the bluetongue virus in cattle in China from 1988 to 2019: A
comprehensive literature review and meta-analysis. Front Vet Sci 2021;7:550381 doi:
10.3389/fvets.2020.550381.
2. Maclachlan NJ. Bluetongue: History, global epidemiology, and pathogenesis. Prev Vet
Med 2011;102(2):107– 111.
3. Chacko N, Mohanty NN, Biswas SK, Chand K, Yogisharadhya R, et al. A coiled-coil

motif in non-structural protein 3 (NS3) of bluetongue virus forms an oligomer. Virus
Genes 2015;51(2):244–251 doi 10.1007/s11262-015-1230-9.
4. Roy P. Bluetongue virus proteins and particles and their role in virus entry, assembly
and release. Adv Virus Res 2015;64:69–123. doi.org/10.1016/S0065-3527(05)64004-3.
5. Schwartz-Cornil PP, Mertens V, Contreras B, Hemati F, Pascale E, Breard et al.

Bluetongue virus: virology, pathogenesis and immunity. Vet Res 2008;39(5):46. doi:
10.1051/vetres:2008023.
6. Sperlova A, Zendulkova D. Bluetongue: a review. Vet Med 2011;56:430–452.
7. Bumbarov V, Golender N, Jenckel M, Wernike K, Beer M, Khinich E, Zalesky O, Erster

O. Characterization of bluetongue virus serotype 28. Transb Emer Dis 2020;67(1):171–
182. doi.org/10.1111/tbed.133338.
8. Maclachlan NJ, Drew CP, Drew CP, Darpel KE, Worwa G. The pathology and
pathogenesis of bluetongue. J Comp Pathol 2009;141:1-16.
doi.org/10.1016/j.jcpa.2009.04.003.
9. Drew CP, Heller MC, Mayo C, Watson JL, Maclachlan NJ. Bluetongue virus infection
activates bovine monocyte-derived macrophages and pulmonary artery endothelial cells.
Vet Immunol Immunopathol 2010;136(3–4):292–296.
doi:10.1016/j.vetimm.2010.03.006.
10. OIE. Enfermedades, infecciones e infestaciones de la Lista de la OIE en vigor en 2019.

http://www.oie.int/es/sanidad-animal-en-el-mundo/enfermedades-de-la-lista-de-la-oie-
2018/. Consultado 16 Nov, 2019.
11. Rojas JM, Rodríguez-Martín D, Martín V, Sevilla N. Diagnosing bluetongue virus in

domestic ruminants: current perspectives. Vet Med Res Report 2019;10:17–27.
119
12. Barratt-Boyes SM, Maclachlan NJ. Dynamics of viral spread in bluetongue virus
infected calves. Vet Microbiol 1994;40(3–4):361–371. doi.org/10.1016/0378-
1135(94)90123.
13. Falconi C, López-Olvera JR, Gortázar C. BTV infection in wild ruminants, with
emphasis on red deer: a review. Vet Microbiol 2011;151(3-4)209-219.
doi.org/10.1016/j.vetmic.2011.02.011.
14. Drolet S, Rijn P, Howerth E, Beer M, Mertens P. A review of knowledge gaps and tools
for Orbivirus research. Vector-borne Zoon Dis 2015;15(6): 339-347. doi:
10.1089/vbz.2014.1701.
15. Gay GC. Orbiviruses: A gap analysis. Vector Borne Zoonotic Dis- 2015;15(6):333-334.
doi:10.1089/vbz.2015.28999.cgg.
16. Suzan VM, Misao O, Romero EA, Yosuke M. Prevalence of bovine herpesvlrus-1,
paraenfluenza-3, bovine rotavirus, bovine viral diarrhoea, bovine adenovirus 7, bovine
leukemia virus and bluetongue virus antibodies in cattle in Mexico. Jpn J Vet Res
1983;31(3-4): 125-132.
17. Vilchis C, Gay J, Batalla D. Determinación de anticuerpos contra el virus de lengua azul
en ovinos por la técnica de inmunodifusión. Tec Pecu Méx 1986;51:116-121.
18. Lozano-Rendón JA, Contreras-Balderas AJ, Fernández-Salas I, Zarate-Ramos J,

Avalos-Ramírez R. Molecular detection of bluetongue virus (BTV) and epizootic
hemorrhagic disease virus (EHDV) in captured Culicoides spp. in the northeastern
regions of Mexico. Afr J Microbiol Res 2015;9(45):2218-2224.
19. Boletín Informativo de la CPA. AVISE. No 9 Febrero, 2021.

https://issuu.com/boletinavise/docs/boletin_avise_ed09_febrero.
20. Gonzalez-Arto M, Hamilton dos STR, Gallego M, Gaspar-Torrubia E, Aguilar D,

Serrano-Blesa E, et al. Evidence of melatonin synthesis in the ram reproductive tract.
Andrology 2016;4(1):167-171. doi: 10.1111/andr.12117.
21. Celma CC, Bhattacharya B, Eschbaumer M, Wernike K, Beer M, Roy P. Pathogenicity

study in sheep using reverse-genetics-based reassortant bluetongue viruses Vet
Microbiol 2014;174(1-2):139-47. doi: 10.1016/j.vetmic.2014.09.012.
22. SIAP. Producción por Estado. 2016.

http://infosiap.siap.gob.mx/anpecuario_siapx_gobmx/apecnal.jsp?id=5.
23. McHolland LE, Mecham JO. Characterization of cell lines developed from field
populations of Culicoides sonorensis (Diptera: Ceratopogonidae). J Med Entomol
2003;40(3):348-51. doi: 10.1603/0022-2585-40.3.348.
120
24. Maan S, Maan NS, Belaganahalli MN, Potgieter AC, Kumar V, Batra K, et al.
Development and evaluation of Real Time RT-PCR assays for detection and typing of
Bluetongue Virus. PLoS ONE 2016;11(9): e0163014. doi:
10.1371/journal.pone.0163014.
25. Bonneau KR, DeMaula CD, Mullens BA, Maclachlan NJ. Duration of viraemia
infectious to Culicoides sonorensis in bluetongue virus-infected cattle and sheep.
Amsterdam: Elsevier Scientific Publishing Co; 2002.
26. Moorhead JR. Estudio de la presencia de anticuerpos precipitantes contra el virus de la

Lengua Azul en ovinos y bovinos sacrificados en el Rastro de Ferrería de la Ciudad de
México, DF [tesis]. Facultad de Medicina Veterinaria y Zootecnia, Universidad
Nacional Autónoma de México;1981.
27. Vilchis CM, Gay GJ, Batalla D. Determinación de anticuerpos contra el virus de lengua
azul en ovinos por la técnica de inmunodifusión. Tec Pecu Méx 1986;51:116-121.
28. Stott JL, Blanchard-Channell M, Osburn BI, Riemann HP, Obeso RC. Serologic and
virologic evidence of bluetongue virus infection in cattle and sheep in Mexico. Am J
Vet Res 1989;50(3):335–340.
29. Mertens PP, Diprose J, Maan S, Singh KP, Attoui H, et al. Bluetongue virus replication,
molecular and structural biology. Vet Italiana 2004;40(4):426-437.
121
Article
Insulin-like growth factor 1 (IGF-1) concentrations in synovial fluid
of sound and osteoarthritic horses, and its correlation with
proinflammatory cytokines IL-6 and TNF
Fernando García-Lacy F. a
Sara Teresa Méndez-Cruz b
Horacio Reyes-Vivas b
Victor Manuel Dávila-Borja c
Jose Alejandro Barrera-Morales d
Gabriel Gutiérrez-Ospina e
Margarita Gómez-Chavarín f*
Francisco José Trigo-Tavera g
a
Universidad Nacional Autónoma de México. Facultad de Medicina Veterinaria y Zootecnia.
Departamento de Medicina, Cirugía y Zootecnia para Équidos. Ciudad de México. México.
b
Instituto Nacional de Pediatría. Laboratorio de Bioquímica Genética. Ciudad de México.
México.
c
Instituto Nacional de Pediatría. Laboratorio de Oncología Experimental. México.
d
SEDENA. Centro Ecuestre de Alto Rendimiento. Ciudad México. México.
e
Universidad Nacional Autónoma de México. Departamento de Fisiología. Instituto de
Investigaciones Biomédicas. Ciudad de México. México.
f
Universidad Nacional Autónoma de México. Facultad de Medicina. Departamento de
Fisiología. Ciudad de México. Mexico.
g
Departamento de Patología. Ciudad de México. México.
122
*Corresponding author: margaritachavarin@gmail.com
Abstract:
Insulin-like growth factor I (IGF-1) is the most important known growth factor for cartilage
repair in horses. It promotes mitosis of chondrocytes, collagen II expression, and extra
cellular matrix production. Osteoarthritis (OA) is the most common musculoskeletal
condition that causes lameness and poor performance in sport horses. A total of 11 lame
horses were clinically and radiographically evaluated, and all were confirmed to suffer a front
metacarpophalangeal lameness by a positive flexion test, a low-4-point nerve block and an
intraarticular block. Total protein, IGF-1, IL-6 and TNF were determined by ELISA,
demonstrating changes and different correlations between clinical condition, radiographic
changes and degree of inflammation. All horses with joint associated pain and therefore
associated lameness, demonstrated a significant increase of total protein (P<0.0001) and
IGF-1 concentration (P<0.05). Concentrations of IL-6 and TNF between controls and lame
horses demonstrated significant differences (P<0.01 and P<0.001 respectively). Horses with
less radiographic changes, demonstrated the highest IGF-1 expression in synovial fluid, and
horses with more chronic OA conditions had very similar IGF-1 expression levels than
control joints. In all lame joints, it was identified by Western blot a lighter isoform of IGF-1
(~7.5 kDa) which was inflammation related and it is the molecular weight of the mature
peptide, and all control joints expressed a heavier isoform (~12 kDa). This finding could lead
to new research for sequencing and targeting the isoform which is not expressed during an
inflammatory process within a joint, and to have a better understanding of its role in the
horse’s joint.
Key words: Insulin growth factor 1 (IGF-1), Horse, Osteoarthritis (OA), Lame.
Introduction
Insulin-like growth factor I (IGF-1) is the most important known growth factor for cartilage
repair in horses, because it stimulates proteoglycan synthesis, and therefore extra cellular
matrix (ECM), and promotes mitosis of chondrocytes. It has an important growth-promoting
123
activity not only in articular cartilage, but in several tissues, mainly in muscle, bones and
brain. It activates the mitogen-activated protein kinase (MAPK) pathway, having several
effects in promoting: cell survival, growth, proliferation, protection to hypoxia, inflammation
regulation in muscle injuries and in bone growth plates(1-4). It has also a key role in brain
development, along with estradiol, regulating a variety of developmental and neuroplastic
events(5). In structure, it is very similar to insulin. When insulin is instilled in a joint, IGF-1
expression in synovial fluid is enhanced(6).
Injuries of the articular cartilage are normally repaired by substitution with fibrocartilage,
which leads to loss of function of the joint resulting in osteoarthritis (OA)(7). Lameness is the
most frequent reason for which equine practitioners are required from horse owners, and OA
represents more than 60 % of all lameness cases in sport horses(8). The main problem in OA
is inflammation, conditioning an imbalance between catabolism and anabolism in the
articular cartilage. In this particular tissue, the only cellular component is constituted by
chondrocytes, which are responsible of ECM synthesis, in order to maintain adequate
cartilage function.
There is evidence regarding exogenous efficacy of IGF-1 in vitro, which enhances

proteoglycan synthesis by stimulated chondrocytes. Other therapies, such as chondrocyte
transplantation from mature and neonatal chondrocytes, gene therapy strategies to upregulate
IGF-1 expression by transfected chondrocytes, require general anesthesia, a surgical
procedure and therefore specialized equipment and personnel(9).
On a pilot study conducted by the authors in which 13 synovial fluid samples obtained from
different joints (distal interphalangeal joints, metacarpophalangeal joints, shoulder joints,
tarsometatarsal joints, and stifles) from horses with associated lameness AAEP (American
Association of Equine Practitioners) grade: 2/5, no radiographic changes but a positive
response on 1-minute flexion test. By ELISA, a significant increase of IGF-1 and a positive
correlation between total protein and IGF-1 levels in synovial fluid (data not shown) were
found. In this study it was obtained synovial fluid samples from 21 horses with different
degrees of OA (confirmed by intraarticular block and radiographic changes) in the
metacarpo-phalangeal joint (MCPJ), where IGF-1 and total protein correlated positively in
horses with acute OA, and negatively in horses with chronic OA and marked bone
remodeling. In horses with mild of non-radiogrphic changes (acute OA), IGF-1 correlated
negatively with interleukin-6 (IL-6) and tumoral necrosis factor alpha (TNF). Interestingly,
were able to find by western blot, at least two functional isoforms of IGF-1 expressed in
synovial fluid, one present only in control horses, and the other in lame horses.
To our knowledge, there is no information regarding IGF-1 fluctuations on naturally

occurring OA. There are no in vivo studies regarding IGF-1 levels during OA. Perhaps this
paper can help practitioners to understand the role of IGF-1 for this particular condition and
124
could be used as a baseline for further studies on IGF-1 concentration and its possible use as
an alternative treatment.
Synovial fluid samples were obtained from Warmblood and Thoroughbred horses (n=11)
from two different disciplines: showjumpers (Warmblood) (n= 8), and race horses
(Thoroughbred) (n= 3) with a mean age of 10.5 yr old and a mean weight of 520 kg. Control
(Ctrl) samples were obtained from two geldings, Warmblood horses of 5 and 7 yr old. No
more control horses were available for the study, since they all were sound, it was not easy
to obtain consent from the owners to sample their joints. A complete lameness evaluation
and radiographic assessment were performed in all control horses in order to be included in
this study. None of them showed signs of front limb lameness and were negative to passive
and active flexion tests (30 sec). Additionally, none of them presented any radiographic
changes associated with joint pathology in the metacarpophalangeal joint.
Lameness evaluation
A clinical evaluation was performed on all horses included in this study, in order to find
evidence of lameness associated with the metacarpo-phalangeal joint of the front limbs.
Evaluation consisted on static observation, palpation and passive flexion response; dynamic
evaluation of walk and trot on a straight line and lunged on hard and soft surface. All included
horses demonstrated a 2 and 3/5 lameness (AAEP), with a positive flexion test (1 min).
Additionally, all horses were positive to low-4-point block (lateral and medial palmar nerves
and lateral and medial metacarpal nerves), using 2 and 1.5 ml respectively of 2 %
mepivacaine (Carbocaine, Zoetis Inc.) and further intra-articular block of the metacarpal-
phalangeal joint, using a volume of 6 ml of 2 % Mepivacaine (Carbocaine, Zoetis Inc.) as
previously described(10). Any horse negative to these blocks, was excluded from the study.
Synovial fluid collection
All synovial fluid samples were obtained from the metacarpal-phalangeal joint MCPJ joints
of lame horses, using an aseptic technique on the palmaro-lateral approach as previously
125
described and 5 d after the intraarticular (IA) block(10). Samples were obtained from healthy
horses and were used as controls (n= 4).
Radiographic evaluation
All selected horses were radiographically evaluated from the MCPJ, using four standard
views (dorso-palmar, latero-medial, dorso-lateral palmaro-medial, and dorso-medial
palmaro-lateral) in order to assess the radiologic condition of all horses. Three different
grades of radiologic changes were determined associated with the clinical condition of the
horse (Table 1).
Table 1: Grade of severity and its relation on clinical and radiographic findings on horses
included in this study
Grade Radiographic and clinical findings
I Non to minor changes associated with joint pain and lameness: Irregularity
and loss of normal homogeneity of the sagittal ridge of MTCIII.
II Moderate changes associated with joint pain and lameness: Osselets
(osteophytes) on P1 and MTCIII.
III Severe changes associated with severe lameness and decrease of motion
range: suprachondilar or subchondral lysis, osteophytes and new bone
formation with periostic reaction and loss of articular space.
(Modified from: Verwilghen D, et al. 2009)(11).
Protein concentration determination
The concentration of total protein from all synovial fluid samples was obtained by using the
BCA Protein Assay Kit, (Pierce BCA Protein Assay Kit cat. 23225), according with the
manufacturer’s instructions. For each sample the final concentration was 100 g/50 L for
the ELISA procedure.
126
IGF-1 concentration analysis
Determination of IGF-1 concentration in synovial fluid samples of control and osteoarthritic

horses was made with 50 L using a commercial ELISA kit (Horse IGF1 ELISA kit,
#MBS017382, MyBio-Source®) following the manufacturer’s instructions.
Interleukin 6 (IL-6) concentration
A quantitative determination of IL-6 in synovial fluid of all samples was performed using a
commercial ELISA kit (Horse interleukin-6 ELISA kit, cat. #: CSB-E16634Hs), which is a
sandwich immunoassay technique, where the plates are coated with a specific horse IL-6
antibody, then, a specific biotin-conjugated antibody for IL-6 and then avidin conjugated
horseradish peroxidase (HRP) are added. Protocol is performed following manufacturer’s
instructions.
Tumoral necrosis factor alpha (TNF) concentration
Determination of TNF concentration in synovial fluid samples of control and osteoarthritic

horses was made with 100 l using a commercial ELISA kit (Equine TNF ELISA kit, cat
#: ESS0017 Invitrogen) following the manufacturer’s instructions.
Western Blot analysis
Equal amounts of protein (100 µg per lane), were subjected to a 16 % SDS-PAGE (90V for
30 min and 120 V for 3.5 h). Precision Plus Protein Dual Color Standards marker was used,
containing ten prestained recombinant proteins (10 to 250 kD), including eight blue-stained
bands and two pink reference bands (25 and 75 kD). After electrophoresis, gels were
transferred using a semi-dry transfer system (271mA for 15 min) to PVDF (0.45uM) (Bio-
Rad) membranes, which were blocked using 4% skim milk diluted in PBS (pH 7.4) and
incubated on a shaker at 37 oC, 120 rpm for 2 h. After blocking, membranes were washed 3x
(for 5 min each) using PBS containing 0.05% Tween-20. As a primary antibody, a goat
polyclonal anti-IGF-1 (1:1000) (Sta. Cruz #Sc-1422) was used, incubated on a shaker first at
37 oC, 120 rpm for 2 h, and left overnight at 4 oC; membranes were washed again as
127
previously described and as a secondary antibody, a polyclonal anti-goat IgG (1:5000)

(Millipore #AP180B) was used, and incubated on a shaker at 37 oC, 120 rpm for 2 h and a
final wash of the membranes was performed. Proteins were detected by using an enhanced
chemiluminescence method and visualized using a high-resolution Imaging System (Bio-Rad
ChemiDoc). Membranes were incubated to a 1:1 dilution of luminol and peroxidase (Merck
Millipore, Luminata # WBLUF0500), and exposed at various times, where the optimum time
of exposure was 35 seconds.
Results
A total of 45 horses were examined, from which only 11 horses (22 samples) were included
in this study, and 2 horses (4 samples) as controls. All horses varied from each other in
degrees of lameness and radiographic changes, and all responded positively to the digital
flexion test, low-4-point block and intraarticular (IA) block of the fetlock joint. Six joints
were scored as grade I, five joints were scored as grade II and 8 joints were grade III (Figure
1).
Figure 1: Representative radiographs from horses scored with various grades
Grade 1 (A) Lateromedial view with a mild irregularity of the proximal-dorsal aspect of the sagittal ridge
(arrow); Grade II (B) Dorsolateral palmaromedial oblique view with a visible osteophyte on the proximal
dorso-medial aspect of P1 (arrow); and Grade III (C) Dorsopalmar view where a subchondral bone cyst in the
proximal aspect of first phalanx in the sagittal groove with areas of bone sclerosis (arrow).
128
IGF-1 concentration
All horses with joint associated pain, lameness, and less radiographic changes, demonstrated
a significant increase of IGF-1 concentration (P<0.05) (Figure 2). All samples were repeated
by pairs and read three times in a 5, 10 and 15-min period with no difference between
measurements (data not shown) and the values of linear regression and standard curve were:
P<0.001; r2=0.9931.
IL-6 and TNF analysis
Concentrations of IL-6 between controls and lame horses, showed a significant difference as
well (P<0.01). TNF concentrations between controls and lame horses showed even more
significant differences in terms of concentration (P<0.001), being higher on lame horses
with more severe changes in the affected joints (Grade III). A Pearson’s correlation analysis
was performed demonstrating a positive correlation between total protein and IGF-1
concentrations (r= 1), which was seen in grade I and II horses, whereas in grade III this
correlation is negatively or inversely proportional. In other words, the worse changes a joint
had (as seen in grade III horses), the less IGF-1 concentration in synovial fluid was observed.
Figure 2: A) IGF-1 determination between control (sound) and lame horses, demonstrating
a significant difference (P<0.05)*. B) Concentrations of IL-6 between controls and lame
horses, showed a significant difference as well (P<0.01)**. C) TNF concentrations between
controls and lame horses showing significant differences in concentration (P<0.001)***.
A 30 * B 8
** C 8000 ***
TNF (pg/ml)
6 6000
IL - 6 (pg/ml)
IGF-1 (ng/ml)
20
4 4000
10
2 2000
0 0 0
s s s
s
s
se se
e es
se
se
rs rs
or or ho
or
or
H o
lh eh rl
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129
Western blot analysis
Horses with less radiographic changes demonstrated a higher IGF-1 concentration

concordantly with the ELISA results for IGF-1 (Grade I and II). Horses with more severe
radiographic changes and a chronic state of the pathologic condition (Grade III), were the
ones with the lowest IGF-1 concentrations in both ELISA and WB analysis. Interestingly,
with this analysis it was able to identify in all samples, two different bands, one of ~12 kDa
which was seen only in control (normal) horses with no joint pathology, and another of ~7.5
kDa seen in all lame horses (Figure 3).
Figure 3: Representative photograph of Western blot analysis for IGF-1, demonstrating a

difference in molecular weight between synovial fluid samples indicating the existence of
two different isoforms present in normal joints and during an inflammatory process
1: Protein marker (marking 10 kDa); 2: Samples from a control horse and a horse with OA; 3: Control horse;
4 & 5: Two different samples from horses with OA.
Discussion
Sport horses are exposed to excessive loads to their joints and soft tissue structures. The joint
that can udergo traumatic OA depends on the discipline in which the horse performs. There’s
evidence regarding interventions such as joint injections on acute phases of the disease that
can help modify its course and prevent further damage while the horse is still performing(12).
Impact loads due to exercise are responsible of damaging articular cartilage by first cracking
the surface, and depending on the force applied and the time it is being applied, the depth and
therfore the severity of the development of the disease (OA) are produced. Characterization
130
of mechanical consequences of impact injuries to articular cartilage has been proven to

develop damage, by continuously and directly stressing the joint structures(13).
When inflammation occurs, chondrocytes migrate to the lesion site in an attempt to

regenerate the defect by forming groups of cells or clusters with the ability to synthesize
ECM de novo. Since the cellular component (chondrocytes) of the articular cartilage is only
1-2 % of the whole tissue, they are unable to repair the damaged area, because their ability
to synthesize ECM is surpassed by the matrix metalloprotease (MMP) activity which
degrades the already damaged ECM aggravating the condition by increasing necrosis and
activating local inflammation by releasing intracellular components which act as damage
associated molecular patterns (DAMPs) and proinflammatory cytokines such as
prostaglandins (PGs), nitrous oxide (NO), interleukin-1 (IL-1), interleukin-6 (IL-6), tumoral
necrosis factor alpha (TNF) and substance P. Particularly, TNF inhibits IGF-1 expression
by increasing ECM catabolism, and blocking AKT pathway via activating JNK pathway. If
the cartilage defect reaches subchondral bone, the cartilage repairs forming a low-quality
articular cartilage called fibrocartilage(2,4,7).
Factors that contribute to the inflammation cascade other than citokines, include extracellular
vesicles, which play an important role on promoting joint inflammation and are also involved
on apoptosis and ECM degradation. These vesicles are exosomes, microvesicles and
apoptotic vesicles, which are all released to the articular cavity (into the synovial fluid), and
have intimate relation with cell-cell communication during the inflammatory process(7,8,14).
The aim of this study was to compare IGF-1 concentration in synovial fluid from sound
(control) horses and horses with different degrees of lameness and joint pathology (OA) in
the MCPJ. It was hypothesized that the more severe and chronic conditions of the joint, the
highest IGF-1 levels in synovial fluid would be found, because of the joint’s high demand
for repairing the defect was higher than in horses with mild changes. The rationale behind
the hypothesis was: to our knowledge, there is still no data available regarding IGF-1
concentrations and its correlation with a particular clinical condition in horses, so it was
conducted a pilot study, where a total of 13 synovial fluid samples were collected from
different joints of different horses. All of these horses were high performance show jumpers
with a positive flexion test from the sampled joints (no radiographic evaluation was
conducted on any of these horses). What was found, it was a significant increase of total
protein, with a positive correlation (Pearson’s correlation, P=0.0229) on IGF-1 levels in
synovial fluid when compared to control samples (synovial fluid obtained from sound
horses). This gave sufficient information to hypothesize that horses with more severe clinical
signs and more chronic joint pathology would have higher IGF-1 levels when compared to
control horses.
Interestingly, with the results obtained, this hypothesis was refuted. It was encountered that
the horses with more severe radiographic changes and thus, the more chronic inflammatory
131
conditions within the joint, were the ones that demonstrated a decrease on IGF-1
concentrations, very similar to what control horses had.
Similar results were seen on studies in which experimentally induced lesions on articular
cartilage in horses, make an acute peak of mRNA expression of igf-1, and at 4 weeks tend to
decrease. When IGF-1 decreases, TGF- predominates and it is responsible of new bone
formation and activation of quiescent lymphocytes to Th17(7).
Equine Insulin-like growth factor 1 (IGF-1) has been widely studied, there are several studies
where its importance in cell proliferation, growth and survival, repair and extracellular matrix
production is well documented, although there are not enough studies regarding the different
isoforms and their functionality(15). It is known that the mRNA undergoes post-transcriptional
modifications (alternate splicing) which generates different isoforms along with post-
translation modifications. IGF-1 propeptides are encoded by multiple alternately spliced
transcripts including C-terminal extension peptides called E-peptides, and N-terminal signal
peptides. When an immature protein has signal peptide, mature peptide and E peptide is
called pre-proIGF-1, and when the signal peptide is eliminated leaving only the mature
peptide and the E peptide, is called pro-IGF-1. These E-peptides control the bioavailabilty of
mature IGF-1, by binding to the ECM due to their highly positive charge, preventing its
systemic circulation and therefore, its local use. They also modulate mature IGF-1 re-entry
to the cell in a murine muscle cell-line(1).
In humans, three different IGF-1 isoforms have been identified (IGF-1Ea, IGF-1Eb and IGF-
1Ec, also known as mecano-growth factor or MGF), and have been proposed to have various
functions in muscle repair(16).
Nixon, et al(17) described the igf1 gene consisting of 5 exons with 4 intron sequences, which
undergo both post-transcriptional and post-traslational modifications, where the translated
proteins resulting from alternate splicing of exon 4 form a smaller propeptide (105
aminoacids) transcript named Pre-proIGF-1A consisting of signal peptide (encoded by exons
1 and 2), mature peptide (encoded by exons 2 and 3), and a C-terminal E-peptide encoded by
exons 3 and 5); and when exon 4 is not alternately spliced, a larger transcript is translated
forming Pre-proIGF1B (111 aminoacids)(17). To our knowledge, this was the last research
paper published regarding post-transcriptional and post-traslational modifications and
alternate splicing of IGF-1 mRNA in horses. It was conducted a bioinformatic analysis of
igf1 gene undergoing different types of alternate splicing, which according to Le, et al(18) are:
exon skipping, intron retention, mutually exclusive exons and alternative 5’ donor or 3’
acceptor sites. This analysis revealed that IGF-1 mRNA consisted of not 5, but 4 exons and
3 introns, which transcipts form 4 isoforms: variant 1 (exons 1-3), variant 2 (exons 2 and 3),
variant 3 (exons 1-3, a 93 pb intron retention, and exon 4) and variant 4 (exons 2-4)(18).
132
The Western blot analysis demonstrated the presence of at least two different functional
isoforms of IGF-1, where the one seen in all normal horses is heavier (~12 kDa) than the one
seen in all horses with different degrees of OA (7.5 kDa). Probably the lighter one is the
mature form of IGF-1, although aminoacid sequencing techniques must be carried out in
order to confirm this statement. With this result, can be presumed that the expression of these
two different functional isoforms depends on inflammation.
This could lead to a new line of research which can focus on determine by advanced
sequencing techniques the exact isoforms of IGF-1 and to target overexpression of the
isoform which is not present when there is an inflammatory process of the joint, and its role
on repairing cartilage defects.
Articular cartilage does not regenerate by itself, since is the only connective tissue in
mammals that does not have either blood and lymphatic vessels, or nerves(19). Therefore, it
is virtually impossible to regenerate after an injury, so it is repaired via substitution with
fibrous tissue, which generates a low quality fibrous cartilage called fibrocartilage. There
have been several treatments to improve cartilage regeneration, in humans, osteochondral
allograft transplantation has proven to be effective in function improvement and overall
repair with graft survivorship of up to 80 % of the patients who had undergone previous
surgical treatment: Microfracture, cartilage debridement, forage, abrasion chondroplasty,
osteochondral and periosteal grafts, cartilage flap reattachment, among others(7,17,20).
Local anestethics and steroids have been used widely by practitioners in the field, for
diagnostic and therapeutic reasons respectively. However, excessive use of these
components, have been proven to damage articular cartilage. Intraarticular injection using
local anesthetics and steroids have make a growing concern about inducing potential toxicity
to chondrocytes and synoviocytes. Sherman et al(21), conducted an interesting comparisson
of lidocaine, bupivacaine, betamethasone acetate, methylprednisolone acetate, and
triamcinolone acetonide in a canine model. They found that in vitro, 1 and 0.5 % lidocaine,
0.2 and 0.25 % bupivacaine, betamethasone acetate and methylprednisolone acetate were
severely chondrotoxic and synoviotoxic when compared with 0.625 % bupivacaine and
triamcinolone(21).
For this reason, treatmentwise, the main goal is to have alternatives that could be used in the
field by clinicians, that can provide an alternative other than steroids that can also enhance
cartilage repair without the need of getting the horse under general anesthesia and still have
133
an effect that lead to horses having a long lasting sport career. This paper provides important
information that can serve as a base for further research regarding IGF-1 isoforms and their
role in cartilage repair.
Acknowledgements
The authors would like to thank all CEAR, SEDENA personnel, QFB Alberto Enrique
Fernández Molina and MVZ Jorge Rodríguez Lezama. Fernando García Lacy is a doctoral
student at Doctorado en Ciencias de la Producción y Salud Animal de la Facultad de
Medicina Veterinaria y Zootecnia de la Universidad Nacional Autónoma de México and
received a scholarship from CONACYT. The work reported in this manuscript is part of his
doctoral dissertation.
Conflicts of interest
The authors declare that there are no conflicts of interest.
Literature cited:
1. Pfeffer LA, Brisson BK, Hanquin L, Barton ER. The Insulin-like Growth Factor (IGF-1)
E-peptides modulate cell entry to mature IGF-1 protein. Mol Biol Cel 2009;20:3810-
3817.
2. Choukair D, Hügel U, Sander A, Uhlmann L, Tönshoff B. Inhibition of IGF-1-related

intracellular signaling pathways by proinflammatory citokines in growth plate
chondrocytes. Ped Res 2014;76(3):245-251.
3. Liu Q, Guan JZ, Sun Y, Le Z, Zhang P, Yu D, et al. Insulin-like growth factor 1 receptor-
mediated cell survival in hypoxia depends on the promotion of autophagy via supression
of the PI3K/Akt/mTOR signaling pathway. Mol Med Rep 2017;15:2136-2142.
4. Tonkin J, Temmerman L, Sampson RD, Gallego-Colon E, Barberi L, Bilbao D, et al.

Monocyte/Macrophage-derived IGF-1 orchestrates murine skeletal muscle regeneration
and modulates autocrine polarization. Am Soc Gene Cell Ther 2015;23(7):1189-1200.
5. García-Segura LM, Arévalo MA, Azcoitia I. Interactions of estradiol and insulin-like

growth factor-I signaling in the nervous system: New advances. Prog Brain Res
2010;181:251-272.
134
6- García-Lacy F, Gutiérrez-Olvera L, Bernad M, Fortier L, Trigo-Tavera FJ, Gómez-

Chavarín M, et al. Pharmacokinetic analysis of intraarticular injection of insulin and its
effect on IGF-1 expression in synovial fluid of healthy horses. Rev Mex Cienc Pecu
2022;13(2):391-407.
7. Fortier LA, Balkman CE, Sandell LJ, Ratcliffe A, Nixon A. Insulin-like growth factor-1
gene expression patterns during spontaneous repair of acute articular cartilage injury. J
Orth Res 2001;19:720-728.
8. Frisbie D. Future directions in treatment of joint disease in horses. Vet Clin Equine
2005;21:713-724.
9. Aguilar IN, Trippel SB, Shuiliang S, Bonassar LJ. Comparison of efficacy of endogenous
and exogenous IGF-I in stimulating matrix production and mature chondrocytes.
Cartilage 2015;6(4):264-272.
10. Moyer W, Schumacher J, Schumacher J. A guide to equine joint injection and regional
anesthesia. Yardley, PA: Veterinary Learning Systems, USA. 2007.
11. Verwilghen D, Busoni V, Gangl M, Franck T, Lejeune JP, Vanderheyden L, et al.

Relationship between biochemical markers and radiographic scores in the evaluation of
the osteoarticular status of Warmblood stallions. Res Vet Sci 2009;87(2):319-328.
12. Chu CR, Beynnon BD, Buckwalter JA, Garrett WE Jr, Katz JN, Rodeo SA. Closing the
gap between benck and nedside research for early arthritis therapies (EARTH): report
from the AOOSSM/NIH U-13 Post-joint injury osteoarthritis conference II. Am J Sports
Med 2011;39(7):1569-1578.
13. Bonnevie ED, Delco ML, Fortier LA, Alexander PG, Tuan RS, Bonassar LJ.
Characterization of tissue response to impact loads delivered using a hand-held
instrument for studying articular cartilage injury. Cartilage 2015;6(4):226-232.
14. Buzas EI, Gyrgöry B, Nagy G, Falus A, Gay S. Emerging role of extracellular vesicles
in inflammatory diseases. Nat Rev Reumatol 2014;10(6):3563-3564.
15. www.uniprot.com. .
https://www.afternic.com/forsale/uniprot.com?utm_source=TDFS&utm_medium=sn_
affiliate_click&utm_campaign=TDFS_Affiliate_namefind_direct8&traffic_type=CL3
&traffic_id=Namefind.
16. Phillipou A, Papageorgiou E, Bogdanis G, Halapas A, Sourla A, Maridaki M, et al.

