RMCP Vol. 10, Num 4 (2019): October-December [english version]

Edición Bilingüe Bilingual Edition

Revista Mexicana de Ciencias Pecuarias Rev. Mex. Cienc. Pecu. Vol. 10 Núm. 4 pp. 801-1076, OCTUBRE-DICIEMBRE-2019

ISSN: 2448-6698

Rev. Mex. Cienc. Pecu. Vol. 10 Núm.4, pp. 801-1076, OCTUBRE-DICIEMBRE-2019

REVISTA MEXICANA DE CIENCIAS PECUARIAS Volumen 10 Número 4, OctubreDiciembre, 2019. Es una publicación trimestral de acceso abierto, revisada por pares y arbitrada, editada por el Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias (INIFAP). Avenida Progreso No. 5, Barrio de Santa Catarina, Delegación Coyoacán, C.P. 04010, Cuidad de México, www.inifap.gob.mx Distribuida por el Centro Nacional de Investigación Disciplinaria en Salud Animal e Inocuidad, Km 15.5 Carretera México-Toluca, Colonia Palo Alto, Cuidad de México, C.P. 05110. Editor responsable: Arturo García Fraustro. Reservas de Derechos al Uso Exclusivo número 04-2016-060913393200-203. ISSN: 2448-6698, otorgados por el Instituto Nacional del Derecho de Autor (INDAUTOR).

Responsable de la última actualización de este número: Arturo García Fraustro, Centro Nacional de Investigación Disciplinaria en Salud Animal e Inocuidad, Km. 15.5 Carretera México-Toluca, Colonia Palo Alto, Ciudad de México, C.P. 015110. http://cienciaspecuarias. inifap.gob.mx, la presente publicación tuvo su última actualización en octubre de 2019.

Ganadería de doble propósito en el estado de Tabasco, México.

Fotografía tomada por: Atalo Martínez Lara.

DIRECTORIO FUNDADOR John A. Pino EDITORES ADJUNTOS Oscar L. Rodríguez Rivera Alfonso Arias Medina

EDITOR EN JEFE Arturo García Fraustro

EDITORES POR DISCIPLINA Dra. Yolanda Beatriz Moguel Ordóñez, INIFAP, México Dr. Ramón Molina Barrios, Instituto Tecnológico de Sonora, México Dra. Maria Cristina Schneider, PAHO, Estados Unidos Dra. Elisa Margarita Rubí Chávez, UNAM, México Dr. Javier F. Enríquez Quiroz, INIFAP, México Dra. Martha Hortencia Martín Rivera, Universidad de Sonora URN, México Dr. Fernando Arturo Ibarra Flores, Universidad de Sonora URN, México Dr. James A. Pfister, USDA, Estados Unidos Dr. Eduardo Daniel Bolaños Aguilar, INIFAP, México Dr. Sergio Iván Román-Ponce, INIFAP, México Dr. Jesús Fernández Martín, INIA, España Dr. Sergio D. Rodríguez Camarillo, INIFAP, México Dr. Martin Talavera Rojas, Universidad Autónoma del Estado de México, México Dra. Maria Salud Rubio Lozano, Facultad de Medicina Veterinaria y Zootecnia, UNAM, México Dra. Elizabeth Loza-Rubio, INIFAP, México Dr. Juan Carlos Saiz Calahorra, Instituto Nacional de Investigaciones Agrícolas, España Dra. Silvia Elena Buntinx Dios, Facultad de Medicina Veterinaria y Zootecnia, UNAM, México Dr. José Armando Partida de la Peña, INIFAP, México Dr. José Luis Romano Muñoz, INIFAP, México.

Dr. Alejandro Plascencia Jorquera, Universidad Autónoma de Baja California, México Dr. Juan Ku Vera, Universidad Autónoma de Yucatán, México Dr. Ricardo Basurto Gutiérrez, INIFAP, México.

Dr. Luis Corona Gochi, Facultad de Medicina Veterinaria y Zootecnia, UNAM, México Dr. Juan Manuel Pinos Rodríguez, Facultad de Medicina Veterinaria y Zootecnia, Universidad Veracruzana, México Dr. Carlos López Coello, Facultad de Medicina Veterinaria y Zootecnia, UNAM, México Dr. Arturo Francisco Castellanos Ruelas, Facultad de Química. UADY Dra. Guillermina Ávila Ramírez, UNAM, México.

Dr. Emmanuel Camuus, CIRAD, Francia.

Dr. Héctor Jiménez Severiano, INIFAP., México Dr. Juan Hebert Hernández Medrano, UNAM, México.

Dr. Adrian Guzmán Sánchez, Universidad Autónoma Metropolitana-Xochimilco, México Dr. Eugenio Villagómez Amezcua Manjarrez, INIFAP, CENID Salud Animal e Inocuidad, México Dr. Fernando Cervantes Escoto, Universidad Autónoma Chapingo, México Dr. Adolfo Guadalupe Álvarez Macías, Universidad Autónoma Metropolitana Xochimilco, México Dr. Alfredo Cesín Vargas, UNAM, México.

TIPOGRAFÍA Y FORMATO Nora del Rocío Alfaro Gómez Indizada en el “Journal Citation Report” Science Edition del ISI . Inscrita en el Sistema de Clasificación de Revistas Científicas y Tecnológicas de CONACyT; en EBSCO Host y la Red de Revistas Científicas de América Latina y el Caribe, España y Portugal (RedALyC) (www.redalyc.org); en la Red Iberoamericana de Revistas Científicas de Veterinaria de Libre Acceso (www.veterinaria.org/revistas/ revivec); en los Índices SCOPUS y EMBASE de Elsevier (www.elsevier. com).

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REVISTA MEXICANA DE CIENCIAS PECUARIAS La Revista Mexicana de Ciencias Pecuarias es un órgano de difusión científica y técnica de acceso abierto, revisada por pares y arbitrada. Su objetivo es dar a conocer los resultados de las investigaciones realizadas por cualquier institución científica, relacionadas particularmente con las distintas disciplinas de la Medicina Veterinaria y la Zootecnia. Además de trabajos de las disciplinas indicadas en su Comité Editorial, se aceptan también para su evaluación y posible publicación, trabajos de otras disciplinas, siempre y cuando estén relacionados con la investigación pecuaria.

total por publicar es de $ 5,600.00 más IVA por manuscrito ya editado.

Se publica en formato digital en acceso abierto, por lo que se autoriza la reproducción total o parcial del contenido de los artículos si se cita la fuente.

El envío de los trabajos de debe realizar directamente en el sitio oficial de la revista. Correspondencia adicional deberá dirigirse al Editor Adjunto a la siguiente dirección: Calle 36 No. 215 x 67 y 69 Colonia Montes de Amé, C.P. 97115 Mérida, Yucatán, México.

Tel/Fax +52 (999) 941-5030. Correo electrónico (C-ele): rodriguez_oscar@prodigy.net.mx.

Se publican en la revista tres categorías de trabajos: Artículos Científicos, Notas de Investigación y Revisiones Bibliográficas (consultar las Notas al autor); la responsabilidad de cada trabajo recae exclusivamente en los autores, los cuales, por la naturaleza misma de los experimentos pueden verse obligados a referirse en algunos casos a los nombres comerciales de ciertos productos, ello sin embargo, no implica preferencia por los productos citados o ignorancia respecto a los omitidos, ni tampoco significa en modo alguno respaldo publicitario hacia los productos mencionados.

La correspondencia relativa a suscripciones, asuntos de intercambio o distribución de números impresos anteriores, deberá dirigirse al Editor en Jefe de la Revista Mexicana de Ciencias Pecuarias, CENID Salud Animal e Inocuidad, Km 15.5 Carretera México-Toluca, Col. Palo Alto, D.F. C.P. 05110, México; Tel: +52(55) 3871-8700 ext. 80316; garcia.arturo@inifap.gob.mx o arias.alfonso@inifap.gob.mx.

Inscrita en la base de datos de EBSCO Host y la Red de Revistas Científicas de América Latina y el Caribe, España y Portugal (RedALyC) (www.redalyc.org), en la Red Iberoamericana de Revistas Científicas de Veterinaria de Libre Acceso (www.veterinaria.org/revistas/ revivec), indizada en el “Journal Citation Report” Science Edition del ISI (http://thomsonreuters. com/) y en los Índices SCOPUS y EMBASE de Elsevier (www.elsevier.com)

Todas las contribuciones serán cuidadosamente evaluadas por árbitros, considerando su calidad y relevancia académica. Queda entendido que el someter un manuscrito implica que la investigación descrita es única e inédita. La publicación de Rev. Mex. Cienc. Pecu. es trimestral en formato bilingüe Español e Inglés. El costo

VISITE NUESTRA PÁGINA EN INTERNET Artículos completos desde 1963 a la fecha y Notas al autor en: http://cienciaspecuarias.inifap.gob.mx Revista Mexicana de Ciencias Pecuarias is an open access peer-reviewed and refereed scientific and technical journal, which publishes results of research carried out in any scientific or academic institution, especially related to different areas of veterinary medicine and animal production. Papers on disciplines different from those shown in Editorial Committee can be accepted, if related to livestock research.

Part of, or whole articles published in this Journal may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical, photocopying or otherwise, provided the source is properly acknowledged.

Manuscripts should be submitted directly in the official web site.

Additional information may be mailed to Associate Editor, Revista Mexicana de Ciencias Pecuarias, Calle 36 No. 215 x 67 y 69 Colonia Montes de Amé, C.P. 97115 Mérida, Yucatán, México. Tel/Fax +52 (999) 941-5030. E-mail: rodriguez_oscar@prodigy.net.mx.

The journal publishes three types of papers: Research Articles, Technical Notes and Review Articles (please consult Instructions for authors). Authors are responsible for the content of each manuscript, which, owing to the nature of the experiments described, may contain references, in some cases, to commercial names of certain products, which however, does not denote preference for those products in particular or of a lack of knowledge of any other which are not mentioned, nor does it signify in any way an advertisement or an endorsement of the referred products.

For subscriptions, exchange or distribution of previous printed issues, please contact: Editor-in-Chief of Revista Mexicana de Ciencias Pecuarias, CENID Salud Animal e Inocuidad, Km 15.5 Carretera México-Toluca, Col. Palo Alto, D.F. C.P. 05110, México; Tel: +52(55) 3871-8700 ext. 80316; garcia.arturo@inifap.gob.mx or arias.alfonso@inifap.gob.mx.

Registered in the EBSCO Host database. The Latin American and the Caribbean Spain and Portugal Scientific Journals Network (RedALyC) (www.redalyc.org). The Iberoamerican Network of free access Veterinary Scientific Journals (www.veterinaria.org/ revistas/ revivec). Thomson Reuter´s “Journal Citation Report” Science Edition (http://thomsonreuters.com/). Elsevier´s SCOPUS and EMBASE (www.elsevier.com) and the Essential Electronic Agricultural Library (www.teeal.org).

All contributions will be carefully refereed for academic relevance and quality. Submission of an article is understood to imply that the research described is unique and unpublished. Rev. Mex. Cien. Pecu. is published quarterly in original lenguage Spanish or English. Total fee charges are US $ 325.00 per article in both printed languages.

VISIT OUR SITE IN THE INTERNET Full articles from year 1963 to date and Instructions for authors can be accessed via the site http://cienciaspecuarias.inifap.gob.mx

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REVISTA MEXICANA DE CIENCIAS PECUARIAS REV. MEX. CIENC. PECU.

VOL. 10 No. 4

OCTUBRE-DICIEMBRE-2019

CONTENIDO ARTÍCULOS

Pág.

Diversidad y estructura genética de una población de cabras criollas negras de tres municipios del estado de Querétaro, México Genetic diversity and structure in Criolla Negra goats in Queretero, Mexico Juan Carlos Silva-Jarquin, Héctor Mario Andrade-Montemayor, Héctor Raymundo Vera-Ávila, Marina Durán-Aguilar, Sergio Iván Román-Ponce, Vincenzo Landi, Amparo Martínez-Martínez, Juan Vicente Delgado-Bermejo, Consorcio BioGoat..................................................................................................801

Estudio biométrico del bovino criollo de Santa Elena (Ecuador) Biometric study of Criollo Santa Elena Peninsula cattle (Ecuador)

Ronald Roberto Cabezas-Congo, Cecilio Jose Barba-Capote, Ana María González-Martínez, Orly Fernando Cevallos-Falquez, José Manuel León-Jurado, José Manuel Aguilar-Reyes, Antón Rafael GarcíaMartínez…………………………………………………………………………………………………………………..…819

Efecto de la suplementación con minerales de fuentes queladas o inorgánicas y vitamina E en la calidad y estabilidad oxidativa de la carne de bovinos Effect of supplementation with vitamin E and chelated or inorganic minerals on beef quality and oxidative stability Manuel Andrés González-Toimil, Pedro Garcés-Yépez, Luis Humberto López-Hernández, Diego BrañaVarela, Everardo González-Padilla .................................................................................................... 837

Productive and economic response to concentrate supplementation by grazing dairy cows at high stocking Respuesta productiva y económica a la suplementación con concentrados de vacas lecheras en pastoreo con alta carga animal Benito Albarran-Portillo, Felipe López-González, Miguel Ruiz-Albarrán, Carlos Manuel Arriaga-Jordán .......................................................................................................................................................... 855

Comportamiento productivo e ingestivo de ovinos en crecimiento en sistemas silvopastoriles y de engorda en confinamiento Productive and ingestive behavior in growing hair sheep in silvopastoral and stabled weight-gain systems Carlos Ricardo Villanueva-Partida, Víctor Francisco Díaz-Echeverría, Alfonso Juventino Chay-Canul, Luis Ramírez Avilés, Fernando Casanova-Lugo, Iván Oros-Ortega ................................................... 870

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Indicadores de estrés en bovinos por el uso de prácticas de manejo en el embarque, transporte y desembarque Stress indicators in cattle in response to loading, transport and unloading practices Silvia Larios-Cueto, Rodolfo Ramírez-Valverde, Gilberto Aranda-Osorio, María Esther Ortega-Cerrilla, Juan Carlos García-Ortiz ................................................................................................................... 885

Impacto del peso al nacimiento del lechón sobre los balances de nitrógeno y energía en la fase de crecimiento Impact of piglet birth weight on nitrogen and energy balances in the growth phase Enrique Vázquez-Mandujano, Tércia Cesária Reis-de-Souza, Ericka Ramírez-Rodríguez, Gerardo Mariscal-Landín ................................................................................................................................ 903

Oferta y demanda regional de carne de pollo en México, 1996-2016 Regional supply and demand for chicken meat in Mexico, 1996-2016 Eulogio Rebollar-Rebollar, Alfredo Rebollar-Rebollar, Jaime Mondragón-Ancelmo, Germán GómezTenorio .............................................................................................................................................. 917

Óptimos técnicos para la producción de leche y carne en el sistema bovino de doble propósito del trópico mexicano Technical optimum milk and meat production levels in dual-purpose cattle systems in tropical Mexico Yuridia Bautista-Martínez, José Antonio Espinosa-García, José Guadalupe Herrera-Haro, Francisco Ernesto Martínez-Castañeda, Humberto Vaquera-Huerta, Benigno Estrada-Drouaillet, Lorenzo Danilo Granados-Rivera ............................................................................................................................... 933

Caracterización productiva y socioeconómica del sistema de producción ovina, en un área natural protegida de México Productive and socioeconomic characterization of a sheep production system in a natural protected area in Mexico Daniel Hernández-Valenzuela, Ernesto Sánchez-Vera, William Gómez-Demetrio, Carlos Galdino Martínez-García ................................................................................................................................ 951

Influencia de los valores humanos en el consumo de quesos tradicionales chiapanecos: una comparación de las rutas directa e indirecta Comparison of the direct and indirect routes of human values’ influence on consumption of two traditional cheeses from Chiapas, Mexico Carolina Illescas-Marín, Arturo Hernández-Montes, Esaú Estrada-Estrada, Rolando Murguía-Cozar, Anastacio Espejel-García, Armando Santos-Moreno ......................................................................... 966

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Endoparásitos de Odocoileus virginianus y Mazama temama bajo cautiverio en Veracruz, México Endoparasites in captive Odocoileus virginianus and Mazama temama in Veracruz, Mexico Cristina Salmorán-Gómez, Ricardo Serna-Lagunes, Norma Mora-Collado, Dora Romero-Salas, Dulce María Ávila-Nájera, Pedro Zetina-Córdoba ....................................................................................... 986

REVISIONES DE LITERATURA

Supplementation of ascorbic acid to improve fertility in dairy cattle. Review Suplementación con ácido ascórbico para mejorar la fertilidad del ganado lechero. Revisión Juan González-Maldonado, Raymundo Rangel-Santos, Raymundo Rodríguez-de Lara, Gustavo Ramírez-Valverde, J. Efrén Ramírez-Bribiesca, José Cruz Monreal-Díaz ........................................ 1000

NOTAS DE INVESTIGACIÓN

Efecto del consumo de moringa sobre parámetros productivos y toxicológicos en pollos de engorda Effect of Moringa oleifera intake on productive and toxicological parameters in broiler chickens Martha Karina Fuentes-Esparza, Teódulo Quezada-Tristán, Salvador Horacio Guzman-Maldonado, Arturo Gerardo Valdivia-Flores, Raúl Ortíz-Martínez ...................................................................... 1013

Evaluación productiva y análisis costo-beneficio de cerdas alimentadas con una dieta adicionada con nopal (Opuntia ficus-indica) durante la lactancia Productive evaluation and cost:benefit analysis of lactating sows fed a diet containing nopal (Opuntia ficus-indica) Gerardo Ordaz-Ochoa, Aureliano Juárez-Caratachea, Liberato Portillo-Martínez, Rosa Elena PérezSánchez, Ruy Ortiz-Rodríguez ........................................................................................................ 1027

Rendimiento de materia seca y valor nutritivo de cuatro leguminosas herbáceas en la zona tropical de Hueytamalco, Puebla, México Dry matter yield and nutritional values of four herbaceous legumes in a humid tropical environment in Hueytamalco, Puebla, Mexico Sergio Alberto Lagunes-Rivera, Juan De Dios Guerrero-Rodríguez, Josafath Omar Hernández-Velez, José de Jesús Mario Ramírez-González, Dulce Violeta García-Bonilla, Antonio Alatorre-Hernández1042

Abortion outbreak caused by Campylobacter fetus subspecies venerealis and Neospora caninum in a bovine dairy herd Brote de abortos causado por Campylobacter fetus subespecie venerealis y Neospora caninum en un hato bovino lechero Melissa Macías-Rioseco, Rubén D. Caffarena, Martín Fraga, Caroline Silveira, Federico Giannitti, Germán Cantón, Yanina P. Hecker, Alejandra Suanes, Franklin Riet-Correa .................................. 1054

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Presencia de hidrocarburos aromáticos policíclicos (HAP) en leche comercializada en la Ciudad de México, evaluando diferentes métodos de extracción Polycyclic aromatic hydrocarbons (PAHs) in four milk brands sold in Mexico City: evaluating three fat extraction methods Javier Chay-Rincón, José Jesús Pérez-González, Beatriz Sofía Schettino-Bermúdez, Rey GutiérrezTolentino, Dayana Sosa-Pacheco, Arturo Escobar-Medina, Salvador Vega-y-León......................... 1064

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Actualización: abril, 2018 NOTAS AL AUTOR La Revista Mexicana de Ciencias Pecuarias se edita completa en dos idiomas (español e inglés) y publica tres categorías de trabajos: Artículos científicos, Notas de investigación y Revisiones bibliográficas.

indican, empezando cada uno de ellos en página aparte.

Página del título Resumen en español Resumen en inglés Texto Agradecimientosy conflicto de interés Literatura citada Cuadros y gráficas

Los autores interesados en publicar en esta revista deberán ajustarse a los lineamientos que más adelante se indican, los cuales en términos generales, están de acuerdo con los elaborados por el Comité Internacional de Editores de Revistas Médicas (CIERM) Bol Oficina Sanit Panam 1989;107:422-437.

1.

Sólo se aceptarán trabajos inéditos. No se admitirán si están basados en pruebas de rutina, ni datos experimentales sin estudio estadístico cuando éste sea indispensable. Tampoco se aceptarán trabajos que previamente hayan sido publicados condensados o in extenso en Memorias o Simposio de Reuniones o Congresos (a excepción de Resúmenes).

2.

Todos los trabajos estarán sujetos a revisión de un Comité Científico Editorial, conformado por Pares de la Disciplina en cuestión, quienes desconocerán el nombre e Institución de los autores proponentes. El Editor notificará al autor la fecha de recepción de su trabajo.

3.

El manuscrito deberá someterse a través del portal de la Revista en la dirección electrónica: http://cienciaspecuarias.inifap.gob.mx, consultando el “Instructivo para envío de artículos en la página de la Revista Mexicana de Ciencias Pecuarias”. Para su elaboración se utilizará el procesador de Microsoft Word, con letra Times New Roman a 12 puntos, a doble espacio. Asimismo se deberán llenar los formatos de postulación, carta de originalidad y no duplicidad y formato de derechos patrimoniales disponibles en el propio sitio oficial de la revista.

4.

5.

6.

Por ser una revista con arbitraje, y para facilitar el trabajo de los revisores, todos los renglones de cada página deben estar numerados; asimismo cada página debe estar numerada, inclusive cuadros, ilustraciones y gráficas.

7.

Página del Título. Solamente debe contener el título del trabajo, que debe ser conciso pero informativo; así como el título traducido al idioma inglés. En el manuscrito no es necesaria información como nombres de autores, departamentos, instituciones, direcciones de correspondencia, etc., ya que estos datos tendrán que ser registrados durante el proceso de captura de la solicitud en la plataforma del OJS (http://ciencias pecuarias.inifap.gob.mx).

8.

Resumen en español. En la segunda página se debe incluir un resumen que no pase de 250 palabras. En él se indicarán los propósitos del estudio o investigación; los procedimientos básicos y la metodología empleada; los resultados más importantes encontrados, y de ser posible, su significación estadística y las conclusiones principales.

A continuación del resumen, en punto y aparte, agregue debidamente rotuladas, de 3 a 8 palabras o frases cortas clave que ayuden a los indizadores a clasificar el trabajo, las cuales se publicarán junto con el resumen.

9.

Resumen en inglés. Anotar el título del trabajo en inglés y a continuación redactar el “abstract” con las mismas instrucciones que se señalaron para el resumen en español. Al final en punto y aparte, se deberán escribir las correspondientes palabras clave (“key words”).

10. Texto. Las tres categorías de trabajos que se publican en la Rev. Mex. Cienc. Pecu. consisten en lo siguiente: a) Artículos científicos. Deben ser informes de trabajos originales derivados de resultados parciales o finales de investigaciones. El texto del Artículo científico se divide en secciones que llevan estos encabezamientos:

Los artículos tendrán una extensión máxima de 20 cuartillas a doble espacio, sin incluir páginas de Título, y cuadros o figuras (los cuales no deberán exceder de ocho). Las Notas de investigación tendrán una extensión máxima de 15 cuartillas y 6 cuadros o figuras. Las Revisiones bibliográficas una extensión máxima de 30 cuartillas y 5 cuadros.

Introducción Materiales y Métodos Resultados Discusión Conclusiones e implicaciones

Los manuscritos de las tres categorías de trabajos que se publican en la Rev. Mex. Cienc. Pecu. deberán contener los componentes que a continuación se

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En los artículos largos puede ser necesario agregar subtítulos dentro de estas divisiones a fin de hacer más claro el contenido, sobre todo en las secciones de Resultados y de Discusión, las cuales también pueden presentarse como una sola sección.

apellidos compuestos se debe poner un guión entre ambos, ejemplo: Elías-Calles E. Entre las iniciales de un autor no se debe poner ningún signo de puntuación, ni separación; después de cada autor sólo se debe poner una coma, incluso después del penúltimo; después del último autor se debe poner un punto.

b) Notas de investigación.

Consisten en modificaciones a técnicas, informes de casos clínicos de interés especial, preliminares de trabajos o investigaciones limitadas, descripción de nuevas variedades de pastos; así como resultados de investigación que a juicio de los editores deban así ser publicados. El texto contendrá la misma información del método experimental señalado en el inciso a), pero su redacción será corrida del principio al final del trabajo; esto no quiere decir que sólo se supriman los subtítulos, sino que se redacte en forma continua y coherente.

El título del trabajo se debe escribir completo (en su idioma original) luego el título abreviado de la revista donde se publicó, sin ningún signo de puntuación; inmediatamente después el año de la publicación, luego el número del volumen, seguido del número (entre paréntesis) de la revista y finalmente el número de páginas (esto en caso de artículo ordinario de revista).

Puede incluir en la lista de referencias, los artículos aceptados aunque todavía no se publiquen; indique la revista y agregue “en prensa” (entre corchetes).

c) Revisiones bibliográficas. Consisten en el tratamiento y exposición de un tema o tópico de relevante actualidad e importancia; su finalidad es la de resumir, analizar y discutir, así como poner a disposición del lector información ya publicada sobre un tema específico. El texto se divide en: Introducción, y las secciones que correspondan al desarrollo del tema en cuestión.

En el caso de libros de un solo autor (o más de uno, pero todos responsables del contenido total del libro), después del o los nombres, se debe indicar el título del libro, el número de la edición, el país, la casa editorial y el año.

Cuando se trate del capítulo de un libro de varios autores, se debe poner el nombre del autor del capítulo, luego el título del capítulo, después el nombre de los editores y el título del libro, seguido del país, la casa editorial, año y las páginas que abarca el capítulo.

11. Agradecimientos y conflicto de interés. Siempre que corresponda, se deben especificar las colaboraciones que necesitan ser reconocidas, tales como a) la ayuda técnica recibida; b) el agradecimiento por el apoyo financiero y material, especificando la índole del mismo; c) las relaciones financieras que pudieran suscitar un conflicto de intereses. Las personas que colaboraron pueden ser citadas por su nombre, añadiendo su función o tipo de colaboración; por ejemplo: “asesor científico”, “revisión crítica de la propuesta para el estudio”, “recolección de datos”, etc. Siempre que corresponda los autores deberán mencionar si existe algún conflicto de interés.

12. Literatura citada. Numere las referencias consecutivamente en el orden en que se mencionan 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 paréntesis, sin señalar el año de la referencia. Evite hasta donde sea posible, el tener que mencionar en el texto el nombre de los autores de las referencias.

Procure abstenerse de utilizar los resúmenes como referencias; las “observaciones inéditas” y las “comunicaciones personales” no deben usarse como referencias, aunque pueden insertarse en el texto (entre paréntesis).

En el caso de tesis, se debe indicar el nombre del autor, el título del trabajo, luego entre corchetes el grado (licenciatura, maestría, doctorado), luego el nombre de la ciudad, estado y en su caso país, seguidamente el nombre de la Universidad (no el de la escuela), y finalmente el año.

Emplee el estilo de los ejemplos que aparecen a continuación, los cuales están parcialmente basados en el formato que la Biblioteca Nacional de Medicina de los Estados Unidos usa en el Index Medicus.

Revistas

Artículo ordinario, con volumen y número. (Incluya el nombre de todos los autores cuando sean seis o menos; si son siete o más, anote sólo el nombre de los seis primeros y agregue “et al.”).

I)

Reglas básicas para la Literatura citada Nombre de los autores, con mayúsculas sólo las iniciales, empezando por el apellido paterno, luego iniciales del materno y nombre(s). En caso de

Basurto GR, Garza FJD. Efecto de la inclusión de grasa o proteína de escape ruminal en el comportamiento de toretes Brahman en engorda. Téc Pecu Méx 1998;36(1):35-48.

Sólo número sin indicar volumen.

II) Stephano HA, Gay GM, Ramírez TC. Encephalomielitis, reproductive failure and corneal opacity (blue eye) in

VIII

pigs associated with a paramyxovirus infection. Vet Rec 1988;(122):6-10.

Beltsville Symposium: Biotechnology’s role in genetic improvement of farm animals. USDA. 1996:13.

III) Chupin D, Schuh H. Survey of present status ofthe use of artificial insemination in developing countries.

World Anim Rev 1993;(74-75):26-35.

No se indica el autor.

IV) Cancer in South Africa [editorial]. S Afr Med J 1994;84:15.

Suplemento de revista.

V) Hall JB, Staigmiller RB, Short RE, Bellows RA, Bartlett SE. Body composition at puberty in beef heifers as influenced by nutrition and breed [abstract]. J Anim Sci 1998;71(Suppl 1):205.

Organización, como autor.

VI) The Cardiac Society of Australia and New Zealand.

Clinical exercise stress testing. Safety and performance guidelines. Med J Aust 1996;(164):282-284.

En proceso de publicación.

VII) Scifres CJ, Kothmann MM. Differential grazing use of herbicide treated area by cattle. J Range Manage [in press] 2000.

Libros y otras monografías

Autor total.

VIII) Steel RGD, Torrie JH. Principles and procedures of statistics: A biometrical approach. 2nd ed. New York, USA: McGraw-Hill Book Co.; 1980.

Autor de capítulo.

IX)

Roberts SJ. Equine abortion. In: Faulkner LLC editor.

Abortion diseases of cattle. 1rst ed. Springfield, Illinois, USA: Thomas Books; 1968:158-179.

Memorias de reuniones.

X)

Loeza LR, Angeles MAA, Cisneros GF. Alimentación de cerdos. En: Zúñiga GJL, Cruz BJA editores.

Tercera reunión anual del centro de investigaciones forestales y agropecuarias del estado de Veracruz.

Veracruz. 1990:51-56.

XI)

Olea PR, Cuarón IJA, Ruiz LFJ, Villagómez AE. Concentración de insulina plasmática en cerdas alimentadas con melaza en la dieta durante la inducción de estro lactacional [resumen]. Reunión nacional de investigación pecuaria. Querétaro, Qro. 1998:13.

XII) Cunningham EP. Genetic diversity in domestic animals: strategies for conservation and development. In: Miller RH et al. editors. Proc XX

Tesis.

XIII) Alvarez MJA. Inmunidad humoral en la anaplasmosis y babesiosis bovinas en becerros mantenidos en una zona endémica [tesis maestría]. México, DF: Universidad Nacional Autónoma de México; 1989.

XIV) Cairns RB. Infrared spectroscopic studies of solid oxigen [doctoral thesis]. Berkeley, California, USA: University of California; 1965.

Organización como autor.

XV) NRC. National Research Council. The nutrient requirements of beef cattle. 6th ed. Washington, DC, USA: National Academy Press; 1984.

XVI) SAGAR. Secretaría de Agricultura, Ganadería y Desarrollo Rural. Curso de actualización técnica para la aprobación de médicos veterinarios zootecnistas responsables de establecimientos destinados al sacrificio de animales. México. 1996.

XVII) AOAC. Oficial methods of analysis. 15th ed.

Arlington, VA, USA: Association of Official Analytical Chemists. 1990.

XVIII) SAS. SAS/STAT User’s Guide (Release 6.03). Cary NC, USA: SAS Inst. Inc. 1988.

XIX) SAS. SAS User´s Guide: Statistics (version 5 ed.).

Cary NC, USA: SAS Inst. Inc. 1985.

Publicaciones electrónicas XX) Jun Y, Ellis M. Effect of group size and feeder type 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.

Accessed Jul 30, 2003.

XXI) Villalobos GC, González VE, Ortega SJA. Técnicas para estimar la degradación de proteína y materia orgánica en el rumen y su importancia en rumiantes en pastoreo. Téc Pecu Méx 2000;38(2): 119-134. http://www.tecnicapecuaria.org/trabajos/20021217 5725.pdf. Consultado 30 Ago, 2003.

XXII) Sanh MV, Wiktorsson H, Ly LV. Effect of feeding level on milk production, body weight change, feed conversion and postpartum oestrus of crossbred lactating cows in tropical conditions. Livest Prod Sci 2002;27(2-3):331-338. http://www.sciencedirect. com/science/journal/03016226. Accessed Sep 12, 2003.

13. Cuadros, Gráficas e Ilustraciones. Es preferible que sean pocos, concisos, contando con los datos

IX

necesarios para que sean autosuficientes, que se entiendan por sí mismos sin necesidad de leer el texto.

Para las notas al pie se deberán utilizar los símbolos convencionales.

km kilómetro (s) L litro (s) log logaritmo decimal Mcal megacaloría (s) MJ megajoule (s) m metro (s) msnm metros sobre el nivel del mar µg microgramo (s) µl microlitro (s) µm micrómetro (s)(micra(s)) mg miligramo (s) ml mililitro (s) mm milímetro (s) min minuto (s) ng nanogramo (s)Pprobabilidad (estadística) p página PC proteína cruda PCR reacción en cadena de la polimerasa pp páginas ppm partes por millón % por ciento (con número) rpm revoluciones por minuto seg segundo (s) t tonelada (s) TND total de nutrientes digestibles UA unidad animal UI unidades internacionales

14 Versión final. Es el documento en el cual los autores ya integraron las correcciones y modificaciones indicadas por el Comité Revisor. Los trabajos deberán ser elaborados con Microsoft Word. Las gráficas y figuras se deberán elaborar en Word, Power Point, Corel Draw y enviadas en archivo aparte (nunca insertarlas como imágenes en el texto). Los cuadros no deberán contener ninguna línea vertical, y las horizontales solamente las que delimitan los encabezados de columna, y la línea al final del cuadro.

15. Una vez recibida la versión final, ésta se mandará para su traducción al idioma inglés o español, según corresponda. Si los autores lo consideran conveniente podrán enviar su manuscrito final en ambos idiomas.

16. Tesis. Se publicarán como Artículo o Nota de Investigación, siempre y cuando se ajusten a las normas de esta revista.

17. Los trabajos no aceptados para su publicación se regresarán al autor, con un anexo en el que se explicarán los motivos por los que se rechaza o las modificaciones que deberán hacerse para ser reevaluados.

18. Abreviaturas de uso frecuente: cal cm °C DL50 g ha h i.m. i.v.

J kg

caloría (s) centímetro (s) grado centígrado (s) dosis letal 50% gramo (s) hectárea (s) hora (s) intramuscular (mente) intravenosa (mente) joule (s) kilogramo (s)

vs

versus

xg

gravedades

Cualquier otra abreviatura se pondrá entre paréntesis inmediatamente después de la(s) palabra(s) completa(s).

19. Los nombres científicos y otras locuciones latinas se deben escribir en cursivas.

X

Updated: April, 2018 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 in publishing in this journal, should follow the belowmentioned directives which are based on those set down by the International Committee of Medical Journal Editors (ICMJE) Bol Oficina Sanit Panam 1989;107:422-437.

1.

Only original unpublished works will be accepted.

Manuscripts based on routine tests, will not be accepted. All experimental data must be subjected to statistical analysis. Papers previously published condensed or in extenso in a Congress or any other type of Meeting will not be accepted (except for Abstracts).

2.

All contributions will be peer reviewed by a scientific editorial committee, composed of experts who ignore the name of the authors. The Editor will notify the author the date of manuscript receipt.

3.

Papers will be submitted in the Web site http://cienciaspecuarias.inifap.gob.mx, according the “Guide for submit articles in the Web site of the Revista Mexicana de Ciencias Pecuarias�. Manuscripts should be prepared, typed in a 12 points font at double space (including the abstract and tables). At the time of submission, the application form, must be filled out, as well as a letter of originality and no duplication and patrimonial rights format, available on the official website of the journal.

4.

5.

6.

References Tables and Graphics 7.

Title page. It should only contain the title of the work, which should be concise but informative; as well as the title translated into English language. In the manuscript is not necessary information as names of authors, departments, institutions and correspondence addresses, etc.; as these data will have to be registered during the capture of the application process on the OJS platform (http://cienciaspecuarias.inifap.gob.mx).

8.

Abstract. On the second page a summary of no more than 250 words should be included. This abstract should start with a clear statement of the objectives and must include basic procedures and methodology.

The more significant results and their statistical value and the main conclusions should be elaborated briefly.

At the end of the abstract, and on a separate line, a list of up to 10 key words or short phrases that best describe the nature of the research should be stated.

9.

Text. The three categories of articles which are published in Revista Mexicana de Ciencias Pecuarias are the following:

a) Research Articles. They should originate in primary

works and may show partial or final results of research. The text of the article must include the following parts: Introduction Materials and Methods Results Discussion Conclusions and implications

To facilitate peer review all pages should be numbered consecutively, including tables, illustrations and graphics, and the lines of each page should be numbered as well.

In lengthy articles, it may be necessary to add other sections to make the content clearer. Results and Discussion can be shown as a single section if considered appropriate.

Research articles will not exceed 20 double spaced pages, without including Title page and Tables and Figures (8 maximum). Technical notes will have a maximum extension of 15 pages and 6 Tables and Figures. Reviews should not exceed 30 pages and 5 Tables and Figures.

b) Technical Notes. They should be brief and be evidence for technical changes, reports of clinical cases of special interest, complete description of a limited investigation, or research results which should be published as a note in the opinion of the editors. The text will contain the same information presented in the sections of the research article but without section titles.

Manuscripts of all three type of articles published in Revista Mexicana de Ciencias Pecuarias should contain the following sections, and each one should begin on a separate page.

c) Reviews. The purpose of these papers is to

Title page Abstract Text Acknowledgments

summarize, analyze and discuss an outstanding topic.

The text of these articles should include the following sections: Introduction, and as many sections as

XI

needed that relate to the description of the topic in question.

f.

In the case of a thesis, references should be made of the author’s name, the title of the research, the degree obtained, followed by the name of the City, State, and Country, the University (not the school), and finally the year.

10. Acknowledgements.

Whenever appropriate, collaborations that need recognition should be specified: a) Acknowledgement of technical support; b) Financial and material support, specifying its nature; and c) Financial relationships that could be the source of a conflict of interest.

Examples The style of the following examples, which are partly based on the format the National Library of Medicine of the United States employs in its Index Medicus, should be taken as a model.

People which collaborated in the article may be named, adding their function or contribution; for example: “scientific advisor”, “critical review”, “data collection”, etc.

11. References. All references should be quoted in their original language. They should be numbered consecutively in the order in which they are first mentioned in the text. Text, tables and figure references should be identified by means of Arabic numbers. Avoid, whenever possible, mentioning in the text the name of the authors. Abstain from using abstracts as references. Also, “unpublished observations” and “personal communications” should not be used as references, although they can be inserted in the text (inside brackets).

Journals

Standard journal article (List the first six authors followed by et al.)

I)

Basurto GR, Garza FJD. Efecto de la inclusión de grasa o proteína de escape ruminal en el comportamiento de toretes Brahman en engorda. Téc Pecu Méx 1998;36(1):35-48.

Issue with no volume II) Stephano HA, Gay GM, Ramírez TC. Encephalomielitis, reproductive failure and corneal opacity (blue eye) in pigs associated with a paramyxovirus infection. Vet Rec 1988;(122):6-10.

Key rules for references a.

The names of the authors should be quoted beginning with the last name spelt with initial capitals, followed by the initials of the first and middle name(s).

In the presence of compound last names, add a dash between both, i.e. Elias-Calles E. Do not use any punctuation sign, nor separation between the initials of an author; separate each author with a comma, even after the last but one.

III) Chupin D, Schuh H. Survey of present status of the use of artificial insemination in developing countries.

World Anim Rev 1993;(74-75):26-35.

No author given

b.

The title of the paper should be written in full, followed by the abbreviated title of the journal without any punctuation sign; then the year of the publication, after that the number of the volume, followed by the number (in brackets) of the journal and finally the number of pages (this in the event of ordinary article).

IV) Cancer in South Africa [editorial]. S Afr Med J 1994;84:15.

Journal supplement V) Hall JB, Staigmiller RB, Short RE, Bellows RA, Bartlett SE. Body composition at puberty in beef heifers as influenced by nutrition and breed [abstract]. J Anim Sci 1998;71(Suppl 1):205.

c.

Accepted articles, even if still not published, can be included in the list of references, as long as the journal is specified and followed by “in press” (in brackets).

Organization, as author

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 names(s), the number of the edition, the country, the printing house and the year.

VI) The Cardiac Society of Australia and New Zealand.

Clinical exercise stress testing. Safety and performance guidelines. Med J Aust 1996;(164):282284.

In press

e.

When a reference is made of a chapter of book written by several authors; the name of the author(s) of the chapter should be quoted, followed by the title of the chapter, the editors and the title of the book, the country, the printing house, the year, and the initial and final pages.

VII) Scifres CJ, Kothmann MM. Differential grazing use of herbicide-treated area by cattle. J Range Manage [in press] 2000.

XII

XVIII) SAS. SAS/STAT User’s Guide (Release 6.03). Cary NC, USA: SAS Inst. Inc. 1988.

Books and other monographs

Author(s)

XIX) SAS. SAS User´s Guide: Statistics (version 5 ed.).

Cary NC, USA: SAS Inst. Inc. 1985.

VIII) Steel RGD, Torrie JH. Principles and procedures of statistics: A biometrical approach. 2nd ed. New York, USA: McGraw-Hill Book Co.; 1980.

Electronic publications XX) Jun Y, Ellis M. Effect of group size and feeder type 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.

Accesed Jul 30, 2003.

Chapter in a book IX)

Roberts SJ. Equine abortion. In: Faulkner LLC editor.

Abortion diseases of cattle. 1rst ed. Springfield, Illinois, USA: Thomas Books; 1968:158-179.

XXI) Villalobos GC, González VE, Ortega SJA. Técnicas para estimar la degradación de proteína y materia orgánica en el rumen y su importancia en rumiantes en pastoreo. Téc Pecu Méx 2000;38(2): 119-134. http://www.tecnicapecuaria.org/trabajos/20021217 5725.pdf. Consultado 30 Jul, 2003.

Conference paper X)

Loeza LR, Angeles MAA, Cisneros GF. Alimentación de cerdos. En: Zúñiga GJL, Cruz BJA editores.

Tercera reunión anual del centro de investigaciones forestales y agropecuarias del estado de Veracruz.

Veracruz. 1990:51-56.

XI)

Olea PR, Cuarón IJA, Ruiz LFJ, Villagómez AE. Concentración de insulina plasmática en cerdas alimentadas con melaza en la dieta durante la inducción de estro lactacional [resumen]. Reunión nacional de investigación pecuaria. Querétaro, Qro. 1998:13.

XXII) Sanh MV, Wiktorsson H, Ly LV. Effect of feeding level on milk production, body weight change, feed conversion and postpartum oestrus of crossbred lactating cows in tropical conditions. Livest Prod Sci 2002;27(2-3):331-338. http://www.sciencedirect.com/science/journal/030 16226. Accesed Sep 12, 2003.

XII) Cunningham EP. Genetic diversity in domestic animals: strategies for conservation and development. In: Miller RH et al. editors. Proc XX Beltsville Symposium: Biotechnology’s role in genetic improvement of farm animals. USDA. 1996:13.

12. Tables, Graphics and Illustrations. It is preferable that they should be few, brief and having the necessary data so they could be understood without reading the text. Explanatory material should be placed in footnotes, using conventional symbols.

13. Final version. This is the document in which the authors have incorporated all the corrections and modifications asked for by the editors. Graphs and figures should be submitted separately in Microsoft Word, MS Power Point, or Corel Draw. Figures must not be inserted as images within the text. In Tables do not use internal horizontal or vertical lines.

Thesis XIII) Alvarez MJA. Inmunidad humoral en la anaplasmosis y babesiosis bovinas en becerros mantenidos en una zona endémica [tesis maestría]. México, DF: Universidad Nacional Autónoma de México; 1989.

XIV) Cairns RB. Infrared spectroscopic studies of solid oxigen [doctoral thesis]. Berkeley, California, USA: University of California; 1965.

14. Once accepted, the final version will be translated into Spanish or English, although authors should feel free to send the final version in both languages. No charges will be made for style or translation services.

Organization as author XV) NRC. National Research Council. The nutrient requirements of beef cattle. 6th ed. Washington, DC, USA: National Academy Press; 1984.

15. Thesis will be published as a Research Article or as a Technical Note, according to these guidelines.

16. Manuscripts not accepted for publication will be returned to the author together with a note explaining the cause for rejection, or suggesting changes which should be made for re-assessment.

XVI) SAGAR. Secretaría de Agricultura, Ganadería y Desarrollo Rural. Curso de actualización técnica para la aprobación de médicos veterinarios zootecnistas responsables de establecimientos destinados al sacrificio de animales. México. 1996.

17. List of abbreviations:

XVII) AOAC. Official methods of analysis. 15th ed.

Arlington, VA, USA: Association of Official Analytical Chemists. 1990.

cal cm °C

XIII

calorie (s) centimeter (s) degree Celsius

DL50 g ha h i.m. i.v.

J kg km L log Mcal MJ m Âľl Âľm mg ml mm min ng

P

lethal dose 50% gram (s) hectare (s) hour (s) intramuscular (..ly) intravenous (..ly) joule (s) kilogram (s) kilometer (s) liter (s) decimal logarithm mega calorie (s) mega joule (s) meter (s) micro liter (s) micro meter (s) milligram (s) milliliter (s) millimeter (s) minute (s) nanogram (s)

probability (statistic) p page CP crude protein PCR polymerase chain reaction pp pages ppm parts per million % percent (with number) rpm revolutions per minute sec second (s) t metric ton (s) TDN total digestible nutrients AU animal unit IU international units

vs

versus

xg

gravidity

The full term for which an abbreviation stands should precede its first use in the text.

18. Scientific names and other Latin terms should be written in italics.

XIV

https://doi.org/10.22319/rmcp.v10i4.4908 Article

Genetic diversity and structure in Criolla Negra goats in Queretaro, Mexico.

Juan Carlos Silva-Jarquin a Héctor Mario Andrade-Montemayor b* Héctor Raymundo Vera-Ávila b Marina Durán-Aguilar b Sergio Iván Román-Ponce c Vincenzo Landi d Amparo Martínez-Martínez d Juan Vicente Delgado Bermejo d Consorcio BioGoat e

a

Universidad Autónoma de Querétaro. Facultad de Ciencias Naturales. Doctorado en Ciencias Biológicas. Avenida de las Ciencias S/N Juriquilla, Delegación Santa Rosa Jáuregui, 76230 Querétaro, México. b

Universidad Autónoma de Querétaro. Facultad de Ciencias Naturales. Licenciatura en Medicina Veterinaria y Zootecnia. México. c

INIFAP, Centro Nacional de Investigación Disciplinaria en Fisiología y Mejoramiento Animal. México. d

Universidad de Córdoba. Campus de Excelencia Internacional Agroalimentario ceiA3.

Departamento de Genética. España. e

Proyecto de Biodiversidad Caprina Iberoamericana. España.

*Corresponding author: andrademontemayor@gmail.com

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Rev Mex Cienc Pecu 2019;10(4):801-818

Abstract: Since their introduction to Mexico goats have undergone a long process of adaptation and selection, resulting in highly rustic local animals. However, importation of improved breeds has led to the extinction of some regional breeds. For example, the Criolla Negra goat breed is known for its rusticity and high milk quality, but is in decline. A genetic characterization was done of a Criolla Negra population. Hair samples were collected in three goat herds located in different municipalities of the state of Querétaro, Mexico: Cadereyta de Montes (n= 7); El Marqués (n= 11); and San Juan del Río (n= 27). Thirty microsatellites were used to quantify the number of alleles per marker (NA), median number of alleles (MNA), number of effective alleles (NEA), observed heterozygosis (Ho), expected heterozygosis (He), polymorphic data content (PDC), the fixation index (FIS) and Hardy Weinberg equilibrium (HWE). The Criolla Negra population was compared to thirteen breeds forming part of the BioGoat project. Genetic diversity was found to be high in this population. A total of 243 alleles were identified with an MNA of 8.1 alleles per marker. The markers were informative (PDC= 0.06) for polymorphism.

The He (0.71) and Ho (0.62) values indicate a slight imbalance in the population. Reynolds genetic distance results showed the Criolla Negra breed to be genetically furthest from the Anglonubia breed and nearest the Murciano-Granadina breed. The studied Criolla Negra goat population exhibits a breed structure well differentiated from the other breeds in the analysis.

Key words: Genetic characterization, population genetics, Criolla Negra goats.

Received: 19/05/2018 Accepted: 22/10/2018

Introduction

Goats have closely coexisted with human beings since their domestication began approximately 10,000 years ago(1,2). One of the first domesticated livestock species, goats formed part of the Neolithic agricultural revolution, development of trade and human migrations(3). All these events involved some basic evolutionary mechanisms, such as animal migration, selection, gene drift and even mutation. This helps in explaining goats’ high capacity for adaptation to different ecosystems and the more than 300 breeds currently in existence(1,4).

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Rev Mex Cienc Pecu 2019;10(4):801-818

First brought to the Americas in 1493, goats were initially propagated by the Spanish although native peoples also learned how to manage them and select for certain traits.

New breeds, known as Creoles, consequently arose over time as selection aimed to better adapt them to local environmental conditions(5).

There are currently over 8.7 million goats in Mexico(6). As in other developing countries goats represent a subsistence resource for people living in arid and semi-arid areas with scarce vegetation and poor rangeland(7,8). Goats can be found throughout Mexico but are far more common in three main regions: the Mixtec mosaic; central Mexico (El Bajio); and northern Mexico (El Lagunero)(9). The Criolla Negra (CN) breed is found primarily in the central region, particularly in the states of Querétaro and Guanajuato. Used mostly for dairy production, milk from this breed contains higher total solids content than other goat breeds in Mexico and provides excellent cheese yield(10,11,12). Long considered a Granadina breed based on its morphological characteristics and origin, over 500 yr of independent evolution have genetically differentiated it from this breed. No studies have been done on the genetic status of the CN breed. This is important because improved goat breeds are increasingly being imported into Mexico and are used in indiscriminate crosses, threatening the CN breed’s genetic health(13).

The evaluation of genetic diversity within and between breeds helps to understand a population’s genetic structure, and to establish strategies for conservation, genetic improvement and sustainable use of genetic resources(14). Microsatellite molecular markers are useful in genetic characterization studies within and between populations.

They provide genetic codominance, abundance, random distribution across the genome, high reproducibility, neutrality with respect to selection and high levels of polymorphism(14,15). Numerous genetic diversity studies have been done recently on several cattle species using microsatellite markers and they have become the genetic markers of choice for molecular applications such as genetic diversity(16,17), population structure(18,19), phylogeny(20), paternity evaluation(21), etc. The present study objective was to evaluate genetic diversity and population structure of Criolla Negra goats using microsatellite markers.

Material and methods

Biological samples

Hair samples were collected from 45 individual goats distributed in three herds in three municipalities of the state of Querétaro, Mexico: Cadereyta de Montes (n= 7); El Marqués (n= 11); and San Juan del Río (n= 27). Samples were collected following the non803

Rev Mex Cienc Pecu 2019;10(4):801-818

probabilistic opportunity method. Inclusion criteria were animals must not be related, they must be older than one year of age, have a black coat and erect or semi-lopped ears.

Because genealogical data is unavailable for these populations kinship data provided by the producers was utilized.

The analysis included data for 25 microsatellites from 455 individuals from 13 goat populations: Retinta; Verata; Blanca Serrana; Celtibérica; Malagueña; MurcianoGranadina; Florida; Payoya; Serrana; Formentera; Saanen; Alpina; and Anglonubia. All populations form part of the Biodiversidad Caprina Iberoamericana (BioGoat) project(22).

Molecular analysis

Extraction of DNA was done from the hair samples using a chelating resin (Chelex® 100, Bio-Rad Laboratories, Inc. USA)(23). Thirty microsatellites recommended by the mixed ISAG/FAO committee for analysis of genetic diversity in domestic animals were used(14).

Of these, 25 were found to be held in common among the thirteen BioGoat populations.

Marker amplification was done by polymerase chain reaction (PCR) using florescent primers(24). The amplicons produced with the PCR were separated by capillary electrophoresis (ABI PRISM 3130 Genetic Analyzer, Applied Biosystems) following manufacturer instructions. Allele size was quantified with an internal size standard (GeneScan-400HD ROX, Applied Biosystems), and genotypes were identified with the GENOTYPER 2.5.1. software. Reference samples were included in each sample to confirm the results.

Statistical analysis

The total number of alleles per marker (NA) was determined by direct counts, while the median number of alleles (MNA) was calculated as the sum of all NA data divided by the number of makers used (n= 30). Observed heterozygosis (Ho) was calculated by dividing the number of individual heterozygotes in each marker by the number of individuals positive for each marker. Expected heterozygosis (He) was estimated with Nei’s formula(25). Polymorphic data content, an indicator of marker quality(26), was estimated using the MICROSATELLITE TOOLKIT complement for Microsoft Excel 2010(27). The number of effective alleles (NEA), which is the number of alleles able to pass to the following generation(28), was generated with the POPGENE v. 1.32. software. The exact

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Rev Mex Cienc Pecu 2019;10(4):801-818

test of Hardy-Weinberg equilibrium (HWE), which considers heterozygote deficit, was calculated with the GENEPOP v.4.2 software(29), using the thirty markers for the CN and the Markov chain method (5,000 dememorizations; 100 lots; 10,000 interactions per lot).

A 95%(25,30) confidence interval was used when calculating endogamy coefficients for individuals versus subpopulations (FIS), individuals versus total population (FIT), and subpopulations versus total populations (FST), as well as the genetic differentiation coefficient (GST). All were generated with the GENETIX v. 4.05 software(31).

A matrix for Reynolds genetic distance(32), the minimum normalized Nei distance with a heterozygosis value in the founding population, was calculated with the POPULATIONS v.1.2.28 software. Split Graphs were then generated with the “NeighborNet” algorithm in the SPLITSTREE4 program(33).

Genetic structure origin of the populations included in the study was analyzed with cluster (K) techniques, which represent the number of populations. These use a Bayesian algorithm employing a model based on the Montecarlo Markov Chains (MCMC) method, which estimates the a posteriori distribution of each mix coefficient for each individual.

This was done with the STRUCTURE v.2.3.4 software(34). The MCMC burn-in was 50,000 iterations and 200,000 repetitions, and results were viewed with the DISTRUCT program(35). Optimum K was estimated by fixing values of K2 to K15 and running the analysis with fifteen repetitions for each K value, following the method of Evanno(36) and using the STRUCTURE HARVESTER program(37).