Expression fo IGF-1 isoforms after exercise-induced muscle damage in humans:
Characterization of the MGF E peptide actions in vitro. In vivo 2009;23:567-576.
135
17. Nixon AJ, Brower-Toland BD, Sandell LJ. Primary nucleotide structure of predominant
and alternate splice forms of equine insulin-like growth factor I and their gene
expression patterns in tissues. AJVR 1999;60(10):1234-1241.
18. Le, KQ, Prabhakar B, Hong, WJ. et al. Alternative splicing as a biomarker and potential
target for drug discovery. Acta Pharmacol Sin 2015;36:1212–1218.
19. Geneser F. Tejido esquelético. Cap 12, Geneser F. Histología. 3rd ed. Madrid, España:
Editorial Médica Panamericana; 2001.
20. Briggs DT, Sadr KN, Pulido PA, Bugbee WD. The use of osteochondral allograft
transplantation for primary treatment of cartilage lesions in the knee. Cartilage
2015;6(4):203-207.
21. Sherman SL, Khazai RS, James CH, Stoker AM, Flood DL, Cook JL. In vitro toxicity of
local anesthetics and corticosteroids on chondrocyte and synoviocyte viability and
metabolism. Catilage 2015;6(4):233-240.
136
Article
Use of Wharton's jelly-derived mesenchymal stromal cells for
the treatment of equine recurrent uveitis: a pilot study
María Masri-Daba a*
Montserrat Erandi Camacho-Flores b
Ninnet Gómez-Romero c,d
Francisco Javier Basurto Alcántara c
a
Departamento de Medicina, Cirugía y Zootecnia para Équidos. Ciudad de México, México.
b
Posgrado en Ciencias de la Producción y de la Salud Animal. Ciudad de México, México.
c
Departamento de Microbiología e Inmunología. Ciudad de México, México.
d
Comisión México-Estados Unidos para la prevención de fiebre Aftosa y otras enfermedades
exóticas de los animales. Ciudad de México, México.
*Corresponding author: masri@unam.mx
Abstract:
Equine recurrent uveitis (ERU) is a disease that affects 2 to 25 % of equines worldwide, 56%
of which go blind; therefore, it is considered the most common cause of blindness in horses.
ERU is a spontaneous immune-mediated condition characterized by recurrent intraocular
inflammatory events. Currently, there is no treatment for horses with this disease.
Mesenchymal stromal cells (MSCs) derived from various tissues, such as Wharton's jelly
(WJ), have demonstrated their ability to modulate the immune response by negatively
regulating the inflammatory process. The objective of this pilot study was to evaluate the
137
effect of using MSCs derived from WJ as a treatment for ERU. The WJ was obtained and
processed according to previously described methodologies for obtaining EMF. The horses
involved in this study received a dose of 5x106 MSCs in the subpalpebral area. The research
evaluated the concentration of interleukins (IL: IL-1, IL-2, IL-10, IFN-, and TNF) in tear
samples obtained before treatment inoculation, 30 min after the inoculation, and 7 days post
inoculation. No significant changes in IL concentration were observed suggesting a decrease
in pro-inflammatory ILs. However, horses with ERU treated with MSCs exhibited a positive
response to therapy, evidenced by a decrease in signs of ERU. The results obtained suggest
that treatment of ERU with WJ-derived MSCs is a safe alternative with promising results.
Keywords: Wharton's Jelly, Mesenchymal Stromal Cells, Equine recurrent uveitis,

Therapeutics.
Introduction
Mesenchymal stromal cells (MSCs) are characterized by their ability to differentiate into
various cell lineages; therefore, they may be involved in the regeneration of damaged tissues.
Another important characteristic of MSCs is that they have anti-inflammatory properties and
regulate the immune response by producing a set of immunomodulatory factors such as
interleukin 6 (IL-6), prostaglandin E2 (PEG2), and nitric oxide(1,2). The secretion of these
factors inhibits the proliferation of activated T lymphocytes, reduces the secretion of
proinflammatory cytokines, and increases the population of regulatory T lymphocytes
(Tregs)(2,3,4).
In equines, MSCs can be obtained from bone marrow, adipose tissue, amniotic membrane,
umbilical cord blood, and foal umbilical cord tissue known as Wharton's jelly (WJ)(5). WJ is
the primitive mucous connective tissue of the umbilical cord that lies between the amniotic
epithelium and the umbilical vessels; it consists of a hyaluronic acid and chondroitin sulfate-
based substance with a high concentration of MSCs(6). Due to their molecular characteristics,
such as the absence of the expression of histocompatibility molecules I and II, these cells
offer a unique advantage for autologous and allogeneic application(7). In particular, WJ is
considered an important source of MSCs, both in humans and in other species, with great
potential in the therapeutics of various inflammatory and immune-mediated conditions, such
as equine recurrent uveitis (ERU).
138
ERU, also known as moon blindness, is a disease recognized as the leading cause of blindness
in horses. It has been reported to be prevalent in 2 to 25 % of equines in the USA(8). It is
characterized by recurrent episodes of intraocular inflammation or low levels of persistent
inflammation, predominantly in the iris, ciliary body, and choroid(9). This disease has an acute
presentation that includes signs such as miosis, decreased intraocular pressure, and iris
adhesions, while its chronic presentation results in the development of cataracts, glaucoma,
and blindness.
The triggering or etiological factors of ERU remain unknown; however, it has been reported
that genetic components as well as Leptospira interrogans infections may be involved in the
development of this condition(8,10). Subsequently, the signs that occur in ERU are the result
of T lymphocyte activation, specifically Th1 and Th17, causing destruction of the uveal tract
of the eye(11,12,13). Currently, there is no cure for ERU; therefore, treatment focuses on
decreasing inflammation with the goal of preserving vision, limiting the recurrence of
episodes, and reducing pain with anti-inflammatory and mydriatic drugs(14).
It has been shown that the use of MSCs in immune diseases of dogs, cats, and horses can
induce the switch of proinflammatory T lymphocyte subsets to regulatory T
lymphocytes(15,16,17). Therefore, the use of WJ-derived MSCs in the treatment of ERU is a
promising alternative. This article describes the procurement, culture, characterization and
differentiation of MSCs derived from umbilical cord WJ of foals at foaling and their
preliminary use in horses with ERU.
MSC procurement and cultivation
WJ MSCs were collected from 26 foals of full English blood mares (EBM) aged 5 to 20 yr.
The umbilical cords were collected was performed following the delivery of the placenta.
They were handled and processed under sterile conditions at the Tissue Engineering, Cell
Therapy, and Regenerative Medicine Unit of the National Rehabilitation Institute of Mexico.
In short, a 15 to 20 cm fragment of each cord, still wrapped in amnion, was taken, followed
by two washes with iodine solution interspersed with washes of sterile physiological saline
solution (PSS). The sections were then cut to approximately 5 cm and stored at 4 ºC in
phosphate buffered solution (PBS) with penicillin (10,000 U/ml), amphotericin B (25 µg/ml)
and streptomycin (10,000 µg/ml) for processing in the laboratory. Subsequently, the WG was
separated from the umbilical cord tissue and deposited in a Petri dish with PBS where cuts
were made to facilitate enzymatic digestion. The latter was carried out in 10 ml of Dulbecco's
modified Eagle's medium (DMEM) solution with collagenase (0.8 mg/ml) in incubation at
139
37 ºC for 1 hr. After the incubation time had elapsed, the cells were centrifuged at 700 xg for
7 min at 37 ºC, the supernatant was decanted and the cells were resuspended in DMEM
supplemented with 10 % fetal bovine serum (FBS) and 1% penicillin, amphotericin B and
streptomycin (10,000 U/ml; 25 µg/ml; 10,000 µg/ml). The primary culture was maintained
in 25 cm2 cell culture bottles incubated with 5 % CO2 at 37 ºC, and three passages were
performed once 80 % confluence was reached.
EMF characterization
Cells obtained before the third passage were subjected to surface phenotype evaluation in
order to corroborate their mesenchymal profile by flow cytometry. It was used 2.5 x 105 cells
contained in round-bottom polystyrene tubes resuspended in 1ml PBS. For cell labeling, cells
were incubated for 1h with specific primary antibodies for detection of CD90 (FITC Mouse
Anti-Human CD90 Clone 5E10 555595), CD73 (APC Mouse Anti-Human CD73 Clone
AD2560847), CD105 (PE Mouse Anti-Human CD105 Clone 266 560839), CD45 (FITC
Mouse Anti-Human CD45 Clone G44-26 555478), CD34 (PE Mouse Anti-Human CD166
Clone 34 559263), CD14 (PerCP Mouse Anti-Human CD14 Clone MφP9 340585), and
MHC-II (APC Mouse Anti-Human HLA-DR Clone G46-6 559866). Cells were then washed
twice and analyzed using the FACS-Calibur Becton and Dickinson flow cytometer.
EMF differentiation
WJ MSCs were grown in 12-well plates at a density of 5x104 using DMEM supplemented
with 5% SFB and 1% antibiotic, under the same culture conditions as previously mentioned.
After 48 h, the culture medium was replaced by adipogenic, osteoblastic, and chondrogenic
medium as described below.
For the adipose lineage induction, after 48 h of incubation the cell culture medium was
replaced by differentiation medium formulated with DMEM supplemented with 0.5% SFB,
dexamethasone (1 mM), 3-isobutylmethylxanthine (0.5 mM), insulin (10 %), indomethacin
(50 mM), as well as penicillin (10,000 U/ml), amphotericin B (25 µg/ml), and streptomycin
(10,000 µg/ml). Medium changes were performed every third day for 21 d. Finally, cell
differentiation was evaluated using Nile red staining.
MSC differentiation into osteoblastic lineage was carried out using DMEM culture medium
supplemented with SFB 1%, and with dexamethasone (100 nM), ascorbic acid (0.05 mM),
10 mM/L -glycerophosphate and BMP-7 (10 ng/ml). Likewise, the medium was changed
every third day for 21 d, and osteogenic differentiation was evaluated by Von Kossa staining.
140
The chondrogenic lineage was obtained by using DMEM culture medium added with insulin
(10 %), ascorbic acid (1 mg/ml), transforming β growth factor (TGF-β) (10 ng/ml), sodium
pyruvate (1 %), and bone morphogenic protein 2 (BMP-2) (100 ng/ml). The evaluation of
the differentiation was carried out with Alcian blue staining.
Horses
A total of 15 EBM horses aged 2 to 7 yr were included for this study. Twelve clinically
healthy horses were used as a control group. As part of the experimental group, 3 horses with
at least one episode of ERU with characteristic signs such as miosis, iris hyperpigmentation,
blepharospasm, corneal edema, aqueous flame, hypopyon, hyphema, epiphora, photophobia,
fibrin in the anterior chamber, conjunctival hyperemia, and scleral injection were considered.
The horses used in this study underwent a strict general physical and complete
ophthalmological examination consisting of threat reflex assessment, pupillary response,
consensual reflex, Schirmer's test, corneal sensibility, flourescein stain, Jones test, rose
Bengal stain and fundus observation.
Tear sample collection and EMF inoculation
Once the control and experimental groups were formed, the horses were sedated using
intravenous xylazine at a dose of 0.3 a 0.5 mg/kg. Subsequently, 100 µl of tears were
collected using a sterile capillary tube without additives; the sample was placed in sterile
vials and stored at -80 ºC until use.
Using an insulin syringe, the inoculum, PBS and 5x106 MSCs were collected for six horses
of the control group, as well as for the three horses with ERU of the experimental group; both
in a volume of up to 200 l. This part of the procedure was carried out under sterile
conditions. Prior to inoculum application, the area was aseptically cleaned with alcohol,
avoiding direct contact with the eye. A 25 G needle was inserted into the subpalpebral area
and the syringe was connected to inoculate the contents. The second and third tear samples
were taken 30 min and one week post inoculation, respectively.
Evaluation of interleukins in tear samples
The evaluation of interleukins IL-1, IL-2, IL-10, IFN- and TNF in tear samples obtained
before and after the treatment was performed by multiplex enzyme-linked immunoassay
(ELISA), in order to determine whether there are changes in the pattern of interleukins
detected.
141
For the multiplex ELISA test, the Equine Cytokine/Chemokine Magnetic Bead Panel
Milliplex MAP Kit was used, following the manufacturer's instructions on the Luminex
equipment (Bio-Plex 200, Bio-Rad Laboratories, EU). Briefly, 200 l of wash buffer was
added to each of the 96 wells of the plate, the plate was covered and incubated in agitation
for 10 min at 20 ºC; the contents of the wells were then discarded, removing the excess by
turning the plate over and tapping it on a bed of absorbent towels. Subsequently, 25 l of the
standard and cores were added to the corresponding wells. 25 l of assay buffer were added
to the sample wells. Subsequently, 25 l of matrix solution were added to all wells, and 25
l of tear sample were added to the corresponding wells. Finally, 25 l of the bead mixture
were added to each of the wells of the plate, which was incubated for 18 h under agitation at
4 ºC covered with aluminum foil.
After the incubation time had elapsed, the entire contents of the plate were discarded, and the
plate was washed three times. Subsequently, 25 l of interleukin detection antibodies were
added to all wells, the plate was sealed and incubated in agitation at room temperature for 1
h. Then 25 l of streptavidin-phycoerythrin were added to all wells, the plate was sealed and
incubated in agitation at room temperature for 30 min. Once this step was completed, the
contents were again decanted, and three washes were performed. To each of the wells, 150
l of "Seath Fluid" were added and agitated for 5 min at room temperature. Finally, the
plaque was read to estimate the concentration of interleukins detected in the tear samples
corresponding to the three sampling times.
For the analysis of the results of the interleukin concentration, nonparametric statistics were
used according to the results obtained from the homogeneity of variance (analysis of
residuals) and normal distribution (Shapiro-Wilk test) tests included in the Prism 8.0
statistical program (GraphPad, Software Inc., EEUU).
In particular, the Mann Whitney U test was used to compare the concentration of cytokines
between the media used (PBS and MSC) in the control group. Subsequently, this test was
repeated to compare the concentration of cytokines between the control and experimental
groups. Additionally, the Kruskal Wallis test followed by Dunn's multiple comparison test
was performed to determine possible changes in cytokine concentration at the different times
evaluated (baseline, after 30 min, and after 7 d) in both groups. In all cases, a value of P<0.05
was considered significant.
142
Results
MSC procurement and cultivation
Primary cultures of MSCs obtained from WJ initially showed a rounded morphology and
clustered in clusters of up to 100 m. Once attached, after 120 h they acquired fibroblastoid
morphology (Figure 1).
Figure 1: Primary culture of mesenchymal stromal cells obtained from Wharton's jelly
a) Rounded morphology (white arrow) of MSCs and formation of cell clusters (black arrow); b) adherence
and fibroblastoid morphology of MSCs (black arrow); c) 80 % confluence of the EMF monolayer with
characteristic morphology (black arrow); c) 80 % confluence of the EMF monolayer with characteristic
morphology.
Characterization of EMF
The identification of positive MSC surface markers was carried out by flow cytometry.
CD90, CD73, and CD105 markers should be expressed, while negative markers are CD14,
CD34, CD45, and MHC-II. Figure 2 shows the phenotype characterization of MSCs obtained
from WJ. The lack of expression of CD45, CD34, and CD14 markers is shown; so is the
expression of the positive markers CD73 and CD90. On the other hand, the MScs samples
evaluated showed a reduction in the expression of the CD105 marker.
143
Figure 2: Phenotype of cultured MSCs
Flow cytometry analysis of cultured WJ-derived MSC protein expression labeled with anti CD45 (green),
CD34 (turquoise), CD14 (pink), CD73 (red), and CD90 (blue) antibodies. The histogram in purple indicates
the intensity of the fluorescence of MSCs labeled with the control antibody. Open histograms indicate
positive reactivity with the indicated antibody.
MSC differentiation
MSCs were treated with three formulations of culture medium to evaluate their
differentiation into adipose, osteoblastic, and chondrogenic cell lineages. Differentiation was
assessed by cell staining (Figure 3); shown are representative images of a) adipocytes stained
with Nile red to detect fatty acid vacuoles within cells; b) osteoblasts stained with Von Kossa
stain to identify calcium deposits; c) chondrocytes detected with Alcian blue stain,
mucopolysaccharide staining is visualized in the extracellular matrix of this tissue.
144
Figure 3: MSC differentiation to a) adipose, b) osteoblastic, and c) chondrogenic lineage
Evaluation of horses with ERU
Horse 1. Warmblood, 12-yr-old castrated male Retinto. He presented signs of uveitis and
started treatment with betamethasone, cyclosporine A, and artificial tears. Subsequently, the
MSC treatment protocol was started, and only the left eye was injected; throughout the week,
no clinical changes were observed.
Horse 2. Friesian, 15-yr-old whole male. He exhibited acute uveitis in the left eye, and had
been previously treated with prednisolone and cyclosporine A. He showed signs such as pain,
edema, vascularization, epiphora, and blepharospasm. (Figure 4). One week after the MSC
treatment, he exhibited improvement from the clinical point of view; all the previous signs
decreased slightly, and he showed a better mood.
Figure 4: Horse 2
a) shows blepharospasm and epiphora, b) shows edema and vascularization; palpebral margins are green due
to previous fluorescein staining. c) shows a decrease in blepharospasm of the left eye d) shows decreased
edema.
145
Horse 3. Apaloosa, entire male, 21 yr old. He exhibited an acute condition, but received no
treatment. Both eyes had miosis, epiphora, corneal edema, and neovascularization, scleral
and conjunctival injection, both eyes were treated with MSCs and maintained for one week
with a topical mydriatic. At 7 d both eyes were mydriatic, without pain, minor epiphora,
edema, and conjunctival and scleral injection. Three days after the second visit, he started
medical treatment, but he exhibited even less edema and was much more comfortable (Figure
5).
Figure 5: Horse 3
a) and b) The pictures show acute signs of uveitis of both eyes, with edema, neovascularization, and
conjunctival hyperemia. c) and d) show decreased edema and hyperemia.
Evaluation of interleukins in tear simples
Comparison between the media used (PBS and MSC) for the control group showed no
differences in any of the interleukins measured. However, when comparing the concentration
of these at different times (basal, after 30 min and after 7 d), it was found that the
concentration of IL-1⍺ with the use of PBS as vehicle, showed statistically significant
differences between the basal measurement and the one performed 7 d later (Table 1).
Since no statistically significant differences were found between the two media applied to
the control group, the measurements were considered within a single group and contrasted
with the data obtained in the experimental group. In this regard, no statistically significant
146
differences were found in the concentration of ILs at any of the three times when they were
used when comparing the control group with the treated experimental group.
Discussion
In the field of regenerative medicine, interest in MSC research has increased over the last
decade. These cells, also known as mesenchymal progenitor cells, have the ability to promote
tissue regeneration, modulate the immune response, and regulate the inflammatory
process(18). They are also considered to be cell populations with the ability to self-renew and
differentiate into various types of connective tissue cells(19). Consequently, they have the
potential to act at sites of inflammation by synthesizing interleukins that participate in the
modulation of this process(20).
Specifically, previous studies have described the immunomodulatory effect of MSCs in

horses; additionally, it has been shown that equine-derived MSCs, compared to those of other
species, have a greater capacity to inhibit the proliferation of activated T lymphocytes and to
decrease the production of IFN- and TNF(1,3). Likewise, it has been reported that the use
of equine MSCs as a treatment induces lymphocyte apoptosis and reduces IL-2 receptor
(CD25) expression in lymphocytes T CD4+.
In equine medicine, they are currently used, above all, to treat diseases of the locomotor
system, skin wounds, equine metabolic syndrome, asthma, laminitis, neurological, and
ophthalmological problems(21). Within the latter, ERU is considered the leading cause of
blindness in equines and is described as an autoimmune inflammatory disease with
characteristics similar to human uveitis(22). Horses suffering from ERU are characterized by
an inflammatory phenotype of Th1 lymphocytes (CD4+ IFN-); therefore, the described data
suggest that the use of MScs is a suitable alternative in the treatment of ERU, as well as other
immune-mediated diseases(23). Together, ocular therapeutic benefits have been documented
in equine and other species(24,25,26).
In particular, it has been proven that the application of MSCs in rabbits with corneal surface
damage accelerates the corneal healing process, reduces oxidative stress, and suppresses the
production of proinflammatory interleukins, resulting in a decrease in corneal opacity and
neovascularization of the affected area(27). In rats, the use of MSCs in the treatment of corneal
burns and reconstruction of the corneal surface has yielded positive results(25).
On the other hand, its use in horses as a treatment for immune-mediated keratitis has yielded
promising results; it was observed that 3 out of 4 horses submitted to this therapy had positive
results evidenced by the decrease in opacity, irregularity, and vascularization on the corneal
surface; in addition to maintaining the corneal disease stable for up to one year after MSC
147
treatment. Therefore, it is considered a new alternative immunomodulatory therapy for this

condition(24). Likewise, in another study on equine immune-mediated keratitis, a satisfactory
response to MSC inoculation in the ophthalmic artery and subconjunctival topical application
three times a day for three weeks were reported(28).
In the present study, was evaluated parameters that would indicate the benefits of
subpalpebral administration of EMF in the treatment of ERU. Once the treatment with WJ-
derived MSC was applied in horses with ERU, tear samples were analyzed to evaluate the
pattern of interleukins present in the sample before and after treatment (30 min and 7 d after).
However, no significant changes in interleukin concentrations were observed at the different
times evaluated where a decrease in proinflammatory interleukins and an increase in anti-
inflammatory interleukins were expected, as that horses with ERU are said to maintain a high
concentration of IL-10, IL-1, IFN-, IL-6, and IL-17 in tears(23).
These results may be associated with the route of administration, where MSC inoculation via
the subpalpebral route shows less efficacy in resolving the condition, probably because it
reaches the site of action (eye) in an insufficient proportion, in an inadequate dosage and
frequency of treatment application, and at an inadequate stage of the disease, therefore having
a low capacity to affect the inflammatory process at that level, since most reports indicate a
significant improvement when the treatment is applied at the acute phase of the disease(23).
On the other hand, the absence of proinflammatory IL detection in tear samples may be due
to the peak concentration occurring at different periods of the disease than those evaluated in
this study.
These findings are useful when choosing the route of administration for MSC treatment.
Although success stories of the use of the subconjunctival route have been described, there
is a need to evaluate and compare additional routes of administration that may provide better
results for short- and long-term efficacy. In contrast, intravenous administration of MSCs has
been reported to be completely safe; however, it is not known whether or not it yields better
results(29). Therefore, further studies are needed to establish the necessary conditions for the
treatment of ERU.
Subsequently, in the three patients with ERU who were part of the present pilot study, the
effect of treatment with subpalpebral MSCs was evaluated. No clinical changes indicating
improvement were observed in horse 1. In contrast, horse 2 showed improvement of the
clinical signs presented (pain, edema, vascularization, epiphora, and blepharospasm),
including improvement in mood 7 d after post-treatment. In the case of horse 3, there was
improvement in some clinical signs such as less epiphora, reduction of edema and
conjunctival and scleral injection, as well as improvement in mood.
148
Two of the three treated horses showed improvement and decrease in clinical signs of ERU
7 days post treatment. The positive results obtained when applying equine-derived WJ MSCs
highlight the importance of further studies to establish a uniform treatment and the
development of an efficient MSC application protocol to achieve better results. For example,
treatment options promoting a long-lasting response and treatment efficacy are enhanced by
a specific number of, or multiple applications with a standardized MSC dose, as well as by
co-application with local immunosuppressive therapy(30).
This pilot study describes the experimental use of WJ-derived MSCs for the treatment of
ERU. Although there were certain limitations, e.g. in the number of animals analyzed that
might allow us to reach firm conclusions, the obtainment of positive results in the respective
clinical presentations without generating adverse effects reaffirms the use of MSCs as a
viable alternative to the treatment of ERU. Despite its promising results, controlled studies
of MSC treatment must be carried out in order to demonstrate and confirm the benefits of the
MSC treatment for ERU.
Financing
Funding for the study was granted by Project No. IN228919 of the Support Program for
Research and Technological Innovation Projects (Programa de Apoyo a Proyectos de
Investigación e Innovación tecnológica, PAPIIT), “Use of allogenous stem cells for the
treatment of equine recurrent uveítis” ("Uso de células troncales alógenas para el
tratamiento de uveítis recurrente equina") of the Faculty of Veterinary Medicine and Animal
Husbandry-Universidad Nacional Autónoma de México (Facultad de Medicina Veterinaria
y Zootecnia-Universidad Nacional Autónoma de México).
The authors declare that they have no conflict of interest in regard to the publication of this
article.
Literature cited:
1. Carrade DD, Lame MW, Kent MS, Clark KC, Walker NJ, Borjesson DL. Comparative
analysis of the immunomodulatory properties of equine adult-derived mesenchymal
stem cells. Cell Med 2012;4(1):1-11. doi: 10.3727/215517912X647217.
149
2. Martínez‐Montiel MDP, Gómez‐Gómez GJ, Flores AI. Therapy with stem cells in
inflammatory bowel disease. World J Gastroenterol 2014;20:1211‐1227.
3. Carrade Holt DD, Wood JA, Granick JL, Walker NJ, Clark KC, Borjesson DL. Equine
mesenchymal stem cells inhibit T cell proliferation through different mechanisms
depending on tissue source. Stem Cells Dev 2014;23:1258‐1265.
4. Le Blanc K, Davies LC. Mesenchymal stromal cells and the innate immune response.
Immunol Lett 2015;168:140‐146.
5. Iacono E, Rossi B, Merlo B. Stem cells from foetal adnexa and fluid in domestic
animals: an update on their features and clinical application. Reprod Dom Anim 2015;
50:353–64. doi: 10.1111/rda.12499.
6. Weiss ML, Troyer DL. Stem cells in the umbilical cord. Stem Cell Rev 2016;2:155–
162.
7. Iacono E, Pascucci L, Rossi B, Bazzucchi C, Lanci A, Ceccoli M, et al. Ultrastructural

characteristics and immune profile of equine MSCs from fetal adnexa. Reproduction
2017;154:509–519. doi: 10.1530/REP-17–0032.
8. Gilger BC, Hollingsworth SR. Diseases of the uvea, uveitis, and recurrent uveitis. In:
Gilger BC, editor. Equine ophthalmology. Hoboken, NJ: John Wiley & Sons, Inc. 2016:
369–415. doi: 10.1002/9781119047919.ch8.
9. Gilger BC, Deeg C. Chapter 8‐Equine recurrent uveitis. Gilger BC editor. Equine
Ophthalmology, 2nd ed. Saint Louis, MO: W.B. Saunders; 2011:317‐349.
10. Sauvage AC, Monclin SJ, Elansary M, Hansen P, Grauwels MF. Detection of intraocular
Leptospira spp. by real-time polymerase chain reaction in horses with recurrent uveitis
in Belgium. Equine Vet J 2019;51:299–303.
11. Deeg CA. Ocular immunology in equine recurrent uveitis. Vet Ophthalmol
2008;11(Suppl 1):61‐65.
12. Deeg CA, Ehrenhofer M, Thurau SR, Reese S, Wildner G, Kaspers B.

Immunopathology of recurrent uveitis in spontaneously diseased horses. Exp Eye Res
2002;75:127‐133.
13. Gilger BC, Malok E, Cutter KV, Stewart T, Horohov DW, Allen JB. Characterization
of T‐lymphocytes in the anterior uvea of eyes with chronic equine recurrent uveitis. Vet
Immunol Immunopathol 1999;71:17‐28.
14. Gilger BC, Michau TM. Equine recurrent uveitis: new methods of management. Vet
Clin North Am Equine Pract 2004;20:417–27. doi: 10.1016/j.cveq.2004.04.010.
150
15. Kol A, Walker NJ, Nordstrom M, Borjesson DL. Th17 pathway as a target for
multipotent stromal cell therapy in dogs: Implications for translational research. PLoS
One 2016;11:e0148568.
16. Arzi B, Mills-Ko E, Verstraete FJM, Kol A, Walker NJ, Badgley MR, et al. Therapeutic
efficacy of fresh, autologous mesenchymal stem cells for severe refractory
gingivostomatitis in cats. Stem Cells Transl Med 2016;5:75–86.
17. Holt DDC, Wood JA, Granick JL, Walker NJ, Clark KC, Borjesson DL. Equine
mesenchymal stem cells inhibit T cell proliferation through different mechanisms
depending on tissue source. Stem Cells Dev 2014;23:1258–1265.
18. Stewart MC, Stewart AA. Mesenchymal stem cells: characteristics, sources, and
mechanisms of action. Vet Clin North Am Equine Pract 2011;27(2):243-61. doi:
10.1016/j.cveq.2011.06.004.
19. Madrigal M, Rao KS, Riordan NH. A review of therapeutic effects of mesenchymal
stem cell secretions and induction of secretory modification by different culture
methods. J Transl Med 2014;12:260.
20. Meirelles LS, Fontes AM, Covas DT, Caplan AI. Mechanisms involved in the
therapeutic properties of mesenchymal stem cells. Cytokine Growth Factor Rev
2009;20(5-6):419-27. doi: 10.1016/j.cytogfr.2009.10.002.
21. Cequier A, Sanz C, Rodellar C, Barrachina L. The usefulness of mesenchymal stem cells
beyond the musculoskeletal system in horses. Animals (Basel). 2021;11(4):931.
doi:10.3390/ani11040931.
22. Malalana F, Stylianides A, McGowan C. Equine recurrent uveitis: Human and equine
perspectives. Vet J 2015;206(1):222-9. doi: 10.1016/j.tvjl.2015.06.017.
23. Saldinger LK, Nelson SG, Bellone RR, Lassaline M, Mack M, Walker NJ, Borjesson
DL. Horses with equine recurrent uveitis have an activated CD4+ T-cell phenotype that
can be modulated by mesenchymal stem cells in vitro. Vet Ophthalmol 2020;23(1):160-
170. doi: 10.1111/vop.12704.
24. Davis AB, Schnabel LV, Gilger BC. Subconjunctival bone marrow-derived
mesenchymal stem cell therapy as a novel treatment alternative for equine immune-
mediated keratitis: A case series. Vet Ophthalmol 2019;22(5):674-682. doi:
10.1111/vop.12641.
25. Jiang TS, Cai L, Ji WY, Hui YN, Wang YS, Hu D, Zhu J. Reconstruction of the corneal
epithelium with induced marrow mesenchymal stem cells in rats. Mol Vis
2010;14;16:1304-16.
151
26. Dodi PL. Immune-mediated keratoconjunctivitis sicca in dogs: current perspectives on

management. Vet Med (Auckl) 2015;30;6:341-347. doi: 10.2147/VMRR.S66705.
27. Cejkova J, Trosan P, Cejka C, Lencova A, Zajicova A, Javorkova E, Kubinova S,

Sykova E, Holan V. Suppression of alkali-induced oxidative injury in the cornea by
mesenchymal stem cells growing on nanofiber scaffolds and transferred onto the
damaged corneal surface. Exp Eye Res 2013;116:312-23. doi:
10.1016/j.exer.2013.10.002.
28. Marfe G, Massaro-Giordano M, Ranalli M, Cozzoli E, Di Stefano C, Malafoglia V,

Polettini M, Gambacurta A. Blood derived stem cells: an ameliorative therapy in
veterinary ophthalmology. J Cell Physiol 2012;227(3):1250-1256. doi:
10.1002/jcp.22953.
29. Kol A, Wood JA, Carrade HDD, Gillette JA, Bohannon-Worsley LK, Puchalski SM, et
al. Multiple intravenous injections of allogeneic equine mesenchymal stem cells do not
induce a systemic inflammatory response but do alter lymphocyte subsets in healthy
horses. Stem Cell Res Ther 2015;6(1):73. doi: 10.1186/s13287-015-0050-0.
30. Zhang J, Huang X, Wang H, Liu X, Zhang T, Wang Y, Hu D. The challenges and
promises of allogeneic mesenchymal stem cells for use as a cell-based therapy. Stem
Cell Res Ther 2015;6:234. doi: 10.1186/s13287-015-0240-9.
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Table 1: Measurement of interleukins by median and range technique (<Quantification level)

Control group(n=12) Experimental group(n=3)
Basal 30 minutes 7 days Basal 30 minutes 7 days
Median Range Median Range Median Range Median Range Median Range Median Range
IL-1α 43.27 38.71 - 41.62 36.95 - 38.71* 36.17 - 40.39 38.31 - 41.26 35.19 - 38.31 36.17 -
53.20 51.00 46.36 45.32 43.78 39.06
IFN-γ 33.94 7.450 - 18.52 3.900 - 28.3 3.900 - 93.12 25.22 - 45.18 11.17 - 108.3 14.78 -
175.3 186.6 341.5 97.09 95.31 329.7
IL-2 1.323 0.4534 - 0.781 0.1316 0.8325 0.3679 1.383 0.5992 0.8926 0.4229 - 1.516 0.9307 -
10.75 -14.56 - 30.42 - 1.729 1.611 2.082
IL-10 19.52 6.709 - 19.43 7.694 - 17.84 6.709 - 18.59 10.34 - 12.43 7.857 - 14.89 11.49 -
70.25 46.30 52.23 19.46 14.38 28.56
TNF-α 14.63 2.200 - 2.998 2.200 - 25.61 2.200 - 11.53 2.200 - 2.797 2.200 - 13.86 8.015 -
56.80 52.03 70.88 19.22 15.64 17.99
IL-1⍺ (*) detected 7 d post-treatment showed a statistically significant decrease compared to the baseline measurement (P=0.0356). Values are reported in pg/ml.
153
Article
Scale of production and technical efficiency of beef cattle farming in

Puebla, Mexico
José Luis Jaramillo Villanueva a*
Lissette Abigail Rojas Juárez a
Samuel Vargas López a
a
Colegio de Postgraduados Campus Puebla. Boulevard Forjadores de Puebla No. 205,
Santiago Momoxpan, Municipio de San Pedro Cholula, 72760, Puebla, México.
*Corresponding authotr jaramillo@colpos.mx
Abstract:
The objective of this study was to estimate the degree of technical efficiency and identify the
factors of inefficiency of beef cattle production in the Sierra Norte of Puebla, Mexico. The
data were generated by surveying a statistical sample of 180 bovine production units (BPUs).
Technical efficiency was estimated using the Stochastic Production Frontier and the
explanation of inefficiency was estimated with a multiple linear regression model. The results
indicate that the size of the BPU is positively correlated with efficiency; the small BPU group
showed an average efficiency of 0.72, the medium ones 0.75 and the large ones 0.85. Feed
and labor costs can be reduced, while maintaining the same level of production. The
significant (P≤0.05) explanatory variables of inefficiency are schooling, technical assistance,
experience, and administrative management.
Key words: Cattle, Technical efficiency, Production scale, Production frontier.
154
Introduction
According to official data(1), in 2017 Mexico produced 3.5 million tons of live cattle and 1.9
million tonnes of beef. National consumption for 2019 was 1.83 million tonnes. National
production, in the last 15 yr, shows a mean growth rate (MGR) of 1.6 %, while demand grew
at a MGR of 0.21, reflecting a fall in consumption, explained by the increase in prices(2). In
this regard, per capita consumption went from 18 kg in 2007 to 15.1 in 2017. However, in
2017 imports totaled 136 thousand tonnes(3).
In Mexico, non-specialized beef production presents difficulties in being profitable,

especially that carried out by small and medium-sized bovine production units (BPUs), which
obtain negative or very low rates of return(4). This type of BPU was one million in 2018.
According to the 2014 National Agricultural Survey (5), 62 % of the BPUs have 1 to 10 heads,
26 % from 11 to 35, 9.9 % from 36 to 120, and 1.6 % more than 120 heads. Therefore,
approximately 88 % of BPUs are small. Given the importance of this sector and of cattle to
generate family income, it is necessary to support their development through the analysis of
the technical-economic factors that have a greater impact on their productivity(6).
A factor that negatively affects the economic profitability of small farmers is the low
productivity and technical efficiency at the level of BPU(7). Another important factor is the
growth rate of inputs, which is higher than that of the price of the output(8). Therefore, the
challenges posed by the problems described can be addressed through the improvement of
the productive efficiency of BPUs. Productive efficiency can improve the profitability of
BPUs through lower costs and greater supply to the market.
Productive efficiency(9) is defined as the situation in which a cattle production unit (CPU)
that produces a single product can improve its production only if it increases the use of at
least one of its inputs. The literature on efficiency focuses on two aspects; measurement of
technical and economic efficiency and sources of inefficiency. Efficiency studies have been
carried out in a wide variety of agricultural production activities; grains(10); vegetables(11),
dairy(12), and coffee(13). In the world, few studies have addressed efficiency in beef
cattle(14,15,16). In these it was found that there are significant deviations from the efficient
production frontier.
In Mexico, Morales-Hernández et al(17) conducted the only available study of beef production
efficiency in Mexico. They found that for small producers, as factors of production increase
by a certain proportion, production grows less than proportionally. On the other hand, for the
large ones, as the factors increased by a certain proportion, production grew in greater
proportion. It is not necessary to increase the amount of feed or the area of pasture to increase
155
the total amount of beef, but the number of animals.
The study of the efficiency of BPUs and the sources of inefficiency are therefore important
from a practical and political point of view. On the one hand, farmers could use this
information to improve the productivity of their farm. On the other hand, policymakers could
focus interventions to improve producer income(18).
The objective of this study was to address this gap in knowledge by estimating the degree of
efficiency, and to identify the factors of inefficiency of beef cattle production in the Sierra
Norte of Puebla, Mexico, from an econometric perspective.
For the present study, seven municipalities of the Sierra Norte of Puebla were selected (Table
1). The study area was located at coordinates 19° 59' 10'' and 20° 34' 20'' N; 97° 19' 97'' and
97° 47' 98'' W. The altitude ranged from 10 to 1,700 m asl. The climate is warm humid with
abundant rainfall all year round, except the municipality of Xicotepec, which has a humid
semi-warm climate. The vegetation is composed of pasture (35 %), jungle (13 %) and forest
(6 %)(19). These municipalities contribute 32.1 % of cattle production at the state level(3).
The methodology consisted of four stages: the first was the knowledge of the region, where
the survey of the area was carried out, and interviews were conducted with leading producers
and technicians to know general aspects of cattle farming; the second was the design of the
sampling, of a simple random type, with proportional distribution, according to the number
of producers in each municipality. The population used corresponds to 60,020 BPUs,
reliability was 95 % and accuracy was 7.5 % of the herd size mean, resulting in a sample size
of 180 BPUs. The third stage consisted of the design, testing and application of
questionnaires, distributed proportionally in the municipalities of the study (Table 1). The
fourth stage was the statistical analysis of the data derived from the questionnaire, which
were organized into sociodemographic, technological, and economic variables.
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Table 1: Distribution of the sample size