Results Genetic variation in Criolla Negra population

A total of 243 alleles were identified with the thirty markers used in the analyses. Median number of alleles (MNA) was 8.1 per locus in this population (Table 1). The highest NA (13) was observed for markers MM12 and SRCRSP23, followed by BM6526 and HSC with twelve alleles. The lowest NA (2) was observed in MAF209. The HSC marker had the highest NEA (9.14) while MAF209 had the lowest (1.25). This may have occurred due to the proportion of polymorphic markers, the number of alleles per marker and their frequencies, and sample size. Average population He was 0.71, but varied from 0.20 in MAF209 to 0.90 in HSC. Average Ho was 0.62, and ranged from 0.18 in MAF209 to 0.93 in HSC. Average PDC in the CN population was 0.66. The least informative marker (PDC<0.25) was MAF209 (PDC= 0.18), followed by ETH225 (PDC= 0.26) and SPS115 (PDC= 0.

44). The remaining 27 markers were the most informative (CIP >0.5). Fourteen of the thirty tested microsatellites exhibited significant deviation for the HWE (P≤0.05).

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Table 1: Analyzed microsatellites, number of alleles detected (NA), number of effective alleles (NEA), expected heterozygosis (He), observed heterozygosis (Ho), polymorphic data content (PDC) and Hardy-Weinberg equilibrium (HWE) deviations Microsatellites BM1329 BM1818 BM6506 BM6526 BM8125 CRSM60 CSRD247 CSSM66 ETH010 ETH225 HAUT27 HSC ILSTS011 INRA063 MAF065 MAF209 McM527 MM12 OarFCB011 OarFCB048 OarFCB304 SPS115 SRCRSP08 TGLA122 SRCRSP05 SRCRSP23 SRCRSP24 ILSTS019 INRA005 INRA006 Average

NA

NEA

He

Ho

PDC

8 8 9 12 6 8 6 11 4 4 8 12 6 5 11 2 8 13 10 10 10 3 10 8 7 13 10 6 5 10 8.1

3.44 4.63 3.26 4.93 3.64 4.32 3.25 7.06 2.69 1.41 4.11 9.14 2.87 2.46 5.78 1.25 5.33 7.00 5.73 7.14 3.72 2.02 3.76 2.20 3.09 8.49 3.59 2.27 2.23 5.29 4.20

0.72 0.79 0.70 0.81 0.73 0.78 0.70 0.87 0.63 0.29 0.77 0.90 0.66 0.60 0.84 0.20 0.82 0.87 0.83 0.87 0.74 0.51 0.74 0.55 0.68 0.89 0.73 0.57 0.56 0.82 0.71

0.67 0.71 0.55 0.86 0.62 0.71 0.77 0.34 0.56 0.25 0.71 0.93 0.59 0.64 0.87 0.18 0.82 0.75 0.75 0.78 0.61 0.27 0.70 0.49 0.59 0.68 0.56 0.51 0.45 0.77 0.62

0.68 0.76 0.66 0.78 0.68 0.73 0.66 0.84 0.56 0.26 0.72 0.88 0.59 0.51 0.81 0.18 0.79 0.84 0.80 0.84 0.69 0.44 0.69 0.52 0.63 0.87 0.69 0.53 0.51 0.79 0.66

HWE (P) 0.20 0.12 0.02 0.90 0.02 0.03 0.97 0.00 0.19 0.22 0.28 0.31 0.33 0.72 0.24 0.43 0.58 0.03 0.12 0.01 0.00 0.00 0.23 0.02 0.05 0.00 0.00 0.04 0.03 0.18

P>0.05= Not significant

Interpopulation genetic differentiation

The endogamy coefficients (FIS, FIT and FST) and GST were estimated for each of the 25 microsatellites shared between the CN and the thirteen breeds included in BioGoat(22) 806

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(Table 2). Average FIS was 0.067. Negative values for the markers BM8125 (-0.002) and MAF209 (-0.006) indicate these heterozygotes were present in excess(18,19). Of the 25 markers eleven had a FIS greater than 0.05. Values for GST generally followed a similar trend to those for FST. The marker with the highest GST value was BM6526 (0.114) while the lowest was MAF209 (0.037).

Table 2: Genetic differentiation coefficient and endogamy coefficients for each microsatellite compared between the Criolla Negra breed and breeds in BioGoat (Retinta, Verata, Blanca Serrana, CeltibĂŠrica, MalagueĂąa, Murciano-Granadina, Florida, Payoya, Serrana, Formentera, Saanen, Alpina and Anglonubia) Microsatellite

GST

FIS

FIT

FST

BM1329 BM1818 BM6506 BM6526 BM8125 CRSM60 CSRD247 CSSM66 ETH010 ETH225 HSC ILSTS011 INRA063 MAF065 MAF209 McM527 MM12

0.081 0.077 0.074 0.114 0.076 0.041 0.082 0.083 0.058 0.057 0.068 0.058 0.051 0.073 0.037 0.081 0.060

0.024 0.024 0.044 0.045 -0.002 0.064 0.026 0.271 0.032 0.015 0.095 0.068 0.164 0.025 -0.006 0.087 0.059

0.078 0.085 0.094 0.111 0.065 0.091 0.095 0.316 0.076 0.058 0.141 0.114 0.195 0.084 0.018 0.151 0.098

0.056 0.062 0.053 0.069 0.067 0.029 0.071 0.062 0.047 0.044 0.050 0.049 0.038 0.060 0.024 0.070 0.042

OarFCB011

0.084

0.065

0.134

0.073

OarFCB048

0.060

0.058

0.103

0.049

SPS115

0.097

0.187

0.257

0.086

SRCRSP08

0.106

0.049

0.143

0.099

TGLA122 Promedio

0.091 0.073

0.070 0.067

0.146 0.121

0.082 0.058

GST= genetic differentiation coefficient; FIS= endogamy coefficient of individuals vs. subpopulations; FIT= endogamy coefficient of individuals vs. total population; and FST= endogamy coefficient of subpopulations vs. total population.

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Interpopulation genetic distance and its graphic representation

Genetic distance analysis between the fourteen compared breeds showed the shortest distance to be between the CN and Murciano-Granadina breeds (MG) (0.133), and the longest to be between CN and Anglonubia (ANG) (0.420) (Table 3). A neighbor-net dendrogram was built to assist in interpreting values in the genetic distance matrix (Figure 1).

ANG

ALP

SAAN

FOR

SER

PAY

FLO

MG

MALAG

CELTIB

BLANCA

VERA

RET

Table 3: Reynolds genetic distance matrix between the fourteen studied goat breeds

VERA

0.025

BLANCA

0.025 0.032

CELTIB

0.025 0.036 0.027

MALAG

0.021 0.025 0.023 0.021

MG

0.044 0.045 0.031 0.034 0.041

FLO

0.023 0.023 0.028 0.026 0.015 0.041

PAY

0.034 0.044 0.047 0.036 0.046 0.061 0.038

SER

0.034 0.038 0.035 0.028 0.024 0.048 0.027 0.045

FOR

0.071 0.094 0.081 0.078 0.083 0.099 0.083 0.081 0.107

SAAN

0.071 0.061 0.070 0.069 0.070 0.069 0.061 0.077 0.078 0.119

ALP

0.063 0.056 0.052 0.062 0.055 0.076 0.049 0.064 0.073 0.119 0.071

ANG

0.124 0.125 0.130 0.151 0.132 0.146 0.126 0.178 0.149 0.221 0.161 0.142

CN

0.039 0.052 0.045 0.044 0.046 0.038 0.048 0.067 0.053 0.102 0.083 0.073 0.130

RET= Retinta, VERA= Verata, BLANCA= Blanca Serrana, CELTIB= Celtibérica, MALAG= Malagueña, MG= Murciano Granadina, FLO= Florida, PAY= Payoya, SER= Serrana, FOR= Formentera, SAAN= Saanen, ALP= Alpina, ANG= Anglonubia, CN= Criolla negra.

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Figure 1: Neighbor-net dendrogram built using Reynolds genetic distance data for the fourteen studied goat breeds.

RET= Retinta, VERA= Verata, BLANCA= Blanca Serrana, CELTIB= CeltibĂŠrica, MALAG= MalagueĂąa, MG= Murciano-Granadina, FLO= Florida, PAY= Payoya, SER= Serrana, FOR= Formentera, SAAN= Saanen, ALP= Alpina, ANG= Anglonubia, CCN= Criolla Negra.

Genetic structure analysis

Optimum K for the genetic structure of the studied populations was 9. When shown graphically (Figure 2), each individual is represented by a vertical line divided into color segments indicating to what extent the individual belongs to each group (K).

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Figure 2: Graphic representation of genetic structure in the fourteen studied goat breeds, assuming an ancestral populations number ranging from 2 to 9

RET= Retinta, VERA= Verata, BLANCA= Blanca Serrana, CELTIB= CeltibĂŠrica, MALAG= MalagueĂąa, MG= Murciano-Granadina, FLO= Florida, PAY= Payoya, SER= Serrana, FOR= Formentera, SAAN= Saanen, ALP= Alpina, ANG= Anglonubia, CCN= Cabra Criolla Negra.

Discussion

Genetic variation in Criolla Negra population

When running analyses with the microsatellites used in the present study it is recommended to have at least four alleles, and that the NEA be greater than two to be included in diversity studies and reduce standard error when estimating genetic distances(38). Only two markers (MAF209 and SPS115) had 2 and 3 alleles in the present

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results. Values for NEA were lower than two in the markers ETH225 (1.41) and MAF209 (1.25). This indicates how appropriate the markers were for genetic diversity evaluation of NA and NEA. The MNA value for the CN population (8.1) provided information on genetic diversity in this population. When MNA values are higher diversity is greater and vice versa. For the studied CN population MNA was high compared to other characterization studies done with breeds such as the Criollo Cubano(39); Saudi goats(19); cashmere goats in China(40,41), and some Iranian goat breeds(42). However, the MNA values for CN were similar to those reported for dairy goats in South Africa(43). Average PDC value for the markers used here was 0.66, which is similar to values reported for the Retinta ExtremeĂąa goat breed(44).

Heterozygosity values can also help to understand genetic diversity since they depend on allele number and relative frequency(45). The average heterozygosis values in the present results (He = 0.71, Ho = 0.62) are very similar to those reported for the Blanca Andaluza(46) and Retinta ExtremeĂąa goat breeds(44), though the latter has a higher MNA than the CN. These He and Ho values indicate the presence of notable genetic variability in the CN considering that the population is shrinking in the studied area, which would be expected to promote marked consanguinity. The HWE test identified fourteen markers with significant deviations (Pâ&#x2030;¤0.05), indicating there to be a heterozygous deficit. The fact that some of these markers behave homozygously may be due to actions such as management conditions (e.g. sire loan), low genetic flow in each flock, and markers that could be linked to productive traits due to production-focused selection of milk and weight gain traits regardless of kinship relations(47).

Interpopulation genetic differentiation

Estimated values for FIS and FIT vary from 1 to -1, with positive values indicating heterozygote deficiency and negative values an excess. The present results for both indices (FIS= 0.067 and FIT= 0.121) indicate that some of the markers were homozygous.

Although the values were near zero, they indicate possible mating between genetic relatives, which is consistent with values reported for native goats in China(48), India(47), Spain and Portugal(24). The FST indicated that 94.2% of the genetic variability in the studied breeds was due to differences between individuals within the breed and 5.8% due to genetic differences between breeds. This genetic differentiation coefficient (GST = 0.073) confirmed this result in that it showed 92.7% of variability of total genetic diversity to be intrabreed and 7.3% to be interbreed. The discrepancy between these indicators exists because FST reflects the properties of interpopulation allelic frequency distribution while GST is defined in terms of intrapopulation frequencies(49). Both the FST and GST

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values suggest that the level of genetic variation in the studied breeds has remained relatively constant. Percentages like those in the present study have been reported for other goat populations(17,42,50).

Interpopulation genetic distance and its graphic representation

The neighbor-net dendrogram showed that the Spanish goat breeds included in the study (Retinta, Verata, Blanca Serrana, CeltibĂŠrica, MalagueĂąa, Murciano-Granadina, Florida, Payoya, Serrana and Formentera) remain grouped. Reported in previous studies(51), this effect is caused by the close genetic and geographical relationships between these breeds.

The Saanen and Alpina breeds formed another group towards one end of the dendrogram.

Of particular note is the Anglonubia breedâ&#x20AC;&#x2122;s large genetic distance from the other studied breeds. These kinds of relationships have also been observed in a comparison between Brazilian goat breeds(52). This effect can be attributed to a greater genetic distance between a breed when compared to others and not necessarily to origin or kinship relations. Another important factor in any goat population is that individuals within it have also been selected based on morphological characteristics. Estimates generated from the Reynolds genetic distance data showed the shortest distance (0.038) to be between the CN and MG breeds, suggesting a possible genetic relationship between them.

Genetic structure analysis

Genetic structure analysis was used to evaluate the degree of kinship between the different studied populations, using optimum K (K= 9) to identify interbreed differences. No crossings were found between CN and the other studied breeds. The ANG population separated from the others beginning at K2 and remained so thereafter. Apparently, this population is more genetically distant from the other populations, a phenomenon reported for the population structure of other Creole goats in the Americas(53). The ANG population also preserved its genetic structure, having a low level of mixing of individuals from the other thirteen studied populations. Genetic structure in the Spanish breeds included in this study was more intermixed, similar to the results reported in a study on goat biodiversity(24). Intermixing is often due to geographical proximity between

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populations, which facilitates migration of individuals between populations. The genetic nearness of the CN breed to the MG breed confirm the supposed origin of the studied CN population. However, analysis using optimal K (K9) showed the CN population to have a totally different structure than the MG breed and the other studied breeds. This supports the distances shown in the neighbor-net dendrogram and suggests that the CN breed maintains a unique genetic structure that is differentiated from the populations that may have contributed to its origin.

Conclusions and implications

These are the first published data on genetic diversity and structure in a Criolla Negra goat population. The studied population has a certain degree of genetic diversity based on its level of polymorphism. The genetic distances between the Criolla Negra population and the other races included in the study indicate that this population is clearly differentiated from them and should thus be considered a distinct Mexican goat breed.

The relatively short genetic distance between the Murciano-Granadina and Criolla Negra breeds suggests that both have a common ancestor, most probably the Granadina breed.

The Criolla Negra goat has a defined breed structure and is differentiated from its possible precursor breeds. The Criolla Negra breed is the first breed of goat in central Mexico to be genetically described.

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https://doi.org/10.22319/rmcp.v10i4.4850 Article

Biometric study of Criollo Santa Elena Peninsula cattle (Ecuador)

Ronald Cabezas Congo a Cecilio Barba Capote b* Ana González Martínez b Orly Cevallos Falquez a José Manuel León Jurado c José Manuel Aguilar Reyes a Antón García Martínez b

a

Universidad Técnica Estatal de Quevedo. Facultad de Ciencias Pecuarias., Quevedo.

Ecuador. b

Universidad de Córdoba. Departamento de Producción Animal, Campus de Rabanales.

Edificio Producción Animal. 14071. Córdoba. España. c

Universidad de Córdoba. Departamento de Genética, Córdoba, España.

* Corresponding author: cjbarba@uco.es

Abstract: Biometric characterization is useful in describing cattle breeds, distinguishing between them and assessing their diversity. The Criollo Santa Elena Peninsula (Ecuador) breed was described with a biometric analysis of 217 adult animals (198 females and 19 females) involving fourteen morphometric variables, live weight and fourteen morphometric indices. An analysis of variance was run with only sex as the variation factor. Pearson correlation coefficients were estimated and principal components analysis run based on variable residuals. A multivariate analysis was then run to differentiate between four Ecuadorian Creole cattle populations with a canonical discriminant analysis. This involved fourteen morphometric variables and live weight in a sample of 1,388 adult females (Lojano: 198; Manabí: 794; Santa Elena: 198; Tsachilas: 198). The results indicate the Criollo Santa Elena Peninsula breed has a normal tendency and an 819

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intermediate body format compared to other creole breeds. It is dolichocephalic type, has sublongilinear body proportions and a fine skeleton (particularly in females), highlighting its suitability for dairy production. Overall, the studied population exhibited moderate homogeneity and harmony, with moderate to high sexual dimorphism, suggesting different genetic management of the sexes. The discriminant function significance levels in conjunction with the Mahalanobis and Euclidean distances indicate that each breed in the analysis has a distinct morphometric pattern, suggesting clear morphometric differentiation between the four populations.

Key words: Biometric analysis, Creole breeds, Breed characterization.

Received: 09/04/2018 Accepted: 16/10/2018

Introduction

Ecuador has one of the highest biodiversity index values in the world, although its domestic animal populations are poorly studied. These are vital resources essential to the country’s food security and sovereignty(1). However, the most recent national reports on biodiversity(2) and agrobiodiversity(3) state that deforestation, changes in land use, pollution and the introduction of exotic species are the main factors threatening agrobiodiversity. Incorporation of foreign livestock breeds is the main threat to conserving domestic food animal genetic resources.

The Domestic Animals Diversity Information System (DAD-IS) lists thirteen species of domestic food animals in Ecuador. Four of these are native Andean species (alpaca, 1; guinea pig, 1; llama, 1; vicuña, 1) and one is the native turkey (1). The remaining species are introduced: buffalo (1), cattle (21), goat (1), sheep (5), pig (8) and poultry (chicken, 1; and duck, 1). Cattle (Bos sp.) dominates Ecuadorian livestock production(4). There are five European breed (Bos taurus) populations (Angus, Brown Swiss, Holstein, Jersey, and Normanda), and three Asian breed (Bos indicus) populations (Brahman, Gyr, Nelore). There are also ten creole-type populations (Bravo de Paramo, Chusco, Criollo Santa Elena Peninsula, Ecuadorian Creole, Esmeraldeño, Galapaqueño, Jaspeado Manabita, Macabea, Moro and Zarumeño), and three synthetic populations (Pizan, Sahiwal and Santa Gertrudis).

Research on the creole breeds of Latin America has found that the main problems reported by producers and technical advisors when using these breeds are lack of data and an 820

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absence of characterization and productive behavior studies(5). This definitely holds true for the Criollo Santa Elena Peninsula (CSEP) cattle population. Cattle production is the main livestock activity in Santa Elena Province, Ecuador, and is mainly done using dualpurpose systems. Producers mainly use medium-sized herds.

The FAO(6) considers it a priority to do breed characterization studies as the first phase in implementation of a livestock development program focused on a sustainability in traditional production systems that is linked to adequate land management.

Characterization of animal genetic resources (AnGR) covers all activities associated with the identification, and quantitative and qualitative description of breed populations, and the natural habitat and production systems to which they are adapted(7).

Descriptive biometric analysis has been widely used for breed characterization; for example, in a recent breed characterization of Criollo ManabĂ­ cattle in Ecuador(8).

Principal components analysis (PCA) is useful both in determining the relationship between biometric variables within a population(9) and in differentiating between populations(10). Discriminant analysis is normally used to analyze multivariate differences between groups, to determine the variables most useful in discriminating between groups, and to identify which groups are similar and which are different. It has recently been used in comparative morphometric studies of creole cattle breeds in Argentina(10) and Africa(11), and of other domestic species: horses(12); sheep(13); goats(14); pigs(15); dogs(16); ducks(17); and turkeys(18). Canonical discriminant analysis has been applied to productive characteristics in beef cattle(19,20) and in milking suitability(21).

The present study objective study was to generate a biometric characterization of the Criollo Santa Elena Peninsula breed through a biometric analysis and morphometric differentiation of this breed compared to other Ecuadorian creole cattle breeds, with the goal of developing a purebred breeding program.

Material and methods

Data collection

Located on the central south coast of Ecuador, the study area consisted of Santa Elena Province, which covers 3,763 km2, and has an average altitude above sea level of 62 meters (range= 0 - 800 m asl). Temperatures vary from 17 to 40 °C, and regional 821

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vegetation is dry tropical forest. A total of 722 cattle ranches are located in the province and these contain a total of 10,454 adult animals, of which 7,265 are breeding females(22).

Morphometric characterization was done with a sample of 217 adult CSEP animals, of which 198 were female and 19 were male. A comparative and differentiation analysis was done between CSEP cattle and three other Ecuadorian cattle populations found in four different provinces. The analysis was run using a total of 1,388 adult females: Criollo Lojano (CL, n= 198); Criollo Manabi (CM, n= 794); Criollo Santa Elena Peninsula (CSEP, n= 198) and Criollo Santo Domingo de los Tsachilas (CSDT, n= 198).

After reviewing previous experiences and FAO protocols(23,24), breeders were asked which specimens they considered most characteristic of and adjusted to the CSEP biotype. These animals were measured and recorded. A random selection was made of three to six adult animals per farm, depending on ranch size (i.e. â&#x2030;¤20 or >20 breeding females per production unit).

Morphometric variables

In addition to live weight (LW), fourteen morphometric variables were chosen from among those recommended by ParĂŠs(25): head width (HW); head length (HL); face length (FL); cranium length (CL); withers height (WH); bicostal diameter (BCD); chest floor (CF), dorso-sternal diameter (DSD); thoracic perimeter (TP); cannon bone circumference (CBC); occipital-ischial length (OIL); rump height (RH); rump length (RL); and interiliac width (IIW). Field measurements were taken with a Hauptner measuring cane, a veterinary outside caliper, a non-flexible measuring tape and a scale (Gallagher W210, Uruguay).

Morphometric indices

Fifteen morphometric indices were calculated. Four were ethnological: cephalic index (CEFI = HW * 100 / HL); thoracic index (TORI = BCD * 100 / DSD); pelvic index (PELI = RH * 100 / IIW); and relative weight index (compactness) (RWI = LW * 100 / WH).

Five were focused on production: dactyl costal index (DCI = CBC * 100 / BCD); relative thorax depth index (RTDI = DSD * 100 / AC); relative cannon bone thickness index

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(RCBI = CBC * 100 / WH); cannon bone load index (CBLI = CBC * 100 / LW); and dactyl thorax index (DTI = CBC * 100 / TP). Six additional indices were calculated: anamorphosis index (ANAI = TP2 / WH); Alderson morphological index of inclined height (ALD1 = WH-RH); Alderson morphological index of front leg length evenness (ALD2 = WH-DSD), Skorkowski W1 index (W1 = WH * 100 / FL); Skorkowski W5 index (W5 = WH * 100 / DSD); and Skorkowski W6 index (W6 = DSD * 100 / CF). All indices were calculated following ParĂŠs(25).

Statistical analysis

Initially, a descriptive statistical analysis of the studied quantitative variables was run, as well as a univariate variance analysis of the morphometric variables residuals to compare traits between males and females, using sex as the only fixed effect. Estimates were calculated of the Pearsonâ&#x20AC;&#x2122;s correlation coefficients for the morphometric variables residuals and LW. A PCA was also done of the residuals to determine the number of independent variables responsible for most of the variance in the studied morphometric traits. A univariate variance analysis between the sexes was done of the linear functions of the first six principal components. Finally, a canonical discriminant analysis was applied to morphometric variables to identify possible relationships between four Creole cattle populations in Ecuador, and Mahalanobis distances were calculated to estimate the degree of differentiation between these populations using only data for females. All statistical analyzes were run with the Statistica ver. 10 software(26).

Results

Descriptive statistics of the morphometric variables and the ANOVA results using sex as the only variation factor showed that most of the variables exhibited moderate population variability (Table I). This largely confirms existence of discrete morphometric uniformity in the studied population, except for DSD, HL, IIW and FL in males, HW in females, and CF in both sexes. Variability was generally greater among the males. Most of the morphometric variables differed between males and females (P<0.001). The variables HW and RH differed moderately (P<0.05), and no difference was present for HL, CF, DSD, RH and IIW (P>0.05).

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Table 1: Descriptive and ANOVA results for comparison of morphometric variables between the sexes in Criollo Santa Elena Peninsula cattle Males

Variables

Females

F

P

Mean

SD

Min. Max.

Mean

SD

Min. Max.

HW

18.32

2.03

15.00 22.00

20.63

4.29

16.00 30.00

5.38 0.0213 *

HL

44.63

11.92 20.00 56.00

45.62

2.92

41.00 51.00

FL

19.74

3.99

20.00 36.00

16.84

1.53

14.00 20.00

CL

29.18

3.45

14.00 26.00

28.18

2.76

20.00 34.00

WH

132.00

5.59 120.00 141.00

124.21

5.27 114.00 133.00

BCD

42.28

2.24

39.00 47.00

69.72

10.09 40.00 82.00

CF

49.53

13.53 28.00 70.00

46.01

9.95

28.00 62.00

0.84 0.3604 ns 0.0001* 99.40 ** 0.0001* 53.10 ** 0.0001* 35.67 ** 0.0001* 31.78 ** 2.03 0.1558 ns

DSD

62.58

19.42 40.00 95.00

61.95

8.78

45.00 73.00

TP

173.05

8.85 156.00 185.00

156.21 10.92 90.00 180.00

CBC

19.29

3.70

15.58

OIL

183.61

7.28 172.00 195.00

162.55 12.83 136.00 181.00

RH

137.37

7.46 127.00 150.00

130.51

5.23 121.00 139.00

0.07 0.7972 ns 0.0001* 42.08 ** 0.0001* 44.18 ** 0.0001* 46.99 ** 27.39 0.0127*

RL

43.05

6.03

36.00 55.00

43.26

3.31

0.06 0.8092 ns

IIW

39.11

8.55

20.00 54.00

LW

569.58 10.34 550.00 585.00

3.86 0.0509 ns 0.0001* 395.72 55.39 280.00 540.00 85.68 **

14.00 26.00

42.02

0.62

5.73

14.00 17.00

38.00 50.00 32.00 51.00

HW= head width; HL= head length; FL= face length; CL= cranium length; WH= withers height; BCD= bicostal diameter; CF= chest floor; DSD= dorso-sternal diameter; TP= thorax perimeter; CBC= cannon bone circumference; OIL= occipital-ischial length; RH= rump height; RL= rump length; and IIW= interiliac width. All variables expressed in cm; LW= live weight (kg); N= number of data; CPV= coefficient of percentage variation; SD= standard deviation; Min= minimum value; Max= maximum value; **:P<0.01; ***: P<0.001; ns= not significant.

The descriptive statistics and ANOVA between the sexes for the CSEP morphometric indices showed all to have a generally moderate to high degree of variability in males, especially for the RTDI, ALD2, W5 and TORI (Table 2). The ALD1 and W6 indices were variable in both sexes, resulting in lower variability for the remaining indices for females. When compared between sexes almost all the indices differed (P<0.001), although the significance was lower in the RDIT (P<0.05), and no differences were apparent for PELI, ALD1 and W6 (P>0.05).

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Table 2. Descriptive and ANOVA results for comparison of morphometric indices between the sexes in Criollo Santa Elena Peninsula cattle Variables

Males

Females

SD

Min.

Max.

Mean

SD

Min.

IIW

36.50

3.61

27.78 40.82

43.13

6.98

32.00 61.36 13.17

TORI

74.31

18.22 48.42 107.50 116.15 12.85 98.63 150.98 54.48

PELI

48.18

8.28

RWI

433.06 19.79 402.17 466.67 310.35 35.81 240.60 387.10 93.78

DCI

44.60

7.79

34.15 60.47

21.77

2.28

18.29 30.77 88.57

RTDI

47.74

14.98 29.63 70.77

51.52

5.21

40.16 60.68

RCBI

14.91

2.55

11.36 18.98

12.53

0.71

11.28 13.93 83.31

CBLI

3.39

0.62

2.50

4.51

4.05

0.51

2.96

5.19

DTI

11.20

1.98

8.64

14.61

9.88

0.48

8.89

10.95 44.23

ANAI

229.78 15.99 198.82 248.07 200.94 11.91 178.32 223.21 79.81

34.00 60.61

49.54

7.83

Max.

F

Mean

34.00 64.10

3.00

0.46

5.26

24.93

ALD1

-5.29

3.98 -13.00

2.00

-5.98

4.14 -14.00

0.43

ALD2

69.06

20.36 38.00 95.00

59.75

5.81

W1

64.61

11.40 45.71 100.00 114.21 17.37 89.47 157.89 31.50

W5

228.92 67.33 141.30 337.50 192.20 17.99 164.79 247.92 30.32

W6

126.02 34.23 72.58 166.67 138.90 32.67 76.27 203.13 2.54

50.00 72.00 20.80

P 0.0004** * 0.0001** * 0.4966 ns 0.0001** * 0.0001** * 0.0229* 0.0001** * 0.0001** * 0.0001** * 0.0001** * 0.5107 ns 0.0001** * 0.0001** * 0.0001** * 0.1124 ns

CEFI= cephalic index; TORI= thoracic index; PELI= pelvic index; RWI= relative weight index (compactness); DCI= dactyl-costal index; RTDI= relative thorax depth index; RCBI= relative cannon bone thickness index; CBLI= cannon bone load index ; DTI= dactyl-thoracic index; ANAI= anamorphosis index; ALD1= Alderson 1 index; ALD2= Alderson 1 index; W1= Skorkowski W1 index; W5= Skorkowski W5 index; W6= Skorkowski W6 index; N= number of data; CPV= coefficient of percentage variation; SD= standard deviation; Min= minimum value; Max= maximum value; **: P<0.01; ***: P<0.001; ns= not significant.

The Pearson correlation coefficients for the residuals of the analyzed variables (Table 3), showed a moderate degree of harmony in this population, 55.24% of the coefficients being significant (P<0.05). The correlations were high for the CL and TP variables, but less so for WH, RH, OIL, RL and IIW. The highest phenotypic correlation coefficient values were between BCD and CL, and RL and IIW (r = 0.86), and to a lesser extent between CBC and FL (0.75), WH and RH (0.71) and CL and FL (0.70).

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Table 3. Pearson correlation coefficients matrix for the residuals of the morphometric variables

HW HL FL CL WH BCD CF

HL

FL

CL

WH

BCD

0.05

-0.02

0.05

0.09

0.06 -0.08 -0.09

0.24* 0.16

CF

DSD

TP

CBC

0.01

0.01 -0.10

0.34* 0.10 -0.16 -0.21* 0.22* 0.03

-0.70* 0.50* -0.67* 0.10 -0.03 -0.10

0.86* 0.08 -0.07

OIL

RH

IIW

LW

0.01 -0.05 -0.06

-0.04

0.25* 0.25* 0.51* 0.41* -0.01

0.67* 0.75* 0.54* 0.44* 0.20* 0.14

0.13 -0.33* -0.43* -0.11 -0.06

0.23* -0.09

DSD

0.62*

0.30* 0.28* -0.60*

0.64* 0.28* 0.53* 0.71* 0.52* 0.40* 0.18*

0.29* 0.17 -0.18* -0.50* 0.01 0.15

RL

0.05

0.37* 0.42* -0.63*

0.39* 0.09

0.32* 0.14

0.48* 0.53* -0.04

0.02

0.36* 0.03

0.04

TP

0.08

0.11

-0.04

0.49* 0.62* 0.59* 0.48* 0.42* 0.37*

CBC

0.35* 0.35* 0.01 -0.07

0.48*

OIL

0.61* 0.55* 0.60* 0.22*

RH

0.48* 0.39* 0.12

RL

0.86* -0.12

IIW

-0.11 * = P<0.05.

The first six principal components explained 85 % of total variation (Table 4). Of all fourteen principal components (14), eight (57.0 %) had a value of less than 0.7. Four principal components (PC), accounted for 73.42 % of total variance (Table 5). Factor PC1 was identified with CL, which was characterized by negative correlations versus TP (-0.89), OIL (-0.82), WH (-0.75), RH (-0.75), RL (-0.74) and IWI (-0.70); that is, the animalâ&#x20AC;&#x2122;s body condition decreased as CL increased. This first factor explained 33.46 % of the variation in the original variables. Factor PC2 was associated with BCD, where an increase in this variable corresponded to increased rump size (RL, IIW) and lower CBC. This factor explained 21.82 % of the total variation. The next two factors are PC3, which was associated with CF and explained 10.92 % of variation, and PC4, which was linked to HW and explained 7.22 % of variation. The analysis of variance of the linear functions of the first six PC confirmed the greater weight of PC1 and PC2 by identifying significant differences between sexes only for PC1 and PC2, with the remaining components exhibiting statistical homogeneity.

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Table 4: Principal components analysis (PCA) of Criollo Santa Elena Peninsula cattle based on morphometric variables residuals Principal component

Eigenval ue

Variance explained

Cumulative Eigenvalue

Cumulative variance explained

1 2 3 4 5 6 7 8 9 10 11 12 13 14

4.68 3.06 1.53 1.01 0.94 0.81 0.59 0.40 0.28 0.24 0.19 0.12 0.09 0.07

33.46 21.83 10.92 7.22 6.74 5.76 4.22 2.85 1.98 1.72 1.37 0.85 0.61 0.47

4.68 7.74 9.27 10.28 11.22 12.03 12.62 13.02 13.30 13.54 13.73 13.85 13.93 14.00

33.46 55.28 66.21 73.43 80.16 85.93 90.15 93.00 94.98 96.69 98.07 98.92 99.53 100.00

Table 5. Contribution of variable residuals to principal components analysis (PCA) Variables HW HL FL CL WH BCD CF DSD TP CBC OIL RH AG IIW

Factor 1 0.05 -0.36 -0.69 0.22 -0.75 -0.01 -0.47 -0.15 -0.85 -0.46 -0.83 -0.75 -0.74 -0.70

Factor 2 -0.02 0.15 -0.66 0.81 -0.02 0.91 0.33 0.16 -0.17 -0.62 0.06 0.03 0.50 0.55

Factor 3 -0.24 -0.76 -0.05 -0.19 -0.24 0.11 0.46 0.70 0.08 0.09 0.22 -0.07 -0.17 -0.00

Factor 4 0.86 -0.10 -0.07 0.08 0.11 0.15 -0.19 0.28 0.02 0.09 0.11 0.21 -0.13 -0.14

Significance of the first two canonical discriminant functions produced was tested with Wilk’s Lambda (λ) (values of 0.03 and 0.22, respectively) and chi-square tests (χ2) (2,457.67 and 1,008.03, respectively; P≤0.001) (Table 6). Function 1 explained 72.33% of total variation and Function 2 explained 25.68 %; Function 3 explained less than 2% of the variance. These results validate the discriminant analysis, highlighting that

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Function 1 has the best linear combination of features that allow discrimination between the four studied populations.

Table 6. Summary of canonical discriminant functions from samples from females Variance Canonical Funtion Eigenvalue explained correlation (%) 1 2 3

7.58 2.69 0.21

72.33 25.69 1.98

0.94 0.85 0.41

λ

χ2

Significance level

0.03 0.22 0.83

2457.67 1008.03 127.16

P<0.001 p<0.001 p<0.001

λ = Wilks’-Lambda; X2= Chi-squared.

A bidimensional graph of variables CAN 1 and CAN 2 shows the relationships between the four populations, with significant overlap between CM and CSTD. The CL and CSEP variables are clearly separate from the other variables, creating distinct groups with no overlap.

Figure 1: Graph of canonical discriminant analysis based on morphometric variables of females from four populations of Ecuadorian Creole cattle

The Mahanalobis distances (upper diagonal) and Euclidean distances (lower diagonal) between the four populations clearly show the proximity between CM and CSDT (2.09)

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and the greater distance between CSEP and CSDT (47.56), considering that all values are significant (P<0.05) (Table 7). The individual Euclidean distances confirm the proximity between CM and CSDT and between CSEP and CL, while highlighting the distance between these two groups. Correct classification of individuals was 86.61 % for CM, 43.40 % for CSDT, 93.42 % for CSEP and 83.50 % for CL.

Table 7. Mahalanobis and Euclidian distances between females from four populations of Ecuadorian Creole cattle Population CM CSDT CSEP CL

CM 31.8 66.2 48.2

CSDT 2.09*** 56.2 41.7

CSEP 45.86*** 47.56***

CL 39.71*** 34.92*** 41.29***

31.2

CM= Criollo Manabis; CSDT= Criollo San Diego Tsachilas; CL= Criollo Lojano; *** = P<0.001.

Discussion

Phenotypic variability among the morphometric variables for CSEP was higher than that reported in other Ecuadorian Creole cattle populations: Criollo Lojano(27); Criollo Macabeo(28); Criollo Manabita(8); and Criollo of Santo Domingo de los Tsachilas(29). This was also the case when compared to other Latin American Creole breeds: Patagonian Creole in Argentina(10); Criollo Saavedra in Bolivia(30); Criollo Pantaneiro in Brazil(31); Criollo Casanare in Colombia(32); Barroso or Salmeco Creole in Guatemala(33); Chinampo(34) and Mixteco Creoles in Mexico(35); Pampas Chaco Creole in Paraguay(36); Criollo Limonero from Venezuela(37); and Uruguayan Creole(38), among others. It was also higher than in native breeds from Spain such as the red and black Berrenda breeds(39); the Serrana from Teruel(40); the black Andalusian(41); the Pallaresa(42); and the Morucha(43).

The studied CSEP population was found to have an intermediate body format compared to other Ecuadorian Creole cattle breeds. It is larger than the Uruguayan Creole(38), Mixtec Creole(35) and Creole from Panama(5), among others, but somewhat smaller than the Patagonian Creole(10), Barroso or Salmeco Creole from Guatemala(33), and the Criollo Manabita(8), among other populations. It was also smaller than the native Spanish breeds red and black Berrenda(39); Serrana de Teruel(40); Pallaresa(42); black Andalusian(41); and Morucha(43). The intermediate format and size of CSEP are similar to those of Portuguese native cattle breeds(44). This population can therefore be characterized as having a

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typically normal body format, probably in response to the influence of Iberian breeds(45) and as an adaptive advantage in tropical environmental conditions(8).

Among the morphometric indices, the studied populationâ&#x20AC;&#x2122;s average CEFI value identifies it as a dolichocephalic type with HL predominating over HW. This coincides with index values for other Latin American Creole breeds such as the Criollo Saavedra in Bolivia(30); the Barroso or Salmeco Creole in Guatemala(33); Criollo Limonero from Venezuela(37); and Criollo Manabita in Ecuador(8). It is also the case for native Spanish breeds such as the Asturian Valley, the Bruna of the Pyrenees, Parda de MontaĂąa and Pirenaica(46), and the Serrana de Teruel(40). Of note is that dolichocephaly is much more pronounced in males than in females.

Average RTDI and DCI values are indicative of skeleton fineness and its association with milk production suitability, especially in females. These values were used to characterize predisposition to milking fitness within the studied CSEP populations. The OIL / WH value indicated that this population has a sublongilineal body proportion, another trait compatible with milking fitness and particularly notable in females. Like most native Spanish and Latin American Creole environmental breeds, the CSEP has a dorsolumbar line with an ascending caudal inclination, which favors movement in rough terrain.

Presence of moderate to high sexual dimorphism in the studied CSEP population based on the ANOVA for morphometric variables coincided with the profile of environmental type breeds with minimal selection(47). However, this dimorphism is not as pronounced as in the Uruguayan Creole(38), Criollo Macabeo(28) and Criollo Manabita breeds(8).

Diphorphism in the CSEP is also supported by the CEFI, TORI and RWI indices since they differ between the sexes (P<0.001). When considered in conjunction with the differences present between the remaining productive indices, this confirms the occurrence of low morphostructural uniformity between males and females in this population. This situation suggests that the CSEP may be undergoing a crossbreeding process through use of sires influenced by exotic breeds. Or, in what is essentially the same process, males and females in the CSEP are treated as two distinct subpopulations, receiving different genetic management. If this is the case it would explain the differences identified between the sexes for both the ethnological and productive type morphometric indices, as well as the greater intrinsic variability among males. In contrast, the PELI and ALD1 values were statistically homogeneous in both sexes. This coincides with the adaptive nature of the traits they represent since ascending caudal inclination of the dorsolumbar line is important to movement in difficult terrain (PELI), and pelvis width is linked to ease of parturition (ALD1).

The low level of correlation between the analyzed variables is indicative of the high underlying variability in this population. This is to be expected in this type of population, which historically has been heavily genetically manipulated by producers using mismatched criteria, without properly structured breeding programs, and in the complete absence of uniform breed trait selection criteria(47). Similar results have been reported for Criollo ManabĂ­ cattle in Ecuador(8), as well as for the Spanish native Serrana de Teruel

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breed(40). This is another possible consequence of the use of sires influenced by improved exotic breeds.

The first principal component (PC1) explained more than one third of the observed variance. This component defined cephalic structure versus general animal morphostructure, such that the larger the CL the greater the reduction in their body format in terms of heights, lengths and diameters. The second principal component (PC2) was associated with body capacity, meaning that an increase in BCD improved rump morphostructure, which is linked to adequate pelvic canal width in females based on IIW and RL. Cranium length (CL) is therefore a defining variable in intuitive selection of animal morphostructure. In addition, BCD is clearly linked to body capacity and to rump size as an adaptive advantage for ease of parturition in females. Both variables must then be considered when establishing selection criteria in Ecuadorian Creole cattle.

The canonical discriminant analysis among females showed that each breed has a distinct morphometric pattern, implying clear morphometric differentiation between the four studied populations. This differentiation may be due to the reproductive isolation between them, as well as variation in body mass selection criteria, both of which are related to the geographical distance between these populations. These results are corroborated by the different Mahalanobis distances between the four populations, with CM and CSDT being nearest each other, CL in an intermediate position and CSEP the furthest from the rest.

Conclusions and implications

The CSEP has a medium body format with a normal tendency, sublongilinear body proportions and a dolichocephalic-type cranium. Skeletal structure in females is fine, indicating their suitability for milk production. Clear differences between the sexes in both the morphometric variables and indices confirm the presence of moderate to high sexual dimorphism in the studied population. Indeed, this suggests the coexistence of males and females as two subpopulations subject to different genetic management. The discriminant analysis effectively differentiated between the four analyzed Ecuadorian Creole cattle populations, confirming that Criollo Santa Elena Peninsula cattle are a distinct population with a specific morphometric pattern. The present study suggests that the Criollo Santa Elena Peninsula is a separate breed within the double-purpose tropical creole cattle breeds.

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https://doi.org/10.22319/rmcp.v10i4.4847 Article

Effect of supplementation with vitamin E and chelated or inorganic minerals on beef quality and oxidative stability

Manuel Andrés González Toimil a * Pedro Garcés Yépez b Luis Humberto López Hernández c Diego Braña Varela d Everardo González Padilla e

a

Universidad Nacional Autónoma de México (UNAM). Facultad de Estudios Superiores-Cuautitlán, Departamento de Ciencias Pecuarias. Estado de México, México.

b

UNAM. Facultad de Medicina Veterinaria y Zootecnia, Departamento de Fisiología.

Ciudad de México, México. c

INIFAP. Centro Nacional de Investigación Disciplinaria en Fisiología y Mejoramiento Animal, Laboratorio de Carnes. Ajuchitlán, Querétaro, México. d

Elanco Animal Health, Latinoamérica.

e

UNAM. Facultad de Medicina Veterinaria y Zootecnia, Departamento de Reproducción. Ciudad de México, México.

* Corresponding author: m.a.toimil@gmail.com

Abstract: Diet and supplementation during finishing beef cattle affect meat properties. An evaluation was done of the effects of chelated and inorganic minerals (Cu, Se and Zn), in combination with vitamin E, on beef quality and oxidative stability. A total of 799 zebu x European cattle were used at a commercial feedlot-finishing center in the state of Veracruz, Mexico. Four experimental diets were formulated based on a standard highgrain finishing diet and supplementation with identical doses of Cu, Se and Zn and vitamin E: chelated minerals only; chelated minerals + vit E; inorganic minerals only; 837

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inorganic minerals + vit E. These were fed to the animals for thirty (30) days prior to slaughter. Weight at slaughter was 450.5 ± 30.5 kg. Twelve individuals were randomly selected from each treatment to evaluate quality variables in the Longissimus thoracis muscle. Meat samples were stored at -20 ° C until processing. Samples were defrosted and aged at 4 °C for one and eight days. Water loss from defrosting was lowest in the inorganic minerals treatments (P<0.05). In the chelated minerals treatments, pH, water holding capacity and catalase activity were higher (P<0.05), and shear force was lower (P<0.05). Vitamin E decreased drip water loss (P<0.05). After eight days’ aging, use of inorganic minerals without vitamin E allowed greater oxidative activity, as shown by the thiobarbituric acid-reactive substances values. The combination of chelated minerals and vitamin E resulted in lower water loss, oxidative activities and cutting force, and is recommended for use in finishing diets for beef cattle.

Key words: Meat quality, Oxidative stability, Beef, Chelated minerals, Inorganic minerals.

Received: 06/04/2018 Accepted: 11/03/2019

Introduction

Supplementing the feed of growing animals with chelated minerals and vitamin E is reported to improve meat physicochemical and organoleptic characteristics(1). This kind of supplementation has been found to increase meat quality and degree of marbling(2), increase beef hot carcass weight(3,4), decrease water loss from drip in carcasses(5), and improve color in pork(6). Vitamin E supplementation in beef cattle diets has been found to lower lipid oxidation(7), and oxymyoglobin oxidation(8).

Chelated minerals are more efficiently absorbed(9) and more available(10) in animals, which improves their distribution and retention in tissues(7,11). In contrast, inorganic minerals can dissociate in the reticulum-rumen, omasum and abomasum, forming indigestible compounds(12) and insoluble complexes with other minerals(13). Copper (Cu), selenium (Se) and zinc (Zn) are essential minerals(14). They are cofactors of antioxidant enzymes such as glutathione peroxidase (GPX)(15,16) and superoxide dismutase (SOD)(17), and are involved in protecting the cytoplasmic membrane from oxidative damage.

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Another micronutrient that has a meat-enhancing and preservative effect is vitamin E, a natural antioxidant located in the cell membrane which protects fatty acids from oxidation(18).

The present study objective was to analyze quality and oxidative stability in meat from feedlot-finished beef cattle in the tropics of Mexico fed a high-grain diet supplemented with Cu, Se, and Zn from chelated or inorganic sources, and with or without vitamin E.

Material and methods

The study was carried out at a beef cattle finishing commercial unit corral in the southern portion of the state of Veracruz, Mexico (19°38’00” N; 95°31’00” W).

Experimental animals were 799 beef cattle (713 females and 86 males; Bos taurus x Bos indicus) obtained from stockers in states throughout southern Mexico (Veracruz, Oaxaca, Tabasco and Chiapas). Average initial weight when placed in finishing pens was 315.9 + 4.52 kg. Animals were randomly assigned to house in 16 pens per treatment. Thirty (30) days before slaughter the animals were fed a finishing diet including a basic inorganic mineral base premix (Table 1). One of two Cu/Se/Zn premixes was added to the diets, with the same amount of minerals regardless of their source (Table 2): chelated minerals (Bioplex® Copper, Bioplex® Zinc and SelPlex®, Alltech Mexico); or inorganic minerals (zinc oxide, copper sulfate and sodium selenite).

Each animal consumed an approximate daily dose of 4 g mineral premix containing 93.9 g/kg Zn, 25 g/kg Cu and 0.757 g/kg Se. Vitamin E (DSM, Mexico) was supplemented at 1320 UI/head/day. Both the minerals and vitamin E were administered following a 2 x 2 treatment factorial arrangement: T1) finishing diet plus inorganic minerals; T2) T1 plus vitamin E; T3) finishing diet plus chelated minerals; T4) T3 plus vitamin E. The experimental diets were provided twice a day (40% at 0600 h; 60% at 1200 h). Animals had free access to water.

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Table 1: Calculated composition and nutritional value of the diet fed to beef cattle for the last 30 days finishing period in feedlot Ingredients

%

Rolled corn Wheat bran Barley hay Molasses Soybean meal Soybean oil Inorganic mineral premix1

1

76.7 5.0 4.5 4.0 4.0 3.3 2.5

Nutritional value: Dry matter Net metabolizable energy Crude protein Ether extract Ashes Neutral detergent fiber Net energy for gain Metabolizable energy Digestible energy Rumen degradable protein

87.71 % 2.34 Mcal/kg 12.88 % 7.32 % 4.56 % 12.67 % 1.65 Mcal/kg 3.31 Mcal/kg 3.97 Mcal/kg 60.46 %

Calcium (5.75 g/kg), magnesium (2.35 g/kg), copper (16.42 mg/kg), selenium (0.06 mg/kg), zinc (43.41 mg/kg).

Table 2: Composition of experimental mineral premixes fed beef cattle for the last 30day finishing period in feedlot Chelated mineral premix

Inorganic mineral premix

Ingredients: Zinc proteinate Copper proteinate Selenium yeast

Ingredients: Zinc oxide Copper sulfate Sodium selenite

(mg of mineral/kg) 93900 25000 757

(mg of mineral /kg) 93900 25000 757

Packaging: mineral premixes were prepared in 25-kilogram lots.

Dose: 4 g/head/d.

Cattle transport and slaughter

Using specialized vehicles, the cattle were transported 110 km from the growing area to a Federal Inspection Type (Tipo InspecciĂłn Federal - TIF) slaughterhouse. Twelve animals per treatment (three per pen) were randomly selected for meat quality and oxidative stability measurements. Using a scale (Revuelta RGI model), the animals were weighed individually upon arriving at the slaughterhouse; average weight for males and females was 450.5 Âą 30.5 kg. Transport and slaughter of animals complied with applicable federal regulations: NOM-051-ZOO-1995(19), and NOM-033-ZOO1995(20). The carcasses were electrically stimulated with alternating current (60 Hz, 50 840

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volts x 2 min) immediately after slaughter, with electrodes placed on the Achilles tendon and the nose.

pH measurement and meat samples

The carcasses were cut longitudinally to produce two half carcasses and placed in cold storage (0.3 °C). Measurement of pH was done 45 min postmortem in the semimembranosus muscle using an electrode connected to a potentiometer (Hanna Instruments®). A meat sample (approx. 15 cm long) was taken from the Longissimus thoracis muscle between the fifth and thirteenth intercostal spaces of the left half of each carcass for meat quality analysis. The carcasses remained in refrigeration for 24 ± 2 h.

Color and pH measurements in fresh meat

Color and pH measurements were done of meat samples from each carcass 24 h postmortem. After 30 min blooming, color was measured in triplicate with a spectrophotocolorimeter (MiniScan EZ HunterLab®, USA. Iluminante D65/10°) and recorded as L* (luminosity), a* (red to green tones) and b* (yellow to blue tones).

Measurement of pH was done with a previously calibrated portable digital potentiometer (pH-meter, Hanna Instruments®, USA). All analyses were done in triplicate(21). After the measurements were taken the samples were vacuum packed and frozen at -20 °C until analysis at the Meat Laboratory of the National Animal Physiology and Improvement Disciplinary Research Center (Centro Nacional de Investigación Disciplinaria en Fisiología y Mejoramiento Animal) of the INIFAP in Colón, Querétaro, México.

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Sample preparation and analysis of aged meat

Using a bench saw (St-295-PE, Torrey®, Mexico), each frozen sample was sectioned into five approximately 1-inch-thick cutlets from the region near the cranial area. The cutlets were weighed and numbered as cut, and placed in a vertical refrigerator (Torrey®, Mexico) at 2 °C. Once defrosted they were weighed again to calculate water loss by subtracting defrosted weight from frozen (-20 °C) weight, which was expressed as the percentage weight lost compared to the initial weight. Cutlet No. 1 from each individual was used to evaluate pH(22), color(23,24), water holding capacity(25), concentration of thiobarbituric acid-reactive substances (TBARS)(26), and activities of the enzymes glutathione peroxidase (GPX) and catalase (CAT)(27). Cutlet No. 3 was used to measure drip water loss(28). Cutlet No. 4 was prepared on an electric grill and then measurements were taken of shear force and water loss from cooking(29). Cutlets numbers 2 and 5 were placed on foam trays, covered with plastic wrap and aged for 8 days at 2 °C. After aging, measurements were taken of pH, color, water loss from cooking, shear force, lipid oxidation by TBARS, GPX and CAT activity. All measurements were done in triplicate.

Statistical analysis

All variables were processed with an ANOVA using a 2 x 2 factorial arrangement, and PROC MIXED in the SAS ver. 9.3 package.

Statistical model: Yijk= μ + i +j+ () ij + Eijk μ = effect of general mean; i = effect of i-th treatment of mineral source; j = effect of j-th treatment of vitamin E; () ij = effect of mineral source / vitamin E interaction; Eijk = random error of each observation.

Also using SAS, a multivariate analysis with Pearson correlations was run to evaluate the relationships between the studied variables.

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Results and discussion

Muscle pH values 45 min postmortem

At 45 min postmortem, no differences (P>0.05) in pH were observed due to mineral source, vitamin E or their interaction (Table 3). Values ranged from 6.1 to 6.3, which are similar to those reported in studies using electrical stimulation after bleeding in cattle(30).

Table 3: Response of pH and color to supplementation with Se, Cu and Zn from inorganic or chelated sources, with or without vitamin E in meat from feedlot -finished cattle in the tropics

Variable

Mineral source Inorganic Chelated (n= 24) (n= 24)

Vitamin E No Yes (n= 24) (n= 24)

pH 45 min postmortem 24 h postmortem

6.31+0.05

6.23+0.07

6.21+0.05

6.33+0.07

5.55+0.03 a

5.67+0.05 b 5.64+0.06

5.59+0.02

24 h postmortem a* (red tone)

41.28+0.55

41.74+0.70 41.63+0.74

41.38+0.51

24 h postmortem b* (yellow tone)

19.14+0.53

19.52+0.35 19.13+0.49

19.53+0.41

24 h postmortem 16.95+0.41 a

17.63+0.40 b 17.12+0.49

17.46+0.30

L* (luminosity)

ab

Results are presented as least squares means Âą standard error and the n value.

Different letter superscripts in the same row indicate significant difference (P<0.05) due to mineral source.