Municipality Population (N) Participation (%) Sample (n)
Francisco Z. Mena 6791 11.31 54
Venustiano Carranza 11898 19.82 36
Tenampulco 3909 6.51 27
Pantepec 17919 29.86 20
Xicotepec 4734 7.89 18
Jalpan 8860 14.76 14
Ayotoxco de Guerrero 5909 9.85 12
Total 60020 100 180
The economic characterization of the cattle production units with the aforementioned
variables is very useful for producers, since it allows them to know the behavior of their
company and they can make decisions in their activities to minimize costs, improve
productivity and profitability of the company. Therefore, it is important to distinguish
between accounting costs and economic costs.
The cost accounting perspective emphasizes expenditures incurred, historical costs and
depreciation. Economic costs represent the opportunity cost of the factors of production. One
way to differentiate between these two approaches is to analyze how the costs of various
factors (labor, capital, or business services) and the accounting or monetary costs, which are
the costs incurred by the production unit for the purchase of inputs and assets at market
prices(20), are defined.
For the purposes of this research, the total costs (TC) are the result of the sum of fixed costs
(FC) and variables costs (VC) (TC = FC + VC). Fixed costs are those charges assumed by
the production unit regardless of its level of production, including the option of zero
productions. Variable costs are those that change depending on the level of production of the
LPU. Total costs include: the cost of total labor, based on the sum of eventual labor (brush
clearing and fertilizer application), and permanent labor (commonly known as payment for
the cowboy and the flotante), which they require annually for cattle handling; cost of inputs
(feed, medicines and others); and the cost of machinery and equipment (including
depreciation rate of each asset, considering a value of 10 % per year).
The basis for defining the strata of herd size was the segmentation of livestock units of
SAGARPA(21), which considers a stratum A made up of 20 heads or less, stratum B from 21
157
to 50 heads, and stratum C made up of a herd greater than 50 heads. The above to serve the
CPUs in a differentiated way. Once the groups were formed, the following were carried out:
econometric analysis; estimation of the stochastic production frontier and estimation of an
explanatory model of inefficiency.
Stochastic frontier model
The assumption of a production of a stochastic nature means that the level of production of
a unit of production is limited superiorly by a stochastic frontier, which can be modeled as
in Equation 1:
𝑌 = 𝑓(𝑥) + 𝜀, 𝜀 = 𝑣 − 𝑢 (1)
Where the error term is composed of two parts; a random perturbation v, symmetric that is
assumed to be identically and independently distributed with mean 0, and u is a non-negative
error term, which is distributed independently of v, following a one-tailed distribution(22).
The random component represents events that are not controllable by the CPU (climatic,
social, economic, and political phenomena), while u collects the distance of each company
to its stochastic frontier, representing a measure of technical inefficiency(23). Therefore, the
Stochastic Production Frontier (SPF) is described by Equation 2:
𝑌 ∗= 𝑓(𝑥) + 𝑣 (2)
For SPFs, the technical efficiency index for enterprise i can be calculated with Equation 3:
𝑌𝑖
𝑇𝐸𝑖 = 𝑓(𝑥)+𝑣 (3)
𝑖
The SPF is first proposed in the 1970s of the last century(24,25) where they considered(24) the
case in which u is semi-normally distributed, that is, 𝑢 − |𝑁(0, 𝜎𝑢 )| and v normally
distributed. The implications at the conceptual level of PF being stochastic are very important
for the interpretation of inefficiency. As Schmidt(24) says, “the farmer whose harvest is
devastated by drought or a storm is unfortunate with our measure, but inefficient with the
usual measure”. An important limitation of the first estimates of SPF is that only the average
efficiency of the sample was calculated, and it was not possible to obtain a measure of the
efficiency of each company. Later developments(26) managed to find a measure of individual
efficiency using the conditional distribution of u in ε. The technical efficiency index for each
firm i is:
𝑇𝐸𝑖 = 𝑒𝑥𝑝[−𝐸(𝑢𝑖 |𝜀1 )] (4)
The most commonly used measure of TE is the ratio of observed production and the
corresponding stochastic production frontier, as in Equation 5:
𝑞 𝑒𝑥𝑝(𝑥𝑖´ 𝛽+𝑣𝑖 −𝑢𝑖 )
𝑇𝐸𝐼 = 𝑒𝑥𝑝(𝑥 ´𝑖𝛽+𝑣 = = 𝑒𝑥𝑝( − 𝑢𝑖 ) (5)
𝑖 𝑖 𝑒𝑥𝑝(𝑥𝑖´ 𝛽+𝑣𝑖 )
This measure of technical efficiency takes a value between zero and one. It measures the
output of the i-th CPU relative to the output that a fully efficient CPU could produce using
158
the same input vector. The first step in calculating the TE is to estimate the parameters of the
stochastic production frontier model:
Estimation of parameters
Because model 9.2 includes random terms; the symmetric error (vi) and a non-negative
random variable (ui), the selected estimation method includes assumptions about both terms.
Each vi is distributed independently of each ui and both are uncorrelated with the explanatory
variables. Additionally, the noise component vi is assumed to have properties identical to
those of the classical linear regression model. The inefficiency component has similar
properties except that it has a non-zero mean (ui ≥0), so Ordinary Least Squares cannot be
used. One solution is to make some distribution assumptions regarding the two error terms
and estimate the model using the maximum likelihood (ML) method.
Half-normal model
ML estimators were obtained(24) under the following assumptions: vi =iidN(0,σ2v ) and

ui =iidN+ (0,σ2u ) . This indicates that the vi are normal random variables distributed
independently and identically with means and variances zero and the ui are semi-normal
random variables distributed independently and identically with scale parameter. That is, the
probability density function (pdf) of each ui is a truncated version of a normal random
variable that has zero mean and variance 𝜎𝑢2 .
The log-likelihood function was parameterized(24) for this half-normal model in terms of
𝜎 2 = 𝜎𝑣2 + 𝜎𝑢2 and 𝜆2 = 𝜎𝑢2 /𝜎𝑣2 ≥ 0. If 𝜆 = 0, there are no technical inefficiency effects
and all deviations from the frontier are due to noise. Using this parameterization, the
maximum likelihood function is represented in Equation 6:
1 𝜋𝜎2 𝜀𝑖 𝜆 1
In 𝐿(𝑦|𝛽, 𝜎, 𝜆) = − 2 In ( ) + ∑1𝑖=1 InΦ (− ) − 2𝜎2 ∑1𝑖=1 𝜀𝑖2 (6)
2 𝜎
Where, y is an output vector; 𝜀𝑖 ≡ 𝑣𝑖 − 𝑢𝑖 ≡ In 𝑞𝑖 − 𝑥𝑖´ 𝛽 is the compound error term; and
𝛷(𝑥) is a cumulative distribution function (cfd) of the standard normal random variable
evaluated at x.
The empirical analysis is based on the estimation of a Cobb-Douglas production function in

which both output and inputs are expressed in logarithmic form (Equation 7), so that the
estimated coefficients are interpreted as elasticities(27).
159
Ln(Yi )=β0 +β1 Ln(ARE)+β2 Ln(LA)+β3 Ln(ASS)+β4 Ln(HEA)+β5 Ln(FEED)+ε (7)

In this model, the dependent variable (Yi) is the value of cattle production of the CPUs. The
explanatory variables are;
ARECAT is the area for cattle, in hectares owned by the BPU.
LA is the cost of the labor used in production.
ASS is the value of assets; value of machinery, equipment, and production facilities used in
the cattle activity.
HEA is the expenditures in health; veterinary supplies and services.
FEED is the cost of feeding; cost of meadow maintenance and supplementary feeding.
Model of individual efficiencies
The estimated model of individual efficiencies (Equation 7) considers the measures of

inefficiency estimated in the first stage as a dependent variable. Explanatory variables are a
set of variables that hypothetically affect the performance of the CPU(6). The literature reports
as the most common explanatory variables the age of the head of the CPU, they level of
schooling, experience in the activity under study, characteristics of the CPU, administration,
and environmental factors, among the most cited(28-31). The multiple regression model was
that described in Equation 8:
Ui =δ0 +δ1 Ln(Age)+δ2 Ln(Schoo)+δ3 Ln(Exper)+δ4 Ln(Admon)+δ5 Ln(TA)+ϑi (8)
Where: Age is the age of the head of the CPU; Schoo is the level of schooling (in years) of
the head of the CPU; Exper are the years of experience in the cattle activity; Admon is a
dummy variable that takes the value of zero if the CPU does not have an administration
system and one if they have an administration system; TA is technical assistance, zero if they
did not receive technical assistance and one if they received the service. The variables of the
stochastic frontier model and of the individual inefficiency model are showed in Table 2.
160
Table 2: Variables used in the stochastic frontier production model

Concepts Frequency Percentage
Gender of the head Woman 22 12.0

Man 162 88.0
Schooling of the head of Primary education 69 37.3
the CPU Junior High school 63 34.1
High school 33 17.8
Professional 20 10.8
Administration They do not have a
114 61.6
system
They have a
71 38.4
system
Technical assistance They did not
124 67.0
receive
They did receive 55 33.0
Technological level Low 94 50.8
Medium 45 24.3
High 46 24.9
Strata [number of animal 20 or less 89 48.1
units (A.U.)] 21 to 50 60 32.4
50 or higher 36 19.5
Variable Mean Standard deviation
Age of the head 56.0 13.4
Experience 22.2 13.3
Animal units 62.5 88.6
Meadow area, ha 64.9 129.4
Labor cost, $ 37,837 19,354
Health, $ 10,680 3,292
Feeding costs, $ 125,477 72,226
Assets; annual
35,260 10,500
depreciation, $
Net income, $ 83,488 20,824
Benefit cost (B/C) 1.31 0.26
161
The owners of the BPUs in the Sierra Norte region of Puebla have an average age of 56 yr
and range from 25 to 86 yr. The average schooling is 8 years; just under half of producers
have completed primary education, 28.6 % finished junior high school and 29.2 % completed
high school. The above characteristics are similar to those previously reported(32) for the rural
population of the state of Puebla. The experience of producers in the production of cattle was
27 yr, and they have received technical assistance in topics of feeding, animal health and
carrying capacity.
Half of the CPUs (50.8 %) are dedicated exclusively to the production of live cattle, 22.2 %
are supported by other commercial activities (leases, businesses, and transport), 16.8 % are
supported by agricultural and fruit activities (coffee, banana, corn, orange, beans, and
vanilla), and 10.3 % report other non-agricultural activities. The percentage of household
income generated by non-agricultural productive activities was 55 %, a result similar to that
reported in previous studies(33).
The average herd size was 73 heads, with a minimum of 4 and a maximum of 657, which
shows a great heterogeneity between the production units, hindering the conditions to
compete and achieve a better production process(34). The average area held by the CPUs for
grazing was 64 ha and the value of their assets was $135,261 (vehicles, mill, warehouse,
milking machine, silo, corral, drinkers, feeder, and scale). The average annual income
reported was $83,666, equivalent to 10 % of the herd, for the sale of weaning calves and
discarded animals. In the cost structure, feed represented 60 % of the total cost of production,
contracted and family labor 18 %, fixed costs and depreciation of assets 17 %, and 5 % was
the cost of health.
Results of the econometric model
The results of the stochastic frontier model, using the full sample, are shown in Table 3. The
variables had the expected sign, according to economic theory. The positive sign means that
increasing the use of the production factor increases production, while the magnitude of the
coefficient accounts for the relative importance of each independent variable in explaining
the dependent variable.
162
Table 3: Results of the fit of the stochastic frontier model

[95% confidence
Explanatory variable Coefficient SE t-statistic interval]
Area of pastures (ARE) 0.025 0.015 1.71* -0.023 0.073
Labor (LA) 0.263 0.068 3.89** 0.430 0.696
Value of Assets (ASS) 0.365 0.046 7.87** 0.456 0.274
Health (HEA) 0.411 0.081 5.07** 0.152 0.670
Feeding (FEED) 0.195 0.016 11.82** 0.053 0.327
Intercept -1.777 0.498 -3.57** -2.753 -0.802
sig2v -3.481 0.287 -12.13 -4.044 -2.919
sig2u -2.607 0.369 -7.06 -3.331 -1.884
sigma_v 0.175 0.025 0.132 0.232
sigma_u 0.272 0.050 0.189 0.390
sigma2 0.105 0.021 0.063 0.146
2
lambda and lambda 1.370/1.88 0.072 1.408 1.688
2 2
gamma: 𝛾 = 𝜎𝑢 /𝜎𝑠 0.74
SE= standard error; * and** significant at 10 % and 5 % respectively.
The variables LA, ASS, HEA, and FEED are significant at 5 %. Area for cattle (ARECAT)
was also found significant(30) when studying factors influencing technical efficiency in
southeastern Kenya in 2013; a 10 % increase in area for cattle resulted in a 29 % increase in
cattle production. The LA variable was found to be significant by several authors(31,35,36). In
a study in Botswana(36) conducted with four strata of producers, they found that increasing
the amount of labor by 10 % increases producers’ profits by 15 % and 18 %, respectively.
The ASS variable has not been identified as significant in the studies reviewed. In the present
study, ASS has a positive effect on the production of cattle PUs, as expected by economic
theory(20). The variables HEA and FEED were also reported as significant(14,30,31).
Regarding the fit of the model (7), the estimated stochastic production frontier showed a
normal distribution of residuals (Shapiro-Wilks test), no serial correlation of errors (Durbin-
Watson), no heteroscedasticity of variance and no autocorrelation or multicollinearity
problems. In the values obtained from the general fitted model (Table 3), it was determined
that cattle production presents increasing returns to scale (the sum of the coefficients is
greater than the unit). To confirm this result, the test was performed for returns to scale,
where a value of P= 0.03 < 0.05 was obtained, this causes the existence of constant returns
to scale to be rejected(6).
Regarding the inefficiencies of model 8, it was observed that the variance parameters of the
maximum likelihood (ML) function are estimated from the total variance model defined as:
𝜎𝑠2 = 𝜎𝑣2 + 𝜎𝑢2 and the estimated value in the model for the total variance (𝜎𝑠2 ) was 0.105.
While the lambda value (%) resulted in 1.370, which shows that the variance of the
163
efficiencies is greater than the variance of the random perturbations at 88 % (𝜆2 − 1) and the
gamma value obtained from the relationship between the variances 𝛾 = 𝜎𝑢2 /𝜎𝑠2 states that
73.9 % of the total variance is explained by the variance of the inefficiencies.
The results of the stochastic frontier model for each stratum of cattle producers are shown in
Table 4. Similar to the general model, the models for each estimated stratum showed normal
distribution of residuals, no serial correlation of errors, no heteroscedasticity of variance, and
no autocorrelation. The variables ARECAT, LA, ASS, HEA, and FEED are significant at
5 % in strata two and three.
Table 4: Results of the Stochastic Frontier model for the strata of CPU
Stratum 1 Stratum 2 Stratum 3
Variable Coefficient Z-value Coefficient Z-value Coefficient Z-value
ARE 0.094 1.85 0.027 2.27 0.073 3.31
LA 0.121 1.76 0.086 2.17 0.163 4.55
ASS 0.116 3.33 0.204 3.83 0.210 4.33
HEA 0.118 2.18 0.158 3.55 0.194 8.95
FEED 0.654 13.64 0.607 14.07 0.670 2.35
Constant 0.641 1.08 0.752 1.59 -1.267 -2.96
/lnsig2v -3.704 -24.7 -4.206 -23.02 -37.972 -0.06
/lnsig2u -13.129 -0.07 -13.419 -0.07 -2.339 -9.92
sigma_v 0.157 0.122 0.000
sigma_u 0.001 0.001 0.310
sigma2 0.025 0.015 0.096
lambda 0.009 0.010 5.460
In stratum 1, only HEA and FEED were significant. One possible explanation is that small
producers have lower quality pastures, without agronomic management, use family labor,
little specialized, and the value of their assets is very low, reflecting low-technified CPUs.
The feed variable is the one that has the greatest weight in explaining the production of the
CPUs for the three strata. The value of assets has twice as much relative weight in strata two
and three than in strata one, which means that these CPUs not only have greater investment
in assets, but that it is modern and generates greater productivity. The models for strata 2 and
3 show increasing returns to scale, but not the model of stratum 1 which has decreasing
returns to scale. In this regard(37), in a study in the United States of America, it was found
that as the size of the CPU increases, TE increases, which showed evidence of economies of
scale. A possible explanation for the result of stratum 1 is that small producers have a low
level of capitalization, low-skilled labor, and since they have little pasture area, they make
intensive use, overexploiting the resource(38,39).
164
Frequency distribution of technical efficiency(TE) by UPG stratum
The TE range for cattle producers was between 0.50 and 0.95. Of the total of the 185 CPUs,
29 % have values between 0.50 and 0.70, 63 % between 0.71 and 0.90, and only 8 % TE
values greater than 0.90. Table 5 shows that stratum 3 presents most of the values of 0.91 or
more. In this regard(40), it was found that the CPUs with the largest number of animal units
and the largest area for cattle presented the highest values of technical efficiency.
Table 5: Frequency distribution (percentages) of technical efficiency (TE) by CPU strata
TE TE TE
Strata (no. of heads) (0.50 - 0.70) (0.71-0.90) (> 0.91) Average
Stratum 1 (20 or less) 47.2 22.2 13.3 0.712
Stratum 2 (21 to 50) 41.5 33.3 0.0 0.751
Stratum 3 (greater than 50) 11.3 44.4 86.7 0.844
General 100.0 100.0 100.0 0.789
Results of individual inefficiencies
Table 6 shows the results of the individual inefficiencies model according to Equation (8).
The significant variables, at different levels of significance, and with a negative coefficient,
were Schoo, Exper, Admon and TA. The negative sign of the coefficients indicates an inverse
relationship between the value of the explanatory variable and the value of the inefficiency.
In this regard, previous studies(28,30,36) have reported results that support the results of this
study. It was found that more years of schooling reduces inefficiency in values very similar
to those reported in this research. Similarly, in the case of the Admon(6,14,41) variable, they
found an inverse relationship between having an administration system and inefficiency. For
TA(28,30,41), they reported that receiving this service contributes to reducing the inefficiency
of the CPUs. In the present study, Age is not significant, a result supported by what was
found in the literature(30).
165
Table 6: General explanatory model of inefficiency

Standard Interval
Explanatory variable Coefficient t-value
error
Age (age) 0.02 0.0212 1.1 -0.042 – 0.042
Schooling (Schoo) -0.23 0.0635 3.6 0.010 – 0.635
Experience (Exper) -0.12 0.0739 1.7 -0.012 – 0.024
Administration (Admon) -0.23 0.0824 2.5 -0.001 - 0.048
Technical Assistance
-0.22 0.0136 14.9
(TA) 0.176 - 0.230
Constant -0.47 0.799 -0.6 -0.626 – (-0.310)
Fit (R2)/R2 adjusted 0.7929 / 0.7859
Heteroscedasticity(Cook-
Prob> Ji2=0.000
Weisberg)
Normality: (Shapiro-
0.00002
Wilk)
Inflation factor variance 1.59
The above results suggest that reducing inefficiency should be addressed by providing public
technical assistance services, an activity that, in Mexico, has been at very low levels since
the nineties. In this regard, in a study on the use of livestock innovations in Sinaloa(7), it was
reported that only 3 % of the PUs receive technical assistance services, and of these, the
CPUs represent only 19.3 %. Training in the management of the CPU, including
administrative services, should also be a central aspect, in addition to the technological issues
of cattle farming.
Results of the technical inefficiency model by CPU strata
Table 7 reports the results of the model of technical inefficiency by strata of CPUs. For
stratum 1, Age and Exper are significant, but not Schoo, Admon and TA. The producers of
this stratum have low schooling, 6 years on average, have experience, and most do not have
administration systems and do not receive any type of technical assistance services. For
stratum 2, Schoo, Exper and TA are significant. It was observed that the years of schooling
increase significantly for the producers of this stratum. Finally, for stratum 3, four variables
are significant. It should be noted that the values of the coefficients are in the range of 0.13
to 0.28, which shows an important effect of these variables to reduce inefficiency. Therefore,
improving administration systems and the quality of technical assistance are aspects that can
lead these CPUs to be highly efficient(14,30,41).
166
Table 7: Results of the technical inefficiency model by CPU strata

Stratum 1 Stratum 2 Stratum 3
Variable Coef. t SE Coef. t SE Coef. t SE
Age -0.066 -2.15* 0.031 0.017 0.42 0.041 0.065 1.38 0.047
Schoo -0.001 -0.03 0.007 -0.184 -2.08* 0.088 -0.142 -2.59* 0.055
Exper -0.027 -2.30* 0.012 -0.126 -2.09* 0.060 -0.197 -4.28* 0.046
Admon 0.033 1.63 0.020 0.017 0.81 0.020 -0.281 -5.73* 0.049
TA 0.108 1.42 0.076 -0.150 -7.34* 0.020 -0.134 -5.56* 0.024
Constant -0.238 -2.10 0.113 -0.481 -2.97 0.162 -0.700 -3.89* 0.179
R2/R2 Adj. 0.7935 / 0.7884 0.8027 / 0.7904 0.8214 / 0.7945
D-W 0.0719 0.0005 0.0247
Normality 0.01219 0.69848 0.17108
VIF 1.4 1.23 1.7
SE= standard error; D-W= Durbin-Watson; VIF= variance inflation factor.
The production of live cattle in the study region is carried out with a high degree of efficiency,
however, there is significant room for improvement, especially in small producers. The most
efficient producers have more schooling, receive technical assistance services, use
administration systems, have more pasture area, more heads and use better animal health
systems. Labor, health, food, and asset costs can be reduced while maintaining the same level
of production. Small producers, which are the largest subsector in number, can improve their
production by attending to food and health aspects, with the other variables constant. The use
of technical assistance services reduces inefficiency, through a more intensive and
appropriate use of available livestock technology. Due to the above, it is advisable to make
these services extensive and permanent to all farmers, especially small farmers. The positive
relationship between herd size and productive efficiency may be related to the benefits of
economies of scale, in the case of medium and large producers, so financing to increase the
herd can generate production and efficiency gains.
Literature cited:
1. SIAP. Sistema de Información Agroalimentaria y Pesca. 2017. Avance mensual de la
producción pecuaria. Consultado 13 marzo, 2019.
http://infosiap.siap.gob.mx/repoAvance_siap_gb/pecConcentrado.jsp.
2. FIRA. Fideicomisos Instituidos en Relación con la Agricultura. 2017. Panorama

Agroalimentario. Panorama Agroalimentario Carne de bovino 2017.pdf. Consultado 22
enero, 2019.
167
3. SIAP. Sistema de Información Agroalimentaria y Pesca. 2018. Cosechando números del

Campo; carne de bovino.
http://www.numerosdelcampo.sagarpa.gob.mx/publicnew/productosPecuarios/cargarP
agina/1. Consultado 24 Ene, 2019.
4. Jaramillo-Villanueva JL, Escobedo-Garrido JS, Carranza-Cerda I. Oportunidades

estratégicas para el desarrollo del sector agropecuario en Puebla, sistemas de producción
y procesos de agregación de valor. 1ra ed. México: Plaza y Valdés SA de CV. 2017.
5. ENA. Encuesta Nacional Agropecuaria. 2015. Existencias de ganado bovino según rangos
de edad por entidad federativa.
https://www.inegi.org.mx/programas/ena/2014/default.html#Tabulados. Consultado 22
Ene, 2019.
6. Veloso-Contreras F, Cabas-Monje J, Velasco-Fuenmayor J, Vallejos-Cartes R, Gil-Roig

JM. Eficiencia técnica de los pequeños productores bovinos de la región centro sur de
Chile. Revista Científica 2015;25(2):99-106.
7. Cuevas RV, Baca del Moral J, Cervantes EF, Espinosa GJA, Aguilar ÁJ, Loaiza MA.
Factores que determinan el uso de innovaciones en la ganadería de doble propósito en
Sinaloa, México. Rev Mex Cienc Pecu 2013;4(1):31-46.
8. Ayala-Garay AV, Rivas-Valencia P, Cortes-Espinoza L, De la O-Olán M, Escobedo-

López D, Espitia-Rangel E. La rentabilidad del cultivo de amaranto (Amaranthus spp.)
en la región centro de México. CIENCIA ergo-sum 2014;21(1):47-54.
9. Koopmans TC. An analysis of production as an efficient combination of activities. Activity

Analysis of Production and Allocation 1951;(13):33-97.
10. Martey E, Wiredu AN, Etwire PM, Kuwornu JK. The impact of credit on the technical
efficiency of maize-producing households in Northern Ghana. Agric Finance Rev
2019;79(3):304-322.
11. Wiboonpongse A, Sriboonchitta S, Rahman S, Calkins P, Sriwichailumphun T. Joint

determination of the choice of planting season and technical efficiency of potato in
northern Thailand: A comparison of Greenes versus Heckmans sample selection
approach. Afr J Bus Manag 2012;6(12):4504-4513.
12. Cabrera VE, Solís D, del Corral J. The effect of traditional practices in the efficiency of
dairy farms in Wisconsin. South J Agric Econ Association Annual Meeting Orlando FL,
February 6-9; 2010.
168
13. Cárdenas G, Vedenov DV, Houston JE. Analysis of production efficiency of Mexican
coffee-producing districts. AAEA Annual Meetings, Selected Paper #134280
Providence, RI July 2005.
14. Otieno DJ, Hubbard LJ, Ruto E. Determinants of technical efficiency in beef cattle
production in Kenya. Selected Paper prepared for presentation at the International
Association of Agricultural Economists (IAAE) Triennial Conference Foz do Iguacu
Brazil August 2012;18-24.
15. Iraizoz B, Bardaji I, Rapun M. The Spanish beef sector in the 1990s: impact of the BSE
crisis on efficiency and profitability. Appl Econ 2005;37(4):473-484.
16. Trestini S. Technical efficiency of Italian beef cattle production under a heteroscedastic
non-neutral production frontier approach. Paper presented at the 10th Joint Conference
J Food Agric Environ, Duluth Minnesota 2006 August:27-30.
17. Morales-Hernández JL, González-Razo FDJ, Hernández-Martínez J. Función de

producción de la ganadería de carne en la zona sur del Estado de México. Rev Mex
Cienc Pecu 2018;9(1):1-13.
18. Solís D, Bravo-Ureta B, Quiroga R. Technical efficiency among peasant farmers

participating in natural resource management programs in Central America. J Agric
Econ 2009;60:202-219.
19. INEGI. Instituto Nacional de Estadística, Geografía. 2009. Censo Agropecuario 2007.
http://www.inegi.org.mx/est/contenidos/proyectos/Agro/ca2007/Resultados_Ejidal/def
ault.aspx. Consultado 18 Jun, 2019.
20. Nicholson W. Teoría microeconómica. Principios básicos y ampliaciones. Novena

edición. Cengage Learning 2012;212-224.
21. SINIIGA. Sistema Nacional de Identificación Individual de Ganado. 2012.

Estratificación por UPs y vientres bovinos, 32. Veracruz, México.
http://ugrnv.com.mx/web/wp-content/uploads/2012/06/Siniiga%20Presentacion.pdf.
Consultado 17 Jun, 2019.
22. Alvarez PA. La medición de la eficiencia y la productividad. Primera ed. Madrid, España:
Ediciones Pirámide; 2001.
23. Greene WH. Simulated likelihood estimation of the normal-gamma stochastic frontier
function. J Product Anal 2003;19(2):179–190.
24. Aigner DJ, Lovell K, Schmidt P. Formulation and estimation of stochastic frontier
production function models. J Econom 1977;6(1):21–37.
169
25. Meeusen W, Van Den Broeck J. Efficiency estimation from Cobb-Douglas production
functions. Int Econ Rev 1977;18(2):435–444.
26. Jondrow J, Lovell CK, Materov IS, Schmidt P. On the estimation of technical inefficiency
in the stochastic frontier production function model. J Econom 1982;19(2-3):233-238.
27. Kumbhakar SC, Lovell CAK. Stochastic Frontier Analysis. Cambridge University Press.
Cambridge, New York, Melbourne 2003. http://dx. doi.
org/10.1017/cbo9781139174411.
28. Velasco FJ, Ortega SL, Sánchez CE, Urdaneta F. Análisis de sensibilidad del nivel
tecnológico adoptado en fincas ganaderas de doble propósito del Estado Zulia,
Venezuela. Rev Cient 2010;20(1):67-73.
29. Melo-Becerra LA, Orozco-Gall AJ. Technical efficiency for Colombian small crop and
livestock farmers: A stochastic metafrontier approach for different production systems.
J Product Anal 2017;47(1):1-16.
30. Kibara MJ, Kotosz B. Tecnnical efficiency estimation in the livestock industry: Case
study of the southern rangelands of Kenya. Challenges in National and International
Economic Policies 2018;97-114.
31. Latruffe L, Balcombe K, Davidova S, Zawalinska K. Determinants of technical efficiency

of crop and livestock farms in Poland. Appl Econ 2004;36(12):1255-1263.
32. INEGI. Instituto Nacional de Estadística, Geografía. 2018. Censos y Conteos de

Población y Vivienda. https://www.inegi.org.mx/programas/ccpv/2010/. Consultado 17
Ene, 2019.
33. Mora-Rivera J, Martínez-Domínguez M, Jaramillo-Villanueva JL, Chávez-Alvarado

MA. Participación en el sector no agropecuario en el México rural: una perspectiva de
género. R Bras Est Pop Belo Horizonte 2017;34(2):367-389.
34. Escalante S, Roberto I, Catalán H. Situación actual del sector agropecuario en México:
perspectivas y retos. Economía Informa 2008; 350.
http://www.economia.unam.mx/publicaciones/econinforma/pdfs/350/01escala
nte.pdf. Consultado 22 May,2019.
35. Rakipova AN, Gillespie JM, Franke DE. Determinants of technical efficiency in
Louisiana beef cattle production. J ASFMRA 2003;99-107.
36. Bahta S, Baker D. Determinants of profit efficiency among smallholder beef producers
in Botswana. Int Food Agribusiness Manag Rev 2015;18(3):107-130.
170
37. Sabasi D, Shumway CR, Astill GM. Off‐farm work and technical efficiency on US
dairies. Agric Econ 2019;1-15.
38. Perfetti JJ, Balcázar A, Hernández A, Leibovich J. Políticas para el desarrollo de la

agricultura en Colombia. Fedesarrollo, Sociedad de Agricultores de Colombia (SAC),
Incoder, Finagro, Banco Agrario. 2013.
39. Cano CG, Vallejo C, Caicedo E, Amador JS, Tique EY. El mercado mundial del café y
su impacto en Colombia. Borradores de Economía 2012;710.
40. Qushim B, Gillespie JM, Bhandari BD, Scaglia G. Technical and scale efficiencies of US
grass-fed beef production: whole-farm and enterprise analyses. J Agric Appl Econ
2018;50(3):408-428.
41. Ceyhan V, Hazneci K. Economic efficiency of cattle-fattening farms in Amasya province,

Turkey. J Anim Vet Adv 2010;9(1):60-69.
171
Article
Quantile regression for prediction of complex traits in Braunvieh cattle

using SNP markers and pedigree
Jonathan Emanuel Valerio-Hernández a
Paulino Pérez-Rodríguez b*
Agustín Ruíz-Flores a
a
Universidad Autónoma Chapingo. Posgrado en Producción Animal. Carretera Federal
México-Texcoco Km 38.5, 56227, Texcoco, Estado de México, México.
b
Colegio de Postgraduados. Socio Economía Estadística e Informática. Carretera Federal
México-Texcoco Km 36.5, 56230, Texcoco, Estado de México.
*Corresponding author: perpdgo@colpos.mx
Abstract:
Genomic prediction models generally assume that errors are distributed as normal,
independent, and identically distributed random variables with zero mean and equal variance.
This is not always true, in addition there may be phenotypes distant from the population
mean, which are usually removed when making the prediction. Quantile regression (QR)
deals with statistical aspects such as high dimensionality, multicollinearity and phenotypic
distribution different from the normal one. The objective of this work was to compare QR
using marker and pedigree information with alternative methods such as genomic best linear
unbiased prediction (GBLUP) and single-step genomic best linear unbiased prediction
(ssGBLUP) to analyze the birth (BW), weaning (WW) and yearling (YW) weights of
Braunvieh cattle and simulated data with different degrees of asymmetry and proportion of
outliers. The predictive capacity of the models was assessed by cross-validation. The
predictive performance of QR both with marker information alone and with information of
markers plus pedigree, with the actual dataset, was better than the GBLUP and ssGBLUP
methodologies for WW and YW. For BW, GBLUP and ssGBLUP were better, however, only
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quantiles 0.25, 0.50 and 0.75 were used, and the BW distribution was not asymmetric. In the
simulated data experiment, correlations between “true” marker effects and estimated effects,
as well as “true” and estimated signal correlations were higher when QR was used compared
to GBLUP. The advantages of QR were more noticeable with asymmetric distribution of
phenotypes and with a higher proportion of outliers, as was the case with the simulated
dataset.
Key words: Quantile regression, GBLUP, ssGBLUP.
Introduction
The main motivation of the quantile regression (QR) method is that most models for genetic
evaluation assume normality, which is not always true. Another problem is that sometimes
phenotypic records very far from the population average are considered as recording errors
or outliers and therefore removed from the analyses, seen from the genomic point of view,
valuable information of markers associated with certain regions of DNA with strong
influence on characteristics of interest is being lost.
With the QR method, robust results and a broad vision of the explanatory variables on the
dependent ones are obtained(1). The data generated from omics experiments are often
complex and large, so there is a statistical challenge to extract relevant biological information
from the large volume of data(2,3). Using a robust approach such as QR makes inference less
biased and less subject to false positives(2). Recent studies using QR describe various
applications such as etic association studies(4), population genetics(5), gene expression(6,7), and
genomic selection(8–10).
One of the first studies where QR was used to predict individual genetic merit was presented
by Nascimento et al(11), who used simulated data, finding advantages when using QR
compared to conventional methodologies. In the same year(12), results using QR to adjust
growth curves with data from pigs and molecular markers were published; not only did they
successfully adjust the growth curves, but they identified important markers associated with
the studied characteristic. Another similar work by the same team of researchers was
presented by Nascimento et al(13), but with bean data. Recently, Pérez-Rodríguez et al(10)
extended the quantile regression model to include pedigree information through the use of
the additive genetic relationship matrix, further improving the predictive ability of the models
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and at the same time identifying the proportions of the variances attributed to markers,
relationships between individuals and the residual, which allows a precise partitioning of the
phenotypic variance to be obtained.
The objective of the present study was to study the predictive power of the quantile regression
model using simulated data and actual data (birth, weaning and yearling weights) from
Braunvieh cattle and the following models were considered: 1) QR with information of SNP
molecular markers (QRM), 2) QR simultaneously including molecular marker information
and genealogical information derived from pedigree (QRH); 3) GBLUP which, like QRM,
only included molecular marker information, and 4) single-step genomic evaluation
(ssGBLUP) which included marker and pedigree information.
Genotypes
The information used was from 300 animals (236 females, 64 males) born from 2001 to 2016
in eight herds located in Eastern, Central and Western Mexico. Hair samples were collected
for genotyping by the company GeneSeek (Lincoln, https://www.neogen.com/, NE, USA),
using the GeneSeek® Genomic Profiler Bovine LDv.4 panel, with 30,000 and 50,000 SNP
markers, 150 animals with each Chip. Genotyping was performed on two separate occasions,
initially 150 individuals with the 30K Chip and later another group of 150 individuals with
the 50K Chip since the 30K Chip was not available at the time. The SNPs in common between
the 30K and 50K chips (12,835 SNPs) were used. The proportions of missing values were
calculated for each marker and for each individual. The average of missing values per
individual was 2.09 % with a standard deviation of 7.50 %. The average call rate (not missing
proportion for each marker) was 97.90 % with a standard deviation of 4.66 %. Markers with
a call rate of less than 95 % were removed. The genotypes were recoded as AA= 0, AB= 1
and BB= 2, from which a matrix with 300 rows (individuals) and 12,835 columns (markers)
was obtained, whose cells take values in the set {0,1,2, −}, where “−” denotes a missing
value. For the 12,835 common markers of the two chips, the missing values were randomly
imputed, generating samples of the 𝐵𝑖𝑛𝑜𝑚𝑖𝑎𝑙(2, 𝑝̂ ) distribution, where 𝑝̂ is the frequency of
the major allele, calculated from the observed marker genotypes. Monomorphic markers or
those with minor allele frequency (MAF) less than 0.04 were removed. After quality control,
9,628 of the 12,835 SNPs in common between the two chips were available for further
analyses.
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Phenotypes
The phenotypic and pedigree information of the Braunvieh cattle population was obtained
from the database of the Mexican Association of Breeders of Registered Swiss Cattle.
Records of birth (BW), weaning (WW) and yearling (YW) weights were used for analysis.
Phenotype editing was similar for BW, WW and YW, records of animals not genetically
related to those genotyped or with missing information for herd, dam’s age and management
were discarded. Contemporary groups (CG) were defined by removing animals in CG of 2
individuals or with variance equal to zero. For BW, the CGs were defined by combining the
effects of the herd (8 herds), year (1998 to 2016) and season of birth; the seasons of birth
were defined considering the Julian calendar, from 80 to 171d, spring; from 172 to 264 d,
summer; from 265 to 354 d, autumn; from 355 to 366 d and from 1 to 79 d, winter. After
editing data, for BW, 330 records were obtained. For WW and YW, the CGs were defined
by combining the effects of the herd (6 herds), year (from 1998 to 2016), season of birth
(same as BW) and management. In the case of WW, the management groups were defined
in three ways: calves fed their mother’s milk; their mother’s milk plus balanced feed; and
milk from their mother and nurse plus a balanced diet. For YW, the management groups were
defined in three ways: grazing animals; grazing animals with feed concentrate; and housed
animals with a balanced diet. The edition of WW and YW data ended with 267 and 232
records for further analyses. Table 1 shows a summary of the number of animals genotyped,
and phenotyped for BW, WW and YW. Figure 1 shows the violin plots for BW, WW and
YW, the sample mean is represented by the red dot and the sample median by the horizontal
line within the box, from the plot, it is clear that the response variables have an asymmetric
distribution and the circles with solid filling in it suggest the presence of outliers.
Table 1: Number of animals genotyped and phenotyped for the analysis of birth, weaning
and yearling weights of a Braunvieh cattle population
Group Birth weight Weaning Yearling
weight weight
Genotyped 300 300 300