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pH values 24 h postmortem, and after 1 and 8 days’ aging in laboratory

After 24 h refrigeration meat pH differed by mineral source (P<0.05), but no effect was found for vitamin E or the mineral source / vitamin E interaction. In the chelated minerals treatment pH values were higher (5.67 ± 0.05) than in the inorganic minerals treatment (5.55 ± 0.03; Table 3). This same effect on pH has been reported in response to supplementation with Se from chelated and inorganic sources(11), although always within ranges considered normal (5.4 to 5.87)(31,32), that do not affect meat organoleptic characteristics. However, these small variations in pH may have affected the configuration of some proteins, which in turn may be related to the observed differences in meat water holding capacity after defrosting, since pH was more acidic in the inorganic source treatment than in the chelated source treatment. No differences in pH were observed between the one and eight days’ aging treatments.

Color, water holding capacity and oxidative stability after aging

Oxidation of polyunsaturated fats in beef rapidly causes rancidity, but also affects its color, quality and texture(34). In terms of color, luminosity (L*) was not affected (P>0.05) by mineral source, vitamin E, their interaction or aging period (Table 3 and 4).

In contrast, a* and b* values were affected (P<0.05) by aging period in all treatments.

Red and yellow tones decreased in the meat samples as aging period increased (Table 5), which is associated with the negative correlation between the a* value and TBARS(33), and the related red tones with oxidation processes.

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Table 4: Meat quality variables in defrosted meat from feedlot-finished cattle in the tropics after 1 and 8 days’ aging, in response to supplementation with Se, Cu and Zn from inorganic or chelated sources, with or without vitamin E Variable

Mineral source Inorganic Chelated (n=24) (n=24)

Vitamin E No Yes (n=24) (n=24)

2.67+0.024a 7.65+0.47 6.23+0.65a

3.96+0.29b 8.33+0.40 9.44+0.90b

2.98+0.25 8.36+0.44β 7.94+0.84

3.64+0.33 7.62+0.44θ 7.73+0.86

pH 1 day’s aging 8 days’ aging

5.52+0.01 5.61+0.01

5.57+0.04 5.63+0.05

5.57+0.04 5.64+0.05

5.51+0.01 5.59+0.01

L* (luminosity) 1 day’s aging 8 days’ aging

41.65+0.66 41.77+0.62

41.16+0.46 41.52+0.66 42.65+0.65 41.79+0.73

41.29+0.46 42.63+0.52

a* (red tone) 1 day’s aging 8 days’ aging

18.12+0.221 17.85+0.331 18.08+0.271 17.89+0.291 16.73+0.382 16.44+0.372 16.47+0.372 16.70+0.382

b* (yellow tone) 1 day’s aging 8 days’ aging

16.68+0.31a 15.88+0.32b 16.26+0.37 16.32+0.37a 15.61+0.24b 16.05+0.34

16.30+0.28 15.88+0.30

Shear force, kg 1 day’s aging

6.35+0.281

6.07+0.261

6.42+0.301

4.70+0.18a2 3.57+0.122b 4.06+0.192

4.18+0.202

Water loss from defrosting,% Water loss from drip, % Water holding capacity, %

8 days’ aging Water loss from cooking, % 1 day’s aging 8 days’ aging

6.14+0.281

24.44+0.651 25.40+0.681 25.22+0.701 24.62+0.641 22.77+0.682 22.64+0.592 22.31+0.532 23.08+0.712

Results are presented as least means squares ± individual standard error and the n value. ab Different letter superscripts in the same row indicate significant difference (P<0.05) due to mineral source. 1,2 Different numerical superscripts between rows indicate a significant effect of day. β,θ Greek letter superscripts indicate an effect from vitamin E.

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Table 5: Effect of Se, Cu and Zn supplementation from inorganic and chelated sources with and without vitamin E on the oxidative stability of the backs of bovine cattle ended in a pen in the tropics WLD

WHC

WLF

WLC

pH

L*

a*

b*

SF

1.00 0.68*** 0.19

1.00 0.07

1.00

TBARS CAT GPX

WLD

1.00

WHC

0.12

1.00

WLF

0.33*

0.03

1.00

WLC

-0.04

0.03

0.00

1.00

pH

-0.28**

0.14

-0.26

-0.29**

1.00

L*

0.18

0.04

-0.29*

-0.03

-0.42***

a* b* SF

-0.31** -0.02 -0.16

-0.16 -0.18 0.25

-0.01 -0.22 0.04

TBARS

0.26**

0.08

0.40**

-0.17

0.06

-0.04

-0.45*** -0.09 -0.37***

CAT GPX

0.07 0.35***

0.00 0.20

0.18 0.16

-0.04 -0.32**

0.09 0.22*

-0.14 0.03

0.16 -0.16 -0.24* -0.09 1.00 -0.38*** -0.15 -0.60*** -0.59*** 0.00 1.00

1.00

0.05 -0.18* 0.05 0.02 -0.45*** 0.66*** 0.53*** -0.23* -0.13

1.00

WLD= Water loss from drip; WHC= Water holding capacity; WLF= Water loss from defrosting; WLC= Water loss from cooking; SF = shear force; TBARS= thiobarbituric acid-reactive substances; CAT= catalase, GPX= glutathione peroxidase. *P<0.05; **P<0.01; ***P<0.001.

The value of b * was higher in treatments with inorganic minerals (P<0.05) at one and eight days’ aging (Table 4). This difference was due to an increase in oxidation of oxymyoglobin to metmyoglobin(35), which produced a brown color(34). Greater TBARS activity at eight days’ aging in the inorganic mineral treatments was largely responsible for this phenomenon (Table 6).

Table 6: Oxidative stability variables in defrosted meat from feedlot-finished cattle in the tropics after one or eight days’ aging in response to supplementation with Se, Cu and Zn from inorganic or chelated sources, with or without vitamin E

Variable

Inorganic mineral source No vit E Vit E T1 T2 (n=12) (n=12)

TBARS (mg MDA/ kg meat) 1 day’s aging 0.05+0.008 1 8 days’ aging

0.72+0.107 2a

CAT (U/ml extract) 1 day’s aging 10.83+0.89 ab 8 days’ aging 9.72+1.07 a

0.03+0.003 1 0.24+0.017 2

Chelated mineral source No vit E Vit E T3 T4 (n=12) (n=12)

0.05+0.006 1 b †

**

0.33+0.068 2 b

8.32+1.08 a 10.72+1.21 a

12.64+0.69 b 12.62+0.83 b

846

0.05+0.004 1 0.20+0.021 2 b † **

12.92+0.94 b 13.16+0.69 b

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GPX (U/g meat) 1 day’s aging 8 days’ aging

13.29+1.00 1 59.44+4.39 2

13.99+0.77 1 57.83+3.56 2

15.46+0.84 1 49.43+2.80 2

15.34+0.84 1 51.31+3.88 2

Thiobarbituric

acid-reactive substances; Catalase;  Glutathione peroxidase.

Results are presented as least square means ± individual standard error and the n value. ab Different letter superscripts in the same row indicate significant difference (P<0.05) due to mineral source. The † superscript indicates an effect from vitamin E. The ** indicates a significant effect from the mineral source / vitamin E interaction. 1,2 Different numerical superscripts between rows indicate a significant effect of aging time.

Water loss from defrosting

Mineral source affected (P<0.05) water loss from defrosting (WLF), but vitamin E and the mineral source / vitamin E interaction did not. Water loss was greater (P<0.05) in cutlets in the chelated minerals treatment than in the inorganic minerals treatment. At 24 h postmortem cutlet pH was higher in the chelated than in the inorganic minerals treatment (P<0.05), which favored stability of myofibrillar proteins and therefore water retention. A portion of the water retained in the meat in the chelated minerals treatment may have become ice crystals when frozen; when defrosted these crystals would have caused greater water loss during aging. In addition, the higher CAT activity in this treatment may be related to this greater water loss.

Water loss from drip

Vitamin E in the diets affected water loss from drip (WLD) (P<0.05), although mineral source and the mineral source / vitamin E interaction did not (P>0.05, Table 4).

Supplementation with vitamin E favored antioxidant activity at the cell membrane level(36) and allowed the cell to preserve its sarcoplasmic components during storage(37).

This antioxidant action is also linked to the lower TBARS activity in the vitamin Esupplemented treatments (Table 5).

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Water holding capacity

Water holding capacity (WHC) was higher (P<0.05) in the chelated mineral treatments than in the inorganic mineral treatments. Vitamin E and the mineral source / vitamin E interaction had no effect (P>0.05). One of the conditions that can alter the arrangement of myofibrillar proteins and the space between them is net charge. This can be modified by changes in the anion/cation balance, especially the replacement of bivalents with monovalents(38), as in the case of added saline solution. In contrast, carcasses and cutlets from the inorganic mineral source treatments exhibited less water loss, even when showed less retention to the added saline solution used for the water holding capacity test. At 24 h postmortem the pH values in the chelated minerals treatments (Table 3) favored WHC because the myofibrillar proteins were further from their isoelectric point (pH 5.4-5.5)(38), leading to protein stability and their binding to water molecules.

Shear force after 1 and 8 days’ aging

After one day of aging shear force (SF) was unaffected (P>0.05) by mineral source, vitamin E or their interaction. However, at eight days SF had decreased (P<0.05) in response to mineral source, but not due to vitamin E or the mineral source / vitamin E interaction (Table 4). Shear force positively correlated to water loss from cooking (r= 0.53; P<0.001; Table 6) and negatively correlated to pH (r= -0.23, P<0.05). A scale developed by Shackelford et al(39) for SF in beef uses four categories: <3.2 kg= very soft meat; 3.2 to 3.89 kg= soft meat; 3.89 to 4.59 kg= intermediate; and >4.6 kg= hard.

Based on this scale, after one day aging the cutlets in all four treatments would be considered “hard”. By day eight the cutlets in the chelated minerals treatment qualified as “soft”, those in the inorganic minerals only treatment were “intermediate”, and those in the inorganic minerals / vitamin E treatment were “hard”.

Values for pH equal to or greater than 5.8 are considered unacceptable in beef, and produce dark-colored cuts(40). These are characterized by having higher WHC and lower SF(41,42), as well as high pH values (>6.0) which can increase Z disk degradation(43). The cutlets from the chelated mineral treatments exhibited higher pH values, and thus also had greater WHC and lower SF values. Another factor that may have lowered SF is supplementation with chelated Se, which causes greater accumulation of seleno amino acids, and modifies muscle tissue structure in such a way that it lowers SF(11). Softening

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of meat is related to aging during which enzymes such as calpain and cathepsin exercise proteolytic action on muscle fiber structural proteins(38,44). After 8 days’ aging enzymatic action had noticeably softened the cutlets, which was particularly favored by higher pH values in the chelated minerals treatments (Table 3).

Water loss from cooking after 1 and 8 days’ aging

Water loss from cooking of cutlets after one or eight days’ aging was not affected by mineral source, vitamin E, or their interaction. Aging time did have an effect since water loss was higher after one day than at eight days (Table 4). All the cutlets subjected to cooking had been previously frozen. This can modify WHC(45) by causing immobilized water to become ice crystals(44), which, when defrosted, become free water that can be lost during the aging process.

TBARS in cuts after one and eight days’ aging

Thiobarbituric acid-reactive substances (TBARS) concentrations were unaffected by any of the treatments after one day of aging (P>0.05), although after eight days the mineral source / vitamin E interaction did have an effect (P<0.05; Table 5). Values for TBARS increased four- to six-fold in the chelated minerals treatments, and from eightto over 14-fold in the inorganic minerals treatments. The difference in TBARS activity between one and eight days’ aging is due to fat peroxidation, which increased as the meat samples aged under refrigeration. At day eight, the difference between the inorganic only treatment (T1) and the chelated only treatment (T3) may have been caused by the difference in the concentration of cofactors (e.g. Se) stored in the tissue.

For example, in one study muscle Se concentrations were higher in cattle fed chelated minerals’ supplements(46). Vitamin E also prevents oxidative deterioration by neutralizing the effect of free radicals(8,37); in the present results this effect can be seen in the lower TBARS activity in the treatments containing vitamin E.

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Catalase

Activity of the CAT enzyme was higher in the chelated minerals treatment at both aging times (P<0.05; Table 5), but vitamin E and the mineral source / vitamin E interaction had no effect on this variable. Apparently the increased CAT activity was associated with the higher bioavailability of chelated minerals such as Cu. This element participates, via ceruloplasmin, in oxidation of Fe from the heme group, a CAT cofactor, in this enzyme’s first action stage on hydrogen peroxide(17). The higher CAT activity in this treatment also helped to reduce fat oxidation.

Glutathione peroxidase

Neither mineral source, vitamin E nor their interaction affected GPX activity. In contrast, this activity was lower at one days’ aging than at eight days’ (P<0.05; Table 5). This is related to free radical activity which increases with time due to meat exposure to the environment and bacterial multiplication. A previous study also found no effect on GPX activity in response to mineral source (chelated or inorganic) and presence or absence of vitamin E in beef cattle, and concluded that feed Se concentrations were sufficient to meet the requirements in all treatments(8).

Conclusions and implications

Supplementation with inorganic and chelated minerals (Cu, Se, and Zn), with or without vitamin E, modified quality characteristics and oxidative stability in beef lower shear force in the tested cutlets. This is probably associated with the higher absorption and bioavailability of chelated minerals compared to inorganics, which may affect Cu, Se, and Zn concentrations in the meat, as well as pH. Water holding capacity, shear force and CAT enzyme activity were consequently affected. An interaction was observed between vitamin E and mineral source on TBARS in which use of inorganic minerals 850

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without vitamin E allowed greater oxidation in the meat. The combination of chelated minerals and vitamin E produced lower shear force values, higher water holding capacity and greater oxidative stability. All are desirable in the meat industry since they add value to meat products. Producers that grow and finish cattle would therefore benefit from supplementing finishing rations with chelated Se, Cu and Zn in conjunction with vitamin E.

Acknowledgements

Technical and financial support was recieved from the Programa de Maestría y Doctorado en Ciencias de la Producción y de la Salud Animal de la UNAM, Consejo Nacional de Ciencia y Tecnología (CONACyT), Rancho Santa Rita, Alltech de México, DSM México y Nutrimentos Minerales de Hidalgo.

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11. Cozzi G, Prevedello P, Stefani AL, Piron A, Contiero B, Lante A, Gottardo F, Chevaux E. Effect of dietary supplementation with different sources of selenium on growth response, selenium blood levels and meat quality of intensively finished Charolais young bulls. Animal 2011;5(10):1531–1538.

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https://doi.org/10.22319/rmcp.v10i4.4787 Article

Productive and economic response to concentrate supplementation by grazing dairy cows at high stocking

Benito Albarrán-Portillo a* Felipe López-González b Miguel Ruiz-Albarrán c Carlos Manuel Arriaga-Jordán b

a

Universidad Autónoma del Estado de México. Centro Universitario UAEM Temascaltepec. Km 67.5 Carretera Toluca-Tejupilco, 51300.Temascaltepec, Estado de México. México. b

Universidad Autónoma del Estado de México. Instituto de Ciencias Agropecuarias y Rurales. México. c

Universidad Autónoma de Tamaulipas. Facultad de Medicina Veterinaria y Zootecnia.

México.

* Corresponding author: balbarranp@uaemex.mx

Abstract: Small-scale dairy systems contribute to ameliorate rural poverty and to local milk supply.

Their sustainability is limited by high feeding costs, mainly from purchased concentrates (CC); whereas a higher reliance on quality forage may improve profitability; but high stocking rates may justify high CC use. The objective of this work was to assess the productive and economic response by grazing dairy cows to levels of CC under grazing of ryegrass–white clover pastures under high stocking rate (4 cows/ha). Six Holstein milking cows were replicated assigned 3 X 3 Latin Square arrangements. Daily milk yield and composition were recorded, and feed intake estimated from utilised metabolizable energy. Treatments were: T1= 1.0 kg T6= 3.0 kg and T6= 6.0 kg concentrate/cow/d.

There were significant differences (P<0.05) for milk yield, with T6 having higher yields than T1 and T3. There were no significant differences in milk protein or fat content among treatments (P>0.05). Herbage intake was significantly (P<0.05) lower in T6, with no 855

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differences (P>0.05) between T1 and T3. There were no differences in margins over feeding costs, but feeding cost per kg of milk was 2.2 times higher in T6 compared to T1, and margin per kilo of milk was 26 % higher in T1 than T6. Although milk yields are higher with T6, T1 and T3 require less expenditures and margins are similar.

Supplementation may alleviate high grazing pressure that deteriorates pastures, ensuring the long-term sustainability of small-scale dairy farming systems.

Key words: Milk, Supplements, Concentrate, Intake, Stoking rate, Cost.

Received: 17/07/2018 Accepted: 18/09/2018

Introduction

Small-scale dairy systems in Mexico are a rural development option since they enable farming families to overcome poverty indices(1). These systems are important in many areas of the world with common features to the highlands of Mexico(2), like in other Latin American countries as in the Andean highland regions of Peru(3,4) and Uganda(5).

In Mexico, they are defined by small farms with herds between three and 35 cows plus replacements, and rely on their family labour(6). Their sustainability is jeopardised by high feeding costs in the face of stagnated prices for milk, mainly due to their reliance on external inputs of which bought-in commercial compound concentrates represent the highest proportion of costs, since farmers believe that high levels of concentrate supplementation are essential for milk production, even at the moderate milk yields in these systems. Therefore, the economic scale limits their sustainability(6).

A higher use of quality home-grown forages may increase the profitability and hence sustainability of these systems, as is the case for intensive grazing of temperate pastures in farms with access to irrigation, that has been shown to reduce feeding costs in these systems(7).

However, one limitation of grazing dairy cows is the low intake of dry matter(8), particularly under high grazing pressure, so that concentrates may be required in these conditions to sustain milk yields. Small-scale dairy systems traditionally have more cattle than the carrying capacity of their small farms with stocking rates over 3.0 cows/ha of agricultural land(6), so that a high concentrate use may be justified in these systems.

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Therefore, the objective of this work was to assess the productive and economic response of grazing dairy cows at a high stocking rate to increased levels of compound concentrate supplementation, as well as their effect on feed intake.

Material and methods

The work took place in Ejido San Cristóbal, a smallholder campesino village where most families are small-scale dairy farmers, located in the highlands of central Mexico at 19º 24’ N and 99º 51’ W, at an altitude of 2,650 m. The region has a sub-humid temperate climate with a distinct rainy season (May – October) and dry season (November – April), and average annual rainfall of 800 – 1,000 mm and a mean annual temperature of 13°C. The experiment took place during the rainy season, from June 26th to August 27th of 2000.

A demonstration module in feeding strategies for small dairy herds was established in consultation with the community on a 1.5 ha-1 plot of the local school. A local participating farmer managed the module with a herd of six local milking cows following a participatory livestock technology research approach(9) so that results were applicable by farmers in the region and other areas with similar systems.

Experimental design

Li Treatment sequences were randomised for Square 1, and Square 2 followed a mirror image in the treatment sequences to account for carry-over effects. Cows were assigned randomly to treatment sequence in both squares following previous work(10).

Experimental periods lasted 21 d, 14 for adaptation to diet and 7 as measurement period.

Cows were hand milked twice daily at 0500 and 1800 h.

Treatments were: T1= 1.0; T3= 3.0; and T6= 6.0 kg fresh basis/cow/d of commercial compound concentrate with 16 % CP, respectively. Cows continuously grazed for 11 h/d-1 with drinking water provided ad libitum at pasture (Lolium perenne and Trifolium repens). During nights, cows stayed indoors in a tie-stall and no feed was provided.

Milk yield was weighed daily during the 7 measurement days per experimental period with a spring balance using mean daily yield for analysis; and samples of milk taken in a morning and an afternoon milking to determine milk protein and milk fat content.

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Body condition score (1 â&#x20AC;&#x201C; 5 scale) was determined on the last day of each experimental period.

Pasture establishment and grazing management

The 1.5 ha-1 were sown with a mixed pasture of perennial ryegrass (Lolium perenne cv.

Nui), annual ryegrass (L. multiflorum cv. Tama) and white clover (Trifolium repens cv.

Pitaw). The pasture was fertilized every 4 wk with 75 kg of urea (46-0-0)/ha-1, and twice a year with 100 kg/ha of triple super phosphate (0-46-0) and potassium chloride (0-0-60), respectively. Continuous (set-stocked) grazing took place from 0700 to 1800 h daily.

Herbage measurements

Estimation of net herbage accumulation (NHA) was from cutting to ground level with shears 0.5 m2 (2.0 x 0.25 m) quadrants, within five exclusion cages. NHA (Kg DM ha-1 d-1) was the difference between herbage cut inside the cage on d 21 and herbage found outside the cage on d 0, then cages where placed randomly in the sward(11,12,13). Samples of cut herbage were oven-dried (60 °C) air forced for DM analysis. These dry weights were used to calculate the herbage mass on a DM basis. Herbage height (cm) was recorded with a rising plate metre twice weekly, taking 20 recordings following a zigzag pattern(10,11).

Chemical composition of herbage and feeds

Herbage was sampled by hand-plucking at the approximate height to which the cows grazed(14,15). During each measurement period herbage and concentrate were all analysed for dry matter (DM), organic matter by ashing (OM), crude protein (CP), neutral detergent fibre (NDF), and acid detergent fibre (ADF) and estimated metabolizable energy (oME) content of herbage and concentrate were from in vitro digestibility by standard techniques following the procedures reported(1).

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Estimation of voluntary intake

Herbage DM intake was estimated, indirectly, once during each measurement period from animal performance results(15,16) as follows:





Herbage DM intake kg DM d -1  (ME m  ME my  ME lwc  ME g )  (supplemen t ME ) herbage ME

where, MEm, MEmy, MElwc and MEg are the ME requirements for maintenance, milk production, live weight change and gestation, respectively(17), supplement ME supplied by the supplement, and herbage ME is the estimated ME concentration of herbage samples.

Statistical analysis

Animal variables were analysed as a replicated 3 x 3 Latin Square with the following model(10): Yijkl =  + Si + Cj(i) + Pk + tl + eijkl Where: = General mean; S= effect due to squares. i = 1, 2; C= effect due to cows within squares j = 1, 2, 3; P= effect due to experimental periods. k = 1, 2, 3; t= effect due to treatment. l = 1, 2, 3; e= residual error term.

Animal response variables were analyzed using MINITAB general linear model command (2003). Multiple comparisons between least squares means were performed using the Tukey test. Pastures variables (Table 1) were analyzed with one-way ANOVA using Microsoft Excel® data analysis package.

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Economic analysis

The economic analysis was performed using the partial budget approach(18), to determine the economic profits due to the use of supplements, exclusively for milk. Economic analysis results are expressed in US dollars.

Results Average temperature was 13.6oC, with a maximum and minimum of 20.5 and 6.8 oC, respectively. Total rain fall during the experiment was 332 mm, distributed as follows 139, 122 and 61 mm in EP1, EP2 and EP3, respectively.

Table 1 shows results for net herbage accumulation (NHA) per period and per day, as well as mean sward height. Net herbage accumulation and DHA in EP3 were significantly higher than in EP one and two (P<0.01).

Table 1: Net herbage accumulation (NHA) and sward height Period Herbage mass

1

2

3

P=

SD

NHA, OM/ha/period

kg

1073.1a

890.0a

2024.5b

0.01

609.0

Daily NHA, OM/ha/d

kg

51.1a

42.4a

96.4b

0.02

29.0

3.0

2.4

5.5

0.21

1.6

herbage height, cm

a,b

SD= Standard deviation.

Values with different superscript differ

Table 2 shows the chemical composition of the pasture herbage. Crude protein and digestibility were not different across EP (P>0.05). Crude protein ranged from 122 to 162 (EP1 an EP3, respectively), with a mean of 147 g/kg DM. Digestibility (Dig) mean digestibility was 581 (g/kg DM). Dry mater, ash, OM, NDF and ADF were significantly different across EP (P<0.05). Estimated metabolizable energy (eME) was different (P<0.001) among EP, the lowest value was in EP1 (10.1), whereas EP2 and 3, were not different among each other (11.2 and 11.2 MJ ME/kg DM).

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Table 2: Chemical composition of herbage Period

P

SD

Dry matter, g kg

1 2 3 a b 275.5 346.9 262.1a 0.001 45.6

Ash, g kg-1 Organic matter, g kg-1 Crude protein, g kg-1 DM Neutral detergent fibre, g kg-1 DM Acid detergent fibre, g kg-1 DM

265.2a 734.8a 160.4 572.4a 474.3a

-1

230.2a 769.8a 121.5 473.8b 247.9b

97.6b 902.4b 161.9 517.9a 260.5b

0.03 0.001 0.08 0.001 0.01

0.1 32.3 75.3 49.4 127.2

Digestibility of organic matter, g kg-1 DM 602.3 559.7 NA

0.11

347.1

Metabolizable energy, MJ kg-1 DM

0.001 0.91

10.1a

11.2b

11.1b

* Estimated from Menke and Steingass (1988).

SD= Standard deviation. a,b Values with different superscript differ.

Table 3 shows the results for feed intake, with significant differences (P<0.05) among treatments. There were no differences between T1 and T2 in herbage intake but lower intake in T6. Due to concentrate supplementation, total feed intake was not significantly different (P>0.05) between T3 and T6, but total intake was significantly lower (P<0.05) for T1. In time, there was a reduction in herbage intake, in Period 2, in spite of improved grazing conditions; this, lead to a significantly lower total intake (P<0.05) in Period 2, compare to periods one and three.

Table 3: Feed intake by treatments and periods kg (OM/cow/d) Treatments Intake

T1

T3

T6

P

SEM

Concentrate

0.9

2.6

5.3

Herbage

8.2a

7.3a

6.1b

0.001

0.5

Total

9.1

9.9

11.4

0.17

0.7

Period Intake

1

2

3

Concentrate

2.9

2.9

2.9

Herbage

7.3a

6.9b

7.3a

0.04

0.5

Total

10.2a

9.8b

10.2a

0.001

0.7

a,b

SEM = Standard error of the mean.

Values with different superscript differ.

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There were significant differences (P<0.05) among treatments for milk yields (Table 4); with no differences between T1 and T3, which were significantly different (P<0.05) from T6 which had the highest yield.

Table 4: Milk yield and milk composition by treatment and periods Treatment

T1

T3

T6

P

Milk yield, kg/cow/d

11.3a

12.6a

15.8b

0.02

Milk fat content, g/kg

37.8

37.6

33.8

0.59

Milk protein content, g/kg

35.1

32.8

33.0

0.91

Period

1

2

3

P

Milk yield, kg/cow/d

-

11.3

14.1

0.11

Milk fat content, g/kg

38.5

35.6

35.5

0.60

Milk protein content, g/kg

30.6

36.3

34.0

0.11

Body condition score

1.8

1.8

1.8

a,b

SEM = Standard error of the mean; Values with different superscript differ (P< 0.05)

Table 5 shows results for feeding costs (in US dollars). Increased concentrate supplementation increased feeding costs. Total feeding costs in T6 are almost three times the feeding cost in T1, whilst milk income was only 28 % higher, which results in a feeding cost per kilo of milk 2.2 times more expensive in T6 than in T1; with figures for T3 intermediate. Profit per kilogram of milk was therefore 33 % higher in T1 than in T6.