Genotyped and phenotyped 232 218 191
Phenotyped in QRM and GBLUP 232 218 191
Phenotyped in QRH and ssGBLUP 330 267 232
QRM=Quantile regression using marker information, QRH=Quantile regression using marker and pedigree
information, GBLUP=Genomic best linear unbiased predictor, ssGBLUP=Single-step genomic evaluation.
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Figure 1: Violin plots of birth (PN=BW), weaning (PD=WW) and yearling (PA=YW)
weights in a Braunvieh cattle population
The sample mean is represented by the red dot and the sample median by the horizontal line inside the box
Models
Quantile regression model with markers (QRM)
The model for quantile regression is:

𝑦𝑖 = 𝜇 + 𝒙𝑡𝑖 𝜷 + 𝑤𝑖 ,
where 𝑦𝑖 is the value of the phenotype of the i-th animal; 𝜇 is an intercept; 𝒙𝑡𝑖 = (𝑥𝑖1 , … , 𝑥𝑖𝑝 )
𝑡
represents the i-th row of the marker matrix, 𝜷 = (𝛽1 , … , 𝛽𝑝 ) is the vector of regression
coefficients associated with markers and 𝑤𝑖 are independent random variables such that their
quantile 𝜃 ∈ (0,1) is zero. The estimation of the regression coefficients for a fixed interest
quantile 𝜃 is obtained by solving the following minimization problem:
𝑚𝑖𝑛{∑𝑛𝑖=1 𝜌𝜃 (𝑦𝑖 − 𝜇 − 𝒙𝑡𝑖 𝜷) + 𝜆 ∑𝑝𝑗=1|𝛽𝑗 |},
where ∑𝑝𝑗=1|𝛽𝑗 | is the sum of the absolute values of the regression coefficients; 𝜆 is the
penalty parameter that controls the intensity of regularization; and 𝜌𝜃 (⋅) is the function
defined as(1):
𝜏 × 𝑡𝑖 If 𝑡𝑖 ≥ 0
𝜌𝜃 (𝑡𝑖 ) = {
−(1 − 𝜏) × 𝑡𝑖 If 𝑡𝑖 < 0,
𝑡
where 𝑡𝑖 = 𝑦𝑖 − 𝜇 − 𝒙𝑖 𝜷. After estimating the parameters of the model, the breeding values
estimated by markers (GEBV) are obtained by the following expression:
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𝑝
𝐺𝐸𝐵𝑉(𝜏) = 𝑦̂𝑖 (𝜏) = ∑𝑗=1 𝑥𝑖𝑗 𝛽̂𝑗 (𝜏),
where 𝛽̂𝑗 (𝜏) is the effect of the j-th marker, defined by the functional relationship obtained
for the quantile of interest.
The QR model can be extended to include other terms, in particular for growth
characteristics, the following model is used:
𝑦𝑖 = 𝜇 + 𝒔𝑡𝑖 𝝇 + 𝒄𝑡𝑖 𝝔 + 𝒙𝑡𝑖 𝜷 + 𝒘𝑖 ,
where 𝑦𝑖 is the value of the phenotype of the analyzed characteristic (BW, WW or YW) of
the i-th animal, 𝜇 is an intercept; 𝒔𝑡𝑖 = (𝑠𝑖1 , . . . , 𝑠𝑖𝑓 ) the i-th row of the incidence matrix for
fixed effects (sex, dam’s age, management), 𝝇 = (𝜍1 , . . . , 𝜍𝑓 )𝑡 the regression coefficients for
fixed effects, 𝒄𝑡𝑖 = (𝑐𝑖1 , . . . , 𝑐𝑖𝑡 ) the i-th row of the incidence matrix for random effects of
contemporary group (54, 43 and 37 for BW, WW and YW), 𝝔 = (𝜚1 , . . . , 𝜚𝑡 )𝑡 random effects
of contemporary group, the rest of the terms as described above.
GBLUP
The model is given by:

𝑦𝑖 = 𝜇 + 𝒔𝑡𝑖 𝝇 + 𝒄𝑡𝑖 𝝔 + 𝒛𝑡𝑖 𝒖 + 𝑒𝑖 ,
where 𝒛𝑡𝑖 = (𝑧𝑖1 , . . , 𝑧𝑖𝑛 ) is the i-th row of the matrix that connects phenotypes with
genotypes, 𝒖 = (𝑢1 , . . . , 𝑢𝑛 )𝑡 is the vector of random effects for animals. Additive,
contemporary group and residual genetic variances are assumed 𝑉𝑎𝑟(𝒖) = 𝑮𝜎𝑢2 , 𝑉𝑎𝑟(𝒄) =
2
𝐈𝜎𝑐𝑔 , and 𝑉𝑎𝑟(𝒆) = 𝐈𝜎𝑒2 , respectively. The matrix of genomic relationships, 𝑮, is calculated
as described by Lopez-Cruz et al(14) and Pérez-Rodríguez et al(15); briefly, G = WW’/p, where
W is the standardized and centered marker matrix (each marker centered by subtracting the
mean allele frequency and standardized by dividing by the standard deviation of the sample
of the allele frequency), p is the total number of markers, 𝑒𝑖 normal and independent random
variables with normal distribution with mean 0 and variance 𝜎𝑒2 .
Single-step quantile regression (QRH) model
This method is considered an extension of the quantile model for a relationship matrix
constructed using matrices of relationships for genotyped and non-genotyped animals and of
which a pedigree is available. The resulting matrix is known in the literature as matrix H(16,17),
this matrix is given by:
𝟎 𝟎
𝐇 −1 = 𝐀−1 + [𝟎 𝐆−1 − 𝐀−1 ],
𝑎 𝑔𝑔
where, Agg is a submatrix of A for genotyped animals, Ga = βG + α; 𝛽 and 𝛼 are obtained by
solving the system of equations:
𝐴𝑣𝑔(𝑑𝑖𝑎𝑔(𝐆))𝛽 + 𝛼 = 𝐴𝑣𝑔(𝑑𝑖𝑎𝑔(𝐀𝑔𝑔 ))
{ .
𝐴𝑣𝑔(𝐆)𝛽 + 𝛼 = 𝐴𝑣𝑔(𝐀𝑔𝑔 )
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The QRH model is given by:

𝑦𝑖 = 𝜇 + 𝒔𝑡𝑖 𝝇 + 𝒄𝑡𝑖 𝝔 + 𝒛𝑡𝑖 𝒖 + 𝑤𝑖 ,
where 𝑉𝑎𝑟(𝒖) = 𝜎𝐻2 𝑯, the rest of the terms as described above.
Single-step GBLUP regression (ssGBLUP) model
The ssGBLUP model is equivalent to the GBLUP model described above with the difference
that the genomic relationship matrix G is replaced with the extended genetic relationship
matrix H, it is assumed that 𝑉𝑎𝑟(𝒖) = 𝑯𝜎𝐻2 .
Cross-validation
The predictive capacity of the models was evaluated by cross-validation, which was
performed as follows. The dataset was divided into five groups of the same size
{𝑆1 , 𝑆2 , … , 𝑆5 }, 80 % of the data was used for training of the model, the remaining 20 % for
validation. For example, {𝑆2 } is used as a validation group and the set {𝑆1 , 𝑆3 , … , 𝑆5 } for
training of the model. The models were fitted using the training set, and the fitted model was
used to obtain predictions for the validation set. This procedure was repeated five times and
predictions were obtained for each group. Correlations between observed and predicted
phenotypes were calculated and averaged for the test sets(18). Note that because these are
actual values, the true breeding values are not known, but only the observed phenotypes are
available, the fitted model provides predictions for breeding values and predictions of other
fixed and environmental effects, with which a prediction of the phenotype is obtained, which
is contrasted with the true value of the phenotype.
Simulation
In order to evaluate the predictive power of the QR model against GBLUP, an asymmetric
data simulation with the presence of outliers was also carried out; the simulation of the
present work is analogous to that used by Pérez-Rodríguez et al(10). The main idea is to
highlight that the quantile regression model works adequately in the presence of atypical
observations, inhomogeneous variances and response variables with responses with
asymmetric distribution. In the context of selection, for example, it is not unusual to have
asymmetric distributions for phenotypes due to the process itself, since, if one selects for
some characteristic Y, and if there is in addition to this another characteristic of interest O,
then the conditional distribution of Y |O>o(19) is asymmetric. In the context of genomic
selection, it is also common to find subsets of observations that differ significantly from the
rest and these observations could be considered atypical. Montesinos-López et al(20) proposed
a model with Laplace errors and showed that it predicts adequately even in the presence of
outliers, the proposed model is a special case of the quantile regression model that is studied
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in the present work. The 9,628 SNPs resulting from the quality control described above for
300 animals were considered, the simulation of the data was carried out considering the linear
model:
𝑦𝑖 = 𝜇 + ∑9,628
𝑗=1 𝒙𝑖𝑗 𝛽𝑗 + 𝑒𝑖 ,
where 𝑖 = 1, … , 300, with 𝜇 = 39 for BW, it was assumed that the errors come from a biased
normal distribution (𝑆𝑁𝑐 ) with mean 0, variance 𝜎 2 (scale parameter 𝜎) and asymmetry index
𝛾1, that is 𝑒𝑖 ~𝑆𝑁𝐶 (0, 𝜎, 𝛾1 ), with 𝜎 = √1 − ℎ2 , ℎ2 with a value of 0.35, 𝛾1 =
−3/2
2 4 2𝜌2
√ 𝜌3 ( − 1) (1 − ) , 𝜌 ∈ {0.950, 0.975, 0.999} were considered, leading to
𝜋 𝜋 𝜋
different degrees of positive bias. Only positive values of 𝛾1 were considered since the
negative bias is obtained simply by changing the sign of the 𝑒𝑖′ 𝑠 and therefore the conclusions
obtained for the case of positive bias will also be valid for the negative case(21,22). Fifty
markers with non-zero effect were fixed, simulating them from a normal distribution with
mean 0 and variance √1 − ℎ2 ⁄50, the rest of the markers were set at 0; the positions of the
sampled markers were taken at random. To introduce outliers in the phenotypes, a certain
proportion of the residues of 𝑒𝑖 ~𝑆𝑁𝐶 (0,3, 𝛾1 ) were randomly generated, two proportions
were considered, 5 and 10 %, so samples from a mixture of two components of biased normal
distributions were taken. Six datasets were generated, three different asymmetry coefficients
0.950, 0.975, 0.999 with their two alternatives of outlier proportion 5 % and 10 %. The
asymmetric normal distribution has been used in genomic prediction(22) and its use in
channeled selection has also been suggested(23). Once the data were generated, the QRM
model was fitted with 𝜃 = {0.25, 0.50, 0.75} to compare it with GBLUP. The selection of
quantiles was made according to Nascimento et al(11), who consider these three possibilities
when the distribution of phenotypes is asymmetric 𝜃 ∈ {0.25,0.75} or when the distribution
is symmetric 𝜃 0.50, since our fundamental interest in this work focused on the modeling of
possibly asymmetric data and with the presence of outliers. The selection of the parameters
was also made for computational convenience since the fitting of the model is done by using
intensive computational techniques based on Markov chain Monte Carlo, as mentioned in
the section on software and fitting of the models. For each analysis, the correlation between
true and estimated 𝜷′𝑠, the correlation between true 𝑿𝜷 and estimated 𝑿𝜷 ̂ signals and the
component of variance associated with the residuals for each model, which is a way to
evaluate the goodness of fit of the models, were calculated. The Deviation Information
Criterion (DIC) was also considered, which can be used to select candidate models; models
with lower DIC are preferred to models with higher DIC(24).
Software and model fitting
The quantile regression models were fitted using a computational strategy similar to that
described by Pérez-Rodríguez et al(10). Adaptations of algorithms to include fixed and
random effects do not present great computational difficulty. The codes for the fitting of the
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models were developed in the programming languages R(25) and C. The codes for the fitting
of the models were organized in such a way that they can be easily run from the statistical
software R and are available by requesting them to the first author of the present study. In all
cases, three quantiles were selected, 𝜃 = {0.25, 0.50, 0.75}. The GBLUP and ssGBLUP
models were fitted with the BGLR library of R(26).
Results
Real data
Tables 2, 3, and 4 show the results of the experiment conducted with BW, WW, and YW
data from a Braunvieh cattle population, evaluated under two scenarios 1) with marker
information only, and 2) marker and pedigree information. In general, the highest correlations
between observed and predicted values were obtained with QR, except for BW, where the
correlations of GBLUP and ssGBLUP were higher than those obtained with QRM and QRH,
however, the correlations of QRM 𝜃 = 0.75 and QRH 𝜃 = 0.75 were close to those obtained
with GBLUP and ssGBLUP (0.7902 vs 0.7924), (0.6981 vs 0.7055), respectively. The lowest
MSE values were obtained with QRM 𝜃 = 0.75 and QRH 𝜃 = 0.75 in the WW
characteristic, while in the BW and YW characteristics, the lowest values were obtained with
GBLUP and ssGBLUP. The variance components associated with the error obtained with
QRM and QRH were lower than those obtained with GBLUP and ssGBLUP. In general, the
lowest DIC values were obtained with QRM 𝜃 = 0.75 and QRH 𝜃 = 0.75, except for BW
with the markers-only scenario, where the lowest DIC was obtained with QRM 𝜃 = 0.25.
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Table 2: Averages of Pearson correlation and standard deviation (in parentheses) between
observed phenotypic values (𝒚) and predicted phenotypic values (𝒚 ̂), mean squared error,
2 2
variance components associated with the error (𝜎𝑒 , 𝜎𝑤 ) and deviation information criterion
(DIC) for birth weight
Model ̂)
Cor(𝒚, 𝒚 MSE 𝝈𝟐𝒆 or 𝝈𝟐𝒘 DIC
QRM 𝜽 = 𝟎. 𝟐𝟓 0.7521 3.9973 2.7260 513.5014
(0.0753) (1.6108) (1.9762) (531.5701)
QRM 𝜽 = 𝟎. 𝟓𝟎 0.5619 7.3249 8.6297 970.7680
(0.1501) (0.4561) (0.2660) (6.9791)
QRM 𝜽 = 𝟎. 𝟕𝟓 0.7902 3.6535 2.4268 716.4237
(0.0766) (0.0943) (0.4829) (35.7161)
GBLUP 0.7924 2.3269 3.0035 803.0675
(0.0874) (0.2063) (0.5578) (31.9814)
QRH 𝜽 = 𝟎. 𝟐𝟓 0.6713 3.5026 2.3645 872.3949
(0.1329) (1.2848) (1.9670) (432.0737)
QRH 𝜽 = 𝟎. 𝟓𝟎 0.6816 2.9988 2.7372 659.1450
(0.1253) (0.7769) (1.8239) (1079.8674)
QRH 𝜽 = 𝟎. 𝟕𝟓 0.6981 4.1405 2.8610 1077.2027
(0.1140) (0.6187) (0.8666) (60.6781)
ssGBLUP 0.7055 2.4463 3.2641 1189.4282
(0.1191) (0.2204) (0.4244) (26.5023)
̂ )=correlation between observed and predicted phenotypes, MSE=mean squared error, 𝜎𝑒2 or
Cor(𝜷, 𝜷
2
𝜎𝑤 =components of variance associated with the error, DIC=deviation information criterion.
2 2
(DIC) for weaning weight
Model ̂)
QRM 𝜽 = 𝟎. 𝟐𝟓 0.5661 476.5293 419.4138 1550.5339
(0.2212) (17.4612) (23.3216) (13.9644)
QRM 𝜽 = 𝟎. 𝟓𝟎 0.5695 357.7328 396.8138 1576.8871
(0.2307) (8.9681) (47.7433) (21.5826)
QRM 𝜽 = 𝟎. 𝟕𝟓 0.5493 175.1298 67.9660 737.2216
(0.2196) (47.6181) (82.0807) (1150.7340)
GBLUP 0.5677 294.5807 376.7794 1583.2355
(0.2377) (36.6279) (24.1379) (16.2187)
QRH 𝜽 = 𝟎. 𝟐𝟓 0.4816 644.1278 551.5150 1962.1296
(0.0672) (50.8464) (64.8091) (20.9916)
QRH 𝜽 = 𝟎. 𝟓𝟎 0.4797 366.5940 356.9005 1537.7760
(0.0274) (56.8604) (238.5303) (903.3492)
QRH 𝜽 = 𝟎. 𝟕𝟓 0.3918 216.1753 5.9471 -706.1573
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(0.0544) (53.2417) (11.7834) (2034.7757)

ssGBLUP 0.4712 303.0404 421.8316 1982.3314
(0.0502) (37.6933) (55.2774) (21.9229)
Cor(𝜷, 𝜷
2
𝜎𝑤 =components of variance associated with the error, DIC=deviation information criterion.
2 2
(DIC) for yearling weight
Model ̂)
QRM 𝜽 = 𝟎. 𝟐𝟓 0.5421 1037.6529 953.6807 1487.1104
(0.1350) (175.2648) (261.8652) (35.8873)
QRM 𝜽 = 𝟎. 𝟓𝟎 0.5341 868.3651 964.4477 1524.0511
(0.1355) (34.0429) (113.1832) (12.4648)
QRM 𝜽 = 𝟎. 𝟕𝟓 0.5115 938.8244 700.7849 1284.0829
(0.1290) (241.2205) (465.2109) (402.9787)
GBLUP 0.5330 725.7579 924.8388 1526.7596
(0.1389) (71.3999) (90.0089) (11.6346)
QRH 𝜽 = 𝟎. 𝟐𝟓 0.5306 1277.9493 1172.2877 1850.7122
(0.1411) (44.0948) (108.7991) (17.2025)
QRH 𝜽 = 𝟎. 𝟓𝟎 0.5098 894.4148 1061.3157 1883.6773
(0.1700) (35.3996) (129.4702) (15.4422)
QRH 𝜽 = 𝟎. 𝟕𝟓 0.5027 915.1871 666.8830 1706.4933
(0.1748) (162.7629) (413.5046) (209.8455)
ssGBLUP 0.4712 778.6416 1071.3096 1891.9029
(0.0502) (84.9871) (128.2878) (17.5592)
Cor(𝜷, 𝜷
𝜎𝑤2 =variance components associated with the error, DIC=deviation information criterion.
Simulated data
The results of the simulation exercise where QR is compared with GBLUP under different
degrees of asymmetry and proportions of outliers are shown in Table 5. Column 2 records
the correlations between the “true” marker effects and the estimated marker effects, the
correlations obtained with QR were higher than those obtained with GBLUP. Column 3
shows the correlations between the “true signals” and the estimated ones, the highest
correlations were obtained with QR. Column 4 records the estimation of the variance
components associated with the error and column 5 the DIC values, the lowest values in both
columns were obtained with QR 𝜃 = 0.75.
182
“true” and estimated marker effects, “true” and estimated signals, variance components
associated with the error and DIC values for simulated data with different degrees of
asymmetry and proportion of outliers
Model ̂)
Cor(𝜷, 𝜷 ̂)
Cor(𝑿𝜷, 𝑿𝜷 𝝈𝟐𝒆 or 𝝈𝟐𝒘 DIC
𝝆 = 𝟎. 𝟗𝟓, 5% outliers
QR 𝜽 = 𝟎. 𝟐𝟓 0.0784 0.4963 0.6821 620.5455
(0.0034) (0.0336) (0.1806) (49.3305)
QR 𝜽 = 𝟎. 𝟓𝟎 0.0766 0.4643 0.6644 665.8219
(0.0042) (0.0493) (0.0703) (16.3032)
QR 𝜽 = 𝟎. 𝟕𝟓 0.0606 0.4269 0.1438 290.6870
(0.0132) (0.0386) (0.1421) (148.9695)
GBLUP 0.0722 0.4910 0.7375 691.6503
(0.0064) (0.0398) (0.0723) (19.9391)
𝝆 = 𝟎. 𝟗𝟓, 10% outliers
QR 𝜽 = 𝟎. 𝟐𝟓 0.0614 0.4369 0.4683 407.6496
(0.0183) (0.0329) (0.4030) (330.6304)
QR 𝜽 = 𝟎. 𝟓𝟎 0.0728 0.4579 0.7947 706.7931
(0.0045) (0.0420) (0.1063) (20.5797)
QR 𝜽 = 𝟎. 𝟕𝟓 0.0574 0.4061 0.4482 381.4644
(0.0092) (0.0399) (0.3225) (474.7138)
GBLUP 0.0654 0.4556 0.8717 731.9104
(0.0057) (0.0314) (0.0890) (21.8563)
𝝆 = 𝟎. 𝟗𝟕𝟓, 5% outliers
QR 𝜽 = 𝟎. 𝟐𝟓 0.0773 0.4835 0.5578 582.4254
(0.0087) (0.0562) (0.2523) (83.0548)
QR 𝜽 = 𝟎. 𝟓𝟎 0.0771 0.4689 0.6369 662.0337
(0.0074) (0.0515) (0.0868) (23.8018)
QR 𝜽 = 𝟎. 𝟕𝟓 0.0598 0.4169 0.2398 219.1691
(0.0128) (0.0450) (0.2033) (444.5060)
GBLUP 0.0703 0.4804 0.7316 692.6392
(0.0056) (0.0333) (0.0831) (24.0645)
𝝆 = 𝟎. 𝟗𝟕𝟓, 10% outliers
QR 𝜽 = 𝟎. 𝟐𝟓 0.0731 0.4386 0.8739 677.0858
(0.0081) (0.0789) (0.1077) (23.5472)
QR 𝜽 = 𝟎. 𝟓𝟎 0.0734 0.4529 0.8154 711.2935
(0.0078) (0.0615) (0.0845) (14.9809)
QR 𝜽 = 𝟎. 𝟕𝟓 0.0541 0.3945 0.3628 385.6030
(0.0056) (0.0583) (0.2572) (393.1935)
GBLUP 0.0640 0.4491 0.8913 736.7880
(0.0077) (0.0517) (0.0654) (14.8343)
𝝆 = 𝟎. 𝟗𝟗𝟗, 5% outliers
QR 𝜽 = 𝟎. 𝟐𝟓 0.0615 0.5286 0.1535 205.6973
183
(0.0144) (0.0271) (0.1657) 277.5807

QR 𝜽 = 𝟎. 𝟓𝟎 0.0741 0.5514 0.4860 614.2282
(0.0037) (0.0167) (0.0663) 15.7647
QR 𝜽 = 𝟎. 𝟕𝟓 0.0467 0.4855 0.0166 -271.4761
(0.0112) (0.0150) (0.0192) 288.4509
GBLUP 0.0737 0.5428 0.5305 625.9703
(0.0030) (0.0121) (0.0353) 11.3632
𝝆 = 𝟎. 𝟗𝟗𝟗, 10% outliers
QR 𝜽 = 𝟎. 𝟐𝟓 0.0768 0.4807 0.7817 650.8593
(0.0080) (0.0687) (0.0888) 22.8417
QR 𝜽 = 𝟎. 𝟓𝟎 0.0696 0.4630 0.6154 511.5287
(0.0148) (0.0600) (0.3369) 412.6645
QR 𝜽 = 𝟎. 𝟕𝟓 0.0507 0.3967 0.0204 -160.1660
(0.0031) (0.0505) (0.0127) 213.0462
GBLUP 0.0659 0.4649 0.7876 709.7240
(0.0065) (0.0418) (0.0528) 14.8566
̂ )=correlation between “true” and estimated marker effects, Cor(𝑿𝜷, 𝑿𝜷
Cor(𝜷, 𝜷 ̂ )=correlation between “true”
𝟐 𝟐
and estimated signals, 𝝈𝒆 or 𝝈𝒘 =variance components associated with the error, DIC=deviation information
criterion.
Discussion
In this study, QR analysis methodologies were compared with GBLUP and ssGBLUP. This
comparison was made with simulated phenotypes with different degrees of asymmetry and
proportions of outliers and actual data for birth, weaning and yearling weights.
Real data
The observed and predicted phenotype correlations obtained from cross-validation with
actual data were higher when using QRM and QRH in the WW and YW characteristics. For
BW, the highest correlations were obtained with GBLUP and ssGBLUP; however, in this
study, only three quantiles 0.25, 0.50 and 0.75 were tested, there is evidence in other studies
where QR is better than GBLUP, as in the case of the work of Nascimento et al(4), who
compared QR with models such as BLASSO, BayesB and RR-BLUP. These authors found
a 15.15 % gain in the predictive capacity of QR compared to RR-BLUP, it should be noted
that, mathematically, RR-is equivalent to GBLUP, in addition to the fact that the datasets
used in this experiment presented asymmetry.
The values of the mean squared error (MSE) measure the average of the squared error, that
is, the difference between the estimator and what is estimated, so low values are preferred;
the MSE averages of QRM and QRH were lower than those obtained with GBLUP and
ssGBLUP only for WW. The residual variance estimator is an indication that how well or
184
poorly the model fits the observed data, low values are preferred; the smallest variance
components of the error were obtained with QRM and QRH for the three characteristics
analyzed. Finally, the DIC value is used to select candidate models and, like MSE and error
variance components, low values are preferred. The lowest DIC values were obtained with
QRM 𝜃 = 0.75 and QRH 𝜃 = 0.75, except in the marker-only scenario and BW, where the
lowest DIC was obtained with QRM 𝜃 = 0.25. The mean squared error, the residual variance
and the DIC are values that help to choose the best fit model. When examining these values
together, it is observed that QRM and QRH are better in some of them, while in others they
are not, that is, QR has a performance equal to or greater than GBLUP and ssGBLUP;
although it should be noted that only three quantiles were tested and that QR has advantages
when used in asymmetric data and outliers, for this case there were only outliers and the
distributions did not present asymmetry. Mendes et al(27) compared QR with the Bayesian
method of LASSO (BLASSO), these authors reported a 6.7 % and 20.0 % increase in
accuracy and considered quantiles 0.15 and 0.45 in the evaluation of carcass yield and bacon
thickness, respectively, however, the characteristics evaluated in their study were
asymmetric.
In the analysis of real data, a limitation of the present study is the sample size, which can
impact the variability of the parameters estimated with the models and consequently the
variability of the predictions, however, all the models were fitted using the same information
and therefore the comparison of the predictive capacity of the models is considered
reasonable, the ideal would be to have large sample sizes, but, due to economic limitations,
this is not always possible. On the other hand, it is currently very common to use prediction
models in which the number of phenotypic records is smaller than the number of predictors
(SNPS), that is 𝑛 ≪ 𝑝, even in this context, numerous studies have shown that Bayesian
methods provide sophisticated tools that allow deriving reasonable predictions as long as the
regularization parameters are selected properly, for example using cross-validation
methods(28–30).
Simulated data
In the simulated data experiment, the correlations between “true” marker effects and
estimated effects as well as correlations of “true” and estimated signals were higher when
QR was used compared to GBLUP. These results are similar to those obtained by other
researchers(10), who simulated data with three different coefficients of asymmetry 0.75, 0.95,
0.999 with 5 % and 10 % of outliers and found that the correlations obtained with QR were
higher than those obtained with Bayesian ridge regression (BRR), in addition, this pattern
was more evident with a greater asymmetry and proportion of outliers. In this study,
simulations with asymmetry coefficients of 0.950, 0.975, 0.999 were carried out and the
quantiles with which higher correlations were obtained varied between 0.25 and 0.50; the
185
advantage of QR is that different quantiles can be tested, obtaining better results depending
on the quantile used, this advantage in the ability to predict the effects of markers and signals
has been taken advantage of by other researchers(4) , who found no trait association using the
traditional GWAS model of single SNP, but, when using QR with extreme quantiles such as
0.1, the model was able to find up to 7 SNPs associated with the characteristics studied.
The coefficients of variance of the error indicate how well the proposed model fits the studied
data, the smaller this value, the better the fit, the DIC is another value that is used to compare
candidate models. Models with a smaller DIC are preferred to models with a larger DIC(24).
The lowest residual variance estimators and DIC values were obtained with QR 𝜃 = 0.75,
perhaps this is because high asymmetry coefficients 0.950, 0.975, 0.999 were used in the
simulation, so therefore a quantile that fits best is expected to be the highest, in this case 0.75.
QR performed equally or better than GBLUP and ssGBLUP to predict growth characteristics
BW, WW and YW, the advantages of this method are more noticeable when the data are
more biased and present a higher proportion of outliers, as in the case of the simulation
experiment.
The predictive performance of QR both with marker information alone and with information
of markers plus pedigree, with the actual dataset, was better than the GBLUP and ssGBLUP
methodologies for WW and YW. For BW, GBLUP and ssGBLUP were better; however,
only quantiles 0.25, 0.50 and 0.75 were used, and the BW distribution was not asymmetric.
In the simulated data experiment, correlations between “true” marker effects and estimated
effects, as well as correlations of “true” and estimated signals were higher when QR was used
compared to GBLUP. The advantages of QR were more noticeable with asymmetric
distribution of phenotypes and with a higher proportion of outliers, as was the case with the
simulated dataset.
Acknowledgements
To the National Council of Science and Technology, Mexico, for the financial support for
the first author during his doctoral studies. The authors also thank the Mexican Association
of Breeders of Registered Swiss Cattle for allowing the use of their databases, and the
cooperating breeders for their kind cooperation in this study.
Conflicts of interest
The authors declare that there are no conflicts of interest.
186
Literature cited:
1. Koenker R, Bassett G. Regression quantiles. Econometrica 1978;46(1):33.
https://doi.org/10.2307/1913643.
2. Briollais L, Durrieu G. Application of quantile regression to recent genetic and -omic
studies. Hum Genet 2014;133(8):951–966. https://doi.org/10.1007/s00439-014-1440-6.
3. Wang L, Wu Y, Li R. Quantile regression for analyzing heterogeneity in ultra-high
dimension. J Am Stat Assoc 2012;107(497):214-222.
https://doi.org/10.1080/01621459.2012.656014.
4. Nascimento M, Nascimento ACC, Silva FF, Barili LD, Do Vale NM, Carneiro JE, Cruz
CD, Carneiro PCS, Serão NVL. Quantile regression for genome-wide association study
of flowering time-related traits in common bean. PLoS One 2018;13(1):1-14.
5. Fisher E, Schweiger R, Rosset S. Efficient construction of test inversion confidence

intervals using quantile regression. J Comput Graph Stat 2016;29:140-148,
http://arxiv.org/abs/1612.02300.
6. Logan JAR, Petrill SA, Hart SA, Schatschneider C, Thompson LA, Deater-Deckard K, de
Thorne LS, Bartlett C. Heritability across the distribution: An application of quantile
regression. Behav Genet 2012;42(2):256–267. https://doi.org/10.1007/s10519-011-
9497-7.
7. Vinciotti V, Yu K. M-quantile regression analysis of temporal gene expression data. Stat

Appl Genet Mol Biol 2009;8(1). https://doi.org/10.2202/1544-6115.1452.
8. Gianola D, Cecchinato A, Naya H, Schön CC. Prediction of complex traits: Robust

alternatives to best linear unbiased prediction. Front Genet 2018;9:195.
https://doi.org/10.3389/fgene.2018.00195.
9. Oliveira GF, Nascimento ACC, Nascimento M, Sant’Anna IdeC, Romero JV, Azevedo
CF, Bhering LL, Moura ETC. Quantile regression in genomic selection for oligogenic
traits in autogamous plants: A simulation study. PLoS One 2021;16(1):1-12.
10. Pérez-Rodríguez P, Montesinos-López OA, Montesinos-López A, Crossa J. Bayesian

regularized quantile regression: A robust alternative for genome-based prediction of
skewed data. Crop J 2020;8(5):713-722. https://doi.org/10.1016/j.cj.2020.04.009.
11. Nascimento M, e Silva FF, de Resende MDV, Cruz CD, Nascimento ACC, Viana JMS,
Azebedo CF, Barroso LMA. Regularized quantile regression applied to genome-enabled
prediction of quantitative traits. Genet Mol Res 2017;16(1).
https://doi.org/10.4238/GMR16019538.
187
12. Barroso LMA, Nascimento M, Nascimento ACC, Silva FF, Serão NVL, Cruz, CD, et al.
Regularized quantile regression for SNP marker estimation of pig growth curves, J.
Anim Sci Biotechnol 2017;8:59. https://doi.org/10.1186/s40104-017-0187-z.
13. Nascimento AC, Nascimento M, Azevedo C, Silva F, Barili L, Vale N, et al. Quantile
regression applied to genome-enabled prediction of traits related to flowering time in
the common bean. Agronomy 2019;9(12):1-11.
https://doi.org/10.3390/agronomy9120796.
14. López-Cruz M, Crossa J, Bonnett D, Dreisigacker S, Poland J, Jannink JL, Singh RP,
Autrique E, de los Campos G. Increased prediction accuracy in wheat breeding trials
using a Marker × Environment interaction genomic selection model. G3 Genes Genom
Genet 2015;5(4):569–582. https://doi.org/10.1534/g3.114.016097.
15. Pérez-Rodríguez P, Crossa J, Rutkoski J, Poland J, Singh R, Legarra A, et al. Single-step

genomic and Pedigree Genotype × Environment interaction models for predicting wheat
lines in international environments. Plant Genome 2017;10(2).
https://doi.org/10.3835/plantgenome2016.09.0089.
16. Christensen O, Lund M. Genomic relationship matrix when some animals are not
genotyped. Genet Sel Evol 2010;42(2):1-8. http://www.gsejournal.org/content/42/1/2.
17. Aguilar I, Misztal I, Johnson DL, Legarra A, Tsuruta S, Lawlor TJ. Hot topic: A unified
approach to utilize phenotypic, full pedigree, and genomic information for genetic
evaluation of Holstein final score. J Dairy Sci 2010;93(2):743-752.
https://doi.org/10.3168/jds.2009-2730.
18. Crossa J, Pérez P, de los Campos G, Mahuku G, Dreisigacker S, Magorokosho C.