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Table 5: Feeding costs for milk production at three levels of concentrate supplementation (US$) Treatments T1

T3

T6

Concentrate

24.5

76.4

152.8

Pasture

37.5

36.4

26.3

Total feeding costs

63.0

112.8

179.1

Milk production, kg

1,311.3

1,459.6

1,691.8

Total returns for milk sales

388.5

432.5

501.3

Margin over feed costs

325.5

319.6

322.1

Returns / feeding costs ratio

6.2

3.8

2.8

Feeding cost, (US$/kg milk)

0.05

0.08

0.11

Sale price of milk, (US$/kg)

0.29

0.29

0.29

Margin per kilo (US$/kg)

0.24

0.21

0.18

Cost of feed inputs:

Returns:

T1 = 1; T3 = 3 y T6 = 6 kg of concentrate DM cow-1/d.

Discussion

The effect of weather condition is reflected in the chemical composition of the herbage throughout the different growing seasons. This grazing season was characterized by low herbage growth rate, short regrowth, low herbage mass availability and low herbage intake. In addition, grass development was based on vegetative growth, characterized with higher proportions of grass leaf, lower proportions of grass stem and dead material, and more digestible than other vegetative state. Temperate herbages used for dairy cows are described as high quality when chemical composition is around 180-240 g DM kg-1, 180 to 250 g CP kg-1 DM, 400 to 500 g NDF kg-1 DM, and 10.47 to 12.14 MJ ME kg-1 DM(19). Under the conditions of this experiment, herbage was characterized by low concentrations of crude protein, low energy and low amounts of non-structural carbohydrates and DM.

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Crude protein content of pasture herbage was lower than a report in southern Brazil(20), and lower than reports of work undertaken in the same study area(21,22); but sufficient to meet protein requirements for moderate yielding dairy cows(23).

Structural carbohydrate content determines digestibility, intake, and the nutritional value of forages. Average values of NDF and ADF of pasture herbage were 521.37 and 327.57 g/kg DM, respectively; lower from reports in a previous work in the same area, but during the dry season(24).

Estimated metabolizable energy was 10.8 MJ/kg DM on average (Table 2) representing an herbage of good quality. However, this value is lower than reports of 12.3 MJ EM/kg DM in the highlands of Mexico(10), or 11.2 MJ EM/kg DM reported in New Zealand(25).

The interaction of lower energy content and herbage availability may explain low intakes and low milk yields observed (Tables 3 and 4).

Net herbage accumulation was low during periods 1 and 2, which given the high stocking rate, resulted in a high grazing pressure and low herbage availability, with very low sward metre heights. Improved grazing conditions in terms of herbage growth and availability for Period 3, enabled cows to recover milk yields similar to those of Period 1, overcoming the loss of almost 3 kg/cow/d-1 from Period 1 to Period 2 as grazing conditions deteriorated.

Difficult grazing conditions with low herbage availability and moderate milk yields resulted in low herbage intakes, which were significantly decreased (P<0.05) by the high supplementation rate in T6, with high substitution rates.

It has been reported that 0.31 kg/d-1 of concentrate supplemented to grazing dairy cows results in a 1.0 kg DM reduction in herbage intake(26). Nonetheless, herbage intake in the experiment herein reported was similar to reports by(27,28) with grazing dairy cows in low herbage mass pastures during winter in France, reporting a mean daily intake of 7.2 kg DM/cow.

Observed milk yields were lower that results for grazing cows reported by in the USA(26), in the UK(29), in southeast USA(30), and in Mexico(24). However, observed milk yields were similar to reports by others(27) from cows under difficult grazing conditions in western France, illustrating the fact that difficult grazing conditions limit intake, and yields, particularly during late lactation.

Milk fat contents of milk are similar to results reported in France(27), in Spain(31), and in Chile(32). Although there were no statistical differences in milk fat content (P>0.05) there was a trend towards lower content in the high supplementation treatment T6.

Protein content of milk was higher than reports from works in Mexico with small-scale dairy farmers(33,34), as well as higher than reports from Chile(32); but similar to milk protein content from a study in the UK(29).

In terms of feeding costs and returns, in spite of lower yields for T1 and T3 compared to T6, the margin over feeding costs is similar for the three treatments. However, in terms 864

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of feeding cost per kg of milk and profit over feeding costs, T1 has lower cost and higher profit. Feeding cost per kilo of milk was 2.5 times higher in T6 compared to T1, and margin was 25 % higher in T1 than T6, with T3 showing intermediate economic results.

Low feeding costs and similar margins among treatments result in a very high returns / feeding costs ratio for T1 compared to T6.

Conclusions and implications

In conclusion, there is no economic benefit of increased concentrate supplementation since total margins over feeding costs are similar; and farmers need a greater cash flow to cover the increased costs of higher amounts of concentrate used. However, there is a need for long-term experiments since supplementation alleviates the grazing pressure due to high stocking rates. NHA was low resulting in difficult grazing conditions limiting the intake of cows. Forage or by-products of lower cost than commercial compound feeds may also be an alternative to sustain cow and pasture productivity. An optimal compromise in productive, economic and in the soundness of the pasture will ensure the long term sustainability of these small-scale farming systems.

Acknowledgements

To all the members of Ejido San Cristóbal, particularly to Mr. Hermenegildo ReyesReyes, the participating farmer in charge of the demonstration module, for his enthusiastic and full support to the project. Ms. Laura Edith Martínez-Contreras and Ms. Irma LópezAnaya (RIP) for chemical analysis. Thanks are given to Consejo Nacional de Ciencia y Tecnología - CONACYT for funding this work (grant 28888-B).

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10. Celis-Alvarez MD, López-González F, Martínez-García CG, Estrada-Flores JG, Arriaga-Jordán CM. Oat and ryegrass silage for small-scale dairy systems in the highlands of central Mexico. Trop Anim Health Prod 2016;48:1129-1134.

11. Sainz-Sánchez PA, López-González F, Estrada-Flores JG, Martínez-García CG, Arriaga-Jordán CM. Effect of stocking rate and supplementation on performance of dairy cows grazing native grassland in small-scale systems in the highlands of central Mexico. Trop Anim Health Prod 2017;49:179-186.

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12. Shakhane LM, Mulcahy C, Scott JM, Hinch GN, Donald GE, Mackay DF. Pasture herbage mass, quality and growth in response to three whole-farmlet management systems. Anim Prod Sci 2013;53:685-698.

13. McCarthy B, Pierce KM, Delaby L, Brennan A, Fleming C, Horan B. The effect of stocking rate and calving date on grass production, utilization and nutritive value of the sward during the grazing season. Grass Forage Sci 2013;68:364-377.

14. Soca P, Gonzalez H, Manterola H, Bruni M, Mattiauda D, Chilibroste P, Gregorini P. Effect of restricting time at pasture and concentrate supplementation on herbage intake, grazing behaviour and performance of lactating dairy cows. Livest Sci 2014;170:35-42.

15. Sheahan AJ, Gibbs SJ, Roche JR. Timing of supplementation alters grazing behavior and milk production response in dairy cows. J Dairy Sci 2013;96:477–483.

16. Rojas-Garduño M, Balocchi O, Vicente F, Pulido R. Effect of supplementation with cracked wheat or high moisture corn on milk fatty acid composition of grazing dairy cows. Chilean J Agric Res 2018;78:96-105.

17. AFRC - Agricultural and Food Research Council. Energy and protein requirements of ruminants. Wallingford, UK, CAB International;1993.

18. Espinoza-Ortega A, Espinosa-Ayala E, Bastida-López J, Castañeda-Martínez T, Arriaga-Jordán CM. Small-Scale dairy farming in the highlands of central Mexico: technical, economic and social aspects and their impact on poverty. Exp Agr 2007;43:241-256.

19. Ruiz-Albarran M, Balocchi O, Wittwer F, Pulido R. Milk production, grazing behavior and nutritional status of dairy cows grazing two herbage allowances during winter. Chilean J Agric Res 2016;76:34-39.

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21. Heredia-Nava D, Espinoza-Ortega A, González-Esquivel CE, Arriaga-Jordán CM. Feeding strategies for small-scale dairy systems based on perennial (Lolium perenne) or annual (Lolium multiflorum) ryegrass in the central highlands of México. Trop Anim Health Prod 2007;39:179-188.

22. Hernández-Ortega M, Heredia-Nava D, Espinoza-Ortega A, Sánchez-Vera E, Arriaga-Jordán CM. Effect of silage from ryegrass intercropped with winter or common vetch for grazing dairy cows in small-scale dairy systems in Mexico. Trop Anim Health Prod 2011;43:947-954.

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23. ARC – Agricultural Research Council. The nutrient requirements of ruminant livestock. Slough, UK, Published on behalf of the Agricultural Research Council by Commonwealth Agricultural Bureaux. 1980.

24. Albarrán B, García A, Espinoza A, Espinosa E, Arriaga CM. Maize silage in the dry season for grazing dairy cows in small-scale production systems in Mexico’s Highlands. Indian J Anim Res 2012;46:317-324.

25. Bryant RH, Dalley DE, Edwards GR. Effect of grazing management on herbage protein concentration, milk production and nitrogen excretion of dairy cows in midlactation. Grass Forage Sci 2013;69:644-654.

26. Bargo F, Muller LD, Delahoy JE, Cassidy TW. Milk response to concentrate supplementation of high producing dairy cows grazing at two pasture allowances. J Dairy Sci 2002;85:1777–1792.

27. Pérez-Prieto LA, Peyraud JL, Delagarde R. Pasture intake, milk production and grazing behaviour of dairy cows grazing low-mass pastures at three daily allowances in Winter. Livest Sci 2011;13:151–160.

28. Pérez-Prieto LA, Peyraud JL, Delagarde R. Substitution rate and milk yield response to corn silage supplementation of late-lactation dairy cows grazing low-mass pastures at 2 daily allowances in autumn. J Dairy Sci 2011;94:3592–3604.

29. Hernández-Mendo O, Leaver JD. Production and behavioral responses of high- and low-yielding dairy cows to different periods of access to grazing or to a maize silage and soybean meal diet fed indoors. Grass Forage Sci 2006;61:335-346.

30. Macoon B, Sollenberger LE, Staples CR, Portier KM, Fike JH, Morell JE. Grazing management and supplementation effects on forage and dairy cow performance on cool-season pastures in the southeastern United States. J Dairy Sci 2011;94:3949– 3959.

31. Vázquez-Yañez OP, González-Rodríguez A, López-Díaz JE. Efecto de la suplementación con concentrado sobre el aprovechamiento de la hierba y el rendimiento de vacas lecheras durante el pastoreo de primavera en la costa norte de Galicia. Pastos 2010;40:83–104.

32. Ruiz-Albarrán M, Balocchi OA, Noro M, Wittwer F, Pulido RG. Effect of increasing pasture allowance and grass silage on animal performance, grazing behaviour and rumen fermentation parameters of dairy cows in early lactation during autumn.

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33. Bernal-Martínez LR, Rojas-Garduño MA, Vázquez-Fontes C, Espinoza-Ortega A, Estrada-Flores J, Castelán-Ortega OA. Determinación de la calidad fisicoquímica de la leche cruda producida en sistemas campesinos en dos regiones del Estado de México. Vet Mex 2007;38:395-407.

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34. Martínez-García CG, Rayas-Amor AA, Anaya-Ortega JP, Martínez-Castañeda FE, Espinoza-Ortega A, Prospero-Bernal F, Arriaga-Jordán CM. Performance of smallscale dairy farms in the highlands of central Mexico during the dry season under traditional feeding strategies. Trop Anim Health Prod 2015;47:331-337.

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https://doi.org/10.22319/rmcp.v10i4.4724 Article

Productive and ingestive behavior in growing hair sheep in silvopastoral and stabled weight-gain systems Carlos Ricardo Villanueva-Partida a Víctor Francisco Díaz-Echeverría a* Alfonso Juventino Chay-Canul b Luis Ramírez-Avilés c Fernando Casanova-Lugo a Iván Oros-Ortega a

a

Tecnológico Nacional de México, Instituto Tecnológico de la Zona Maya, División de

Estudios de Posgrado e Investigación, Km 21.5 carretera Chetumal a Escárcega, Ejido Juan Sarabia Quintana Roo, México. b

Universidad Juárez Autónoma de Tabasco, División Académica de Ciencias

Agropecuarias, Villahermosa Tabasco, México. c

Universidad Autónoma de Yucatán, Facultad de Medicina Veterinaria, Xmatkuil Yucatán,

México.

*Corresponding autor: diazvic@prodigy.net.mx

Abstract: In sheep silvopastoral systems, forage legumes are promising alternatives to commercial feed since they can lower costs. An evaluation was done comparing productive and ingestive behavior parameters in hair sheep in silvopastoral and stabled systems for four months. Nine variables were evaluated: dry matter intake (DMI); organic matter intake (OMI); crude protein intake (CPI); neutral detergent fiber intake (NDFI); daily weight gain (DWG); feeding time (FT); rumination (RT); walking (WT); and other activities time (OAT).

Experimental animals were eighteen sheep (initial weight= 16.83 x 2.57 kg) distributed in a completely random design, with three treatments: a silvopastoral system (SPS) including the

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legume Leucaena leucocephala and the grass Cynodon plectostachyus (L+E); another SPS including L. leucocephala and the grass Panicum maximum (L+M); and a stabled weightgain system (SWS). Orthogonal contrasts were applied to compare the SWS vs SPSs and the two SPSs. Compared to the two SPSs, all intakes (DMI= 1246.2 g; OMI= 1073.0 g; CPI= 157.1 g; and NDFI= 364.0 g animal d-1) and DWG (195 g animal d-1) were higher (P<0.001) in the SWS. Daily weight gain did not differ between the SPSs (102 and 114 g animal d-1), but all intakes were higher (P<0.001) in L+M (DMI= 772.2 g; OMI= 662.7 g; CPI= 124.0 g; and NDFI= 334.2 g animal d-1) than in L+E (DMI= 548.9 g; OMI= 86.20 g; CPI= 483.0 g; NDFI= 252.3 g animal d-1). Feeding time (FT) was shortest (P<0.001) in the SWS (148.33 min). Between the SPSs, FT was shortest (P<0.05) in the L+E (318.3 min) than in the L+M (344.6 min). Time dedicated to other activities was longer (P<0.001) in the SWS (247.9 min) than in the SPSs. Silvopastoral systems combining legumes and grasses provided sufficient growth in hair sheep although the stabled weight gain system produced faster growth in the studied time period.

Key words: Grazing, stabled, productive and ingestive parameters.

Received: 18/12/2017 Accepted: 13/09/2018

Introduction Sheep production in the tropics of Mexico has been growing in response to increasing demand and attractive market prices. However, productivity in grazing sheep is limited mainly by low pasture availability and quality during the dry season. This affects lamb growth and development and consequently the time required for finishing animals for sale(1).

An alternative for addressing problems of low fodder availability and quality is the use of forage trees and shrubs which have higher nitrogen content than grasses, a compound that promotes proper rumen functioning(2). Forage trees and shrubs are also an excellent source of digestible energy and can provide bypass protein, which is required to ensure a productive response in grazing animals under tropical conditions(3).

Integration of forage trees and bushes into sheep production systems in the tropics has grown as silvopastoral systems (SPSs) are implemented as an alternative in livestock production.

These systems associate trees and shrubs with traditional elements (i.e. grasses and animals) within a comprehensive management system that provides high fodder availability and quality for animal feed year-round as well as providing environmental benefits(4). However, 871

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it is important to understand sheep ingestive behavior in these systems. For example, animals with limited grazing in SPS use most of their time for feeding, with minimal time dedicated to walking or extended rest and ruminating periods(5).

Most studies show positive effects on voluntary intake and animal weight gain when controlled quantities of forage tree species are included in diets(6,7). However, these studies have been done under controlled conditions with stabled animals and may not express the actual potential of forage tree species since grazing animals have greater energy expenditure, which may limit maximum productive expression of this feed alternative(2).

The leguminous forage tree Leucaena leucocephala has been widely studied and is commonly used for establishing SPS in the tropics. In Mexico little is known about this legume’s potential when used in SPS and how it affects weight gain in ruminants(8) in comparison to stabled conditions. More knowledge is needed on the use of alternative forage plants to replace commercial feeds, which increase production costs. Although SPSs have been extensively studied in other countries, in southeast Mexico further evaluation is needed of these systems when animals are directly involved in the system through grazing, and to assess if their use is feasible for weight gain and finishing of animals intended for market.

The present study objective was to evaluate productive and ingestive parameters in hair sheep grazing two SPSs based on L. leucocephala and compare them to a stabled weight gain system (SWS).

Material and methods Study area The study was done at the Instituto Tecnológico de la Zona Maya (18°31’ N; 88° 29’ W), in the state of Quintana Roo, Mexico. Regional climate is warm sub-humid (Aw1), with annual mean temperatures ranging from 24.5 to 25.8 °C(9). Based on the IUSS classification system, soils are predominantly haplic greysols(10). The study was done during the rainy season, from September to December 2016.

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Animals, handling and feeding Experimental animals were 18 males (Pelibuey × Blackbelly) with a 16.83 ± 2.57 kg initial weight. These were randomly divided into three groups of six animals each, corresponding to three treatments. The animals were weighed at the beginning of the trial and deparasitized with 1% ivermectin at 1 ml / 50 kg live weight (LW) (Iverfull®, ArandaLab®) and 5% closantel at 1 ml / 10 kg LW (Closantel-Panavet® 5%). They were administered two vitamin supplements: 2 ml per animal of a multivitamin containing retinol (Vit. A), colecalcipherol (Vit. D3) and tocopherol acetate (Vit. E) (Vigantol ADE-Bayer); and 5 ml per animal of a B complex (Complejo-B, Virbac). They were allowed 15 d adaptation before the treatments were begun. Disease treatment was done following established national animal welfare and health guidelines (NOM-062-ZOO-1999).

Three treatments were evaluated: SWS, based on a commercial feed concentrate and fresh grass; L+E, based on L. leucocephala and Cynodom plectostachyus; and L+M, based on L. leucocephala and Panicum maximum.

Evaluated variables Nine variables were evaluated. Five were productive variables (g animal-1 d-1): daily weight gain (g animal-1 d-1; DWG); dry matter intake (DMI); crude protein intake (CPI); organic matter intake (OMI); and neutral detergent fiber intake (g animal-1 d-1; NDFI). Another four addressed ingestive behavior (minutes): feeding time (FT); ruminating time (RT); walking time (WT); and other activities time (OAT).

Available forage estimation

Before the grazing treatments, random samples were collected of forage in each area following an established technique(11). Briefly, a one square-meter frame was placed over an area containing grass and this cut to 10 to 15 cm above soil surface, approximating the height at which the animals were observed to graze it. Leucaena leucocephala was cut at 40 cm above soil surface. Samples were weighed fresh. A subsample was dried in a forced air stove for 72 h at 60 °C to constant weight and weighed. These data were used to calculate dry matter content per hectare. All the samples were ground for later chemical composition analyses.

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Pens and feeding

In the SWS, animals were housed in individual pens divided by sheep fencing in a building with concrete walls and floors, and a sheet metal roof. Each pen was equipped with feeders and water bottles, and feed and water were freely available. The commercial feed (Maltacleyton Premium) was adjusted according to production stage (15% CP in months 1 and 2, and 13% CP in months 3 and 4). Chopped fresh grass (P. maximum cv. Mombaza) was provided separately. Throughout the experimental period the feed ratio was 70 % feed concentrate and 30 % grass.

In the SPSs the animals were grazed in pastures containing 36,000 L. leucocephala plants with grass between the rows. These plots were established in 2014 under seasonal conditions and without application of fertilizers. Electric fence was used to divide each SPS pasture (1 ha) into four 2,500 m2 paddocks. These were trimmed every 60 d at a height of 40 cm for L. leucocephala and 10 to 20 cm for the associated grasses. The daily area used per animal lot inside the paddocks averaged 178 Âą 29.54 m2. This was calculated based on average dry matter intake (DMI) per animal (3 to 4.5 % LW) and adjusted every fourteen days. Rotational grazing was used with polywire fence from 0700 to 1600 h; a container of clean water was provided during this time. After grazing the animals were stabled in individual pens like those used in the SWS treatment, where they were provided with a portion equivalent to 20 % commercial feed concentrate based on the quantity offered animals in the SWS (70 % of total diet). Grazing time and feed in all three treatments were adjusted every 14 d.

Voluntary intake (VI) Voluntary intake (VI) in the SWS was determined using the difference between the feed offered and that rejected during 24 h. Values for DMI, OMI, CPI and NDFI were estimated using VI and the chemical composition of the feed concentrate and grass.

In the SPSs, VI was estimated individually with the external marker technique(12), using chromium oxide (Cr2O3) as a marker. This was administered the animals for eleven days during the second and fourth months of testing. Stool samples were collected directly from the rectum over three days, dried in a forced air stove at 60 °C to constant weight, and ground.

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Daily weight gain (DWG) Daily weight gain was measured by weighing animals on a digital scale, after a 15-hour fast, at 0, 14, 28, 42, 56, 75, 84, 98, 112 and 140 d. Weight was measured before feeding, and DWG estimated as the slope of the linear regression between VI and days.

Ingestive behavior

The activities involved in ingestive behavior were documented following an established methodology(13) in which three sheep were randomly selected per treatment and one observer assigned each animal. Animal activities were recorded every 5 min during a 9-hour period (equivalent to grazing time) over four consecutive days every month from October to December. The total time allotted each activity by the animal was calculated based on behavioral activity times in five-minute intervals.

Laboratory analysis and calculations Samples were taken of the commercial feed, grasses, legumes and weeds eaten by the animals to measure DM, OM and CP contents with AOAC techniques(14), while NDF content was measured according to Van Soest et al(15).

Feces Cr2O3 content was measured with a microwave plasma atomic emission spectroscope (MP-AES 4200, Agilent) coupled to a nitrogen generator (Genius 5200, Peak). In vitro dry matter digestibility (IVDMD) was quantified in forage samples from each SPS (16). Feces production and DMI were estimated using published formulas(17), and adding DMI corresponding to the feed concentrate provided in the pens:

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Estimates of DMI, CPI, OMI, and NDFI were made using VI values and chemical composition for the grasses, weeds, L. leucocephala and feed concentrate.

Experimental design and statistical analysis Response variables were compared with an orthogonal contrast test in a completely random design with three treatments and six replicates. Calculation of differences between SWS vs L+E and L+M, as well as L+E vs L+M were done with the PROC GLM in the SAS statistics program(18).

Results and discussion Forage availability and chemical composition

The L+M system provided more (P<0.05) dry matter than the L+E system, which is unexpected since C. plectostachyus had higher DM, NDF and OM contents than P. maximum.

Table 1: Chemical composition of forage species and feed concentrate, and forage availability in the silvopastoral systems.

Content L. leucocephala C. plectostachyus P. maximum Feed concentrate DM, % 25.08 ± 3.71 28.5 ± 5.52 21.30 ± 3.96 89.05 ± 2.70 CP, % 21.38 ± 1.17 8.21 ± 1.68 8.36 ± 1.38 13.33 ± 0.43 NDF, % 37.58 ± 1.97 57.50 ± 1.31 55.15 ± 1.88 12.25 ± 0.75 OM, % 86.46 ± 0.92 89.37 ± 0.49 84.42 ± 1.24 85.51 ± 0.11 -1 Forage availability (t DM ha ): System Total L+E 3.56 ± 0.71 3.73 ± 0.77 7.30 ± 1.49 b L+M 2.01 ±0.19 8.64 ±0.09 10.65 ±0.28 a P value 0.012 L+E = Leucaena leucocephala + Cynodon plectostachyus; L+M = Leucaena leucocephala + Panicum maximum. ab Different letter superscripts in the same column indicate significant difference (P<0.05).

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Weight gain

Daily weight gain (DWG) was higher in the SWS (P<0.001) than in the SPSs, which did not differ (P>0.05). Animals in the SWS had higher DMI, PCI, OMI and NDFI (P<0.001), than in the SPSs, and those in the L+M had higher values (P<0.001) than those in the L+E (Table 2).

Table 2: Daily weight gain and nutrient intake in hair sheep in a stabled weight gain system (SWS) and two silvopastoral systems (g animal-1d-1) Treatments Contrasts Parameters SWS L+E L+M SE Cont. 1 Cont. 2 DWG 195 102 114 8.7 ** NS DMI 1246.2 548.9 772.2 22.6 ** ** CPI 157.1 86.2 124.0 3.4 ** ** OMI 1073.0 483.0 662.7 19.7 ** ** NDFI 364.0 252.3 334.2 11.5 ** ** DWG = daily weight gain; DMI = dry matter intake; CPI = crude protein intake; OMI = organic matter intake; NDFI = neutral detergent fiber intake; Cont. 1 = SWS vs. SPSs; Cont. 2 = L+E vs. L+M; SE = standard error between means. **= P<0.001; NS= P>0.05.

The higher DWG in the SWS was associated with the increased DMI, PCI, OMI and NDFI in this treatment. Similar results have been reported in sheep fed diets containing high proportions of concentrate (14% CP and 8.36 MJ/kg DM) in which higher concentrate consumption may have led to greater DM ruminal degradability and ruminal flow rate, and consequently more weight gain(19). In another study the production parameters in goats fed diets supplemented with peanut cake were directly linked to nutrient intake which influences dietary fiber digestibility and weight gain during growth(20), similar to the present results.

The lack of significant difference in DWG between L+E and L+M may indicate that the higher DMI (223.24 g animal-1 d-1), OMI (179.66 g animal-1 d-1) and CPI (37.80 g animal-1 d-1) values in L+M were insufficient to digest the higher NDFI (81.94 g animal-1 d-1) present in both SPSs. This coincides with a previous report suggesting that consumption of higher amounts of cellulose and lignin in ruminants fed tropical grasses and legumes reduced forage transit rate, leading to longer rumen retention times, and consequently modifying DWG(21).

In the SWS, the DWG values (195 g animal-1 d-1) were slightly higher than those reported for sheep fed different proportions of grain silage (maize, sorghum and millet) plus 50 % commercial feed concentrate (120 to 180 g animal-1 d-1)(22). In contrast, the DWG values for L+E (102 g animal-1 d-1) and L+M (114 g animal-1 d-1) were similar to those reported for hair 877

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sheep grazed in a SPS containing L. leucocephala (35,000 plants/ha) and P. maximum (106 g animal-1 d-1)(23). Weight gain in the two studied SPSs was within DWG ranges reported for lambs fed feed concentrate plus the grass Pennisetum purpureum plus 30 % inclusion of the forage trees Moringa oleifera or Trichanthera gigantea (96 to 155 g animal-1 d-1)(24), and for sheep fed the grass C. plectostachyus and different inclusion levels of the forage trees Guรกzuma ulmifolia, L. leucocephala or Gliricidia sepium (54 to 137 g animal-1 d-1)(25).