Genomic selection and prediction in plant breeding. J Crop Improv 2011;25(3):239-261.
https://doi.org/10.1080/15427528.2011.558767.
19. Arnold BC, Beaver RJ. Hidden truncation models. Shankhya. Indian J Stat
2000;62(1):23–35. http://www.jstor.org/stable/25051286. Accessed Jul 6, 2022.
20. Montesinos-López A, Montesinos-López OA, Villa-Diharce ER, Gianola D, Crossa J. A

robust Bayesian genome-based median regression model. Theor Appl Genet
2019;132(5):1587-1606. https://doi.org/10.1007/s00122-019-03303-6.
21. Pérez-Rodríguez P, Villaseñor-Alva JA. On testing the skew normal hypothesis. J Stat
Plan Inference 2010;140(11):3148-3159. https://doi.org/10.1016/j.jspi.2010.04.013.
22. Pérez-Rodríguez P, Acosta-Pech R, Pérez-Elizalde S, Cruz CV, Espinosa JS, Crossa J. A

Bayesian genomic regression model with skew normal random errors. G3
Genes|Genom|Genet 2018;8(5):1771–1785. https://doi.org/10.1534/g3.117.300406.
188
23. Domínguez-Viveros J. Parámetros genéticos en la varianza residual de variables de

comportamiento en toros de lidia. Arch Zoot 2020;69(267):354–358.
https://doi.org/10.21071/az.v69i267.5354.
24. Spiegelhalter DJ, Best NG, Carlin BP, van der Linde A. Bayesian measures of model
complexity and fit. J R Stat Soc: Series B Stat Methodol 2002;64(4):583–639.
https://doi.org/10.1111/1467-9868.00353.
25. R Core Team. R: A language and environment for statistical computing. R Foundation
for statistical computing 2021. Vienna, Austria. https://www.R-project.org/.
26. Pérez P, de los Campos G. Genome-wide regression and prediction with the BGLR
statistical package. Genetics 2014;198(2):483-495.
https://doi.org/10.1534/genetics.114.164442.
27. Mendes dos Santos P, Nascimento ACC, Nascimento M, Fonseca e Silva F, Azevedo CF,
Mota RR, et al. Use of regularized quantile regression to predict the genetic merit of
pigs for asymmetric carcass traits. Pesqui Agropecu Bras 2018;53(9):1011–1017.
https://doi.org/10.1590/S0100-204X2018000900004.
28. Gianola D. Priors in whole-genome regression: The Bayesian alphabet returns. Genetics
2013;194(3):573-596. https://doi.org/10.1534/genetics.113.151753.
29. de los Campos G, Hickey JM, Pong-Wong R, Daetwyler HD, Calus MPL. Whole-genome
regression and prediction methods applied to plant and animal breeding. Genetics
2013;193(2):327-345. https://doi.org/10.1534/genetics.112.143313.
30. Ferragina A, de los Campos G, Vazquez AI, Cecchinato A, Bittante G. Bayesian

regression models outperform partial least squares methods for predicting milk
components and technological properties using infrared spectral data. J Dairy Sci
2015;98(11):8133-8151. https://doi.org/10.3168/jds.2014-9143.
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Article
Seasonal growth analysis of a white clover meadow (Trifolium repens L.)
Edgar Hernández Moreno a
Joel Ventura Ríos b
Claudia Yanet Wilson García c
María de los Ángeles Maldonado Peralta d*
Juan de Dios Guerrero Rodríguez e
Graciela Munguía Ameca a
Adelaido Rafael Rojas García d
a
Colegio de Postgraduados - Campus Montecillo. km 36.5 Carretera México-Texcoco,
56250, Texcoco, Estado de México, México.
b
Universidad Autónoma Agraria Antonio Narro. Departamento de Producción Animal.
Saltillo Coahuila, México.
c
Universidad Autónoma Chapingo. Sede San Luis Acatlán. San Luis Acatlán, Guerrero,
México.
d
Universidad Autónoma de Guerrero. Facultad de Medicina Veterinaria y Zootecnia N° 2.
Cuajinicuilapa, Guerrero, México.
e
Colegio de Postgraduados – Campus Puebla, México.
*Corresponding author: mmaldonado@uagro.mx
Abstract:
The objective of the present study was to assess a growth analysis of white clover (Trifolium
repens L.) and determine the optimal harvest time per season. The experiment was carried
190
out at the Colegio de Postgraduados, Campus Montecillo, Texcoco, Mexico. Twenty-four

3.7 X 1.7 m plots were used, distributed in a completely randomized design, with eight
treatments and three replicates per station. The treatments consisted of successive weekly
cuts, during a regrowth cycle of 8 wk, in each season of the year. At the beginning of the
study, a uniform cut was made and the residual forage was determined. The evaluated
variables were: accumulation of dry matter, botanical and morphological composition, and
leaf area index of white clover. The highest forage accumulation (P<0.05) occurred in the
eighth week in spring (2,688 kg DM ha-1). Leaf production was higher (P<0.05) in spring,
autumn and winter. The highest leaf area index was reached in the eighth week in spring (3.0;
P<0.05). It is recommend exploiting the white clover meadow in the sixth week of the spring-
summer period and in the seventh week of autumn-winter.
Key words: Growth analysis, Trifolium repens L., Dry matter accumulation, Botanical
composition.
Introduction
In the central zone of Mexico, white clover (Trifolium repens L.), perennial ryegrass (Lolium
perenne L.), orchard grass (Dactylis glomerata L.), and alfalfa (Medicago sativa L.) planted
on 171,520 hectares are the forage species that have exhibited the best performance under
grazing conditions in pure or mixed pastures(1,2,3). However, due to its chemical composition,
its persistence resulting from its creeping growth habit, and its adaptability to temperate
zones, white clover is the species of greatest agronomic importance among the almost 300
species of the genus Trifolium(4). In addition, it can also improve soil fertility by supplying
nitrogen in a proportion of up to 450 kg N ha-1 through symbiotic fixation(5,6,7).
Forage production patterns in Mexico are influenced by climate variations, with temperature
and precipitation being the main factors(8); therefore, it is important to know the seasonal
growth patterns of the most widely used forage species in each one of the ecological regions
of the country(9). Previous works mention that the management strategies of a meadow,
intensity and frequency through cutting or grazing can modify the botanical composition,
yield, and nutritional quality(10,11). The severity in the use of the meadow can modify the
191
carbohydrate reserves in the plant, which affects the growth pattern, reducing the number of
stems, number of sprouts, and number of leaves in the plant(12).
Evaluating the seasonal growth of a pure or mixed meadow helps to understand the behavior
of plants and the association of different species, since the balance between growth rate and
tissue loss varies with the season throughout the year(9). Dry matter yield, crop growth rate,
leaf area index, botanical and morphological composition, intercepted radiation, plant height,
leaf: stem ratio, and leaf: non-leaf component are structural variables that help to understand
the behavior of a meadow and must be considered to understand the response under a mowing
or grazing system(13,14).
In a study conducted by Moreno et al(15), they reported that, when associated with irrigated
grasses, white clover produced an average of 1,581 kg DM ha-1 (P<0.05) in its first year of
evaluation, while Maldonado et al(3) reported an increase of 376 % (equivalent to 7,532 kg
DM ha-1) in their fourth year of evaluation under mixed pastures with irrigation due to their
stoloniferous growth and persistence in the meadow. In another research(9), the highest leaf
area index occurred at week five in summer (P0.05), and the leaf was the largest component
in spring. There is little research analyzing the growth of white clover in Mexico. The
objective of the present study was to evaluate growth analysis of white clover (Trifolium
repens L.) in order to determine the optimal physiological moment of grazing in each season
of the year.
Materials and methods
The study was performed in a white clover meadow in the experimental field of the College
of Postgraduates (Colegio de Postgraduados), in Montecillo, Texcoco, State of Mexico, at
19º 29' N and 98º 53' O, at 2,240 m asl. Broadcast sowing was carried out in February 2009
with a viable pure seed density of 6 kg ha-1. The local climate is temperate sub-humid, with
an average annual precipitation of 636.5 mm, and a rainfall regime in the summer (from June
to October) and an average annual temperature of 15.2 ºC(16). The local soil is sandy loam,
with a slightly alkaline (pH 7.8) and 2.4 % organic matter(17).
In the middle of each season of 2012, a uniform grazing was made and later a growth analysis
was carried out; the treatments consisted of an eight-week growth analysis in spring-summer
and a nine-week one in autumn-winter, since low temperatures promote a slower forage
growth. Sheep were used as defoliators until the remaining forage was left at a height of
approximately 5 cm above ground level, and, for better management, an electric fence was
established in the experimental plots.
192
The plots were distributed in a completely randomized design with three replications.
Twenty-four 3.7 x 1.7 m plots were drawn, among which the treatments were randomly
distributed. During the dry season, the meadows were irrigated by gravity at field capacity;
16 irrigations were carried out every 2 wk with approximately 32 mm for each one, which
gave a total of 512 mm of water, and the meadows were not fertilized.
Climate data
The monthly averages of outdoor temperature (maximum and minimum) and monthly
rainfall during the study period were obtained from the agrometeorological station of the
College of Postgraduates (Colegio de Postgraduados), located at a distance of 100 m from
the experimental site (Figure 1). The maximum monthly temperature ranged from 22.1 to
30.2 °C, while the minimum temperature was from -2.6 to 11 °C. The highest temperature
occurred in spring, registering a maximum of 30.2 °C in April, and the lowest temperature,
of -2.6 °C was recorded in December. The accumulated precipitation from March 2012 to
April 2013 was 312.3 mm, 70 % of which occurred in June, July, August, and September
2012, accumulating a precipitation of 220 mm.
Figure 1: Average maximum and minimum monthly temperature and accumulated

precipitation per month during the study period (March 2012 - April 2013)
35 100
90
30
80
25 70
Temperature (ºC)
Precipitation (mm)
20 60
50
15 40
10 30
20
5
10
0 0
Mar Abr May Jun Jul Ago Sep Oct Nov Dic Ene Feb Mar Abr
Precipitación (mm) Temp. Máxima (ºC)
193
Dry matter accumulation
After uniform grazing, three 0.25 m2 squares were cut at a height of 5 cm from the ground in
each experimental plot for eight weeks. The forage harvested in each quadrant was washed
and dried in labeled paper bags in a forced air oven at 55 °C during 72 h in order to estimate
the amount of dry matter per hectare at the various regrowth ages.
Botanical and morphological composition
For the purpose of determining the botanical composition of the forage, a subsample of this
was collected for a dry matter yield of approximately 20 %(11) and was separated into the
following components: dead material, weeds, grasses and white clover. The morphological
components of white clover (leaf, petiole, runner and flower) were separated. Each separate
component was dried in a labeled paper bag and left in a forced air oven at 55 °C during 72
h in order to estimate its dry weight.
Leaf area index
The leaf area index was calculated by separating the leaves of five stolons were separated for
each week’s replicate and placing them in a leaf area integrator (LI 3100 LI-COR Inc.) from
which the leaf area readings in cm2 were obtained. These readings together with the number
of stolons per square meter allowed estimating the leaf area index by means of the following
formula:
LAI= LA * SD
Where: LAI= leaf area index; LA=leaf area per stem; and SD= stolon density (m-2).
The data were analyzed by the GLM procedures of SAS(18), for a completely randomized
experimental design, where the treatments were the weeks of evaluation with three repetitions
per season and a regression analysis for each variable. The means were compared with the
Tukey test (α= 0.05).
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Dry matter accumulation
Figure 2 shows the results of the accumulated dry matter yield, which increased with
regrowth age, reaching its maximum in the eighth week of spring (2,688 kg DM ha-1;
P<0.05), the seventh week of summer (2,241 kg DM ha-1; P<0.05), the eighth week of fall
(1,781.3 kg; P<0.05), and the sixth week of winter (1,643 kg; P<0.05). The biomass
accumulated in spring was higher by 20 % (447 kg DM ha-1), 51 % (907 kg), and 64 % (1,045
kg), compared to summer, autumn and winter, respectively (P<0.05). The leaf for spring,
autumn and winter increased as the regrowth weeks increased and it was greater than the
petiole, however, in summer there was a higher petiole yield and less leaf. During the
evaluation period in winter (March 3, 2013), an intense frost occurred; this coincided with
the sixth week of regrowth, which limited the biomass yield for said sampling, drastically
increasing the dead material.
For their part, in their assessment of white clover in the highlands of Mexico, Gutiérrez et
al(9) mention an accumulation of forage as the age of the plant increased in all seasons,
reaching the maximum yield for spring, autumn, and winter in the eighth week, with 2,953,
1,592, and 1,791 kg DM ha-1, respectively, and in the seventh week for summer with 1,971
kg DM ha-1 (P<0.05).
When establishing associations of white clover with perennial ryegrass (Lolium perenne) and
orchard grass (Dactylis glomerata L.), Moreno et al(15) found a maximum white clover yield
of 513 kg DM ha-1 in their first sampling year. However, Maldonado et al(3) registered a
significant increase in the yield of white clover in these same associations in their third and
fourth year of production, adding up to an average of 7,220 kg DM ha-1. These same authors
mention that white clover dominates over time in the meadow because it is a kind of
stoloniferous growth habit that allows rapid growth compared to grasses, which are tufted.
In another study(19), they reported that 65% of the annual yield occurred in spring and
summer, 23 % in winter, and autumn was the season that exhibited the lowest value, of 12 %
(P<0.05).
According to various authors(18,20), white clover requires temperatures of 18 to 30 °C, the

optimal being 24 °C, and precipitations of 750 mm for best performance. These temperatures
were reached in spring (Figure 1), favoring the growth of the meadow as a result of the
increase in the leaf area per plant and probably due to the increase in leaf appearance and
elongation rates(19). Conversely, the dry matter yield was low in winter; in this regard, various
195
authors (3,12,21) argue that low temperatures limit growth and forage accumulation, due to their
direct influence on a lower leaf appearance and foliar rate.
Botanical and morphological composition
Based on the morphological components and the estimated accumulated biomass yield, the
plant morphology was variable (P<0.05). The highest leaf percentage, of 68 %, was produced
in the third week in spring; likewise, in summer the highest percentage of leaf, of 46 to
40 %, was produced from the first to the third week, decreasing drastically in the fourth week,
to 30 % (P<0.05). In autumn, the highest leaf percentage occurred in the first week (70 %)
and remained until the seventh week (59 %), after which it decreased. Finally, in winter, the
highest leaf percentage (60 %) occurred in the fifth week (Figure 3).
On the other hand, the greatest contribution of the petiole, of 38 %, occurred in summer
(P<0.05), while the highest percentage of stolon was reported in spring, being greater only
in the first two weeks of growth, when it amounted to 20 % in average (P<0.05). As for
pastures, the largest percentage, an average of 15 %, occurred in the summer. The
contribution of weeds and flowers was minimal in all seasons and weeks of regrowth, being
3 % in average.
Winter was the season that reported the largest amount of dead material and from the seventh
week on there was a drastic increase, of 100 %, since there was a decrease in temperature
(Figure 1) frost causing the death of white clover. In this regard, Brock and Tilrock(8) mention
that all plants have an optimal growth temperature and when these surpass it or decrease
drastically, there may be cell death, which causes a drastic increase in dead material. On the
other hand, as the age of the plant increased, the dead material also built up (P<0.05), due to
the maturation of the senescent leaves of the lower strata(9).
The large proportion of leaves with respect to the petiole and stolon indicates that it is a high-
quality forage since this allows it to be more digestible. In addition to this, as observed in all
the assessment stages, the content of flower was minimal (P>0.05), which indicates that this
forage is not precocious and allows it to increase its nutritional value in the first weeks. As
has been shown, the association of this legume with grasses confers it forage value, since it
augments the total yield per surface unit to up to 14 t DM ha-1; however, these qualities can
also be affected by the season of the year(19,22).
196
In addition to this, white clover under grazing conditions is less susceptible to loss of apical
meristems due to the horizontal arrangement of leaves and leaf primordia, which allow
efficient capture of solar radiation compared to Gramineae(8,14).
Leaf area index
The leaf area index (LAI) allows to estimate the photosynthetic capacity of a plant per unit
area and helps to understand its ability to assimilate solar energy and transform it into dry
matter yield(23). The highest LAI was reported during spring and autumn, with 3.0 and 2.6,
respectively, in week eight, while in summer and winter the highest index was obtained in
week five with 2.6 (P<0.05). On the other hand, in all seasons there was a close relationship
between the LAI and the regrowth weeks, the highest r2, of 0.97, occurring in spring, and the
lowest, of 0.83, in summer (Figure 4).
The temperature range of 22 to 30 °C during the spring and summer and the higher
precipitation in June, July, August, and September resulted in 70 % of the total accumulated
precipitation in the experimental period, contributing to the greater growth of the plant, which
profited from the biochemical and photosynthesis processes for its optimal development.
However, the conditions were not favorable in the fall and winter, when the low temperatures
ranged from -2 to 11 °C, causing a reduction of the tissue turnover in winter and thereby
affecting the growth and development of the plant, which exhibited the lowest LAI and the
lowest yield of accumulated biomass during this season.
In a trial to assess white clover(9), the highest leaf area index, of 3.0, was observed in the
eighth week of spring; later, in the fifth week of summer, it was 1.7, and in the eighth week
of autumn and of winter, it had a value of 1.4 and 1.6, respectively ―results that agree with
those of this research. In a study directed by Zaragoza et al(1), they reported the highest LAI
(P<0.05) for the alfalfa crop in week five of spring (3.5), of summer (2.8), and of autumn
(2.0), and in the sixth week of winter(1.9). However, the values were different when
evaluating the orchard grass, since the highest LAI (P<0.05) occurred in week six of regrowth
in spring(2.3), summer(1.4) and autumn (1.1), and in the seventh week of winter (1.0). Other
researches on alfalfa(23,24) reported behaviors similar to those observed in this experiment,
since the highest values of LAI (P<0.05), of 3.3 and 4.9, respectively, were recorded in
spring-summer.
The LAI varies for each crop and depends on the environmental conditions present. Matthew
et al(14) points out that the LAI is optimal when the net forage production is at a maximum
197
point and the highest LAI is reached simultaneously; however, the LAI can be affected
indirectly by low temperatures, according to the type of crop and the time of sampling.
The accumulated biomass yield was higher in the spring and lower in autumn and winter. As
the regrowth age increased, so did the dry matter. It is recommend profiting from the
defoliation of the meadow in the sixth week of spring and summer and the seventh week of
autumn and winter, based on the fact that an adequate dry matter yield, greater leaf
component, and less dead material were obtained at those times, consequently optimizing the
forage nutrients.
Literature cited:
1. Zaragoza EJ, Hernández GA, Pérez PJ, Herrera HJG, Osnaya GF, Martínez HPA,
González MSS, et al. Análisis de crecimiento estacional de una pradera asociada alfalfa-
pasto ovillo. Téc Pecu Méx 2009;47(2):173-188.
2. Parfitt RL, Couper J, Parquinson R, Schon NL, Stevenson BA. Effect of nitrogen fertilizer
on nitrogen pools and soil communities under grazed pastures. NZ J Agr Res
2012;55(3):217-233.
3. Maldonado PMÁ, Rojas GAR, Torres SN, Herrera PJ, Joaquín CS, Ventura RJ, Hernández
GA, Hernández GFJ. Productivity of orchard grass (Dactylis glomerata L.) alone and
associated with perennial ryegrass (Lolium perenne L.) and white clover (Trifolium
repens L.). Rev Brasi Zootec 2017;46(12):890-895.
4. Randazzo CP, Rosso BS, Pagano EM. Identificación de cultivares de trébol blanco
(Trifolium repens L.) mediante SSR. J Basic Appl Gen 2013;24(1):19-26.
5. Black AD, Laidlaw AS, Moot DJ, O’Kiely P. Comparative growth and management of
white and red clovers. Irish J Agric Food Res 2009;48(2):149-166.
6. Phelan P, Keogh B, Casey A, Necpalova M, Humphreys. The effects of treading by dairy

cows on soil properties and herbage production for three white clover‐based grazing
systems on a clay loam soil. Grass Forage Sci 2012;68(4):548-563.
7. Unkovich M. Nitrogen fixation in Australian dairy systems: review and prospect. Crop
Past Sci 2012;(63):787-804.
198
8. Brock JL, Tilbrook JC. Effect of cultivar of white clover on plant morphology during the
establishment of mixed pastures under sheep grazing. NZ J Agric Res 2000;43(3):335-
343.
9. Gutiérrez-Arenas AF, Hernández-Garay A, Vaquera-Huerta H, Zaragoza-Ramírez JL,

Luna-Guerrero MJ, Reyes-Castro S, Gutiérrez-Arenas DA. Análisis de crecimiento
estacional de trébol blanco (Trifolium repens L.). Agroproductividad 2018;11(5):62-68.
10. Olivo CJ, Ziech MF, Meinerz GR, Agnolin CA, Tyska D, Both JF. Valor nutritivo de
pastagens consorciadas com diferentes espécies de leguminosas. Rev Brasi Zoot
2009;38(8):1543-1552.
11. Rojas GAR, Hernández GA, Ayala W, Mendoza PSI, Joaquín CS, Vaquera HH, Santiago
OMA. Comportamiento productivo de praderas con distintas combinaciones de ovillo
(Dactylis glomerata L.), ballico perene (Lolium perenne L.) y trébol blanco (Trifolium
repens L.). Rev Fac Cienc Agra 2016;48(2):57-68.
12. Rojas GAR, Hernández GA, Rivas JMA, Mendoza PSI, Maldonado PMA. Joaquín CS.
Dinámica poblacional de tallos de pasto ovillo (Dactylis glomerata L.) y ballico perenne
(Lolium perenne L.) asociados con trébol blanco (Trifolium repens L.). Rev Fac Cienc
Agra 2017;49(2):35-49.
13. Rojas GAR, Torres SN, Joaquín CS, Hernández-Garay A, Maldonado PMA, Sánchez SP.
Componentes del Rendimiento en diferentes variedades de alfalfa (Medicago sativa L.).
Agrociencia 2017;51(7):697-708.
14. Matthew C, Lemaire G, Sackville-Hamilton NR, Hernández-Garay A. A modified

selfthinning equation to describe size/density relationships for defoliated swards. Ann
Botany 1995;76(6):579–587.
15. Moreno CMA, Hernández-Garay A, Vaquera HH, Trejo LC, Escalante EJA, Zaragoza
RJL. Joaquín TBM. Productividad de siete asociaciones y dos praderas puras de
gramíneas y leguminosas en condiciones de pastoreo. Rev Fito Mex 2015;(38):101-108.
16. García E. Modificaciones al sistema de clasificación climática de Koppen. 4ed.

Universidad Nacional Autónoma de México. México, DF. 2004;217.
17. Ortiz SC. Colección de Monolitos. Depto. Génesis de suelos. Edafología, IRENAT.
Colegio de Postgraduados. Montecillo, Texcoco, Estado de México. 1997.
18. SAS, Institute. 2009.SAS/STAT® 9.2. Use Guide Release. Cary, NC: SAS Institute. USA.
19. Castro RR, Hernández GA, Pérez PJ, Hernández GJ, Quero CAR, Enríquez QJF.
Comportamiento productivo de cinco asociaciones gramíneas-leguminosas bajo
condiciones de pastoreo. Rev Fito Mex 2012;35(1):87-95.
199
20. Lane LA, Ayres JF, Lovett JV. The pastoral significance, adaptive characteristics, and
grazing value of white clover (Trifolium repens L.) in dryland environments in
Australia: a review. Austr J Exper Agric 2000;40(7):1033-1046.
21. Hernández GA, Hodgson J, Matthew C. Effect of spring grazing management on

perennial ryegrass/white clover pastures. 1. Tissue turnover and herbage accumulation.
NZ J Agr Res 1997;40:25-35.
22. Karsten HD, Carlassare M. Describing the botanical compositions of a mixed species
northeastern U. S. Pasture rotationally grazed by cattle. Crop Sci 2002;(42):882-889.
23. Rojas GAR, Hernández GA, Joaquín CS, Maldonado PMA, Mendoza PSI, Álvarez VP.
Joaquín TBM. Comportamiento productivo de cinco variedades de alfalfa. Rev Mex
Cienc Agríc 2016;7(8):1855-1866.
24. Álvarez-Vázquez P, Hernández-Garay A, Mendoza-Pedroza SI, Rojas-García AR,

Wilson-García CY, Alejos-de la Fuente JI. Producción de diez variedades de alfalfa
(Medicago sativa L.) a cuatro años de establecida. Agrociencia 2018;52:841-851.
200
Figure 2: White clover growth curves by season and by morphological component during a growth cycle of 8 and 9 weeks
201
Figure 3: Botanical and morphological composition of white clover during an 8 and 9 week growth cycle. mm= dead material
202
Figure 4: Leaf area index of white clover during a growing season
Spring= 4.67/(1+0.11*exp(-0.0619t)) Autumn= 3.13/(1+26.85*exp(-0.0666t))

r= 0.97 r= 0.98
Summer= 0.03+0.11t-0.00152 r= 0.83 Winter= 1.70/(1+18.91*exp(-0.1142t)) r= 0.96
203
Review
Aspects related to the importance of using predictive models in sheep

production. Review
Antonio Leandro Chaves Gurgel a*
Gelson dos Santos Difante a
Luís Carlos Vinhas Ítavo a
João Virgínio Emerenciano Neto b
Camila Celeste Brandão Ferreira Ítavo a
Patrick Bezerra Fernandes a
Carolina Marques Costa a
Francisca Fernanda da Silva Roberto c
Alfonso Juventino Chay-Canul d
a
Universidade Federal de Mato Grosso do Sul, Faculdade de Medicina Veterinária e
Zootecnia. Avenida Senador Filinto Müler, 2443 - Pioneiros, 79074-460, Campo Grande,
Mato Grosso do Sul, Brasil.
b
Universidade Federal do Rio Grande do Norte, Unidade Acadêmica Especializada em
Ciências Agrárias. Macaíba, Rio Grande do Norte, Brasil.
c
Universidade Federal da Paraíba, Centro de Ciências Agrárias. Areia, Paraíba, Brasil.
d
Universidad Juárez Autónoma de Tabasco, División Académica de Ciencias
Agropecuarias. Villahermosa, Tabasco, México.
* Corresponding author: antonioleandro09@gmail.com
Abstract:
Sheep production systems face numerous challenges, which make decision-making a

process fraught with risks and uncertainties. Modelling is a helpful tool in this respect, as
it allows decision-makers to evaluate the behaviour of variables and their
204
interrelationships, in addition to using previous or related information to predict results

and simulate different scenarios. The advent of prediction models has made it possible to
monitor the weight of an animal and determine the best time for its sale. Additionally, it
allows producers to estimate the weights of the carcass and major marketable cuts before
slaughter. All this information is directly associated with the profitability and success of
the production activity. Therefore, in view of the different applications of mathematical
models in production systems, this literature review examines concepts in modelling
studies and the importance of using prediction models in meat sheep production.
Furthermore, it addresses the practical application of modelling studies in predicting dry
matter intake and carcass traits of meat sheep through correlated variables.
Key words: Biometric measurements, Carcass, Intake prediction, Mathematical

equations, Meat sheep farming, Tropical pasture.
Introduction
Sheep farming for meat production in Brazil has expanded in the last decade because of
the increased demand for this type of meat in the market. Thus, producers have sought
ways to establish production systems capable of efficiently generating quality meat at a
low cost(1,2,3). However, the productivity of these animals in Brazil is still incipient due to
deficiencies in genetic and nutritional management, poor financing, inadequate
management systems of the various rearing stages, and low ability to properly organize
the production chain(4). Another relevant fact is that over 50 % of sheep production is
carried out on natural pastures without management(4). These peculiarities make the
decision-making process in sheep production systems fraught with risks and
uncertainties.
Despite being an inherent characteristic of animal production systems, the risk (likelihood
of occurrence of an event) can be minimized through the adoption of tools that help
decision-making(5). In this sense, a lack of knowledge will result in the impossibility of
estimating risk (uncertainty). Some information within and outside the production system
is essential to reduce uncertainties associated with decision making(6). Outside the
production system, the producer has little control over the actions that impact the
profitability of production. In contrast, actions within the farm will directly impact the
success of the activity.
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In this context, an adequate methodology for decision-making analysis requires accurate

information about the problem as well as efficiency in handling the system, so the planned
goals can be achieved(7). Modelling is a tool that can aid the decision-making process, as
it allows decision-makers to evaluate the behaviour of variables and their
interrelationships, in addition to using previous or related information to predict results
and simulate different scenarios(8).
The use of mathematical models allows producers to estimate some important

information that would be difficult to obtain in practical terms, e.g., herbage intake using
correlated variables(9). Modelling also makes it possible to monitor the weight of an
animal and determine the best time for its sale(10,11). In addition, it allows producers to
estimate the weight of the carcass and major cuts before slaughter(12,13). All this
information is directly associated with the profitability and success of the production
activity.
Thus, because of the different applications of mathematical models in production

systems, this literature review examines concepts in modelling studies and the importance
of using predictive models in the production of meat sheep.
Mathematical models
The use of mathematical models has become an indispensable tool for public
policymakers and scientists(14). Pool(15) suggested that the act of modelling would become
a third domain of science, joining the traditional domains of theory and experimentation.
In this sense, important political decisions, such as the effect of global warming on
terrestrial biology(16,17), public health, and pandemic management(18), have come to
depend heavily on modelling studies. In addition, researchers have started to use
modelling in the most diverse fields of science, e.g. medicine(19), economics(20),
physics(21), chemistry(22), engineering(23), law(24), animal science(25,26), and many others.
There are several concepts for mathematical models. Hamilton(14) defined them as the
expression of theory, which provides a possibility of comparing the theory with data
obtained in the physical environment. For Tedeschi(25), models are mathematical
representations of mechanisms that govern natural phenomena that are not fully known,
controlled, or understood. More recently, Tedeschi and Mendez(8) postulated that
mathematical models are arithmetic representations of the behaviour of real devices and
life processes. All these authors also considered that models are an abstraction and a
representation of reality(8,14,25).
The use or non-use of mathematics defines whether the model is predictive or descriptive,
respectively. Descriptive models theoretically address the performance of variables and
their interrelationships. In contrast, predictive models are aimed at using prior
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information to predict results or simulate different scenarios(8). In this respect, Tedeschi

and Mendez(8) categorized mathematical (predictive) models into three main classes: in a
temporal context, models can be classified as static or dynamic; in a natural context, as
empirical or mechanistic; and, in a behavioural context, as deterministic and stochastic.
Dynamic models are those that describe changes that have occurred and the obtained
responses as a function of time. The non-linear models used to describe animal growth
have a dynamic character(27,28). Static models, on the other hand, are those that generate
a response for a fixed instant, that is, they do not include time as a variable (8). However,
Tedeschi and Mendez(8) warn that the concept of static versus dynamic depends on the
time scale used, as a biological phenomenon can be better represented by a dynamic
model when daily changes occur, but when years are used as a time variant, a static model
may work better than a dynamic model, since daily changes are irrelevant to the variable
of interest.
Empirical models are obtained from observational data. These models are applied in
experimental studies that evaluate dose-response relationships, e.g., the effect of nitrogen
rates on the crude protein content of forage plants. Thus, it is possible to estimate the
concentration of a crude protein (variable Y) at any nitrogen rate (variable X) through
polynomial regression fitting(29). Mechanistic models, on the other hand, consider the
underlying conceptual mechanisms and the combination of elements from different
hierarchical levels. The main purpose of these models is to explain how an element at a
higher level behaves or responds to a range of elements at a lower level. This type of
model can be better exemplified in the modelling of the herbage accumulation dynamics
of a given forage plant(30). In this case, the mechanistic model seeks to explain the
sequence of actions of abiotic factors at the level of molecules, cells, tissues, organs,
tillers, plants, clumps, and the forage canopy.
Stochastic models are those that associate a risk or probability with the decision.
Stochasticity is associated with a lack of understanding of the biological phenomenon.
Accordingly, a greater understanding of the phenomenon would translate into a less
stochastic model. An example of a stochastic model was developed by Nadal-Roig et al(5)
to address tactical decisions, plan production, increase flexibility, and improve the
coordination and overall production of swine under the uncertainty associated with the
price of animal sales. The authors concluded that the stochastic model was efficient in
predicting the best scenario for the production system. Furthermore, due to the market
uncertainty of the sales price for swine, the stochastic version led to more precise and
realistic results than the deterministic version.
In contrast, deterministic models do not associate any probability with a given estimate(8).
Therefore, whenever the model is run without changes in the input variables, the same
output information will be obtained. An example of the use of this type of model was
proposed by the NRC(31) to estimate dry matter intake by sheep. According to the NRC(31),
the dry matter intake (kg/day) of sheep is determined by the following input variables:
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adult body weight (kg), body weight, and standard reference weight, which would
correspond to an adult animal. If female, it should be considered non-pregnant and non-
lactating, with a body score of 2.5 on a scale of 1 to 5, and having already undergone
complete skeletal growth. In this way, every time the model is run using the same input
information, the predicted intake will be the same. However, the model does not give any
probability that the predicted intake will indeed be observed.
Regardless of the type of model, its basic structure is composed of variables, parameters,
and constants, but not all models exhibit these three components simultaneously.
Variables—dependent or independent—are changed according to the individual;
parameters vary depending on the model, and the constants do not vary in any situation.
In this sense, two processes are commonly used for the creation of models: 1) Establishing
ideas and concepts through an in-depth study of the literature and then creating parameters
for the model variables, or 2) Analyzing experimental data that explain a biological
phenomenon and then combining them into an equation(25). In both situations, proper
statistical analysis to assess the fit of the models is an indispensable step.
Evaluation of mathematical models
There are conceptual differences between the terms ‘validation’, ‘verification’, and
‘evaluation’ of mathematical models(14,25). However, the term ‘model validation’ was
frequently questioned by researchers(25,32). Because models are considered an abstraction
of reality and an approximation of the real system(8,14,25), it is impossible to prove that all
model components will truly predict the behaviour of a biological system. Tedeschi(25)
proposed the terms ‘evaluation’ or ‘test’ to indicate the degree of robustness of the model
based on pre-established criteria. The author also highlighted that mathematical models
cannot be proved valid, except if they are suitable for the purpose for which they are
intended, under certain conditions.
In modelling studies, a protocol must be followed to define the best prediction model for
the established goal. Thus, the process first requires an extensive review of the literature
on the topic addressed. After a theoretical understanding of the phenomenon to be
modelled is achieved, the next step is to adjust, evaluate, and compare the defined models
and, finally, interpret the results and make inferences about the application of the selected
models. Therefore, it is understood that evaluation is a fundamental step in the adjustment
of prediction models(25), as this step defines whether the model is suitable for its intended
purpose. According to Hamilton(14), model evaluation is a comparison of predicted over
observed data, which uses statistical tools to support conclusions.
Accuracy and precision are two important concepts when evaluating mathematical
models. Accuracy indicates the proximity of predicted to observed mean values.
Precision, on the other hand, is the model's ability to consistently predict values(25).
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Therefore, an accurate but not precise model (situation 1 in Figure 1) estimates an average
value close to the true average value, but with a high standard deviation. In contrast, a
model with low accuracy and high precision (situation 4 in Figure 1) predicts a mean
different from the observed data but denotes a low standard deviation in the predictions.
In situations 2 and 4 (Figure 1) the models are equally accurate, yet only model 2 shows
the characteristics of accuracy and precision, as the points are distributed compactly in
the center of the target.
Figure 1: Schematic representation of precision and accuracy concepts (Adapted(25))
The first and simplest assessment of the goodness-of-fit of models (precision and
accuracy) is moment analysis of predicted and observed data. In this type of assessment,
a good model is expected to estimate mean, maximum, and minimum values as well as
data variance and standard deviation close to the observed values(33). Spearman's
correlation coefficient value has also been used initially to assess the classification of
predicted and observed data values. This coefficient assesses whether the highest
predicted value is also the highest observed value, thus creating a classification among
all data(34).
Linear regression between observed and predicted values is commonly used to evaluate
models. The hypothesis that the predicted data are equal to the observed data is tested by
the regression equation Y = β0 + β1 × X, where Y is the observed value; β0 and β1
represent the intercept and slope of the regression equation, respectively; and X is the
value predicted by the equations. Model-predicted values are plotted on the X-axis,
whereas observed values are plotted on the Y-axis(25). In this graph format, the data points
located above and below the equality line indicate overestimation or underestimation by
the model, respectively(26).
To test the hypothesis (β0 = 0 and β1 = 1), Dent and Blackie(35) suggested simultaneously
209
evaluating whether the intercept is different from zero and the slope is different from one.
For this purpose, the simultaneous F test for the identity of the regression parameters
predicted by observed data was used(36). However, Tedeschi(25) warned that the F test is
valid only for deterministic models and should not be used for stochastic models. In
addition, due to the assumption that the data are independent, which is not always
observed in a modelling study, the simultaneous F test can result in errors of acceptance
or rejection of the tested hypothesis(36).
After obtaining the linear regression, it is possible to calculate the coefficient of

determination of the equation (R2). The coefficient of determination indicates the
percentage of variation in Y that is explained by X. Therefore, R2 evaluates the proximity
of the data to the fitted regression line. It is noteworthy that the interpretation of the R2
value is often wrong(33). When used in isolation, this information is not a good indicator
of the quality of the model, as R2 measures the precision and not the accuracy of the
equation. Coupled with this is the fact that a high coefficient of determination does not
necessarily imply that there is a linear relationship between predicted and observed data
since the relationship can be curvilinear(25).
Another way to evaluate the regression equation is by the mean square error (MSE),
which evaluates the precision of the adjusted linear regression using the difference
between the observed values and the values estimated by the regression. Analla(37)
recommended MSE as the best criterion to select the model with the best fit when
comparing several models. It should be noted that although several methods are used to
assess the adequacy of the regression equation, its use may generate ambiguous results
when data do not show the normal distribution and in cases in which residual errors are
low(25). In this context, some additional evaluations are carried out.
The MSE is similar to the mean squared error of prediction (MSEP). The fundamental
difference between the two parameters is that MSEP is the difference between the
observed values and the values predicted by the model, while MSE, as seen above, is the
difference between the observed values and the values estimated by the regression.
Tedeschi(25) considered MSEP the most common and reliable measure to determine the
predictive accuracy of a model; however, the author warned that its reliability will
decrease as the number of observations decreases. In addition, the author highlighted that
MSEP does not provide any information about the precision of the model and that a
disadvantage of MSEP is that deviations are weighted by their squared values, which
removes the negative data, thus giving greater emphasis to larger values.
Bunke and Droge(38) proposed a decomposition of MSEP that takes into account the
source of variation of the parameters. By this fractionation, MSEP is divided into mean
error, systematic error, and random error. When most errors are attributed to the mean
error, it means that there is a deficiency in the placement of the equality line, which can
be corrected with an additive correction factor. Systematic error, on the other hand,
indicates a fault in line displacement, which can be corrected with a multiplicative
210
correction.
The model's coefficient of determination (CD), which shows the proportion of the total
variance of observed values that is explained by the predicted data, has long been used to
evaluate mathematical models. However, the CD has been replaced by the concordance
correlation coefficient (CCC) in studies of continuous variables(39,40). The CCC
simultaneously assesses the accuracy and precision of equations, which makes it a
powerful measure. The CCC value is obtained by an equation of two components: 1)
Correlation coefficient, which measures precision; and 2) Bias correction factor, which
indicates accuracy(41).
Numerous statistical techniques are used to assess the precision and accuracy of models.
However, no technique used in isolation is capable of adequately evaluating model
performance(25). Therefore, the best way to assess the predictive performance of a model
is to associate it with a set of statistical methods. It is important to emphasize that this
review addresses the main methods used in modelling studies predicting the dry matter
intake and carcass traits of sheep(39,40,42). A further discussion on the evaluation of models
from a statistical point of view was presented by Neter et al(33) and Tedeschi(25).
Application of predictive models in meat sheep production
Due to the diverse applications of mathematical models in sheep production systems, this
literature review will address the application of modelling studies in predicting dry matter
intake by grazing sheep as well as the body weight and carcass traits of sheep through
biometric measurements. This information is difficult to obtain under practical
conditions; however, it is directly associated with the profitability and success of the
production activity. It is highlighted that the possibilities of using modelling in sheep
production are as diverse as possible and it would be difficult to summarize all this
information in a single review.
Prediction of dry matter intake by grazing sheep
In the case of feedlot animals, the chemical and physical characteristics of ingredients
that make up the diets and their interactions have a great effect on dry matter intake
(DMI)(43,44). In short, the animal's energy demand defines the consumption of diets with
high caloric density(45). On the other hand, when the animal is fed diets of low nutritional
value and low energy density, the physical capacity of the gastrointestinal tract determines
the potential for DMI(46). In this respect, Mcdowell(47) mentioned that herbage intake is
primarily influenced by body size since the size of the animal, is positively correlated
211
with the nutritional requirements of maintenance(42,43,45), followed by energy density and

the rate of digestion of the diet. Furthermore, the author observed that DMI is positively
correlated with organic matter digestibility.
The neutral detergent fibre (NDF) content of a diet or herbage is an efficient parameter to
express the action of these two mechanisms to control dry matter intake, as it is positively
related to the rumen-fill effect and inversely related to the energy concentration of the
diet(48).
Animal-related factors such as breed, sex, age, body weight, physiological stage (growth,
pregnancy, or lactation), and body composition influence the nutrient requirements and
intake of sheep(45). Mertens(48) suggested that nutrient intake depends on important factors
related to feeding management (feed availability, linear trough area, feed accessibility,
frequency of supply, physical form, and processing), in addition to environmental
conditions and animal welfare related to the energy concentration of the diet(48).
Regarding grazing animals, in addition to all the aforementioned factors acting on dry
matter intake, the complex interactions between animal and pasture characteristics affect
the nutrient intake rate(49). Feeding behaviour is known to be the most efficient way to
demonstrate the interactions between pasture structure and herbage intake(50).
According to a mechanistic view, the daily DMI for grazing sheep is the result of the time
spent by the animal in searching and prehending the herbage and the intake rate during
this period(50), which, in turn, is the product of biting rate and bite weight. The rate and
weight of a bite change when the amount of herbage per bite (bite volume) is changed.
The bite volume is sensitive to oscillations in bite depth and herbage bulk density, which
in turn is determined by canopy height and herbage mass (Figure 2). The pasture structure
(herbage mass, height, etc.) also changes the time spent by the animal on the grazing
activity(51,52).
212
Figure 2: Schematic representation of the feeding behaviour of grazing animals

(Adapted53)
To exemplify the relationship between pasture structure, feeding behaviour, and intake,
one must simply imagine a situation with limitations on the herbage supply. In this
circumstance, there is a reduction in bite-size, whereas grazing time and biting rate
increase(52). Therefore, to some extent, it is possible to obtain constant herbage intake in
pastures with different canopies. Nonetheless, if the herbage allowance is too low, the
increase in grazing time will not be able to maintain intake due to the reduction in intake
rate(54).
Thus, because of the existence of several factors influencing the DMI of grazing sheep,
the modelling of this parameter becomes very complex. For this reason, most sheep DMI
prediction models are obtained from experiments conducted in feedlot
conditions(31,42,43,45). This may lead to inconsistencies if they are used to predict the DMI
of grazing sheep, as they do not consider the characteristics of the pasture and the
interactions between the animal and the forage plant.
Most models used to predict intake by grazing animals are mechanistic, focusing on the
digestive process and the selectivity of ingestion under grazing conditions, and they
mainly consider pasture height or the amount of herbage removed(50,55). Pittroff and
Kothmann(56) undertook a comparative analysis of quantitative models predicting the feed
intake of sheep and observed that about 55 % of the equations took into account some
213
pasture trait, with a predominance of herbage availability. The researchers concluded that
there is a weak conceptual framework in the development of the models.
Of the models presented in the review by Pittroff and Kothmann(56), that developed by
Freer et al(57) focused more on pasture characteristics. The equation predicts intake by
sheep as the product of potential feed intake (Imax) and the proportion of that potential
(relative intake) that the animal can obtain from the available amount of feed. Imax was
defined as the amount ingested (kg/d of DM) when the animals are allowed unrestricted
access to feed with a DM digestibility of at least 80 %, which depends on the standard
weight of an adult animal (standard reference weight) and the ratio between body weight
and standard reference weight (equation 1). It is noteworthy that in the case of tropical
grasses, the minimum digestibility of 80 % is hardly reached(58,59,60).
(1) Imax= 0.04 × SRW × Z × (1.7 - Z)
Where SRW= standard reference weight; Z= relative animal size, the ratio of body weight
to standard reference weight.
Relative intake was described as the product of two feed attributes: relative availability
and relative ingestibility. For grazing animals, relative availability is mainly predicted
from the herbage mass, whereas relative ingestibility is predicted from the digestibility of
the pasture collected by grazing simulation (hand plucking)(57).
To simulate the ruminant intake dynamics during grazing, Baumont et al(50) developed a
theoretical model of an intake rate that combines the pasture structure and the animal's
decision to graze or perform other activities. The authors defined dry matter intake as the
sum of instantaneous intake rates, which, in turn, are determined as a function of potential
intake rates in grazing horizons, preferences that determine the proportions selected in
both pasture horizons, and animal satiety levels (equation 2).
(2) IR= (PREFi × PIRi) + (PREFi+1× PIR + 1) / SL
Where IR= intake rate (g DM/min); PREFi and PREFi + 1= relative preference
determined from a grazing decision sub-model that defines how the animal distributes
intake between the highest available horizon (i) and the next available horizon (i + 1),
according to the relative preferences PREFi and PREFi+1; SL= satiety level; PIRi=
potential intake rate (g DM/min) obtained from the time taken by the animal to perform
the bite and the weight of that bite in the highest available horizon (i), and the next
available horizon.
McCall(61) proposed a model to estimate herbage intake in pastures where perennial

ryegrass is the predominant forage species. The author modelled the actual DMI of sheep
on pasture as a function of the maximum intake multiplied by the correction factor
(equation 3). The correction factor is obtained from herbage allowance and the animal's
214
potential intake (equations 4 and 5). Herbage allowance was estimated by harvesting the
forage contained within 1-m2 metal frames.
(3) HI= Imax × M

(4) M= A × EXP (-1.016 × EXP (1.0308 × HAM)
(5) A= 1-1.42 × (-0.00198 × HM),
Where HI= herbage intake (kg/day); Imax= maximum herbage intake (kg/day); M=
correction factor; EXP= exponential (2.7182); HAM= herbage allowance divided by
maximum intake; and HM= herbage mass minus dead material (kg/ha).
Medeiros(62) used the model proposed by McCall(61) to estimate the intake of sheep on
Cynodon spp. under different grazing intensities in a continuous grazing system and
concluded that the McCall(61) model overestimated the animals’ intake. This
overestimation indicated by Medeiros could be due to the type of grass since McCall
worked with a C3 grass that is more digestible than the C4 with which Medeiros worked.
Thus, Medeiros(62) suggested replacing the green herbage allowance (leaf + stem) in the
equation with a green leaf allowance. Only then was the estimated intake statistically
equal to that observed.
Similarly, Gurgel et al(9) evaluated different models predicting DMI in tropical pastures
using the adjustment factor proposed by McCall(61) and concluded that the equations do
not accurately predict the DMI of meat sheep and generate overestimated values in
tropical climate pastures. The authors proposed that the DMI estimate for lambs on
tropical pasture should consider the following model (equation 6):
(6) DMI (% LW)= 7.16545 - 0.21799 × LW + 0.00273 × LW2 - 0.00688 × GT +

0.000007 × GT2 + 0.00271 × GHA
Where DMI= dry matter intake (% LW); LW= live weight (kg); GT= grazing time
(min/day); and GHA= green herbage allowance (kg DM/100 kg LW), which corresponds
to herbage allowance minus dead material.
Therefore, the models proposed for a temperate climate do not correctly estimate herbage
intake by sheep on tropical pasture. In this way, studies to estimate intake by sheep in
tropical regions are necessary, especially in systems that adopt pasture as the primary
source of nutrients, as this information is of fundamental importance for nutritional
planning.
215
Prediction of body weight and carcass traits of sheep through

biometric measurements
Body weight is one of the main pieces of information that guides decision-making in
production systems due to its direct relationship with the nutritional requirements of
animals(31). In addition, monitoring the growth curve of ruminants makes it possible to
identify the phases in which the animal is more capable of converting the consumed feed
into body tissue and the best time for its sale(10,11,63).
Animal growth is evaluated using direct measuring equipment, such as livestock scales.
However, due to the conditions in which traditional sheep production systems operate(4),
the direct determination of the animals’ body weight often represents a challenge for
producers because of the high cost of acquiring and maintaining scales(64,65,66,67). In most
cases, this causes producers to market animals based on visual scores, which leads to
errors in the estimation of body weight and affects the profitability of production
systems(68).
The estimation of body weight by indirect methods can be an easily adopted, low-cost
alternative. In this sense, biometric measurements are a viable option to predict body
weight due to the correlation between these traits and the body weight of animals(65,69).
This method consists of developing mathematical models that allow producers to estimate
bodyweight using some biometric measurements (Figure 3) from linear and multiple
regression analyses. These body measurements can be obtained with a horse measuring
stick and a measuring tape(12,41), easy-to-handle and inexpensive instruments that do not
require sophisticated periodic maintenance.
The main biometric measurements (Figure 3) evaluated in sheep are as follows(70): withers
height (WH) – from the highest point of the withers to the ground (1); rump height (RH)
– the vertical distance from the highest point of the rump to the ground (2); body length
(BL) – from the scapulohumeral joint to the caudal part of the ischium (3); chest width
(CW) – the measurement between the tips of the scapulae (4); rump width (RW) – the
distance between the ischial tuberosities (5); heart girth (HG) – taken around the chest
cavity (6); abdominal circumference (AC) – taken around the abdominal cavity (7); leg
length (LL) – taken from the ischial tuberosity to the ground (8); and leg circumference
(LC) – taken around the middle portion of the thigh (9).
216
Figure 3: Main biometric measurements performed on sheep
2
1
7
5
3
6
9 4
Withers height (1); rump height (2); body length (3); chest width (4); rump width (5); heart girth (6);
abdominal circumference (7); leg length (8); leg circumference (9).
Some studies were conducted to develop linear and multiple equations to estimate the
bodyweight of sheep from biometric measurements(65,66,71,72,73). The authors concluded
that HG is the most important biometric measurement for predicting animal body weight
(Table 1). In contrast, Canul-Solís et al(66) used RW to estimate the body weight of
Pelibuey sheep. However, when more than one measurement is used, the predictive
capacity of the equations increases(68,69,73,74).
217
Table 1: Equations to predict the body weight (BW) of sheep using biometric
measurements (cm)
Author Breed Equation R2
Chay-Canul et
Pelibuey BW (kg) = -47.97 + 1.07 × HG 0.86
al(65)
Canul-Solís et
Pelibuey BW (kg) = - 19.17 + 3.46 × RW 0.96
al(66)
Charolais; Kent;
Málková et al(68) BW = (kg) = -3.997 + 0.225 × HG 0.78
crossbred
Charolais; Kent; BW (kg) = -4.672 + 0.243 × CBC +
Málková et al(68) 0.80
crossbred 0.198 × HG
BW (kg) = 0.45 × HG - 0.58× AC +
Gurgel et al(69) Santa Inês 0.88
0.005 × AC2 + 0.002 ×RH2
Kumar et al(71) Harnali BW (kg) = -63.72 + 1.23 × HG 0.87
Kebeles; Arsi-
Worku(73) BW (kg) = -39.51 + 0.91× HG 0.71
Bale
Kebeles; Arsi- BW (kg) = 45.77 + 0.59 × HG + 1.99 ×
Worku(73) 0.81
Bale CBC + 0.30 × CD + 0.5 × RH
BW = (kg) -107.16 + 1.40 × HG + 0.60
Grandis et al(74) Texel 0.88
× WH
HG= heart girth, RW= rump width; CBC= cannon bone circumference; AC= abdominal circumference;
RH= rump height; CD= chest depth; WH= withers height.
Another way to use biometric measurements to predict the body weight of sheep is from
body volume, which is obtained by the formula used for calculating the volume of a
cylinder, including the HG and BL measurements(75):
Radius (cm)= HG / 2π,

Body volume (dm3)= (π × r2 × BL) / 1000,
where, r= radius of the circumference (cm); π= 3.1416; HG= heart girth (cm); and BL=
body length (cm).
Salazar-Cuytun et al(67) compared three equations (linear, quadratic, and exponential) to

assess the relationship between body volume and weight in Pelibuey lambs and ewes. The
authors observed a correlation coefficient of 0.89 between body volume and weight.
Additionally, the quadratic model was found to have the best performance, according to
the adequacy assessment. Le Cozler et al(76) reported that body volume is strongly
correlated with weight in lactating Holstein cows.
In addition to being an efficient method to estimate body weight, biometric measurements

are used to predict sheep carcass traits(12,40,77). Determining the yield of carcass or major
cuts before slaughter is valuable information for production systems, as it allows the
producer to estimate the gross income of the farm. In this regard, the use of biometric
218
measurements taken before slaughter is of greater interest in commercial production

conditions due to the low additional cost for producers(40,78,79).
Because it is directly related to producer remuneration, the carcass weight has been the
variable most predicted by biometric measurements, with slaughter weight explaining
47.0 to 99.0 % of the variation in ruminant carcass weight(79,80). However, when biometric
measurements are used in association with body weight in linear and multiple equations
to predict carcass weight, there is an increase in the coefficient of determination(40). In
this respect, Gurgel et al(81) showed that the measurements of CW, LC, and RW, together
with body weight, explained 91.0 % of the variation in the carcass weight of Santa Inês
lambs finished on tropical pasture. For Pelibuey lambs, Bautista-Díaz et al(78)
recommended an equation to estimate the carcass weight that is associated with the
measurements of BL, HG, and AC and abdominal width (R2= 0.89). In predicting the hot
carcass weight of Morada Nova lambs, Costa et al(12) recommended an equation without
using body weight as an independent variable. According to the authors, the
measurements of BL, WH, CW, AC, and body condition scores are the most important in
predicting the carcass weight of the studied sheep (R2= 0.80).
Sheep meat is sold mostly in the form of half carcasses or whole carcasses. Nevertheless,
one way to add value to the meat is by selling it through cuts obtained by sectioning the
carcass(82). Thus, the carcass is initially divided into the major cuts of shoulder, neck, loin,
leg, and rib, which are smaller and facilitate marketing, conservation at home, and
preparation for consumption(3,82,83).
Biometric measurements are highly correlated with the major cuts of the carcass(2).
Therefore, studies were developed to test the hypothesis that biometric measurements
would be efficient in predicting the yield of these cuts. Shehata(13) developed regression
models to predict the weight of the major cuts of the carcass of Barki lambs from
biometric measurements and found that HG explained 67.0 % of the variation in leg
weight, and when HG was associated with BL, this value rose to 72.0 %. In addition,
Shehata(13) observed that the HG and BL precisely estimate the weights of the loin roast,
shoulder, and loin chop cuts. Abdel-Moneim(84) indicated BL as an efficient variable to
predict the shoulder weight of Barki sheep.
The application of biometric measurements is not restricted to predicting carcass weight

and major cuts. When used in equations, these measurements estimate the amount of
internal fat and carcass trimmings, ribeye area, and the yield of non-carcass components,
muscles, bones, and adipose tissue(12,40,77). Thus, the monitoring of biometric
measurements is a management tool that can help production systems increase revenues
and shorten the time needed for animals to reach slaughter weight.
It is noteworthy that, for the most part, these measurements are carried out on feedlot-
finished animals and/or in wool sheep, which does not represent the reality of production
systems in tropical regions, since tropical forage grasses are the food base of small and
219
large ruminants and are responsible for most of the meat produced in the tropics.
Therefore, modelling studies must be developed to estimate the weight and carcass traits
of hair sheep finished in tropical pastures through biometric measurements, taking into
account that genotype, sex, age, rearing system, and health can change carcass traits and
composition(85,86).
Despite its low adoption rate, modelling has great potential to help in decision-making in
meat sheep production. Modelling is a tool capable of predicting the dry matter intake,
bodyweight, carcass weight, and major marketable cuts of sheep with high precision and
accuracy, through correlated measurements. These equations can be used by researchers,
producers, technicians, and the meat industry, thus facilitating activity planning.
However, further research is warranted to increase the databases so that the equations can
be applied in the most diverse scenarios. In addition, more studies are needed to predict
herbage intake using information more easily obtained in practical production conditions.
Literature cited:
1. Trindade TFM, Difante GS, Emerenciano Neto JV, Fernandes LS, Araújo IMM,
Véras ELL, et al. Biometry and carcass characteristics of lambs supplemented in
tropical grass pastures during the dry Season. Biosci J 2018;34(1):172-179.
2. Gurgel ALC, Difante GS, Emerenciano Neto JV, Costa MG, Dantas JLS, et al.
Supplementation of lamb ewes with different protein sources in deferred marandu
palisadegrass (Brachiaria brizantha cv. Marandu) pasture. Arq Bras Med Vet Zootec
2020;72(5):1901-1910.
3. Araújo CGF, Costa MG, Difante GS, Emerenciano Neto JV, Gurgel ALC, et al.
Carcass characteristics, meat quality and composition of lambs finished in cultivated
pastures. Food Sci Technol 2021; Ahead of Print: 1-6.
4. Hermuche PM, Maranhão RLA, Guimarães RF, Carvalho Júnior OA, Gomes RAT,
Paiva SR, Mcmanus C. Dynamics of sheep production in Brazil. Int J Geoinformatics
2013;2(3):665-679.
5. Nadal-Roiga E, Plà-Aragonèsa EM, Pagès-Bernausa A, Albornoz VM. A two-stage

stochastic model for pig production planning in vertically integrated production
systems. Comput Electron Agric 2021;176:e105615.
6. Calvano MPCA, Brumatti RC, Barros JC, Garcia MV, Martins KR, Andreotti R.
Bioeconomic simulation of Rhipicephalus microplus infestation in different beef
cattle production systems in the Brazilian Cerrado. Agric Syst 2021;194:e103247,
2021.
220
7. Calvano MPCA, Brumatti RC, Garcia MV, Barros JC, Andreotti A. Economic
efficiency of Rhipicephalus microplus control and effect on beef cattle performance
in the Brazilian Cerrado. Exp Appl Acarol 2019;79:459-471.
8. Tedeschi LO, Menendez HM. Mathematical modeling in animal production. In:

Bazer FW, Lamb GC, Wu G editors. Animal agriculture sustainability, challenges
and innovations. 1rst ed. Cambridge: Academic Press; 2020:431-453.
9. Gurgel ALC, Difante GS, Emerenciano NJV, Santana JCS, Fernandes PB, Santos
GT, et al. Prediction of dry matter intake by meat sheep on tropical pastures. Trop
Anim Health Prod 2021;53:e479.
10. Silva FL, Alencar MM, Freitas AR, Packer IU, Mourão GB. Curvas de crescimento
em vacas de corte de diferentes tipos biológicos. Pesqui Agropecu Bras
2011;46(3):262-271.
11. Sousa JER, Façanha DAE, Bermejo LA, Ferreira JB, Paiva RDM, Nunes SF, Souza
MSM. Evaluation of non-linear models for growth curve in Brazilian tropical goats.
Trop Anim Health Prod 2021;53:e198.
12. Costa RG, Lima AGVDO, Ribeiro NL, Medeiros AND, Medeiros GRD, Gonzaga
Neto S, Oliveira RL. Predicting the carcass characteristics of Morada Nova lambs
using biometric measurements. Rev Bras Zootec 2020;49:e20190179.
13. Shehata MF. Prediction of live body weight and carcass traits by some live body
measurements in Barki lambs. Egyptian J Anim Prod 2013;50(2):69-75.
14. Hamilton MA. Model validation: an annotated bibliography. Commun Stat - Theory
Methods1991;20(7):2207-2266.
15. Pool R. Is it real, or is it Cray?. Science 1989;244(4811):1438-1440.
16. Devi S, Mishra RP. A mathematical model to see the effects of increasing
environmental temperature on plant–pollinator interactions. Modelo Earth Syst
Environ 2020;6:1315-1329.
17. Mandal S, Islam MS, Biswas MHA, Akter S. A mathematical model applied to
investigate the potential impact of global warming on marine ecosystems. Appl Math
Model 2022;101:19-37.
18. Dover DC, Kirwin EM, Hernandez-Ceron N, Nelson KA. Pandemic Risk
Assessment Model (PRAM): a mathematical modeling approach to pandemic
influenza planning. Epidemiol Infect 2016;144:3400-3411.
19. Waters SL, Schumacher LJ, Haj AJE. Regenerative medicine meets mathematical
modelling: developing symbiotic relationships. Regen Med 2021;6:e24.
221
20. Brandt AR. Review of mathematical models of future oil supply: Historical overview
and synthesizing critique. Energy 2010;35:3958-3974.
21. Madadelahi M, Acosta-Soto LF, Hosseini S, Martinez-Chapa SO, Madou MJ.

Mathematical modeling and computational analysis of centrifugal microfluidic
platforms: a review. Lab Chip 2020;20:1318-1357.
22. Yu PY, Craciun G. Mathematical analysis of chemical reaction systems. Isr J Chem
2018;58:733-741.
23. Shamsi M, Mohammadi A, Manshadia MKD, Sanati-Nezhad, A. Mathematical and

computational modeling of nano-engineered drug delivery systems. J Control
Release 2019;307:150-165.
24. Eriksson K. The accuracy of mathematical models of justice evaluations. J Math

Sociol 2012;36:125-135.
25. Tedeschi LO. Assessment of the adequacy of mathematical models. Agric Syst 2006;
89:225-247.
26. Zanetti D, Prados LF, Menezes ACB, Silva BC, Pacheco MVC, Silva FAZ, et al.
Prediction of water intake to Bos indicus beef cattle raised under tropical conditions.
J Anim Sci 2019;97:1364-1374.
27. Richards FJ. A flexible growth function for empirical use. J Exp Bot. 1959;10:290-
301.
28. Fernandes HJ, Tedeschi LO, Paulino MF, Detmann E, Paiva LS, Valadares SC, Silva
AG, Azevêdo JAG. Evaluation of mathematical models to describe growth of
grazing young bulls. Rev Bras Zootec 2012;41:367-373.
29. Leite RG, Cardoso AS, Fonseca NVB, Silva MLC, Tedeschi LO, Delevatti LM, et
al. Effects of nitrogen fertilization on protein and carbohydrate fractions of Marandu
palisadegrass. Sci Rep 2021;11:e14786.
30. Brunetti HB, Boote KJ, Santos PM, Pezzopane JRM, Pedreira CGS, Lara MAS, et
al. Improving the CROPGRO Perennial Forage Model for simulating growth and
biomass partitioning of guineagrass. Agron J 2021;113:1-16.
31. NRC. National Research Council. Nutrient requirements of small ruminants: sheep,
goats, cervids and new world camelids. Washington, DC, USA: National Academy
Press; 2007.
32. Oreskes N, Shrader-Frechette K, Belitz K. Verification, validation, and confirmation

of numerical models in the earth sciences. Science 1996;263:641-646.
222
33. Neter J, Kutner MH, Nachtsheim CJ, Wasserman W. Applied linear statistical
models. New York, USA: McGraw-Hill Publishing Co.; 1996.
34. Agresti, A. Categorical data analysis. New Jersey, USA: John Wiley & Sons. 2002.
35. Dent JB, Blackie MJ. Systems simulation in agriculture. London: Applied Science;
1979.
36. Mayer DG, Stuart MA, Swain AJ. Regression of real-world data on model output:
an appropriate overall test of validity. Agric Syst 1994;45:93-104.
37. Analla M. Model validation through the linear regression fit to actual versus
predicted values. Agric Syst 1998;57:115-119.
38. Bunke O, Droge B. Estimators of the mean squared error of prediction in linear
regression. Technometrics 1984;26:145-155.
39. Morais MG, Menezes BB, Ribeiro CB, Walker CC, Fernandes HJ, Souza ARDL, et
al. Models predict the proportion of bone, muscle, and fat in ewe lamb carcasses
from in vivo measurements of the 9th to 11th rib section and of the 12th rib. Semin
Cienc Agrar 2016;37:1081-1090.
40. Bautista-Díaz E, Mezo-Solis JA, Herrera-Camacho J, Cruz-Hernández A, Gomez-

Vazquez A, Tedeschi LO, et al. Prediction of carcass traits of hair sheep lambs using
body measurements. Animal 2020;10:e1276.
41. King TS, Chinchilli VM. A generalized concordance correlation coefficient for
continuous and categorical data. Stat Med 2001;20:2131-2147.
42. Cabral LS, Neves EMO, Zervoudakis JT, Abreu JG, Rodrigues RC, Souza AL,
Oliveira IS. Nutrients requirements estimative for sheep in Brazilian conditions. Rev
Bras Saúde Prod Anim 2008;9:529-542.
43. Vieira PAS, Pereira LGR, Azevêdo JAG, Neves ALA, Chizzotti ML, Santos RD, et
al. Development of mathematical models to predict dry matter intake in feedlot Santa
Ines rams. Small Ruminant Res 2013;122:78-84.
44. Allen MS. Effects of diet on short-term regulation of feed intake by lactating dairy
cattle. J Dairy Sci 2000;83:1598-1624.
45. Oliveira AP, Pereira ES, Pinto AP, Silva AMA, Carneiro MSS, Mizubuti IY, et al.
Estimativas dos requisitos nutricionais e utilização do modelo Small Ruminant
Nutrition System para ovinos deslanados em condições semiáridas. Semin Cienc
Agrar 2014;35:1985-1998.
223
46. Romera AJ, Gregorini P, Beukes PC. Technical note: a simple model to estimate
changes in dietary composition of strip-grazed cattle during progressive pasture
defoliation. J Dairy Sci 2010;93:3074-3078.
47. McDowell LR. Nutrient requirements of ruminants. In: McDowell LR. Nutrition of
grazing ruminants in warm climates. Cambridge: Academic Press 1985:21-36.
48. Mertens DR. Regulation of forage intake. In: Fahey Junior GC. Forage quality,
evaluation and utilization. Am Soc Agron 1994:450-492.
49. Carvalho PCF. Harry Stobbs Memorial Lecture: Can grazing behavior support
innovations in grassland management?. Trop Grassl-Forrages 2013;1:137-155.
50. Baumont R, Cohen-Salmão D, Prache S, Sauvant D. A mechanistic model of intake

and grazing behaviour in sheep integrating sward architecture and animal decisions.
Anim Feed Sci Technol 2004;112:5-28.
51. Bremm C, Carvalho PC, Fonseca L, Amaral GA, Mezzalira JC, Perez NB, et al. Diet
switching by mammalian herbivores in response to exotic grass invasion. Plos One
2016;11:e0150167.
52. Gonçalves RP, Bremm C, Moojen FG, Marchi D, Zubricki G, Caetano LAM, et al.
Grazing down process: The implications of sheep's ingestive behavior for sward
management. Livest Sci 2018;214:202-208.
53. Hodgson J. Grazing management. Science into practice. Longman Group UK Ltd.,
1990.
54. Guzatti GC, Duchini PG, Sbrissia AF, Mezzalira JC, Almeida JGR, Carvalho PCF,
Ribeiro-Filho HMN. Changes in the short-term intake rate of herbage by heifers
grazing annual grasses throughout the growing season. Grassl Sci 2017;63:255-264.
55. Gregorini P, Beukes PC, Romera AJG, Hanigan MD. A model of diurnal grazing
patterns and herbage intake of a dairy cow, MINDY: Model description. Ecol Model
2013;270:11-29.
56. Pittroff W, Kothmann MM. Quantitative prediction of feed intake in ruminants: I.

Conceptual and mathematical analysis of models for sheep. Livest Prod Sci
2001;71:131-150.
57. Freer M, Moore AD, Donnelly JR. GRAZPLAN: Decision support systems for
Australian grazing enterprises—II. The animal biology model for feed intake,
production and reproduction and the Graz Feed DSS. Agric Syst 1997;54:77-126.
224
58. Leal ES, Ítavo LCV, Valle CB, Ítavo CCBF, Dias AM, Difante GS, et al. Influence
of protodioscin content on digestibility and in vitro degradation kinetics in Urochloa
brizantha cultivars. Crop Pasture Sci 2020;72:278-284.
59. Ítavo LCV, Ítavo CCBF, Valle CB, Dias AM, Difante GS, Morais MG, et al.
Brachiaria grasses in vitro digestibility with bovine and ovine ruminal liquid as
inoculum. Rev Mex Cienc Pecu 2021;12:1045-11060.
60. Euclides VPB, Montagner DB, Araújo AR, Pereira MA, Difante GS, Araújo IMM,
et al. Biological and economic responses to increasing nitrogen rates in Mombaça
guinea grass pastures. Sci Rep 2022;12:1937.
61. Mccall DG. A systems approach to research planning to North Island hill country.
[Doctoral thesis]. New Zealand, DF: Massey University; 1984.
62. Medeiros HR. Avaliação de modelos matemáticos desenvolvidos para auxiliar a

tomada de decisão em sistemas de produção de ruminantes em pastagens. [Doctoral
thesis]. Brazil, SP: Universidade de São Paulo; 2003.
63. Gurgel ALC, Difante GS, Emerenciano NJV, Fernandes HJ, Itavo LCV, Itavo
CCBF, et al. Evaluation of mathematical models to describe lamb growth during the
pre-weaning phase. Semin Cienc Agrar 2021;42:2119-2126.
64. Conrado VDC, Arandas JKG, Ribeiro MN. Modelos de regressão para predição do
peso da raça Canindé através de medidas morfométricas. Arch Zootec 2015;64:277-
280.
65. Chay-Canul AJ, García-Herrera RA, Salazar-Cuytún R, Ojeda-Robertos NF, Cruz-

Hernández A, Fonseca MA, Canul-Solís JR. Development and evaluation of
equations to predict body weight of Pelibuey ewes using heart girth. Rev Mex Cienc
Pecu 2019;10:767-777.
66. Canul-Solis J, Angeles-Hernández JC, García-Herrera RA, Razo-Rodríguez D, Lee-

Rangle HA, Piñeiro-Vázquez AT, et al. Estimation of body weight in hair ewes using
an indirect measurement method. Trop Anim Health Prod 2020;52:2341-2347.
67. Salazar-Cuytun R, Garcia-Herrera RA, Munoz-Benitez AL, Ptacek M, Portillo-

Salgado R, Bello-Perez EV, Chay-Canul AJ. Relationship between body volume and
body weight in Pelibuey ewes. Trop Subtrop Agroecosyst 2021;24:e125.
68. Málková A, Ptáček M, Chay-Canul A, Stádník L. Statistical models for estimating

lamb birth weight using body measurements. Ital J Anim Sci 2021;20:1063-1068.
69. Gurgel ALC, Difante GS, Emerenciano NJV, Santana JCS, Dantas JLS, Roberto
FFS, et al. Use of biometrics in the prediction of body weight in crossbred lambs.
Arq Bras Med Vet Zootec 2021;73:261-264.
225
70. Oliveira DP, Oliveira CAL, Martins ENM, Vargas Junior FM, Barbosa-Ferreira M,
Seno LO, et al. Morphostructural characterization of female and young male of
naturalized Sul-mato-grossenses “Pantaneiros” sheep. Semin Cienc Agrar 2014;35:
73-986.
71. Kumar S, Dahiya SP, Malik ZS, Patil CS. Prediction of body weight from linear
body measurements in sheep. Indian J Anim Res 2018;52:1263-1266.
72. Huma ZE, Iqbal F. Predicting the body weight of Balochi sheep using a machine
learning approach. Turk J Vet Anim Sci 2019;43:500-506.
73. Worku A. Body weight had highest correlation coefficient with heart girth around
the chest under the same farmers feeding conditions for Arsi Bale sheep. Int J Food
Sci Technol 2019;5:6-12.
74. Grandis FA, Fernandes Junior F, Cunha LFC, Dias CBA, Ribeiro ELA, Constantino
C, et al. Relação entre medidas biométricas e peso corporal em ovinos da raça Texel.
Vet Zootec 2018;25:1-8.
75. Paputungan U, Hendrik MJ, Utiah W. Predicting live weight of Indonesian Local-
Bali cattle using body volume formula. Livest Res Rural Dev 2018;30:8
76. Le Cozler Y, Allain C, Xavier C, Depuille L, Caillot A, Delouard JM, et al. Volume
and surface area of Holstein dairy cows calculated from complete 3D shapes
acquired using a high-precision scanning system: Interest for body weight
estimation. Comput Electron Agric 2019;165:e104977.
77. Gomes MB, Neves MLMW, Barreto LMG, Ferreira MA, Monnerat JPIS, Carone
GM, Morais JSASC. Prediction of carcass composition through measurements in
vivo and measurements of the carcass of growing Santa Inês sheep. Plos One
2021;16:1-17.
78. Bautista-Díaz E, Salazar-Cuytun R, Chay-Canul AJ, Herrera RAG, Piñeiro-Vázquez