Intake values in the SWS (DMI, 1,246.24 g; CPI, 157.11 g; OMI, 1073.03 g; and NDFI, 363.99 g animal-1 d-1) are near those for growing Pelibuey lambs fed the grass Cenchrusaris ciliaris Link. and a conventional concentrate including exogenous enzymes (DMI, 1029 to 1120 g; OMI 928 to 976 g; CPI 130 to 137 g; NDFI 447 to 470 g animal-1 d-1)(26).

One aspect to consider in the present study is that the animals were allowed to graze for nine hours. Given the season, forage intake can decline between 1100 and 1400 h due to extreme heat, which may have contributed to reducing intake and thus lowering DWG in the SPS treatments. Moreover, feeding of 20 % concentrate in the pens after grazing may have conditioned the animals to wait for this ration, consequently decreasing their feeding time and intake on the pasture.

Feeding behavior

Animals in the SWS spent less time feeding than those in the SPSs (P<0.001). In addition, animals in the L+E spent less time feeding than those in the L+M (P<0.05). Time spent walking (WT) and ruminating (RT) did not differ between the SWS and the SPSs (P>0.05).

This is of note since the SWS included less fiber in the diet and would be expected to have had a shorter RT than in the SPSs. Perhaps this is due to the animals in the SPSs choosing young grass and legume leaves, which would lower their fiber intake and result in similar rumination times in the three studied systems. Other activities (OAT) occupied a greater portion of the time in the SWS than in the SPSs (P<0.01), although OAT values did not differ between L+E and L+M (P>0.05) (Table 3).

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Table 3: Ingestive behavior in the stabled weight gain system and two silvopastoral systems based on L. leucocephala and associated grasses Treatments Contrasts Activity SWS L+E L+M SE Cont. 1 Cont. 2 Feeding time, min 148.33 318.33 344.58 8.28 ** * Walking time, min 20.00 24.17 30.00 7.25 NS NS Ruminating time, min 62.08 53.75 31.67 10.33 NS NS Other activities, min 247.92 83.75 73.75 12.90 ** NS SWS = stabled weight gain system; L+E = Leucaena leucocephala + Cynodon plectostachyus; L+M = Leucaena leucocephala + Panicum maximum; SE= standard error between means; Cont. 1 = SWS vs. SPSs; Cont. 2 = L+E vs. L+M. *= P<0.05; **= P<0.001; NS= P>0.05.

Feeding times (FT) in the present results were higher than those reported in sheep fed ammoniated C. ciliaris L. in which this variable accounted for 38.4 % of daily activities(27); these differences are probably due to use of a 9 h daily observation period in the present study compared to a 24-h period in the previous study. However, the present results are similar to those reported for other ruminants (bovines) in a restricted grazing regime including Bracharia humidicola and P. maximum infested with legume creepers(28), and another regime involving Cynodon nlemfuensis supplemented with corn distillers grains in the dry season(29).

The present ingestive behavior results also coincide with those reported for goats in an SPS using P. maximum, L. leucocephala, native legumes and natural pastures(5), in which the animals spent most of their time walking and feeding (152 min and 36.28 % during 8 h observation). In another study goat kids grazing C. nlemfuensis were found to prefer grazing pasture in the daytime, spending from 82 to 83 % of their time doing it(30). The fact that FT in the present results was shortest (15.44 min) and least frequent (31.34 %) in the SWS treatment may be associated with several factors such as feed supplementation. This has been reported elsewhere in ruminants grazing C. nlemfuensis supplemented with 1.5 or 2.5 kg corn distillers grains in which feeding time was shorter and thus allowed more time for other activities or resting(29).

Differences in FT between the SWS and the SPSs, as well as between the two SPS treatments, may be due to variations in diet NDF content. For example, in a report on sheep fed ammoniated C. ciliaris L. hay higher dietary fiber content was related to longer rumination time, suggesting that the time spent on feeding and ruminating was influenced by diet composition and was probably proportional to cell wall content(27). This does not coincide with the rumination times and frequencies in the SPSs studied here which had shorter rumination times and frequencies than previous reports(26); worth noting is that after grazing the animals did engage in rumination once penned. However, the previous results are similar

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to the rumination time and frequency in the SWS, perhaps due to lower grass (and therefore fiber) content in the diet.

More research is needed to corroborate hair sheep productive and ingestive behavior yearround. The number of observations used in the present study was limited due to the difficulty of observation during the rainy season. Despite some limitations, the present results provide an encouraging picture of the use of silvopastoral systems in sheep production in southern Quintana Roo.

Conclusions and implications Hair sheep in the stabled weight-gain system exhibited greater weight gain than the silvopastoral systems due to higher dry matter, crude protein, organic matter and neutral detergent fiber intake, and they required less time to feed. Weight gain in the two tested silvopastoral systems did not differ despite higher intake levels in the L. leucocephalaMombaza grass association, probably due to longer feeding time. Walking and ruminating times did not differ between the three production systems. Silvopastoral systems are promising alternative production systems in the tropics because they incorporate tree legumes such as L. leucocephala. These are rich in nutrients, particularly protein, and promote higher feed intake in ruminants consequently increasing livestock production under grazing conditions and reducing dependence on external inputs.

Acknowledgements The research reported here formed part of the Master’s degree of Carlos Ricardo VillanuevaPartida, and was financed by the Programa para el Desarrollo Profesional Docente (superior), Tecnológico Nacional de México (proyecto: ITLZM-CA-1) and the Consejo Nacional de Ciencia y Tecnología (proyecto: 270666).

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27. Fernández PA, Homen NSP, Lolato RO, Mauro SE, Giordano PG, Silva OJ et al. Intake and ingestive behavior of lambs feed diets containing amoniated buffel grass hay. Trop Anim Health Prod 2017;49:717-724.

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https://doi.org/10.22319/rmcp.v10i4.4561 Article

Stress indicators in cattle in response to loading, transport and unloading practices

Silvia Larios-Cueto a Rodolfo Ramírez-Valverde a* Gilberto Aranda-Osorio a María Esther Ortega-Cerrilla b Juan Carlos García-Ortiz a

a

Universidad Autónoma Chapingo, Posgrado en Producción Animal, Km 38.5 Carretera México-Texcoco, Chapingo, Estado de México, México. b

Colegio de Postgraduados, Estado de México, México.

* Corresponding author: rodolforv@correo.chapingo.mx

Abstract: Transporting of cattle can cause multiple physical and psychological stressors that may affect profitability. An evaluation was done of the effects of stress produced by management practices before, during and after cattle transport, based on changes in physiological indicators and weight prior to the finishing stage. Animals were 124 weaned calves transported to the State of Mexico from the states of Veracruz (500 km) and Chiapas (851 km), Mexico, for finishing in feedlots. Four treatments were used: 1) preloading reception management at place of origin (PRE); 2) PRE + preloading application of a β-blocker (PREβ); 3) reception management upon unloading at feedlot (POST); and 4) POST + preloading application of a β-blocker (POSTβ). The data were analyzed with the GLM procedure in the SAS program. The experimental design was a completely random 2 x 4 factorial arrangement (two transport distances and four treatments). Average live weight (LW) decreased 42 kg per animal (11.3 %) at unloading, but 10 days after unloading had 885

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recovered by 35 kg. Live weight loss was lower in the PREβ treatment (37.6 kg) than in the PRE (47.5 kg) or POSTβ (44.5 kg) treatments. The PREβ did not differ (P>0.05) from the MD (38.7 kg). The most important stress indicators were changes in live weight, glucose and cortisol, with differences (P<0.05) identified by treatment and transport distance. No differences (P>0.05) were present in the other indicators (free fatty acids, β-hydroxybutyrate, total proteins, and sodium and potassium concentrations). The combination of preloading reception management and a β-blocker produced the least amount of stress in the animals.

More attention is needed on the period between loading and unloading cattle for transport to finishing to establish optimum conditions.

Key words: Cortisol, Carazolol, Finishing, Transport.

Received: 22/07/2017 Accepted: 04/06/2019

Introduction Transport of cattle for finishing and slaughter subjects animals to multiple physical and psychological stressors(1,2), which can negatively impact health and productive behavior.

Response to transport stress can vary according to different factors, such as nature of the trip, grouping of unknown animals, use of an electric sweeper, the presence of noise, high load density, vehicle type, driver skill, road conditions and trip duration, among others(1,3,4,5).

Behavioral, pathological and physiological indicators have been applied to assess the effects of transport stress on cattle(2,6). Research on cattle stress during transport from finishing corral to slaughterhouse has shown increases in cortisol levels (5.2 to 14.3 ng.ml-1)(7), total blood serum proteins (67.7 to 74.4 g.l-1)(8), and blood glucose (>10% after 16 h transport)(9), as well as loss of live weight (7.9 to 10.5%)(10,11,12). No evaluations have been done to date of the effects of transport from the breeding system to the finishing corral in a variety of conditions.

In one study done in Chile of calves intended for grazing finishing(8), transport for more than 48 hours raised cortisol levels from 10 ng.ml-1 before loading to 15 ng.ml-1 at unloading, and caused 13% live weight loss.

Drugs such as allostatic modulators, sedatives and β-blockers have been tested to reduce the negative effects of cattle transport to the slaughterhouse(13). Beta-blockers have been shown to lower stress levels in animals due to their antagonistic effect on β-adrenergic receptors 886

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which decreases the effects of catecholamines, including their glycolytic action(14,15,16).

However, results have been variable and at times contradictory. Therefore, assessment is needed of the direct effects of transport stress on production parameters (e.g. live weight and recovery time) under specific situations.

In Mexico, it is common practice in cattle production to finish cattle acquired post-weaning from growth systems. No literature is currently available documenting the effects of cattle transport from growth systems to finishing corral. Commercial livestock carriers use various management practices when receiving animals for transport and at unloading, including application of drugs before transport to reduce adverse effects.

The present study objective was to evaluate the effect of use of reception management practices before or after transport and pre-loading application of a commercial β-blocker (Simpanorm®) in weaned calves on different stress indicators and live weight gain after transport to the finishing feedlot.

Material and methods Study area and animals

Data were collected from July 2013 to July 2014 during commercial transport of calves from Acayucan, Veracruz (17°56’ N; 94°54’ W) and Pichucalco, Chiapas (17°31’ N; 93°05’ W) to finishing feedlots in Texcoco, State of Mexico (19°30’ N; 98°52’ W)(17). Three trips were evaluated per place of origin (6 animals in the first trip, and 5 in the remaining two). The evaluated animals were 124 commercial cross (Bos taurus - Bos indicus) calves (from grazing systems) with a 375 ± 44 kg average weight. The animals from Acayucan were loaded directly at the growing facility and thus had not been transported previously. Those from Pichucalco had been transported a short distance from small production units to the collection point, although the conditions of this initial transport were unknown.

Transport conditions

Exact transport time depended on the conditions specific to each of the three trips; those from Acayucan (500 km) lasted 16, 16 and 13 h, while those from Pichucalco, Chiapas (821 km) lasted 18, 17 and 25 h(17). Transport was done on paved roads and a toll highway, in tractor887

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trailer rigs equipped with cattle cages (14.2 m [48 ft.] long, six internal sections). The effects of section were partially mitigated by transporting the evaluated animals in the two lowest sections of the trailer; the resulting allocated space per animal was 0.83 m2 in the trailers from Acayucan and 0.86 m2 in those from Chiapas.

Environmental conditions during transport were temperatures from 10 to 32 °C in the trips from Pichucalco; and from 12 to 31 °C in those from Acayucan. Temperatures were highest at the place of origin in both cases.

Treatments and response variables

The evaluated animals (n= 124) were randomly and evenly distributed among four treatments based on routine practices used by the participating commercial carriers: 1) preloading reception management at the place of origin (PRE); 2) PRE + administration of β-blocker before loading (PREβ); 3) post-unloading reception management at finishing feedlot (POST); and 4) POST + application of β-blocker before loading (POSTβ).

Data for the response variables were collected during two periods: P1) loading to unloading (i.e. values at loading minus values at unloading); and P2) unloading to 10 d after arrival in finishing feedlot (i.e. unloading values minus values 10 d after unloading). Analyses were done of eight variables: live weight (LW); glucose (GLU); β-hydroxybutyrate (βHB); free fatty acids (FFA); total proteins (TP); cortisol (COR); sodium (Na); and potassium (K).

Fieldwork

Before loading, the animals were weighed individually on a livestock scale (max. capacity 5,000 kg), marked for identification and assigned to the treatments, applying the corresponding reception management (i.e. PRE, PREβ, POST or POSTβ). Data were also collected on each animal’s breed (i.e. Zebu, European or cross) based on external phenotypic characteristics, and the presence of horns.

Reception management in all four treatments consisted of deworming (Ivomec F®, Merial ivermectin 1 ml 50 kg-1 LW); vaccinating (Protector 5®, Lapisa, Michoacán, Mexico, and Blacklegol®, Bayer, North Rhine-Westphalia, Germany); vitamin supplementation (Synt ADE®, Zoetis, New Jersey, United States); and a growth promoter implant (Revalor, MSD, New Jersey, United States; one implant per animal: 140 mg trenbolone acetate plus 20 mg 888

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17ß estradiol). A β-blocker was administered in the PREβ and POSTβ treatments. This consisted of carazolol hydrochloride (Simpanorm®, Schütze-Segen, Mexico City, Mexico) and was applied by intramuscular injection at 0.02 ml kg-1 LW, 30 min before loading into the cattle trailer.

For all the evaluated trips the animals were loaded in the afternoon (1600-1900 h), after an eight-hour fast. Time elapsed and temperature inside the trailer were recorded during transport. Temperature readings were taken using a digital thermometer (Mod. 445702, Extech, Hong Kong, China) at eight times during the trip within each of the two divisions (indicated as 1 and 2) inside the trailer where the evaluated animals were kept.

At the feedlot, the animals were transferred to cargo trucks with steel railing for transfer to the finishing corrals (approximate travel time= 15 min, space per animal= 1.1 m2). Reception management for the animals in the POST and POSTβ treatments was done at the feedlot.

Once in the finishing corrals, the evaluated animals from each trip were kept together to begin adaptation. For the first 1 to 2 d, hay-only forage (barley straw) was provided, followed by feed concentrate (PC = 14%; ME = 2.8 MCal) which was gradually increased by 15 % of total daily ration. Water was freely available.

The variables LW, GLU and βHB were measured three times: upon loading, at unloading, and 10 days after arrival in the finishing corral. For GLU and βHB, a drop of blood was taken from the coccygeal vein and analyzed using a glucometer (Mod. Optium Xceed, Abbott Laboratories, Chicago, United States), which used strips specific to each test. The βHB strips contain the enzyme hydroxybutyrate dehydrogenase which oxidizes to acetoacetate, with simultaneous reduction of NAD+ to NADH; this is proportional to the βHB concentration.

The system is valid for concentrations of 0 to 6 mmol l-1(18).

Blood samples and laboratory analysis

Blood samples were taken upon loading, at unloading and 10 d after arrival at the finishing corral. The coccygeal vein was punctured using vacuum tubes without anticoagulant (20 ml per animal). The samples were allowed to coagulate at room temperature and centrifuged at 3,500 rpm for 15 min. The serum was separated from the clotted blood to avoid hemolysis, deposited in storage tubes and transported to the laboratory in coolers (temperature no higher than 4 °C). Cortisol levels (COR) were quantified by the ELISA (enzyme-linked immunoabsorbant essay) technique using rabbit antibody and horseradish peroxidase as an antigen (Cat. No. 6101-17, Diagnostic Automation, Inc., Calabazas, California, United States). Readings were done at a 410 µm wavelength and two replicates done of all 889

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measurements. Free fatty acids (FFA) were quantified by enzymatic colorimetry with acylCoA oxidase (ACOD) (Randox kit, Mod. FA115, Crumlin, County Antrim, Ireland), in two replicates.

Total proteins (TP) were measured by the refractometry technique using an ATAGO® refractometer and deionized water as a blank(19); three replicates were done of each measurement. Electrolytes (Na and K) were measured by atomic absorption spectrophotometry(20).

Statistical analysis

Analysis of changes in the stress indicator variables was done with a completely randomized experimental design using a 2 x 4 factorial arrangement. Four treatments (PRE, PREβ, POST and POSTβ) and two transport distances (500 and 851 km) were used as well as the interaction of these effects. With the purpose of removing variability due to other effects reported as important in similar studies(3,21,22), other independent variables were considered such as cattle breed group based on external phenotype traits (Zebu, European or cross); presence of horns (yes or no); transport time per trip (covariate); average ambient temperature inside trailer during transport; initial value (upon loading) of response variable (covariate); and animal position inside trailer during transport (section one or two). These effects and their simple interactions were initially included in the analysis of each variable and the nonsignificant ones (P>0.05) removed from the final analyses.

Under the present study conditions, in P1 the variables removed from the final analyses for all response variables were breed and presence of horns. Transport time was removed from the final models for changes in LW and βHB; while the initial value for this variable was excluded only for changes in FFA. Ambient inside trailer temperature was only considered for changes in GLU and TP; while position within the trailer was used only for changes in LW and FFA. In P2, only the presence of horns variable was excluded from all the models in all response variables. Transport time was removed only for changes in βHB, Na and K. Animal breed was only considered in the final models for changes in FFA and COR, while inside trailer temperature was only included for changes in GLU, COR and TP. Variable initial value was used only for changes in GLU and TP, and position inside trailer only for changes in LW and TP. The data were analyzed by period: P1 (loading to unloading) and P2 (unloading to 10 days in corral). The response variable complied with the assumptions of normality (Shapiro-Wilk W test), so the GLM SAS procedure and a Tukey test were used to compare the means(23).

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Results Descriptive statistics by sampling period

As expected, stress indicator variability values were high (CV between 12 and 84%) due to the influence of the various factors (e.g. treatments, transport distance, etc.) (Table 1).

Table 1: Overall averages ± standard deviations (coefficient of variation, %) of stress indicators during calf transport at loading, unloading and 10 days after unloading (10dUL).

Indicator Live weight, kg Glucose, mg.dl-1 βHB, mmol.l-1 FFA, mmol.l-1 TP, g.dl-1 Cortisol, pg.dl-1 Na, mg.dl-1 K, mg.dl-1

Loading 375 ± 44.3 (12) 78 ± 21.1 (27) 0.39 ± 0.21 (55) 0.58 ± 0.36 (63) 8.05 ± 1.32 (16) 3.33 ± 2.12 (64) 4407 ± 1747 (40) 147 ± 32 (22)

Unloading 333 ± 45.5 (14) 91 ± 21.3 (23) 0.38 ± 0.23 (61) 0.71 ± 0.22 (31) 8.83 ± 1.08 (12) 3.79 ± 2.48 (65) 4718 ±2075 (44) 145 ± 34 (23)

10d UL 368 ± 51.2 (14) 69 ± 12.5 (18) 0.28 ± 0.18 (65) 0.23 ± 0.18 (78) 7.94 ± 0.92 (12) 2.47 ± 2.07 (84) 5020 ± 2126 (42) 162 ± 37 (23)

βHB= β-hydroxybutyrate; FFA= free fatty acids; TP= total proteins; Na= sodium; K= potassium.

Transport resulted in weight loss with an average 42 kg (11.3%) drop in LW per animal at unloading, followed by a 35 kg recovery after 10 days in the feedlot. The COR and βHB levels remained within normal biological ranges (COR = 0-20 ng.ml-1; βHB = 0.02-0.46 mmol.L-1)(24,25), although COR levels did vary widely (CV> 63%). Average GLU values were above the normal range (50-70 mg.dl-1)(24) during loading and unloading, but returned to normal values after 10 days in the feedlot. Total protein (TP) values were above the normal value (6.8 g.dl-1)(26) at all sampling times. Sodium (Na) values were generally higher than the proposed normal biological range (3,105-3,405 ppm)(27), and increased by 311 ppm after transport. In contrast, K levels were low during loading and unloading, even lower than the normal range (160-200 ppm)(27).

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Stress indicators during transport (P1)

During P1 the most important stress indicators were changes in LW, GLU and COR, since differences (P<0.05) occurred due to treatment (Table 2) and transport distance (Table 3).

No differences were observed in the other stress indicators (P>0.05). Differences were present between treatments (P<0.05) for changes in LW (Table 2), with 26.3% less (P<0.05) loss of LW in the PREβ treatment than in the PRE (21%) or POSTβ (16%) treatments; PREβ did not differ from POST (P>0.05). Average loss of LW in the animals not in the PREβ treatment during P1 was 9.9 kg per animal. Transport distance was one of the main factors affecting weight loss during transport (P<0.05; Table 3): those that traveled 500 km lost less weight than those that traveled 851 km. In addition, the animals that traveled 851 km had COR levels 2.5 times higher than those that traveled 500 km (P<0.05).

Table 2: Least squared means ± standard errors for changes in stress indicators due to the treatments in P1 (loading to unloading).

Treatments Indicators PRE PREβ POST POSTβ b a ab Live weight, kg -47.5 ± 3.0 -37.6 ± 2.5 -38.7 ± 1.9 -44.5 ± 3.8 b Glucose, mg.dl-1 15.1 ± 5.2 ab 20.1 ± 5.9 a 8.8 ± 4.3 b 7.2 ± 6.8 b βHB, mmol.l-1 -0.03 ± 0.08 -0.01 ± 0.06 -0.02 ± 0.05 -0.01 ± 0.06 . -1 FFA, mmol l 0.04 ± 0.80 0.13 ± 0.70 0.25 ± 0.60 0.05 ± 0.70 . -1 TP, g dl 0.71 ± 0.30 0.64 ± 0.18 0.67 ± 0.20 0.82 ± 0.28 . -1 Cortisol pg dl 0.75 ± 0.53 1.01 ± 0.56 0.11 ± 0.36 0.28 ± 0.63 . -1 Na, mg dl 496 ± 487 186 ± 457 556 ± 680 -32 ± 457 . -1 K, mg dl -12.2 ± 9.1 -5.2 ± 6.8 12.2 ± 7.8 -3.0 ± 11.8 PRE= preloading reception management at point of origin; PREβ= PRE + β-blocker; POST= reception management after unloading at feedlot; POSTβ= POST + β-blocker at loading; βHB= β-hydroxybutyrate; FFA= free fatty acids; TP= total proteins; Na= sodium; K= potassium. ab Different letter superscripts in the same row indicate significant difference (P<0.05).

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Table 3: Least squared means ± standard errors for changes in stress indicators due to place of origin in P1 (loading to unloading) Transport distance (km) Indicators 500 851 Live weight, kg -39.2 ± 1.8 a -45.1 ±2.1 b Glucose, mg.dl-1 25.6 ± 3.3 a -3.0 ± 3.5 b . -1 βHB, mmol l 0.05 ± 0.06 -0.03 ± 0.04 . -1 FFA, mmol l 0.12 ± 0.05 0.11 ± 0.05 .

-1 TP, g dL 0.71 ± 0.15 0.69 ± 0.20 . -1 a Cortisol, pg dl 0.28 ± 0.22 0.81 ± 0.31 b Na, mg.dl-1 586 ± 441 97 ± 333 1 K, mg.dl-11.9 ± 6.8 -8.1 ± 6.1 βHB= β-hydroxybutyrate; FFA= free fatty acids; TP= total proteins; Na= sodium; K= potassium. ab Different letter superscripts in the same row indicate significant difference (P<0.05).

Glucose levels (GLU) differed between treatments (P<0.05). However, the treatment interaction by transport distance for GLU and FFA was also significant (P<0.05). Animals in the PRE and PREβ treatments originating in Veracruz (500 km) exhibited a greater increase in GLU than those in the POST and POSTβ treatments. In contrast, GLU decreased in animals in the PREβ, POST and POSTβ treatments originating in Chiapas (851 km) and remained essentially unchanged in PRE. Concentrations of FFA behaved inversely, with higher increases in all treatments for animals from Chiapas (851 km). In the animals from Veracruz (500 km) the increases in FFA for PREβ, POST and POSTβ were lower and below that of PRE (P<0.05).

Cortisol (COR) increased in all treatments, but none of the differences were significant (P>0.05). Neither were differences present due to treatment or transport distance for βHB, TP, Na and K (P>0.05; Tables 2 and 3). However, K concentration was affected by the interaction between the treatment and transport distance (P<0.05). Animals in the PREβ treatment from Veracruz (500 km) had higher K levels than the other treatments (PRE = 5.3, PREβ = 21.6, POST = 5.3, POSTβ = 12.6 mg.dl-1). Those from Chiapas (851 km) had overall lower K levels, but particularly so in the POSTβ and PREβ treatments (PRE = -14.5, PREβ = -38.2, POST = -17.5 and POSTβ = -38.6 mg.dl-1).

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Stress indicators post-transport (P2)

During P2 (unloading to 10 d post-unloading) no differences were present between treatments (P>0.05) (Table 4). In all the treatments the animals recovered almost 85 % of the weight lost during transport, representing an average daily weight gain of 3.5 kg per animal.

The other physiological indicators (COR, GLU, βHB, FFA and TP) decreased.

Table 4: Least squared means ± standard errors for changes in stress indicators due to the treatments in P2 (unloading to 10 days post-unloading) Treatment Indicators PRE PREβ POST POSTβ Live weight, kg 33.8 ± 15.6 33.1 ± 19.6 35.5 ± 6.4 39.4 ± 25.9 . -1 Glucose, mg dl -20.9 ± 4.7 -21.9 ± 3.0 -15.8 ± 4.5 -26.4 ± 3.8 . -1 βHB, mmol l -0.12 ± 0.05 -0.13 ± 0.07 -0.11 ± 0.05 -0.01 ± 0.07 . -1 FFA, mmol l -0.47 ± 0.04 -0.50 ± 0.05 -0.54 ± 0.04 -0.45 ± 0.06 . -1 TP, g dl -0.61 ± 0.25 -1.18 ± 0.24 -0.71 ± 0.23 -1.13 ± 0.23 . -1 Cortisol, pg dl -1.78 ± 0.43 -1.71 ± 0.54 -1.55 ± 0.44 -1.73 ± 0.59 . -1 Na, mg dl -4 ± 515 651 ± 836 -106 ± 410 -1093 ± 619 . -1 K, mg dl 18.1 ± 7.0 14.5 ± 9.5 17.2 ± 7.8 21.3 ± 11.9 βHB= β-hydroxybutyrate; FFA= free fatty acids; TP= total proteins; Na= sodium; K= potassium.

PRE= preloading reception management at point of origin; PREβ= PRE + β-blocker at loading; POST= reception management after unloading at feedlot; POSTβ= POST + β-blocker at loading;

During P2 transport distance only affected (P<0.05) COR and TP concentrations (Table 5).

Animals from Chiapas (851 km) had lower COR levels than those from Veracruz (500 km), while TP decreased more in animals from Veracruz than those from Chiapas. No differences were observed in the other stress indicators after transport due to transport distance (P>0.05).

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Table 5: Least squared means ± standard errors for changes in stress indicators due to place of origin in P2 (unloading to 10 days post-unloading).