ÁT, Monforte JGM, et al. Determination of carcass traits in Pelibuey ewes using
biometric measurements. Small Ruminant Res 2017;147:115-119.
79. Alves AAC, Pinzon AC, Costa RM, Silva MSS, Vieira EHM, Mendonça IB, et al.
Multiple regression and machine learning based methods for carcass traits and
saleable meat cuts prediction using non-invasive in vivo measurements in
commercial lambs. Small Ruminant Res 2019;171:49-56.
80. Castilhos AM, Francisco CL, Branco RH, Bonilha SFM, Mercadante MEZ,
Meirelles PRL, et al. In vivo ultrasound and biometric measurements predict the
empty body chemical composition in Nellore. J Anim Sci 2018;96:1678-1687.
226
81. Gurgel ALC, Difante GS, Emerenciano Neto JV, Araujo CGF, Costa MG, Itavo
LCV, et al. Prediction of carcass traits of Santa Inês lambs finished in tropical
pastures through biometric measurements. Animal 2021;11:e2329.
82. Costa RG, Ribeiro NL, Cavalcante ITR, Roberto FFS, Lima PR. Carne de caprinos
e ovinos do Nordeste: Diferenciação e agregação de valor. Rev Cient Prod Anim
2019;21:25-33.
83. Oliveira JPF, Ferreira MA, Alves AMSV, Melo ACC, Andrade IB, Urbano SA, et
al. Carcass characteristics of lambs fed spineless cactus as a replacement for
sugarcane. Asian Australas J Anim Sci 2018;31:529-536.
84. Abdel-Moneim AY. Body and carcass characteristics of Ossimi, Barki and Rahmani
ram lambs raised under intensive production system. Egypt J Sheep Goats Sci
2009;4:1-16.
85. Ekiz B, Yilmaz A, Ozcan M, Kocak O. Effect of production system on carcass

measurements and meat quality of Kivircik lambs. Meat Sci 2012;90:465-471.
86. Hopkins DL, Mortimer SI. Effect of genotype, gender and age on sheep meat quality
and a case study illustrating integration of knowledge. Meat Sci 2014;98:544-555.
227
Technical note
Preference for eight plants among captive white-tailed deer Odocoileus

virginianus in Veracruz, Mexico
Hannia Yaret Cueyactle-Cano a
Ricardo Serna-Lagunes a*
Norma Mora-Collado a
Pedro Zetina-Córdoba b
Gerardo Benjamín Torres-Cantú a
a
Universidad Veracruzana. Facultad de Ciencias Biológicas y Agropecuarias región Orizaba-
Córdoba, Calle Josefa Ortiz de Domínguez s/n Peñuela. Amatlán de Los Reyes, Veracruz,
México.
b
Universidad Politécnica de Huatusco. México.
*Corresponding author: rserna@uv.mx
Abstract:
Wild white-tailed deer Odocoileus virginianus consume a diversity of high energy plants.
Captive deer, however, do not have access to this diversity, which may affect their productive
capacity. A cafeteria test was used to evaluate intake of and preference for eight plant species
among captive deer in Veracruz, Mexico. Three replicates were done of five consecutive
days of feeding with the selected plants followed by a 15-d evaluation period. One kilogram
of material from each plant species was offered each day and intake recorded.
Physicochemical analyses were done of all eight species. Intake results were evaluated with
an analysis of variance and a Tukey test, and a partial least squares regression analysis was
applied to relate intake to plant characteristics. Intake was highest for four plants: Zapoteca
acuelata, Bidens pilosa, Pennisetum purpureum and Parthenium hysterophorus. Preference
for these species was determined by their fiber and protein contents, and °Brix and pH levels.
228
Diversifying the diet of captive deer could provide additional feed options for producers and
increase animal productivity parameters.
Key words: Proximate analysis, Diet, Cervidae, Intake.
White-tailed deer (Odocoileus virginianus; Artiodactyla: Cervidae) is distributed throughout

the Americas, from Canadian forests, to coniferous and xerophytic forests in the US, in most
forests in Mexico and even in portions of South America(1). It is widely hunted in Mexico(2),
and is raised in Wildlife Conservation Management Units (Unidades de Manejo para la
Conservación de la Vida Silvestre - UMA) to produce trophies, meat, skin, brood stock, and
ornaments, among other products(3).
In the wild, O. virginianus is an opportunistic selective herbivore, foraging a selection of

plant parts (e.g. shoots, fruits, leaves, bark, and seeds), especially those with high nutritional
value(4). When the dry season occurs in deciduous tropical forests plant abundance decreases
and their nutritional quality diminishes(5). Under these circumstances, O. virginianus can
experience deficiencies in development, such as a lower than standard weight, become prone
to disease and limit its reproduction(4). These same responses are often observed in captive
O. virginianus. Captive deer, fed diets based on sheep and commercial deer feeds as well as
alfalfa(6), produce single rather than twin births, have low birth weight offspring, and longer
intervals between births(7).
Adult deer require 5.5 to 9 % crude dietary protein for adequate physiological
development(8,9). Protein requirements may be related to ontogeny(9), since captive fawns
require between 13 and 20 % protein for adequate development, while, for optimal antler
development, 15 to 18 % protein is required(9). Females require from 11 to 18 % protein in
pre-breeding, mating, pregnancy, lactation, and to increase offspring count(10). Diet
diversification in O. virginianus UMAs is imperative to complement basic feed nutritional
value and improve productive characteristics(11). If animal feed preferences, nutrients
contained in preferred plants and the nutritional requirements of animals at given weights
can be interrelated, then animal productive behavior can be estimated(11).
Estimates of the nutritional content of plants consumed by wild deer have been done using
various methodologies(12,13), but none have been done for captive deer. Cafeteria tests allow
quantification and analysis of how animals modify dietary behavior to balance their
nutritional needs. Essentially a multiple choice test, animals are offered one or several plants
229
and their nutritional preferences documented(14). The present study objective was to use a
cafeteria test to quantify the dietary preferences of captive O. virginianus offered eight plants
as feed.
The study was carried out at the El Pochote UMA (Secretaría de Medio Ambiente y Recursos
Naturales registry: UMA-IN-CR-0122-VER/og), located in Ixtaczoquitlán municipality, in
the state of Veracruz, Mexico (coordinates: 18°52’13.70” N; 97°02’59.97” W; 1,137 m asl).
Regional climate is predominantly semi-warm humid (Cwa) with abundant summer rains, an
average annual temperature of 18 to 24 °C, and average annual rainfall of 1,900 to 2,600 mm.
Vegetation near the UMA consists of remnant semi-evergreen tropical forest and secondary
vegetation.
Experimental animals were two-year-old deer (3 males and 3 females, n = 6), all healthy and
with similar body conditions. The cafeteria feeding trial was done over a 60-d period, that is,
three replicates of 5 d feeding with the eight selected plants, followed by a 15-d evaluation.
Feeding with the selected plants was done for five consecutive days at 0900 h. Independent
feeders were randomly distributed within the pen, and 1 kg fresh material (leaves, shoots and
green branches) from each of the tested plants placed in separate feeders (Table 1). To reduce
animal subjectivity (deer tend to repeat feeding behaviors), feeder positions were changed
daily. After 2 h, the feeders and the remaining plant material were removed from the pen.
Intake was quantified with the equation consumption = grams material offered – grams
material rejected.
Table 1: Intake (grams) of eight tested plants species by captive white-tailed deer O.
virginianus during a cafeteria feeding trial
Standard Standard Coefficient of
Plant species Mean Min Max
deviation error variation
Bidens pilosa 999.6 0.69 0.4 0.07 998.8 1000
Bursera simaruba 516 112.93 65.2 21.89 393 615
Fetusca sp 594.4 44.39 25.63 7.47 559 644.2
Pennisetum 975.67 23.86 13.78 2.45 949 995

purpureum
Phartenium 966.27 33.00 19.05 3.45 928.8 991
hysterophorus
Saccharum 797.47 10.71 6.18 1.34 787 808
officinarum
Vachelia 616.4 43.99 25.4 7.14 587.2 667
farnesiana
Zapoteca acuelata 1000 0 0 0 1000 1000
230
Proximate analyses were done of the eight tested plant species. Three samples of 100 g of
mixed material were collected from each plant and incinerated for 2 h at 600 °C. Organic
matter, ash, °Brix, pH and acidity were estimated; crude protein was quantified with the
Kjeldahl method (N x 6.25) and ether extract in a Soxhtel extractor(15). The intake and
physicochemical analysis data were analyzed with descriptive statistics using a central
tendency. Intake levels by animal were analyzed with an analysis of variance (ANOVA) and
a Tukey means test (α=0.05). A partial least squares (PLS) regression analysis was applied
in which the dependent variable was intake per plant species, the categorical variables were
the eight plants, and the predictor variables were each plant’s physicochemical
characteristics. All analyses were run with the Infostat ver. 2017 software.
The average intake results (Table 1) showed Bursera simaruba to have the highest coefficient
of variation and the lowest average intake. The ANOVA identified Zapoteca aculeata,
Bidens pilosa, Parthenium hysterophorus and Pennisetum purpureum as having the highest
intake (correlation coefficient: R²= 0.96, coefficient of variation= 5.94; P<0.05; Table 2).
These levels exceeded those of the other evaluated plants (Tukey: minimum significant
difference = 135.68 g, error= 2204.01, gl= 16; Figure 1). This is supported by the coefficients
of variation, since only these four plants were clearly preferred by the animals. The tested
plant species varied in terms of protein, fiber and °Brix (Table 3). The PLS regression
analysis explained 61.7 % of the correlation for intake preference of V. farnesiana, B. pilosa,
Z. acuelata and S. officinarum, which was related to fiber and protein contents and °Brix
level (Figure 2).
Table 2: ANOVA results for plant intake by captive O. virginianus
Source of Degrees of
variation Sum of squares freedom Mean square F P-value
Plant species 883335.23 7 126190.75 54.77 <0.0001
Error 36864.08 16 2304.01
Total 920199.31 23
231
Figure 1: Tukey means test results identifying plants with highest intake by O. virginianus
Consumption
Plant species
Table 3: Average physicochemical values of eight plants fed captive O. virginianus

Plant Moistur Protein Fat Fiber Ash pH Bri Acidity
species e (%) (%) (%) (%) x
(%) (°)
Bidens 48.937 18.15 4.728 23.94 1.505 5.5 7.8 0.224
pilosa
Bursera 58.437 8.88 3.484 6.03 1.902 5.3 2.7 0.352
simaruba
Phartenium 63.174 16.02 6.475 39.04 2.202 6.0 2.4 0.032
hysterophor
us
Saccharum 63.510 11.19 4.555 17.03 1.164 4.6 6.8 0.256
officinarum
Vachellia 48.016 18.1 0.474 29.04 2.245 5.0 4.5 0.192
farnesiana
Pennisetum 48.795 14.1 3.011 46.4 2.438 6.0 3.1 0.032
purpureum
Zapoteca 41.771 20.5 5.224 22.06 0.352 4.5 9.3 0.64
aculeata
Festuca sp. 32.375 15.02 6.873 48.02 1.432 4.3 7.8 0.16
232
Figure 2: Relationship of plant physicochemical characteristics to intake by O. virginianus
Consumption 1
Consumption 3
Consumption 2
Fiber
Protein
Acidity
Humidity
% Ashes
% Fats
Of the tested plants, Z. aculeata, B. pilosa, P. purpureum, P. hysterophorus and S.

officinarum were preferred by the captive O. virginianus. That these include two herbaceous
plants and a grass is of note since wild deer consume mostly shrubs and trees, choose
herbaceous species only seasonally, and consume few grasses throughout the year(16,17).
Voluntary consumption of bushy, herbaceous and grassy plants reflects nutritional need(18,19),
and is focused on species with the best physicochemical characteristics(20), such as
carbohydrates (°Brix) and fiber, both vital to digestibility(21).
All eight tested plant species meet deer protein requirements according to ontogenic stage.
To reach above-average weight male deer require 15 % dietary protein(21), and females
require 13 %(22). In young males, optimal growth requires from 13 to 16 % protein, while
20 % will augment their reproductive activity(22).
These results suggest that at least five of the tested plant species could be used to diversify
the diet of captive O. virginianus, which represents more dietary options for UMAs in this
region. In addition, the tested plants have physicochemical characteristics that make them apt
for use as deer feed while meeting the productive and reproductive requirements of O.
virginianus.
233
Acknowledgements
The research reported here was financed by the project “Caracterización de recursos
zoogenéticos de las altas montañas, Veracruz: aplicación de la filogeografía y modelación
ecológica2 (PRODEP: 511-6/18-9245/PTC-896). The authors thank María del Rosario
Dávila for carrying out the physicochemical analyses.
Literature cited:
1. Ortega S, Mandujano S, Villarreal J, Di Mare MI, López-Arévalo H, Molina M, Correa-
Viana M. Managing White-tailed deer: Latín América. In: Hewitt DG editor. Biology
and management of White-tailed deer. Boca Ratón, Fl, USA: CRC Press. 2011:565-585.
2. Mandujano S, Delfín-Alfonso CA, Gallina S. Comparison of geographic distribution

models of white-tailed deer Odocoileus virginianus (Zimmermann, 1780) subspecies in
Mexico: biological and management implications. Therya 2010;1:41-68.
3. Gallina S, Mandujano S, Bello J, López-Fernández H, Weber M. White-tailed deer

Odocoileus virginianus (Zimmermann, 1780). In: Barbanti DJM, González S editors.
Neotropical Cervidology: Biology and Medicine of Latin American Deer.
FUNEP/IUCN. 2009:101-118.
4. Ramírez-Lozano RG. Nutrición del venado cola blanca. Universidad Autónoma de Nuevo
León. Monterrey, Nuevo León, México. 2004.
5. Arceo G, Mandujano S, Gallina S, Pérez-Jiménez LA. Diet diversity of white-tailed deer

(Odocoileus virginianus) in a tropical dry forest in México. Mamm 2005;69:159-168.
6. Fulbright TE, Ortega-Santos JA. Ecología y manejo de venado cola blanca. Texas A&M
University Press. 2007.
7. Henke SE, Demaris S, Pfister JA. Digestive capacity and diets of White-tailed deer and
exotic ruminants. J Wild Management 1998;52:595-598.
8. Holter JB, Hayes HH, Smith SH. Protein requirement of yearling white-tailed deer. J Wild
Management 1979;1979:872-879.
9. Smith SH, Holder JB, Hayes HH, Silver H. Protein requirements of white tailed deer fawns.
J Wild Management 1975;39:582-589.
234
10. Jones PD, Strickland BK, Demarais S, Wang G, Dacus DC. Nutrition and ontogeny
influence weapon development in a long-lived mammal. Canad J Zool 2018;99:1-8.
11. Plata FX, Ebergeny S, Resendiz JL, Villarreal O, Bárcena R, Viccon JA, Mendoza GD.
Palatabilidad y composición química de alimentos consumidos en cautiverio por el
venado cola blanca de Yucatán (Odocoileus virginianus yucatanensis). Archiv Med Vet
2009;41:123-129.
12. Miller R, Kaneene JB, Fitzgerald SD, Schmitt SM. Evaluation of the influence of
supplemental feeding of white-tailed deer (Odocoileus virginianus) on the prevalence of
bovine tuberculosis in the Michigan wild deer population. J Wild Dis 2003;39:84-95.
13. Champagne E, Moore BD, Côté SD, Tremblay JP. Spatial correlations between browsing
on balsam fir by white‐tailed deer and the nutritional value of neighboring winter forage.
Ecol Evol 2018;8(5):2812-2823.
14. Hartley A, Jones GE. Process oriented supplier development: building the capability for
change. Inter J Purch Mat Management 1997;33:24-29.
15. AOAC. Official Methods of Analysis. Association of Official Analytical Chemists. 15th
ed. Washington, DC, USA. 1990.
16. Gallina S. White-tailed deer and cattle diets in La Michilia, Durango, Mexico. J Range
Management 1993;46:487-492.
17. Granados D, Tarango L, Olmos G, Palacio J, Clemente F, Mendoza G. Dieta y

disponibilidad de forraje del venado cola blanca Odocoileus virginianus thomasi
(Artiodactyla: Cervidae) en un campo experimental de Campeche, México. Rev Biol
Trop 2014;62:699-710.
18. Ramírez GR, Quintanilla JB, Aranda J. White-tailed deer food habits in northeastern
Mexico. Small Ruminant Res 1997;25:141-146.
19. López-Pérez E, Serrano-Aspeitia N, Aguilar-Valdés BC, Herrera-Corredor A.

Composición nutricional de la dieta del venado cola blanca (Odocoileous virginianus
ssp. mexicanus) en Pitzotlán, Morelos. Rev Chap serie Cienc Forest Amb 2012;18:219-
229.
20. Aguilera-Reyes U, Sánchez-Cordero V, Ramírez-Pulido J, Monroy-Vilchis O, García-

López GI, Janczur M. Hábitos alimentarios del venado cola blanca Odocoileus
virginianus (Artiodactyla: Cervidae) en el Parque Natural Sierra Nanchititla, Estado de
México. Rev Biol Trop 2013;61:243-253.
235
21. Clemente F, Riquelme E, Mendoza GD, Bárcena R, González S, Ricalde R. Digestibility

of forage diets of white-tailed deer (Odocoileous virginianus, Hays) using different
ruminal fluid inocula. J Appl Anim Res 2005;27:71-76.
22. Ullrey DE, Youatt WG, Johnson HE, Fay LD, Bradley BL. Protein requirement of white-
tailed deer fawns. J Wild Management 1967;31:679-685.
236
Technical note
Yield and nutritional value of forage brassicas compared to traditional

forages
David Guadalupe Reta Sánchez a*
Juan Isidro Sánchez Duarte b
Esmeralda Ochoa Martínez b
Ana Isabel González Cifuentes c
Arturo Reyes González b
Karla Rodríguez Hernández b
a
Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias (INIFAP). Campo
Experimental Delicias. Km. 2 Carretera Delicias-Rosales. 33000, Centro, Cd. Delicias,
Chihuahua, México.
b
INIFAP. Campo Experimental La Laguna. Matamoros, Coahuila, México.
c
Universidad Juárez del Estado de Durango. Facultad de Agricultura y Zootecnia. Gómez
Palacio, Durango, México.
*Corresponding author: reta.david@inifap.gob.mx
Abstract:
The high nutritional value of brassicas can increase productivity in traditional forage
production systems. The objective of the study was to compare the nutritional value and yield
of dry matter (DM) and nutrients between forage brassicas and traditional autumn-winter
species. The forage brassicas were Winfred, Hunter and Graza radish and the traditional
forages were oats, triticale, barley, wheat and berseem clover. The study was conducted in
Matamoros, Coahuila, Mexico in the 2018-2019 cycle, under a randomized complete block
experimental design with four repetitions. The regrowth capacity, the nutritional composition
237
of the forage and the yields of DM and nutrients were determined. All species showed
regrowth capacity, with three cuts in berseem clover in 154 d, and with two cuts in brassicas
(150-154 d) and cereals (133-144 d). The brassicas had nutritional composition similar to
that of berseem clover and better than that of cereals, mainly due to their higher content of
net energy of lactation (NEL; 6.57 to 7.32 MJ kg-1 DM). The DM yields of the brassicas were
similar to those observed in traditional forages; however, due to their high nutritional
composition, the brassicas were equal to or superior in production of crude protein (CP)
(1,608 to 2,986 kg ha-1) and NEL (62,819 to 84,044 MJ ha-1) to traditional forages. In general,
forage brassicas can increase nutrient yield with respect to cereals and berseem clover,
especially in the production of NEL (27.5 to 47.3 %).
Key words: Alternative crops, Dry matter, Regrowth, Crude protein, Energy.
Intensive cow’s milk production is one of the main economic activities in the Comarca
Lagunera, Mexico. The forage required by livestock is produced in a production system
where the main crops are corn, sorghum, alfalfa, oats and triticale. The production of these
crops faces problems of water scarcity, salinity in the soil and high environmental
temperatures(1), conditions that will worsen in the next decades due to climate change(2). This
situation makes it necessary to look for new crop options that allow increasing the nutritional
value and yields of dry matter and nutrients. An alternative is to increase forage production
in autumn-winter using species with regrowth capacity, and good nutritional and production
characteristics.
In the Comarca Lagunera, cereals in autumn-winter are produced with one or two cuts in the
stages of booting or beginning of heading, which are usually ensiled. Forage brassicas that
include species of canola, rapeseed, turnips, suede, kale and radish are a viable alternative
for the region due to their production potential, nutritional quality, in addition to their
capacity for regrowth(3,4) and silage of their forage(5,6). Brassicas produce 8,000 to 15,000 kg
ha-1 of dry matter (DM) in a period of 80 to 150 days after sowing (das). This means that
their DM yields may be equal to or higher than autumn-winter forage cereals(3,7). The main
benefit of the brassicas is their ability to produce forage with high nutritional value for a
relatively long period, since the crude protein (CP) content and the digestibility of DM(8) do
not decrease markedly with age. The CP content in brassica forage varies from 134 to 255 g
kg-1; the digestibility of DM fluctuates from 85 to 93 %(8,9); the neutral detergent fiber (NDF)
238
content reaches values from 199 to 516 g kg-1(9,10); and it has high concentrations of energy
(NEL) (1.79 to 1.87 Mcal kg-1 DM)(11).
In studies with stabled dairy cows, it is indicated that brassica forage can be used in the diet
of dairy cows without effects on milk production and composition(12,13). Other studies show
positive effects of brassica forage with increases in milk production, without negative effects
on cow health(14,15). In addition, in studies where the inclusion of brassica forage did not
affect milk production and composition, an increase in profitability was observed when
pasture silage and commercial concentrates were replaced with forage brassicas(15,16). It is
also reported that the use of brassica forage has a favorable environmental effect due to the
lower methane production compared to ruminants fed on pasture-based diets(11,17). The
objective of the study was to compare the nutritional value and yield of dry matter (DM) and
nutrients between forage brassicas and traditional species during the autumn-winter cycle.
The study was carried out at the La Laguna Experimental Station (CELALA, for its acronym
in Spanish) of the National Institute of Forestry, Agricultural and Livestock Research
(INIFAP, for its acronym in Spanish), located in Matamoros, Coahuila, Mexico (103° 13’
42” W and 25° 31’ 41” N, at an altitude of 1,100 m asl). The soil of the experimental site has
a clayey-loamy texture, with a depth greater than 1.8 m, water availability values of 150 mm
m-1(18), organic carbon content of 0.75 % and a pH of 8.14(1). The preparation of the land
consisted of a fallow, double harrowing and leveling of the terrain with laser. Before sowing,
each experimental plot was manually fertilized with granular ammonium sulfate and
monoammonium phosphate at doses of 50 kg N and 80 kg P2O5, respectively.
Sowing was done manually on October 12, 2018, on this date, sowing irrigation with a 15
cm irrigation sheet was also applied. Eight days after sowing, an overirrigation with a 6 cm
sheet was applied to facilitate the emergence of seedlings. The species and cultivars evaluated
were the following: oats (Avena sativa L.), Cuauhtémoc variety; triticale (x Triticosecale
Wittmack), Río Nazas variety; barley (Hordeum vulgare L.), Narro 95 variety; wheat
(Triticum aestivum L.), AN265 variety; berseem clover (Trifolium alexandrinum L.),
Multicut variety; brassica, Winfred cultivar (Brassica oleracea L. x Brassica rapa L.);
Hunter cultivar (Brassica rapa L. x Brassica napus L.) and forage radish, Graza cultivar
(Raphanus sativus L. x Brassica oleracea L., Raphanus maritimus L.). During the production
cycle, six supplemental irrigations with a total sheet of 75 cm were applied in oats, triticale,
wheat, clover, and Hunter brassica; while in barley, Winfred brassica and Graza radish, five
supplemental irrigations with a sheet of 63 cm were applied. The nitrogen fertilization dose
(250 kg ha-1) was also completed with 55 kg ha-1 at 33 das, 90 kg ha-1 after the first cut in
each species between 77 and 112 das, and 55 kg ha-1 before the second cut between 112 and
135 das.
239
A randomized complete block experimental design with four repetitions was used. The
experimental plot consisted of 20 furrows 0.18 m apart and 6 m long. The useful plot for
determining forage yield was 14.4 m2, harvesting 16 central furrows of 5 m in length. At
harvest, fresh forage and DM yields were determined. The DM content was obtained in a
random sample of 0.72 m2, sampling two of the central furrows of each plot of 2 m in length.
The sampled plants were dried at 60 °C in a forced-air oven until reaching constant weight.
DM yield was determined by multiplying the fresh forage yield by the DM content of each
plot. In cereals, two harvests were made in the booting stage; clover was harvested three
times in the vegetative stage, while the cultivars of brassica and radish were harvested twice
in the vegetative stage. The leaf area index (LAI) was determined weekly in all plots of the
experiment. For this, an AccuPAR ceptometer model Lp-80 PAR/LAI (Decagon Devices,
Inc., Pullman, WA, USA) was used. Three readings per plot were taken between 1200 and
1400 h solar time. Three measurements were made above and below the canopy, parallel to
the soil surface. The sensor was placed at an angle of 45° with respect to the furrows.
Plants sampled for the determination of DM content were also used to analyze the nutritional
value of forage. The dry samples were ground in a Wiley® mill (Thomas Scientific,
Swedesboro, NJ, USA) with a 1 mm mesh. The nitrogen content in each sample was
determined using the Dumas combustion method number 990.03 of AOAC, in which the
Thermo Scientific Flash 2000 equipment was used, and the result was multiplied by 6.5 to
obtain the percentage of crude protein (CP)(19). The neutral detergent fiber (NDF) and the
acid detergent fiber (ADF) were obtained according to Goering and Van Soest(20). The
content of net energy of lactation (NEL) was estimated following the methodology proposed
by Weiss et al(21). CP and NEL yields per hectare were determined by multiplying the CP and
NEL contents by the DM yield per hectare estimated for each experimental plot.
For the evaluation of regrowth capacity, the data on DM yield and LAI were analyzed by
harvest, using the MIXED procedure for repeated measurements of SAS (P≤0.05)(22). For
DM, CP and NEL yields, data from the two or three harvests in each crop were added together
to perform the statistical analysis. For the data on the nutritional value of the forage, a
weighted average of each parameter evaluated in the harvests carried out was obtained,
considering the DM yields. Analyses of variance (P≤0.05) were performed for the variables
of nutritional composition and yields of DM and nutrients. The means of these parameters
were compared with the protected Fisher’s least significant difference test (P≤0.05). The
analysis of the information was performed with the SAS(22) statistical program.
All the species evaluated had regrowth capacity, but berseem clover was superior with three
cuts in 156 days. The rest of the species produced two cuts; where the alternative species
Winfred brassica, Hunter brassica and Graza radish required the total available period (150
to 154 d); while cereals produced the cuts between 133 and 144 d. This behavior of cereals
240
allows starting earlier the preparation of the land for the next crop in the spring cycle.
However, if this is not so important in the production system, the later harvest of alternative
crops does not represent a disadvantage in the use of irrigation water, since these crops
required less or equal irrigation sheet than that used in cereals (63 to 75 cm of water sheet).
The regrowth capacity of the hybrids of brassica and the forage radish for the production of
two or three harvests in this study has also been observed in other works, where it is indicated
that several grazings can be carried out in these crops(3,4). Their good regrowth capacity is
observed in the little or no reduction of the LAI in regrowth and the higher yields of DM in
regrowths compared to the first harvest in Winfred brassica, Hunter brassica and Graza radish
(Table 1).
Table 1: Growth cycle, dry matter yield recovery (DMY) and leaf area index (LAI) at
regrowth after the first cut in traditional and alternative crops evaluated in the autumn-
winter cycle of 2018-2019
Treatments Cycle DMY (kg ha-1) LAI

Cut 1 Cut 2 Cut 3 Cut 1 Cut 2 Cut 3
(days)
Cuauhtémoc oat 144 4694 a 6550 a - 6.08 a 4.48 b -
Río Nazas 141 3718 b 5684 a - 4.20 a 3.55 a -

triticale
Narro 95 barley 133 4089 a 5697 a - 5.98 a 5.76 a -
AN265 wheat 144 4779 a 6534 a - 5.64 a 2.92 b -
Berseem clover 156 3924 a 4183 a 2094 b 3.65 b 6.19 a 3.10 b
Winfred brassica 150 4586 b 7430 - 7.20 a 6.26 b -
Hunter brassica 154 3391 a 5178 - 5.82 a 6.30 a -
Graza radish 154 4483 a 5999 - 6.44 b 8.03 a -

ab
Means followed by different letters in each row are significantly different (Tukey-Kramer P≤0.05).
The regrowth capacity observed in traditional crops is in accordance with what is commonly
observed in other studies carried out in the Comarca Lagunera. In berseem clover, it has been
reported that the Multicut variety produces up to 13.1 t ha-1 of DM in six cuts(23). In cereals
such as triticale, oat and barley, it has been observed that they have good capacity to
regrowth(24,25), with two to three cuts(26). Generally, greater capacity is observed in winter
genotypes, followed by facultative ones and lower in spring ones(27,28). In the present study,
241
the spring cultivars of Cuauhtémoc oat, Río Nazas triticale and Narro 95 barley had regrowth
capacity similar to that observed in the facultative wheat AN265, which had a lower recovery
of LAI due to its later growth cycle. This represents a disadvantage in an intensive forage
production system, since AN265 wheat did not reach its maximum growth in regrowth as
spring cereals did.
Of the traditional crops, berseem clover had the best forage nutritional composition, with
lower concentrations of NDF (417 g kg-1) and ADF (289 g kg-1), as well as higher contents
of CP (286 g kg-1) and NEL (6.44 MJ kg-1 DM) with respect to the values observed in all
cereals. Among cereals, Rio Nazas triticale was outstanding for its lower ADF content (372
g kg-1), and higher concentrations of NEL (5.52 MJ kg-1 DM) and CP (189 g kg-1) (Table 2).
Table 2: Nutritional composition of traditional and alternative crops evaluated in the

autumn-winter cycle of 2018-2019
Treatments CP (g kg-1) NDF (g kg-1) ADF (g kg-1) NEL (MJ kg-1

DM)
Cuauhtémoc oat 148.7 d 612.3 a 395.8 c 5.27 e
Río Nazas triticale 189.1 c 606.6 a 372.2 d 5.52 d
Narro 95 barley 204.7 c 567.3 b 488.7 a 4.27 g
AN265 wheat 165.1 d 628.6 a 418.7 b 5.02 f
Berseem clover 286.4 a 417.1 d 288.6 e 6.44 c
Winfred brassica 248.8 b 431.3 d 239.5 f 6.99 b
Hunter brassica 187.8 c 277.0 e 210.4 g 7.32 a
Graza radish 198.4 c 456.6 c 280.7 e 6.57 c
CP= crude protein; NDF= neutral detergent fiber; ADF= acid detergent fiber; NE L= net energy of lactation;
DM= dry matter.
†Means followed by different letters in each column are significantly different (MSD P≤0.05).
The alternative crops, brassicas and radish, had a better nutritional composition than that
observed in cereals, due to their high CP content, lower fiber concentration and higher NEL
content. In CP concentration, Winfred brassica (249 g kg-1) exceeded cereals (149 to 205 g);
while Hunter brassica (188 g) and Graza radish (198 g) obtained values similar to or higher
than those observed in cereals. In berseem clover, the CP content (286 g kg-1) was higher
than that observed in the alternative crops, while in concentration of NEL, Winfred brassica
242
and Graza radish (6.57 to 6.99 MJ kg-1 DM) were higher than that obtained in berseem clover
(Table 2).
The results of the nutritional composition of the present study in the forage of brassicas and
radish were within the typical range observed in forage brassicas of other works, which were
characterized mainly by their high contents of CP (134 to 255 g kg-1)(8,9) and NEL (7.49 to
7.82 MJ kg-1 of DM)(11). However, in this study in Winfred brassica and Graza radish, higher
ADF and NDF contents than those obtained in previous studies were observed, with ADF
values of 118 to 217 g kg-1 and 166 to 334 g in NDF(10,11,29). It has been indicated that these
NDF concentrations do not meet the minimum values for the proper functioning of the rumen
in cows (350 g)(30). In the present study, NDF values in Winfred brassica (431 g) and Graza
radish (457 g) were greater than 350 g, and similar to those observed in berseem clover (417
g); while in Hunter brassica (277 g), NDF values were lower than this amount. The high
content of NEL in the forage of Hunter and Winfred brassicas, Graza radish and berseem
clover was associated with the lower contents of ADF and NDF, in relation to the values
observed in cereals harvested in the booting stage.
The alternative crops, Winfred brassica and Graza radish, were outstanding in DM yield
(12,016 to 10,482 kg ha-1). These yields were similar to those obtained by berseem clover
(10,201 kg) and to the best cereals, Cuauhtémoc oat, Narrro 95 barley and AN265 wheat
(9,786 to 11,313 kg). In nutrient production, only berseem clover obtained CP yields (2,871
kg) similar to those of Winfred brassica (2,986 kg), the rest of the crops obtained lower CP
yields (1,608 to 2,082 kg). In yield of NEL, Winfred brassica (84,044 MJ) exceeded all other
crops evaluated (from 41,689 to 68,722 MJ ha-1) (Table 3).
243
Table 3: Yields of dry matter (DM), crude protein (CP) and net energy of lactation (NEL)
in traditional and alternative crops evaluated in the autumn-winter cycle of 2018-2019
Treatments DM (kg ha-1) CP (kg ha-1) NEL (MJ ha-1)
Cuauhtémoc oat 11244 ab 1672 b 59442 bc
Río Nazas triticale 9402 bc 1781 b 52074 cd
Narro 95 barley 9786 abc 1996 b 41689 d
AN265 wheat 11313 ab 1854 b 57045 bc
Berseem clover 10201 abc 2871 a 65923 bc
Winfred brassica 12016 a 2986 a 84044 a
Hunter brassica 8569 c 1608 b 62819 bc
Graza radish 10482 abc 2082 b 68722 b

abc
Means followed by different letters in each column are significantly different (MSD P≤0.05).
The DM yields obtained in the brassicas with two cuts are similar to the best yields reported
in other studies in brassicas (10,134 to 14,000 kg ha-1)(31,32). This level of yield in brassicas,
and their higher contents of CP and NEL with respect to cereals resulted in higher yields of
these nutrients per hectare. In relation to berseem clover with a high CP content, the brassicas
obtained similar CP yields for their high DM yield; however, in NEL yields, Winfred brassica
was superior to all species as a result of a combined effect of a high NEL content (Table 2)
and a high DM production (Table 3).
An aspect to highlight in the study was the ability of forage brassicas to produce yields of
DM and nutrients similar to or greater than those obtained with traditional species, with
irrigation sheets (63 to 75 cm) less than or equal to those used in traditional crops. These
results are important in a forage production system such as that of the Comarca Lagunera,
which has a shortage of water for irrigation.
In conclusion, forage brassicas have the potential to increase productivity in forage

production in autumn-winter due to their high nutritional value, good regrowth capacity and
high production of DM and nutrients. Of the species evaluated, Winfred brassica was
outstanding with respect to traditional crops mainly due to its higher content and production
of NEL (27.5 to 47.3 %).
244
Literature cited:
1. Santamaría CJ, Reta SDG, Chávez GJFJ, Cueto WJA, Romero PRJI. Caracterización del
medio físico en relación a cultivos forrajeros alternativos para la Comarca Lagunera.
Libro Técnico Núm. 2. INIFAP-CIRNOC-CELALA. México; 2006.
2. Andrade VM, Montero MJ. Nuevas proyecciones de cambio de precipitación y

temperatura para el siglo XXI en el Norte de México. Herrera E, López M, Carrillo J
editores. Memorias del segundo congreso cambio climático del Estado de Chihuahua.
Primera ed. 2014:26-35.
3. Bell LW, Watt LJ, Stutz RS. Forage brassicas have potential for wider use in drier, mixed
crop-livestock farming systems across Australia. Crop Pasture Sci 2020;71(10):924-
943. https://doi.org/10.1071/cp20271.
4. Umami N, Prasojo YS, Haq MS. Morphological characteristics and biomass production
Brassica rapa var. Marco during the dry season. Anim Prod 2022;24(1):31-36.
https://doi.org/10.20884/1.jap.2022.24.1.107.
5. Sánchez DJI, Serrato CJS, Reta SDG, Ochoa ME, Reyes GA. Assessment of ensilability
and chemical composition of canola and alfalfa forages with or without microbial
inoculation. Indian J Agric Res 2014;48(6):421-428. https://doi.org/10.5958/0976-
058x.2014.01325.0.
6. Kilic U, Erisek A, Garipoğlu AV, Ayan I, Onder H. The effects of different forage types
on feed values and digestibilities in some brassica fodder crops. Turkish J Agric Natural
Sci 2021;8(1):94-102. https://doi.org/10.30910/turkjans.747031.
7. Watt LJ, Bell LW, Cocks BD, Swan AD, Stutz RS, Toovey A, De Faveri J. Productivity
of diverse forage brassica genotypes exceeds that of oats across multiple environments
within Australia´s mixed farming zone. Crop & Pasture Sci 2021;72(5):393-406.
https://doi.org/10.1071/CP21034.
8. Villalobos LA, Brummer JE. Forage brassicas stockpiled for fall grazing: yield and
nutritive value. Crop Forage Turfgrass Management 2015;1(1):1-6.
https://doi.org/10.2134/cftm2015.0165.
9. Dillard SL, Billman ED, Soder KJ. Assessment of forage brassica species for dairy and
beef-cattle fall grazing systems. Appl Anim Sci 2020;36(2):157-166.
https://doi.org/10.15232/aas.2019-01921.
10. Omokanye A, Hernández G, Lardner HA, Al-Maqtari B, Singh Gill K, Lee A. Alternative
forage feeds for beef cattle in Northwestern Alberta, Canada: forage yield and nutritive
value of forage brassicas and forbs. J Appl Anim Res 2021;49(1):203-210.
https://doi.org/10.1080/09712119.2021.1933990.
245
11. Dillard SL, Roca-Fernández AI, Rubano MD, Elkin KR, Soder KJ. Enteric methane
production and ruminal fermentation of forage brassica diets fed in continuous culture.
J Anim Sci 2018;96(4):1362-1374. doi: 10.1093/jas/sky030
12. Keim JP, Castillo M, Balocchi O, Pulido R, Pacheco D, Muetzel S. Brief communication:
milk production responses and rumen fermentation of dairy cows supplemented with
summer brassica crops. NZ J Anim Sci Prod 2018;78:122-124.
13. Vargas-Bello-Pérez E, Geldsetzer-Mendoza C, Ibáñez RA, Rodríguez JR, Alvarado-

Gillis C, Keim JP. Chemical composition, fatty acid profile and sensory characteristics
of chanco-style cheese from early lactation dairy cows fed winter Brassica crops. Animal
2021;11(1):107. https://doi.org/10.3390/ani11010107.
14. Williams SRO, Moate PJ, Deighton MH, Hannah MC, Wales WJ, Jacobs JL. Milk
production and composition, and methane emissions from dairy cows fed lucerne hay
with forage brassica or chicory. Anim Prod Sci 2016;56(3):304-311.
https://doi.org/10.1071/AN15528.
15. Keim JP, Daza J, Beltrán I, Balocchi OA, Pulido RG, Sepúlveda-Varas P, Pacheco D,
Berthiaume R. Milk production responses, rumen fermentation, and blood metabolites
of dairy cows fed increasing concentrations of forage rape (Brassica napus ssp. biennis).
J Dairy Sc 2020;103(10):9054-9066. https://doi.org/10.3168/jds.2020-18785.
16. Castillo-Umaña M, Balocchi O, Pulido R, Sepúlveda-Varas P, Pacheco D, Muetzel S,

Berthiqume R, Keim JP. Milk production responses and rumen fermentation of dairy
cows supplemented with summer brassicas. Animal 2020;14(8):1684-1692.
https://doi.org/10.1017/S175173112000021X.
17. Sun XZ. Invited review: glucosinolates might result in low methane emissions from
ruminants fed brassica forages. Frontiers in Vet Sci 2020;7:588051.
https://doi.org/10.3389/fvets.2020.588051.
18. Santamaría CJ, Reta SDG, Orona CI. Reducción del rendimiento potencial de maíz
forrajero en calendarios con tres y cuatro riegos. Terra Latinoamericana 2008;26(3):235-
241.
19. AOAC (Association of Official Agricultural Chemists). Official methods of analysis.

Dumas method (99003). 15th edition Washington DC, USA. 2005.
20. Goering HK, Van Soest PJ. Forage fiber analysis Apparatus, reagents, procedure and
some applications Agric Handbook 379 ARS. Washington, DC, USDA; 1970.
246
21. Weiss WP, Conrad HR, St-Pierre NR. A theoretically-based model for predicting total
digestible nutrient values of forages and concentrates. Anim Feed Sci Technol
1992;39(1-2):95-110. https://doi.org/10.1016/0377-8401(92)90034-4.
22. SAS Institute. The SAS system for windows, release 93. Cary, NC: Statistical Analysis
Systems Inst; 2011.
23. Núñez HG, Quiroga GHM, Márquez OJ de J, de Alba AA. Producción y calidad de trébol
de Egipto (Trifolium alexandrinum L.) para ganado lechero en el Norte y Centro de
México. Agrociencia 1997;31(2):157-164.
24. Keles G, Ates S, Coskun B, Koc S. Re-growth yield and nutritive value of winter cereals.
In: Proc 22nd Int Grassland Cong. 2013.
25. Wilson GCY, López ZNE, Ortega CME, Ventura RJ, Villaseñor MHE, Hernández GA.
Acumulación de forraje, composición morfológica e intercepción luminosa en dos
variedades de avena. Interciencia 2018;43(9):630-636.
26. Zamora VVM, Lozano del RAJ, López BA, Reyes VMH, Díaz SH, Martínez RJM,
Fuentes RJM. Clasificación de triticales forrajeros por rendimiento de materia seca y
calidad nutritiva en dos localidades de Coahuila. Téc Pecu Mex 2002;40(3):229-242.
27. Lozano del RAJ, Rodríguez SA, Díaz SH, Fuentes RJM, Fernández BJM, Fernando
NMJM, Zamora VVM. Producción de forraje y calidad nutritiva en mezclas de triticale
(X Triticosecale Wittmack) y ballico anual (Lolium multiflorum L.) en Navidad, N.L.
Téc Pecu Méx 2002;40(1):17-35.
28. Ye CWE, Díaz SH, Lozano del RAJ, Zamora VVM, Ayala OMJ. Agrupamiento de
germoplasma de triticale forrajero por rendimiento, ahijamiento y gustosidad. Téc Pecu
Méx 2001;39(1):15-29.
29. Villalobos L, Brummer J. Evaluation of Brassicas for fall forage. In: Proc Western States
Alfalfa and Forage Symp, Reno, NV, December, 2013. UC Cooperative Extension,
Plant Science Department University of California, Davis, CA 95616.
30. Kolver ES. Nutrition guidelines for the high producing dairy cow. Proc Ruakura Farmers
Conference; 2002.
31. Stewart AV, Moorhead AJ. The development of a fodder radish suitable for multiple
grazing. Agronomy NZ 2004;34:1-7.
32. McGrath S, Sandral G, Holman B, Friend M. Lamb growth rates and carcass
characteristics of White Dorper and crossbred lambs grazing traditional and novel
pastures during spring in souther Australia. Anim Prod Sci 2020;61(11):1151-1159.
https://doi.org/10.1071/AN18769.
247
Technical note
Genetic characterization of bovine viral diarrhea virus 1b isolated from

mucosal disease
Roberto Navarro-López a
Juan Diego Perez-de la Rosa b
Marisol Karina Rocha-Martínez b
Marcela Villarreal-Silva a
Mario Solís-Hernández a
Eric Rojas-Torres a
Ninnet Gómez-Romero a*
a
Comisión México-Estados Unidos para la prevención de fiebre Aftosa y otras enfermedades
exóticas de los animales, Carretera México-Toluca Km 15.5 Piso 4 Col. Palo Alto.
Cuajimalpa de Morelos. 05110. Ciudad de México. México.
b
Centro Nacional de Servicios de Constatación en Salud Animal (CENAPA), Morelos,
México.
*Corresponding author: ninnet.gomez.i@senasica.gob.mx; ninna_gr@hotmail.com
Abstract:
This report describes a fatal case of mucosal disease in a two-year-old bull. For causal agent
detection, scab, whole blood, and feces samples were tested by RT-PCR, PCR, ELISA, and
viral isolation. RT-PCR positive amplification was obtained in blood samples for bovine
viral diarrhea virus (BVDV). Viral isolation from the scab samples confirmed BVDV as the
causative agent of the clinical manifestations. Subsequently, genetic characterization based
on phylogenetic analysis of three partial sequences revealed the presence of BVDV
subgenotype 1b in analyzed samples. Due to the development of clinical manifestation
248
named mucosal disease, these findings suggest the detection of BVDV persistently infected
(PI) bull; therefore, these results demonstrate the importance of establishing BVDV control
programs that rely on testing the presence of PI in cattle from Mexico.
Key words: Bovine viral diarrhea virus, Cattle, Mucosal disease, Persistent infection,
Mexico.
Bovine viral diarrhea (BVD) remains one of the most common endemic diseases of cattle
and other ruminant populations worldwide. Furthermore, BVD has a significant economic
impact on the cattle industry due to its negative effects on cattle reproduction and health
conditions(1,2). BVD is caused by a positive-sense single-stranded RNA genome virus termed
bovine viral diarrhea virus (BVDV), belonging to the Flaviviridae family within the
Pestivirus genus. BVDV is currently divided into three species: Pestivirus A (Bovine viral
diarrhea virus 1, BVDV-1), Pestivirus B (Bovine viral diarrhea virus 2, BVDV-2), and
Pestivirus H (HoBi-like pestivirus), which are segregated into subgenotypes(3). Pestivirus A
is subdivided into up to 21 subgenotypes (1a to 1u), Pestivirus B, and Pestivirus H into four
subgenotypes each (a to d)(4). Further, BVDV strains are classified in cytopathic (CP) and
non-cytopathic (NCP) biotypes according to their effect on replication and morphological
changes induced in cell culture. This classification is relevant because cytopathogenicity in
vitro is not related to cytopathogenicity in vivo. Thus, NCP strains are predominant in the
field, involved in most natural infection cases and persistent infections. In contrast, CP
strains are rare and isolated almost exclusively from a fatal form of BVD named mucosal
disease (MD)(5).
BVDV infection is characterized by clinical manifestations, including respiratory,

gastrointestinal, and reproductive disorders. However, reproductive failures such as
abortions, mummification, stillbirth, congenital defects, and the birth of persistently infected
animals (PI) are considered of major economic importance(6).
PI animals are generated as a result of transplacental infection with NCP BVDV strain during
the first 125 d of gestation. Such animals acquire immunologic tolerance towards the
infecting BVDV strain and develop persistent infection; hence, a PI calf will not induce an
immune response by antibodies or T-cells against the virus(7). Additionally, PI cattle shed
the virus in body secretions like nasal and oral discharges, milk, urine, feces, and semen
249
throughout their entire lives. Therefore, they are considered a permanent source of viral
infection and play an essential role in BVD pathogenesis and epidemiology(8).
Calves born as PI appear normal and sometimes as weak animals but are characterized by
reduced growth rates, immunosuppression, and high death rate(2). Moreover, PI has
increased morbidity and mortality rates owing to susceptibility to other diseases and may
eventually die from pneumonia or MD. Most PI calves succumb to MD, usually between 6
to 24-mo old(9,10). Nevertheless, older PI cattle of 3, 5, and 7-yr old have been previously
reported, implying a broader viral dissemination period(2,11,12).
MD is a sporadic fatal condition restricted to PI cattle that occurs when the PI causative NCP
BVDV mutates into CP as a result of a recombination event or when the PI animal is co-
infected with an antigenically homologous related strain of CP BVDV(13,14). Therefore, both
biotypes can be consistently found in animals with MD(15,16). The outcome of MD is death
occurring within two weeks after the onset of the clinical signs. Erosions and extensive
ulceration of the gastrointestinal tract are the main lesions found(17). Conversely, late MD
onset after several months has also been described(18). Other clinical signs include anorexia,
fever, dehydration, diarrhea, dermatitis, necrosis of lymphoid tissue, poor condition, and
death(19).
This case report describes the onset of MD in a two-year bull with severe clinical signs
suggesting the description of PI cattle from Mexico for the first time.
On June 2021, a 2-year-old bull was reported with 15 d course of clinical signs including
anorexia, depression, ptyalism, severe hemorrhagic watery diarrhea, dehydration, nasal
discharge, and deep and extensive ulceration in muzzle, nares, lips, gums, and hard palate
(Figures 1, 2, and 3). The affected animal belonged to a traditional backyard farm located in
Texcoco, State of Mexico, Mexico. The farm kept four bovines, four horses, six dogs, and
three pigs, apparently healthy at the report. No similar clinical manifestations were registered
in the neighboring farms prior to the event. According to the owner, no animal mobilization
among nearby farms, and new animals were introduced.
250
Figure 1: Two years old bull with mucosal disease presentation showing erosive lesions in
nasal discharge, extensive ulceration in muzzle and nares
Figure 2: Erosive lesions in lips and gums
251
Figure 3: Superficial erosions in hard palate
Scab samples from skin lesions, whole blood, and feces samples were obtained and
submitted for diagnosis to the Immunology, Cellular and Molecular Biology Laboratory
from the Comisión México-Estados Unidos para la prevención de fiebre Aftosa y Otras
enfermedades exóticas de los Animales (CPA). The case report was identified with the
number CPA-0861-21. Main vesicular cattle diseases were considered for differential
diagnosis, including foot and mouth disease (FMD), vesicular stomatitis (VS), malignant
catarrhal fever disease (MCF), and BVD using RT-PCR, PCR, ELISA, and virus isolation.
Negative results were obtained on viral isolation in cell culture, RT-PCR, and ELISA for
FMD and VS. Similarly, the MCF virus was not detected by PCR in surveyed samples.
Conversely, BVDV was isolated from scab samples, and positive amplification was obtained
from whole blood samples using RT-PCR. Consequently, the BVDV isolate was submitted
to the Molecular Biology Laboratory of the Centro Nacional de Servicios de Diagnóstico en
Salud Animal (CENASA) for partial sequencing. The 5'UTR, Npro, and E2 BVDV
sequences obtained were deposited in GenBank under accession numbers OM812936,
OM812937, and OM812938, respectively. Moreover, phylogenetic analysis was performed
based on 5'UTR, Npro, and E2 regions. Partial 5'UTR (360 bp), Npro (504 bp), and E2 (1482
bp) sequences obtained in this study were compared to BVDV reference strains to
characterize BVDV isolate. The evolutionary history was inferred using the Maximum
likelihood method with a Kimura 2-parameter substitution model(20) for 5'UTR and Npro
sequences, and a Tamura 3-parameter substitution model(21) for the E2 sequences was
conducted in commercial software MEGA7 using 1000 bootstrap replicates each (Figure.
252
4). A discrete gamma distribution with two categories was used to model evolutionary rate
differences among sites, with some sites being evolutionary invariable for Npro and E2
sequences.
Figure 4: Phylogenetic tree based on 5'UTR region (a), Npro (b), and E2 (c) sequences
253
Phylogenetic inference was conducted in MEGA 7 according to maximum likelihood method. Analysis was
supported by 1000 bootstraps replicates. Reference sequences are identified by GenBank accession number.
Mexican nucleotide sequences are highlighted with symbol ""
BVD continues to be a significant concern to the cattle industry, with substantial economic
impact mainly associated with reproductive disorders(2). Depending on the stage of
pregnancy at the time of infection, transplacental infections with BVDV NCP strains may
result in the birth of immunotolerant PI calves. These animals are consistently viremic,
BVDV spreads through most organs in the animal, but no apparent lesions are developed(22).
Consequently, PI cattle sustain lifelong viral replication and excretion in all body
secretions(23). Thus, PI animals represents the main transmission and maintenance source of
BVDV within and between herds. Moreover, NCP BVDV can also be transmitted from
acutely infected cattle and by fomites such as contaminated surgical and handling material,
rectal examination, bovine sera used in embryo transfer, and vaccine production, infected
semen, and contaminated vaccines(24-27).
Further, BVDV infections directly impact PI animals' fertility, i.e., PI bulls can produce
semen of acceptable quality. However, they are associated with poor fertility related to
spermatozomal abnormalities and low motility(28). Likewise, BVDV infections alter ovarian
function by causing hypoplasia and reduced ovulations in PI cows(29). Nevertheless, bulls
and PI cows can still sire normal PI offspring, which may recirculate BVDV in susceptible
dams(30).
254
Continual exposure of healthy animals to BVDV from a PI animal may lead to the
perpetuation of BVDV infections(31); thus, herd infertility, immunosuppression, and
generation of new PI calves may arise(32). Furthermore, acute NCP infections compromises
herd fertility by producing retarded and reducing follicle growth(33) and diffuse interstitial
ovaritis(34), and conception failure by preventing embryo implantation(22). In addition,
embryonic death before d 79 of gestation in pregnant cows or congenital malformations
between days 79 and 150 can also occur(35).
In areas where adequate BVDV control measures are implemented, the estimated prevalence
of PI animals is around 1%-2 %(1); however, no report of MD outbreaks nor presentation in
the Mexican bovine population has been previously described. In addition, the current
proportion of PI's calves in the country remains unknown. Recently, limited information
regarding the BVDV genetic characterization and prevalence in Mexico has begun to be
surveyed(36).
In the present study, it was described a case of MD by BVDV-1b affecting a beef bull in
which ulcerative lesions in the gastrointestinal tract were predominant. BVDV-1b is
currently defined as the most common strain found in the field; thus, it is considered the
predominant subgenotype worldwide, followed by 1a and 1c(4). BVDV-1b is also described
as the most prevalent strain in PI calves(37). Similar to these studies, the genetic
characterization of the virus isolated from the evaluated bull in this study, reveals the
identification of BVDV subgenotype 1b. The latter correlates to a previous study where
BVDV-1b was described as an endemic virus circulating in Mexican cattle, together with
1a, 1c, and 2a(38). Despite these initial efforts to report BVDV cases, BVD remains a non-
regulated disease hence no control strategies nor prevention measures are officially
implemented.
Consequently, vaccination protocols are based on voluntary procedures, and monitoring and
biosafety measures are applied depending on cattle producers' BVD knowledge. The
evaluated bull from this clinical case belongs to a farm where scarce sanitary measures and
no vaccination practices against BVDV are applied. BVDV positive tests and clinical
presentation suggest an MD case developed in a PI bull of 2 yr old.
The latter has important implications for BVD control in the nation. These results confirm
the presence of BVDV-1b circulating in Mexican cattle, similar to the findings reported by
Gómez-Romero et al(38). Clinical presentation from the case highlights the severe outcome
of MD and the relevance of underdiagnoses of PI animals and, therefore, BVDV
epidemiological status. Furthermore, national BVD case reports will impulse the
development of control strategies that allow producers to detect BVDV and remove PI calves
from the herd. Moreover, when vaccination is applied, the choice of a specific vaccine should
be evaluated for protection provided against circulating BVDV. In Mexico, the recent
255
addition of BVDV-1b as vaccine antigen has been included in one commercial vaccine;
however, vaccination alone is not adequate for the BVD control programs. The finding of
BVDV-1b in a non-vaccinated bull demonstrates the crucial role of biosecurity and disease
surveillance to mitigate the effects of BVDV infections in cattle populations.
The authors declared no conflict of interest regarding the authorship or publication of this
manuscript.
Literature cited:
1. Houe H. Epidemiological features and economical importance of bovine virus diarrhoea
virus (BVDV) infections. Vet Microbiol 1999;64(2-3):89-107. doi: 10.1016/s0378-
1135(98)00262-4. Erratum in: Vet Microbiol 2003;93(3):275-6.
2. Houe H. Economic impact of BVDV infection in dairies. Biologicals 2003;31(2):137-

43. doi: 10.1016/s1045-1056(03)00030-7.
3. Smith DB, Meyers G, Bukh GJ, Gould EA, Monath T, Scott-Muerhoff A, et al. Proposed
revision to the taxonomy of the genus Pestivirus, family Flaviviridae. J General Virol
2017;98(8), 2106–2112. https://doi.org/10.1099/jgv.0.000873.
4. Yesilbag K, Alpay G, Becher P. Variability and global distribution of subgenotypes of

bovine viral diarrhea virus. Viruses 2017;9(6):128. doi: 10.3390/v9060128.
5. Neill J. Molecular biology of bovine viral diarrhea virus. Biologicals 2013;41:2–7. doi:
10.1016/j.biologicals.2012.07.002.
6. Houe H, Lindberg A, Moenning V. Test strategies in bovine viral diarrhea virus control
and eradication campaigns in Europe. J Vet Diagn Invest 2006;18(5):427-36. doi:
10.1177/104063870601800501.
7. Meyers G, Thiel HJ. Molecular characterization of pestiviruses. Adv Virus Res 1996;
47:53-118. doi: 10.1016/s0065-3527(08)60734-4.
8. Brodersen BW. Bovine viral diarrhea virus infections: manifestations of infection and
recent advances in understanding pathogenesis and control. Vet Pathol 2014;51(2):453-
64. doi: 10.1177/0300985813520250.
9. Odeón AC, Leunda MR, Faverín C, Boynak N, Vena MM, Zabal O. In vitro
amplification of BVDV field strains isolated in Argentina: effect of cell line and culture
conditions. Rev Argent Microbiol 2009;41:79–85.
256
10. Uzal FA, Platnner BL, Hostetter JM. Alimentary system in pathology of domestic
animals. In: Maxie, MG, editor. Jubb, Keneddy and Palmers pathology of domestic
animals.;6th ed. St. Louis, Missouri: Academic Press Inc; 2016:122–130.
11. Brock KV, Grooms DL, Ridpath JF, Bolin SR. Changes in levels of viremia in cattle
persistently infected with bovine viral diarrhea virus. J Vet Diagn Invest1998;10:22-26.
12. Bedekovic T, Lemo N, Lojkic I, Cvetnicz Z, Cac Z, Madic J. Bovine viral diarrhoea: A
seven year old persistently infected cow - a case report. Veterinarski Arch 2012;82
(6):637-643.
13. Brownlie J. The pathways for bovine virus diarrhoea virus biotypes in the pathogenesis
of disease. Arch Virol Suppl 1991;3:79-96. doi: 10.1007/978-3-7091-9153-8_10.
14. Tautz N, Thiel HJ. Cytopathogenicity of pestiviruses: cleavage of bovine viral diarrhea
virus NS2-3 has to occur at a defined position to allow viral replication. Arch Virol
2003;148(7):1405-12. doi: 10.1007/s00705-003-0106-9.
15. Bolin SR, McClurkin AW, Cutlip RC, Coria MF. Severe clinical disease induced in
cattle persistently infected with noncytopathic bovine viral diarrhea virus by
superinfection with cytopathic bovine viral diarrhea virus. Am J Vet Res
1985;46(3):573-6.
16. Kummerer B, Tautz MN, Becher P, Thiel H, Meyers G. The genetic basis for
cytopathogenicity of pestiviruses. Vet Microbiol 2000;77(1-2):117-28. doi:
10.1016/s0378-1135(00)00268-6.
17. Baker JC. The clinical manifestations of bovine viral diarrhea infection. Vet Clin North
Am Food Anim Pract 1995;11(3):425-45. doi: 10.1016/s0749-0720(15)30460-6.
18. Fritzemeier J, Haas L, Liebler E, Moennig V, Greiser-Wilke I. The development of early

vs. late onset mucosal disease is a consequence of two different pathogenic mechanisms.
Arch Virol 1997;142(7):1335-50. doi: 10.1007/s007050050164.
19. Wilhelmsen CL, Bolin SR, Ridpath JF, Cheville NF, Kluge JP. Lesions and localization
of viral antigen in tissues of cattle with experimentally induced or naturally acquired
mucosal disease, or with naturally acquired chronic bovine viral diarrhea. Am J Vet Res
1991;52(2):269-75.
20. Kimura M. A simple method for estimating evolutionary rate of base substitutions
through comparative studies of nucleotide sequences. J Molec Evol 1980;16:111-120.
21. Tamura K. Estimation of the number of nucleotide substitutions when there are strong
transition-transversion and G + C-content biases. Molec Biol Evol 1992;9:678-687.
257
22. Fray MD, Paton DJ, Alenius S. The effects of bovine viral diarrhoea virus on cattle
reproduction in relation to disease control. Anim Reprod Sci 2000;60-61:615-27. doi:
10.1016/s0378-4320(00)00082-8.
23. Kameyama K, Konishi M, Tsutsui T, Yamamoto T. Survey for detecting persistently

infected cattle with bovine viral diarrhea in Japan J Vet Med Sci 2016;78(8):1329–31.
24. Gunn HM. Role of fomites and flies in the transmission of bovine viral diarrhoea virus.
Vet Rec 1993;132(23):584-5. doi: 10.1136/vr.132.23.584.
25. Brock KV, Redman DR, Vickers ML, Irvine NE. Quantitation of bovine viral diarrhea
virus in embryo transfer flush fluids collected from a persistently infected heifer. J Vet
Diagn Invest 1991;3(1):99-100. doi: 10.1177/104063879100300127.
26. Lang-Ree JR, Vatn T, Kommisrud E, Loken T. Transmission of bovine viral diarrhoea
virus by rectal examination. Vet Rec 1994;135(17):412-3. doi: 10.1136/vr.135.17.412.
27. Gómez-Romero N, Velázquez-Salinas L, Ridpath JF, Verdugo-Rodríguez A, Basurto-

Alcántara FJ. Detection and genotyping of bovine viral diarrhea virus found
contaminating commercial veterinary vaccines, cell lines, and fetal bovine serum lots
originating in Mexico. Arch Virol 2021;166(7):1999-2003. doi: 10.1007/s00705-021-
05089-9.
28. Revell SC, Chasey D, Drew TD, Edwards S. Some observations on the semen of bulls
persistently infected with bovine virus diarrhoea virus. Vet Rec 1988;123(5):122-5. doi:
10.1136/vr.123.5.122.
29. Grooms DL, Ward LA, Brock KV. Morphologic changes and immunohistochemical
detection of viral antigen in ovaries from cattle persistently infected with bovine viral
diarrhea virus. Am J Vet Res 1996;57(6):830-3.
30. Meyling A, Jensen AM Transmission of bovine virus diarrhoea virus (BVDV) by

artificial insemination (AI) with semen from a persistently-infected bull. Vet Microbiol
1988;17(2):97-105. doi: 10.1016/0378-1135(88)90001-6.
31. Roeder PL, Harkness JW. BVD virus infection: prospects for control. Vet Rec
1986;118(6):143-7. doi: 10.1136/vr.119.6.143.
32. Hamers C, Lecomte C, Kulcsar G , Lambot M, Pastoret PP. Persistently infected cattle
stabilise bovine viral diarrhea virus leading to herd specific strains. Vet Microbiol
1998;61(3):177-82. doi: 10.1016/s0378-1135(98)00185-0.
33. Grooms DL, Brock KV, Pate JL, Day ML. Changes in ovarian follicles following acute
infection with bovine viral diarrhea virus. Theriogenology. 1998;49(3):595-605. doi:
10.1016/s0093-691x(98)00010-7.
258
34. Ssentongo YK, Johnson RH, Smith JR. Association of bovine viral diarrhoea-mucosal
disease virus with ovaritis in cattle. Aust Vet J 1980;56(6):272-3. doi: 10.1111/j.1751-
0813.1980.tb05722.x.
35. Windsor P. Abnormalities of development and pregnancy. Noakes ED, et al, editors.
England, Vet Rep Obst (Tenth ed), W.B. Saunders, 2019; ISBN 9780702072338,
https://doi.org/10.1016/B978-0-7020-7233-8.00009-4.
36. Gómez-Romero N, Ridpath JF, Basurto-Alcántara FJ, Verdugo-Rodríguez A. Bovine

viral diarrhea virus in cattle from Mexico: Current Status. Front Vet Sci 2021;8:673577.
doi: 10.3389/fvets.2021.673577.
37. Fulton RW, Whitley EM, Johnson BJ, Ridpath JF, Kapil S, Burge LJ, Cook BJ, Confer
AJ. Prevalence of bovine viral diarrhea virus (BVDV) in persistently infected cattle and
BVDV subtypes in affected cattle in beef herds in south central United States. Can J Vet
Res 2009;73(4):283-91.
38. Gómez-Romero N, Basurto-Alcántara FJ, Verdugo-Rodríguez A, Bauermann FV,

Ridpath JF, Genetic diversity of bovine viral diarrhea virus in cattle from Mexico. J Vet
Diagn Invest 2017;29(3):362-365. doi: 10.1177/1040638717690187.
259
Revista Mexicana de Ciencias Pecuarias
Edición Bilingüe
Rev. Mex. Cienc. Pecu. Vol. 14 Núm. 1, pp. 1-259, ENERO-MARZO-2023 Bilingual Edition
ISSN: 2448-6698
CONTENIDO
CONTENTS
ARTÍCULOS / ARTICLES Pags.

Identification of candidate genes and SNPs related to cattle temperament using a GWAS analysis coupled with an interacting network analysis
Identificación de genes candidatos y SNP relacionados con el temperamento del ganado utilizando un análisis GWAS junto con un análisis de redes interactuantes
Francisco Alejandro Paredes-Sánchez, Ana María Sifuentes-Rincón, Edgar Eduardo Lara-Ramírez, Eduardo Casas,
Felipe Alonso Rodríguez-Almeida, Elsa Verónica Herrera-Mayorga, Ronald D. Randel......……………………………………………………………………........………………....…....…....…....…....…....….................…..........….........1
Revista Mexicana de Ciencias Pecuarias Rev. Mex. Cienc. Pecu. Vol. 14 Núm. 1, pp. 1-259, ENERO-MARZO-2023
Efecto de la consanguinidad y selección sobre los componentes de un índice productivo en ratones bajo apareamiento estrecho
Effect of consanguinity and selection on the components of a productive index, in mice under close mating
Dulce Janet Hernández López, Raúl Ulloa Arvizu, Carlos Gustavo Vázquez Peláez, Graciela Guadalupe Tapia Pérez………………………...................................................................................................................23
Variabilidad genética en biomasa aérea y sus componentes en alfalfa bajo riego y sequía
Genetic variability in aerial biomass and its components in alfalfa under irrigation and drought
Milton Javier Luna-Guerrero, Cándido López-Castañeda......……..…….....…….....……................……..…...............................................................................................................................................................…….39
Estimación de masa de forraje en una pradera mixta por aprendizaje automatizado, datos del manejo de la pradera y meteorológicos satelitales
Estimation of forage mass in a mixed pasture by machine learning, pasture management and satellite meteorological data
Aurelio Guevara-Escobar, Mónica Cervantes-Jiménez, Vicente Lemus-Ramírez, Adolfo Kunio Yabuta-Osorio, José Guadalupe García-Muñiz….........…….....…….....…..….....……..........…….....…….....……...........61
Thymol and carvacrol determination in a swine feed organic matrix using Headspace SPME-GC-MS
Determinación de timol y carvacrol en una matriz orgánica de alimento para cerdo utilizando Headspace SPME-GC-MS
Fernando Jonathan Lona-Ramírez, Nancy Lizeth Hernández-López, Guillermo González-Alatorre,
Teresa del Carmen Flores-Flores, Rosalba Patiño-Herrera, José Francisco Louvier-Hernández.........….........…......…......…......…......…......…......…......…......…......…......…......…......…......…......…..……..78
Cambios en el recuento de cuatro grupos bacterianos durante la maduración del Queso de Prensa (Costeño) de Cuajinicuilapa, México
Changes in the count of four bacterial groups during the ripening of Prensa (Costeño) Cheese from Cuajinicuilapa, Mexico
José Alberto Mendoza-Cuevas, Armando Santos-Moreno, Beatriz Teresa Rosas-Barbosa, Ma. Carmen Ybarra-Moncada, Emmanuel Flores-Girón, Diana Guerra-Ramírez….....…….......……..........….............94
Detección molecular de un fragmento del virus de lengua azul en borregos de diferentes regiones de México
Molecular detection of a fragment of bluetongue virus in sheep from different regions of Mexico
Edith Rojas Anaya, Fernando Cerón-Téllez, Luis Adrián Yáñez-Garza, José Luis Gu�érrez-Hernández, Rosa Elena Sarmiento-Salas, Elizabeth Loza-Rubio….............…………...…….…………........................…......110
Insulin-like growth factor 1 (IGF-1) concentrations in synovial fluid of sound and osteoarthritic horses,
and its correlation with proinflammatory cytokines IL-6 and TNFα
Concentraciones del factor de crecimiento similar a la insulina 1 (IGF-1) en el líquido sinovial de caballos
sanos y osteoartríticos, y su correlación con las citoquinas proinflamatorias IL-6 y TNFα
Fernando García-Lacy F., Sara Teresa Méndez-Cruz, Horacio Reyes-Vivas, Victor Manuel Dávila- Borja, Jose Alejandro Barrera-Morales,
Gabriel Gu�érrez-Ospina, Margarita Gómez-Chavarín, Francisco José Trigo-Tavera……..….....…….....…....….....……...……..…..……..…..……..…..……..…..……..…..……..…..……..…..……..…..……..…..……..….......……..….122
Uso de células estromales mesenquimales derivadas de la gelatina de Wharton para el tratamiento de uveítis recurrente equina: estudio piloto
Use of Wharton's jelly-derived mesenchymal stromal cells for the treatment of equine recurrent uveitis: a pilot study
María Masri-Daba, Montserrat Erandi Camacho-Flores, Ninnet Gómez-Romero, Francisco Javier Basurto Alcántara…………………….…….......….…………...............................................................................…….137
Escala de la producción y eficiencia técnica de la ganadería bovina para carne en Puebla, México
Scale of production and technical efficiency of beef cattle farming in Puebla, Mexico
José Luis Jaramillo Villanueva, Lisse�e Abigail Rojas Juárez, Samuel Vargas López…………………………………….....……..........…….....…….....…….....….....…….....…….....…….......…….....…….......…….....……..........…......154
Regresión cuantil para predicción de caracteres complejos en bovinos Suizo Europeo usando marcadores SNP y pedigrí
Quantile regression for prediction of complex traits in Braunvieh cattle using SNP markers and pedigree
Jonathan Emanuel Valerio-Hernández, Paulino Pérez-Rodríguez, Agus�n Ruíz-Flores……………………....…….......….....…….......….....…......…......…......…......…......…......…......…......…......…..........….....…….....……...172
Análisis de crecimiento estacional de una pradera de trébol blanco (Trifolium repens L)

Seasonal growth analysis of a white clover meadow (Trifolium repens L.)
Edgar Hernández Moreno, Joel Ventura Ríos, Claudia Yanet Wilson García, María de los Ángeles Maldonado Peralta,
Juan de Dios Guerrero Rodríguez, Graciela Munguía Ameca, Adelaido Rafael Rojas García....................................................................................…………………………………………………………….……………...........…190
REVISIONES DE LITERATURA / REVIEWS

Aspects related to the importance of using predictive models in sheep production. Review
Aspectos relacionados con la importancia del uso de modelos predictivos en la producción ovina. Revisión
Antonio Leandro Chaves Gurgel, Gelson dos Santos Difante, Luís Carlos Vinhas Ítavo, João Virgínio Emerenciano Neto, Camila Celeste Brandão Ferreira Ítavo,
Patrick Bezerra Fernandes, Carolina Marques Costa, Francisca Fernanda da Silva Roberto, Alfonso Juven�no Chay-Canul……....……....……....……....……....……....……....……....……....……....……....……....…….......204
NOTAS DE INVESTIGACIÓN / TECHNICAL NOTES

Preferencia de ocho plantas por Odocoileus virginianus en cautiverio
Preference for eight plants among captive white-tailed deer Odocoileus virginianus in Veracruz, Mexico
Hannia Yaret Cueyactle-Cano, Ricardo Serna-Lagunes, Norma Mora-Collado, Pedro Ze�na-Córdoba, Gerardo Benjamín Torres-Cantú..……..………………..………..………..………..……….........………...……....…..……228
Rendimiento y valor nutricional de brásicas forrajeras en comparación con forrajes tradicionales

Yield and nutritional value of forage brassicas compared to traditional forages
David Guadalupe Reta Sánchez, Juan Isidro Sánchez Duarte, Esmeralda Ochoa Mar�nez, Ana Isabel González Cifuentes, Arturo Reyes González, Karla Rodríguez Hernández...........................…………..…..237
Genetic characterization of bovine viral diarrhea virus 1b isolated from mucosal disease
Caracterización del virus de la diarrea viral bovino subtipo 1b aislado de un caso de la enfermedad de las mucosas
Roberto Navarro-López, Juan Diego Perez-de la Rosa, Marisol Karina Rocha-Mar�nez, Marcela Villarreal-Silva, Mario Solís-Hernández, Eric Rojas-Torres, Ninnet Gómez-Romero........................…………...248
Rev. Mex. Cienc. Pecu. Vol. 14 Núm. 1, pp. 1-259, ENERO-MARZO-2023

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Rev. Mex. Cienc. Pecu. Vol. 14 Núm. 1, pp. 1-259, ENERO-MARZO-2023

TIPOGRAFÍA Y FORMATO: Oscar L. Rodríguez Rivera

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REV. MEX. CIENC. PECU. VOL. 14 No. 1 ENERO-MARZO-2023

Identification of candidate genes and SNPs related to cattle temperament using a

Efecto de la consanguinidad y selección sobre los componentes de un índice

Variabilidad genética en biomasa aérea y sus componentes en alfalfa bajo riego y

Estimación de masa de forraje en una pradera mixta por aprendizaje automatizado,

Detección molecular de un fragmento del virus de lengua azul en borregos de

Insulin-like growth factor 1 (IGF-1) concentrations in synovial fluid of sound and

Uso de células estromales mesenquimales derivadas de la gelatina de Wharton para

Escala de la producción y eficiencia técnica de la ganadería bovina para carne en

Regresión cuantil para predicción de caracteres complejos en bovinos Suizo

Análisis de crecimiento estacional de una pradera de trébol blanco ( Trifolium repens L)

Aspects related to the importance of using predictive models in sheep production.

Preferencia de ocho plantas por Odocoileus virginianus en cautiverio

Rendimiento y valor nutricional de brásicas forrajeras en comparación con forrajes

Genetic characterization of bovine viral diarrhea virus 1b isolated from mucosal

INSTRUCTIONS FOR AUTHORS

Revista Mexicana de Ciencias Pecuarias is a scientific

11. Literature cited. All references should be quoted in

Identification of candidate genes and SNPs related to cattle temperament

Francisco Alejandro Paredes-Sánchez a

Ana María Sifuentes-Rincón b*

Edgar Eduardo Lara-Ramírez c

Felipe Alonso Rodríguez-Almeida e

Elsa Verónica Herrera-Mayorga f

*Corresponding author: asifuentes@ipn.mx

Key words: Beef cattle, Behaviour, BTA6, Candidate genes, Temperament.

Genome-wide association studies (GWAS), based on high-throughput single nucleotide

In a related context, exome-specific resequencing of specific regions using next-generation

Material and methods

Description of animals and biological sample sources

animals’ EV measurements. Animals with EV measurements ≤1.9 m/s were classified as

GWAS analysis and gene discovery

Pathway analysis and protein-protein interactions

Candidate genes resequencing

Bioinformatics analysis of sequencing data

Prediction of the effect of non-coding SNPs on splice sites

GWAS analysis and candidate gene identification coupled to protein-

CHR rs ID Position Frecuency P-value

6 rs81144933 36,655,249 0.20 0.70 3.48 E-05

Figure 2: Protein-protein interactions reported for bovine MMRN1 in the STRING

Figure 3: Protein-protein interactions reported for bovine GPRIN3 in the STRING

Finally, SNCA protein, some GO terms identified (GO:0045920, GO:004241 and

Genetic variation in candidate genes

Connecting gene networks to explain cattle temperament

Conclusions and implications

1. Buchenauer D. Genetics of behaviour in cattle. In: Fries R Ruvinsky A, editors. The

2. Mormède P. Molecular genetics of behaviour: research strategies and perspectives for

4. Hiendleder S, Thomsen H, Reinsch N, Bennewitz J, Leyhe-Horn B, Looft C, et al.

5. Garza-Brenner E, Sifuentes-Rincón AM, Randel RD, Paredes-Sánchez FA, Parra-

6. Garza-Brenner E, Sifuentes-Rincón AM, Rodríguez-Almeida FA, Randel RD, Parra-

7. Garza-Brenner E, Sifuentes-Rincón AM, Rodríguez Almeida FA, Randel RD, Parra-

19. Mi H, Huang X, Muruganujan A, Tang H, Mills C, Kang D, Thomas PD. PANTHER

30. Brighina L, Riva C, Bertola F, Saracchi E, Fermi S, Goldwurm S, et al. Analysis of

31. Shadrina M, Nikopensius T, Slominsky P, Illarioshkin S, Bagyeva G, Markova E, et al.

38. Kanehisa M, Furumichi M, Tanabe M, Sato Y, Morishima K. KEGG: New perspectives

41. Lobjois V, Liaubet L, San Cristobal M, Glénisson J, Fève K, Rallières J. A muscle

44. Chacko E, Ranganathan S. Genome-wide analysis of alternative splicing in cow:

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Effect of consanguinity and selection on the components of a