Transport distance (km) Indicators 500 851 Live weight, kg 36.6 ± 2.8 34.2 ± 4.0 . -1 Glucose, mg dl -24.8 ± 3.0 -18.8 ± 3.0 . -1 βHB, mmol l -0.11 ± 0.05 -0.09 ± 0.04 . -1 FFA, mmol l -0.50 ± 0.04 -0.48 ± 0.03 . -1 a TP, g dl -1.21 ± 0.17 -0.53 ± 0.17 b Cortisol, pg.dl-1 -1.29 ± 0.23 a -2.17 ± 0.44 b Na, mg.dl-1 601.2 ± 467.2 54.4 ± 314.4 . -1 K, mg dl 16.6 ± 6.8 16.2 ± 5.5 βHB= β-hydroxybutyrate; FFA= free fatty acids; TP= total proteins; Na= sodium; K= potassium. ab Different letter superscripts in the same row indicate significant difference (P<0.05).

Discussion Descriptive statistics

The data in this section provide a useful initial record of the changes in physiological and LW indicators during cattle transport under specific conditions in Mexico.

The estimated LW losses observed in the present results are similar to previous reports(4,8,9).

These losses may be a consequence of the prolonged food and water deprivation involved in long distance transport. They have also been associated with dehydration, as well as stressinduced increases in urination and defecation(24). One estimate is that more than 80% of weight loss in cattle during transport during a 24-h period is due to water and feed deprivation and the remaining 20 % is due to transport stress(28). Physiologically this could be explained by lipolysis of fatty tissue, dehydration and muscle degradation to replace energy deficiencies(25). The LW losses in the present results are higher than the 4-7 % reported in a study done in the United States(3). In this study, the economic cost of transport from growing to finishing systems (USD 624 million) in the USA was mainly due to mortality during transport caused by long distances and respiratory diseases. No precise data is available in Mexico on the financial impact of respiratory diseases in cattle intended for finishing; however, respiratory problems were not observed during the present study, suggesting that any financial losses from transport would derive from LW loss.

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The LW recovery observed in the present results 10 d after unloading is higher than the estimated 43 % reported for cattle under grazing conditions 14 d after transport(8).

The differences in LW loss between the animals that traveled 500 and 851 km (5.9 kg) from the place of origin to the finishing feedlots confirm that LW loss was higher the greater the transport distance and time. This is probably due to higher stress levels, greater physical exhaustion and feed and water deprivation(6). Weight loss may have financial implications for livestock transporters when marketing animals.

The behavior of the LW, TP, Na, COR and FFA indicators in the present results at the different sampling times was similar to that described in another study(24). The occurrence of normal COR and βHB values suggest that growth conditions were adequate prior to the transport and adaptation of the animals to stress stimuli such as dizziness, loading and unloading, and contact with people and other animals not from the herd of origin(29,30). Small transport-induced increases in COR like those in the present results have been reported elsewhere(31,32). The small decreases in βHB in the present study may be due to pretransport feeding and handling practices since βHB concentrations are not a good indicator of acute stress but are an accurate reflection of chronic stress(9).

The higher GLU levels recorded at unloading may have been due to the interaction of glycolysis and gluconeogenesis processes stimulated by transport-induced stress and longterm fasting(32). This coincides with another study in which GLU levels increased in cattle after transport or after fasting for 3 and 16 h, which is attributed mainly to release of catecholamines in response to stress rather than to fasting period(9). Free fatty acids (FFA) levels have been proposed as an indicator of fasting(24), which agrees with the present results in which FFA increased upon unloading and then subsequently decreased. Above normal Na values(27) at all sampling times may indicate dehydration or hemoconcentration caused by water deprivation; however, the highest value was recorded at unloading which coincides with levels reported for cattle after 36 h transport(8). Prolonged water deprivation and consequent dehydration can favor increases in serum Na(33), which is consistent with the present results.

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Stress indicators during transport (P1) The lower LW losses in the PREβ treatment during P1 may be because the β-blocker used in the study (carazolol) acts on the β-adrenergic receptors. It reduces the sympathetic nervous system stimulation via epinephrine in the heart, blood vessels and smooth muscle, preventing the glycolytic effect of catecholamines and reducing the stress response(16,34). These results suggest that the addition of the β-blocker to pre-loading management practices could provide financial benefits since the price per dose of carazolol was approximately half the price of one kg LW of the studied animals.

The greater LW losses with the 851 km transport distance were reflected in higher COR concentrations than with the 500 km transport distance. Regardless of the differences between treatments and transfer distances in the present results, GLU levels were generally higher than normal. Post-transport increases in GLU have been attributed to the action of catecholamines (adrenaline and norepinephrine) in the initial stress response(25). These hormones stimulate hepatic gluconeogenesis which is further favored by increases in hormones such as COR since these have effects opposed to insulin (affecting GLU transporters), consequently reducing GLU efficiency and use in tissues and increasing its blood concentration(35).

Stress indicators post-transport (P2)

Post-unloading (i.e. P2) recovery of LW lost during P1 may be partially explained by a portion of the weight loss during transport being due to dehydration and gastrointestinal filling(36); this can account for 12 to 25 % of animal weight(37). Other factors may be the normal recovery process regulated by homeostasis mechanisms after stressful processes incurring body tissue loss, and possible compensatory growth(38). An additional consideration is that the cattle studied here were grown in poor feeding systems (seasonal grazing), and therefore may have greatly improved their nutrient use efficiency after arriving at the finishing feedlots and feeding on balanced rations high in protein and energy(37,38,39).

Neither the treatments nor transport distances affected LW or other physiological indicators of transport stress during P2, suggesting that, under the study conditions, these effects had no financial consequences. Using a practical approach it is clear that further study is needed to understand the implications of handling practices during cattle loading and transport on animal health and financial parameters.

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Conclusions and implications Transport of calves to finishing feedlots produces important changes in the animals which can be quantified using physiological and productive indicators. Some of the main stress indicators in animals are changes in live weight and concentrations of cortisol, glucose and free fatty acids. Differences in these indicators were observed depending on transport distance and management practices (i.e. when reception management was done and use of a β-blocker), with negative effects being less pronounced at the shorter distance and with preloading management. In Mexico, cattle carriers routinely carry out reception management practices and administer a β-blocker prior to loading. This is financially more profitable because it reduces live weight loss during transport. Regardless of management practices, however, the studied animals had largely recovered any lost weight within ten days of arriving in the finishing feedlot; the period from loading to unloading therefore requires more attention to improve animal health and process profitability.

Acknowledgements

The research reported here was financed by a master’s scholarship for the first author from the Consejo Nacional de Ciencia y Tecnología (CONACyT).

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32. García LA, Rodríguez MJA. Metabolismo en el ayuno y la agresión, su papel en el desarrollo de la desnutrición relacionada con la enfermedad. Nutr Hosp 2013;6:1-9.

33. Minka NS, Ayo JO. Physiological responses of food animals to road transportation stress.

Afr J Biotechnol 2009;8(25):7415-7427.

34. Bristow MR. Mechanism of action of beta-blocking agents in heart failure. Am J Cardiol 1997;80:26L-40L. 35. Mamza YP, Udoh AE, Etukudo MH. Evaluation of serum cortisol and growth hormone in type 2 diabetic subjects attending University of Maiduguri Teaching Hospital, Nigeria. IOSR-JDMS 2013;7:53-57.

36. Gallo CS, Gatica MV. Efecto del tiempo de ayuno sobre el peso vivo, de la canal y algunos órganos en novillos. Arch Med Vet 1995;27:69-77.

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38. Hornick JL, Van Eenaeme CGO, Dufrasne I, Istasse L. Mechanisms of reduced compensatory growth. Domest Anim Endocrinol 2000;19:121-132.

39. Molina F, Carmona D, Ojeda A. Evaluaciรณn del crecimiento compensatorio como estrategia de manejo en vacunos de carne a pastoreo. Zootecnia Trop 2007;25(3):149155.

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https://doi.org/10.22319/rmcp.v10i4.5559 Article

Impact of piglet birth weight on nitrogen and energy balances in the growth phase

Enrique Vázquez Mandujano a Tércia Cesária Reis de Souza b Ericka Ramírez Rodríguez c Gerardo Mariscal-Landín c*

a

Universidad Nacional Autónoma de México. Posgrado de Producción y Salud Animal.Ciudad de México. México. b

Universidad Autónoma de Querétaro. Facultad de Ciencias Naturales. Querétaro, Qro., México. c

Instituto Nacional de Investigaciones Forestales Agrícolas y Pecuarias. Centro Nacional de Investigación en Fisiología. km 1 Carretera a Colón, 76280 Ajuchitlán, Querétaro, México.

* Corresponding author: mariscal.gerardo@inifap.gob.mx

Abstract: Low birth weight in pigs may compromise lifelong growth potential and productive performance. An evaluation was done of how birth weight affects nitrogen and energy balances in growing pigs. Assays of nitrogen and energy balances were done of five pairs of sibling piglets (n= 10), each pair consisting of a low birth weight (LBW= 912 ± 40 g) and a normal birth weight (NBW= 1,610 ± 223 g) individual. The pigs were managed normally until 90 days of age, then transferred to metabolic cages for the balance assays.

These were done when both pigs attained 50 kg weight and again when they were the same age (when the LBW pig weighed 50 kg). The NBW pigs digested more (P<0.05) dry matter at 50 kg and at the same age (86.9 vs 86.0). Nitrogen digestibility tended to be higher (P<0.10) in the NBW at 50 kg (77.6 vs 76.7) and was clearly higher (P<0.05) at the same age (78.0 vs 76.7). Retained nitrogen as a percentage of intake was higher (P<0.01) in the NBW (61.1% vs 57.7 %) at the same age, which also occurred (P <0.10) 903

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for nitrogen retained as a percentage of absorbed nitrogen (78.4 % vs 75.2 %). Energy digestibility was higher (P<0.05) in the NBW both at 50 kg (85.1 vs 84.1%) and at the same age (P<0.01) (85.4 vs 84.1 %). Metabolizable energy was higher in the NBW both at 50 kg (P<0.05) (83.0 vs 82.0 %) and at the same age (P<0.01) (83.5 vs 82.0 %). The low birth weight piglets were generally less efficient than the normal birth weight pigs.

Key words: Low birth weight, Energy balance, Nitrogen balance, Swine.

Received: 31/08/2018 Accepted: 24/01/2019

Introduction

The efficiency with which pigs obtain and use energy from feed is possibly determined at birth. Fetal growth is a dynamic process that depends on the harmonious interaction of the mother, the fetus and the placenta. During fetal development the genome plays a limited role(1), meaning fetus growth depends largely on nutrient availability. Genetic selection in pigs has focused on prolificity which has raised the incidence of low-weight births(2-4). Nutrient shortage can impair development of the embryo / fetus or its organs during pregnancy. Because the intrauterine environment modulates expression of the fetal genome it can permanently affect the animal through â&#x20AC;&#x153;fetal programmingâ&#x20AC;?(5); for instance, low birth weight animals may suffer inadequate postnatal development. The impact of low birth weight has been studied mainly in muscle and nervous tissue(6-8). These tissues are used because their postnatal development occurs through hypertrophy(9), meaning that, in principle, any negative effects will persist throughout an animalâ&#x20AC;&#x2122;s life. However, modulation of gene expression in the small intestine and colon can affect various gastrointestinal (GI) tract functions. The affected genes are related to cell metabolism, biosynthesis, signal transduction and cell death(10).

In pigs the GI tract matures physiologically after birth. Changes are mainly induced by the transition from parenteral nutrition to enteral nutrition as well as the presence of a large number of bioactive substances in colostrum and milk(11). Under this scenario the assumption had been that any negative impact of low birth weight on GI tract function would manifest itself only during the early stages of life, however growing evidence suggests negative impacts can continue into adulthood. Piglets have a lower capacity to digest protein than do growth-finishing pigs. This lower digestive capacity is more evident when diet raw materials include vegetal ingredients rich in fiber or anti-nutritional factors(12,13), since no differences between piglets and pigs have been reported when using the highly digestible protein casein(14). However, this type of lower digestive capacity

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differs from developmentally determined digestive deficiencies since the latter would occur in animals of the same chronological age(10,15).

The present study objective was to compare digestibility and nitrogen and energy balances between low birth weight pigs and normal birth weight pigs in animals of same weight or same age.

Material and methods

The study was carried out at the Metabolic Unit of the National Center for Disciplinary Research (CENID) Physiology of the National Institute of Forestry, Agriculture and Livestock Research (INIFAP). The research protocol was reviewed and approved by the Technical Scientific Committee of CENID Physiology. Animal management practices complied with federal guidelines for the production, care and use of laboratory animals(16), as well as the International Guiding Principles for Biomedical Research Involving Animals(17).

Animals

Five pairs of siblings were chosen (n= 10). Each consisted of one low birth weight (LBW) piglet weighing less than one kilogram at birth (0.912 ¹ 0.040 kg), and one normal birth weight (NBW) piglet (1.612 ¹ 0.223 kg) selected from among the siblings weighing nearest the average litter weight. The animals were selected from five litters containing more than twelve live-born piglets. All ten piglets were fed normally and remained on the farm under normal management conditions until ninety days of age. After this period, they were moved to the Metabolic Unit to a room with controlled temperature (19 to 22 °C). Each animal was housed individually in a metabolic cage with a dedicated drinking trough and feeder. A mesh allowed separation and collection of feces, and urine was collected through funnels under the cage floor.

When the NBW pigs had reached 50 kg weight, the first nitrogen and energy balance assays were done, and only involved the NBW animals. Nitrogen and energy balance assays were done of the LBW pigs when they reached 50 kg weight, and the NBW were assayed again. This approach produced data for both groups at 50 kg weight, as well as for both groups at the same age.

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Experimental diet and feed management

At the beginning of the adaptation period to the experimental diet and throughout the experimental period the pigs were fed based on their metabolic weight (555 kcal DM x Kg0.60); they also became accustomed to consuming the allotted food within one hour(18).

The experimental diet (Table 1) was a sorghum-soybean diet enriched with vitamins and minerals and provided the requirements recommended by the NRC for this productive stage(19).

Table 1: Experimental diet composition and estimated nutrient content (g/kg) Ingredients

g/kg 731.6 200.0 24.5 7.9 0.9 0.9 0.0 5.0 6.3 10.3 8.0 4.5

Sorghum Soybean meal Corn oil L-Lysine HCl L-Threonine DL-Methionine L-Tryptophan Salt Calcium carbonate Dicalcium phosphate Mineral premix Vitamin premix Chemical analysis: Dry matter Crude energy NDF ADF Crude protein Estimated analysis Digestible lysine Digestible threonine Digestible sulphur amino acids Digestible tryptophan Calcium Total phosphorous a

% Kcal/kg % % %

95.55 3,985.00 9.36 4.26 14.75

% % % % % %

0.85 0.52 0.48 0.15 0.59 0.52

The trace mineral premix provided the following amounts per kg feed: Co, 0.60 mg; Cu, 14 mg; Fe, 100 mg; I, 0.80 mg; Mn, 40 mg; Se, 0.25 mg; Zn, 120 mg. b The vitamin premix provided the following amounts per kg feed: vitamin A, 4,250 UI/g; vitamin D3, 800 UI/g; vitamin E, 32 UI/g; menadione, 1.5 mg/kg; biotin, 120 mg/kg;

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Rev Mex Cienc Pecu 2019;10(4):903-916 cyanocobalamin, 16 μg/kg; choline, 250 mg/kg; folic acid, 800 mg/kg; niacin, 15 mg/kg; pantothenic acid 13 mg/kg; pyridoxine 2.5 mg/kg; riboflavin 5 mg/kg; thiamin, 1.25 mg/kg.

NDF = Neutral detergent fiber; ADF = Acid detergent fiber.

Nitrogen and energy balance

During the balance assays, water provided to the pigs was limited to 3 L of water per kilo dry matter consumed. This functioned to control water intake for total collection of urine.

Each experimental period consisted of five days of adaptation. At the beginning of the experimental period the feed contained a ferric oxide marker included at a rate of 3 g/kg.

Total feces collection was done every 12 hours beginning from the moment the feed was marked. On the sixth day (the first day after the experimental period), the feed was again marked with ferric oxide (3 g/kg). Feces collection ended when marked feces was excreted again. All collected feces were stored at -20 °C until processing. Urine was collected twice daily for five days. The urine collection container contained 40 ml 6M HCl to acidify the urine and prevent ammonia loss by volatilization. Each day of the experiment the collected urine was filtered through gauze and fiberglass, weighed, and a 5 % aliquot was taken and stored at -20 °C until analysis.

Laboratory analysis

Feces samples were partially dried at 55 °C and ground in a laboratory mill (Arthur H. Thomas Co., Philadelphia, PA) until passing through a 0.5 mm mesh. Established methods were used to quantify dry matter (DM) content (934.01) and crude protein (CP) (976.05) in the raw materials, experimental diets and feces(20). Energy content was measured with an adiabatic calorimetric pump (model 1281, Parr, Moline, IL). Urine nitrogen content was quantified following an established protocol (976.05)(20). The energy content of lyophilized urine was estimated according to Le Bellego(21).

Data analysis

Daily feed intake was recorded to calculate intake of DM (g/day), nitrogen (N) (g/d) and energy (E) (Kcal/day) by multiplying feed intake by each nutrient’s diet concentration.

Excretion of DM (g/day), nitrogen (g/d) and energy (Kcal/d) in feces was estimated by multiplying the amount of feces produced on a dry basis by the nutrient concentration in the feces. Excretion of nitrogen (g/d) and energy (Kcal/d) in urine was estimated by

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multiplying the total urine produced by nutrient concentration in the urine. Fecal digestibility of DM, nitrogen and energy was estimated using the following equation proposed by Adeola(22):

ATD = ((NC-NX)/NC)×100

Where ATD = apparent total digestibility; NC= nutrient consumed (g/d); and NX = nutrient excreted (g/d). Nitrogen retention (g/d) and digestible and metabolizable energy (Kcal/d) were calculated by subtracting the amount of nutrients excreted in feces and urine from the amount of nutrients consumed.

Statistical analysis

Data were analyzed using a completely random block design (each sibling pair was a block) and two comparisons: N and E balances at same weight (50 kg); and N and E balances at same age (when LBW pigs weighed 50 kg)(23). Statistical analyses were run using the GLM procedure in the SAS statistical package(24). Significance was set at P<0.05, and a trend identified when 0.05<P<0.10.

Results

Average birth weight differed (P<0.001) between the LBW piglets (912 ± 40 g) and the NBW piglets (1,610 ± 223 g) (Table 2). The NBW piglets attained 50 kg weight at 109 days, which is faster (P<0.001) than the 127.8 d of the LBW piglets. Resulting daily weight gain (DWG) was 513 g for the NBW pigs and 451 g for the LBW pigs. The difference in average weight between the two pig types at 127.8 d of age was 12.2 kg (63.8 [NBW] vs 51.6 kg [LBW]). This difference did not result from the LBW animals suffering any disease or consuming different diets from the NBW pigs during the growth phase. During the balance assays the animals were offered feed at a rate of 555 kcal per kg0.60, and feed intake did not differ between groups (P>0.10)(545 [NBW] vs 540 [LBW] kcal per kg0.60); indeed, intake of offered feed (kg0.60) was almost the same between the NBW (98.1 %) and LBW (97.2 %). However, the NBW pigs exhibited higher DM digestibility (P<0.05) than the LBW pigs both at 50 kg weigh and at the same age.

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Table 2: Pig weight at birth and beginning of nitrogen and energy balance assays; kilocalorie intake per metabolic weight and balance; and dry matter digestibility Weight

Age

Contrasts P P age4 weight3 0.001 0.001 0.001 0.001 0.001 0.001 NS 0.001 0.001 NS

NBW1 LBW2

NBW

LBW

SEM5

Birth weight, kg Weaning weight, kg ADG, kg Weight beginning balance, kg Age beginning balance (days)

1.610 6.310 0.513 51.4 109.0

0.912 3.616 0.451 51.6 127.8

1.610 6.310 0.540 63.8 127.8

0.912 3.616 0.451 51.6 127.8

ME intake kcal /kg0.60 DM intake, g/d DM excretion, g/d

545.1 1,729 227

540.3 1,722 241

544.9 1,987 261

540.3 1,722 241

NS NS 0.10

NS 0.001 0.05

3.186 14.747 4.730

DM digestibility, %

86.9

86.0

86.9

86.0

0.05

0.05

0.255

0.048 0.222 0.008 0.554 1.564

1NBW=

normal birth weight group; 2LBW= low birth weight group; 3Weight contrast probability; 4Age contrast probability. 5SEM= standard error of the mean; ADG= average daily weight; ME = metabolizable energy; DM = dry matter.

NS= not significant (P>0.10).

Nitrogen balance

At the same age N intake was higher (P<0.001) in the NBW pigs (48.9 g/d) than in the LBW pigs (42.4 g/d) (Table 3). At the same weight (50 kg) N digestibility tended (P<0.10) to be higher in the NBW pigs (77.6) than in the LBW pigs (76.7) but at the same age it was significantly higher (P<0.05) in the NBW pigs (78.0) than in the LBW pigs (76.7). Nitrogen excretion in urine was 8.3 g N/d in both groups regardless of age and weight (P>0.10). At 50 kg the N retained as a percentage of feed intake (57.4 %) did not differ (P>0.10) between groups, but at the same age it was higher (P<0.01) in the NBW pigs (61.1 %) than in the LBW pigs (57.7 %). This difference remained (P<0.10) in the N retained as a percentage of absorbed N, with the NBW pigs having higher values (78.4 %) than the LBW pigs (75.2 %).

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Table 3: Nitrogen balance results

Nitrogen intake, g/d Nitrogen in feces, g/d Digestible nitrogen, % Nitrogen absorbed, g/d Nitrogen in urine, g/d Nitrogen excreted, g/d Nitrogen retained, g/d Nitrogen retained, % intake Nitrogen retained, % absorbed 1NBW=

Weight

Age

NBW1 LBW2

NBW LBW

43.0 9.6 77.6 33.4 8.8 18.5 24.6 57.1 73.6

42.4 9.9 76.7 32.5 8.1 18.0 24.5 57.7 75.2

48.9 10.8 78.0 38.2 8.3 19.0 29.9 61.1 78.4

42.4 9.9 76.7 32.5 8.1 18.0 24.5 57.7 75.2

Contrasts P P age4 weight3 NS 0.001 NS 0.01 0.10 0.05 NS 0.001 NS NS NS 0.05 NS 0.001 NS 0.01 NS 0.10

SEM5 0.432 0.176 0.334 0.383 0.361 0.283 0.514 0.719 0.999

normal birth weight group; 2LBW= low birth weight group; 3Weight contrast probability; 4Age contrast probability; 5SEM= standard error of the mean.

NS= not significant (P>0.10).

Energy balance At the same age energy intake was higher (P<0.001) for the NBW pigs (8,289 kcal/day) than the LBW pigs (7,183 kcal/d)(Table 4). At the same weight energy digestibility was higher (P<0.05) in the NBW pigs (85.1 %) than in the LBW pigs (84.1 %), which was more significant (P<0.01) at the same age (85.4 % [NBW] vs 84.1 % [LBW]). The energy excreted in the urine was 153 kcal/day and did not differ (P>0.10) between the groups.

At 50 kg, metabolizable energy (ME) was higher (P<0.05) in the NBW pigs (83.0 %) than in the LBW pigs (82.0 %). At the same age ME differed even more (P<0.01) (83.5 % [NBW] vs 82.0 % [LBW]).

Table 4: Energy balance results Weight

Energy intake, kcal/d Energy in feces, kcal/d Digestible energy, % Digestible energy, kcal/kg Energy excreted, kcal/d Energy in urine, kcal/d ME, % ME, kcal/kg 1

Age

NBW1

LBW2

NBW

LBW

7,212 1,080 85.1 3,547 1,231 151 83.0 3,459

7,183 1,141 84.1 3,508 1,292 151 82.0 3,420

8,289 1,211 85.4 3,563 1,370 159 83.5 3,483

7,183 1,141 84.1 3,508 1,292 151 82.0 3,420

Contrasts P P age4 weight3 NS 0.001 0.05 0.01 0.05 0.01 0.05 0.01 0.10 0.05 NS NS 0.05 0.01 0.05 0.01

SEM5 63.429 15.453 0.222 9.245 20.237 6.150 0.278 11.529

NBW= normal birth weight group; 2LBW= low birth weight group; 3Weight contrast probability; 4Age contrast probability; 5SEM= standard error of the mean; ME= Metabolizable energy; NS= not significant (P>0.10).

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Discussion

Growth

Low birth weight (LBW) pigs were lighter at weaning and throughout the growth stage, the result of overall lower daily weight gain. The currently higher frequency of LBW piglets in pig production is due to selection for prolificity in females. This has generated a greater frequency of LBW piglets (<1 kg)(2-4); indeed, among all domestic species the pig exhibits the highest rate of low weight births(7). Piglets born with low weight are characterized by a higher mortality rate, lower weight gain during lactation and consequent lower weight at weaning(25). Their post-weaning growth rate is also lower than siblings born with normal weight(3,25,26), which leads to lower carcass quality(26).

Digestibility

The lower N and E digestibility observed in the LBW piglets has been reported mainly in piglets(11,27-31). Various explanations for this phenomenon have been proposed and include lower digestive capacity due to a shorter GI tract length and area in piglets(11,28,30); compromised intestinal histological structure in piglets(10); lower lactase and aminopeptidase concentrations and smaller pancreas size, which may reduce digestive enzyme secretion capacity(27); and lower neutral amino acid transporter concentrations(29).

Studies in this area are fewer for growing animals, although there are reports of decreased aminopeptidase enzyme concentrations and a lower relative GI tract weight in pigs(31), and smaller GI tract size in cattle(32).

Energy and nitrogen balances

The LBW pigs exhibited lower nutrient absorption efficiency, as shown by the lower N retention and ME levels in the LBW pigs when both groups were the same age. Perhaps some enzymes and metabolites were altered in the LBW animals consequently affecting intermediate metabolism. For example, increased plasma concentrations of fructosamine and cholesterol in conjunction with low-density lipoproteins have been reported in LBW piglets, indicating a tendency towards insulin resistance in these animals(26). Low birth weight piglets also exhibit lower serum concentrations of serotonin and tryptophan(33), a lower protein synthesis rate(34), a higher protein degradation rate(35), and a smaller number

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of muscle cells(2,36-38). It is not known if these characteristics continue during postweaning growth. All the aforementioned factors may affect metabolic use of nutrients since lean tissue (protein) deposition capacity is reduced under these conditions. This would imply that weight gain would have a higher metabolic cost, which would agree with the higher relative fat content of these animals(26).

Feed intake

During the balance phase at the same age, feed intake was lower in the LBW, which was a result of the 555 kcal DMS per kg0.60 ration for both groups. Nonetheless, LBW pigs also tend to eat less throughout their lives than NBW piglets(6,36-38). In piglets, feed intake is affected by diet digestibility(39) and LBW animals have lower digestion capacity. Some animals have fewer ghrelin-secreting gastric cells; this hormone stimulates feed intake by generating the hunger sensation(40). All the above characteristics help explain the lower feed intake observed throughout their lives in the LBW animals.

Conclusions and implications

The low birth weight piglets were generally less efficient than the normal birth weight piglets. This phenomenon is due to the lower nitrogen and energy digestibility, and reduced nitrogen retention, in the low birth weight piglets, which makes weight gain more metabolically expensive.

Acknowledgements

The authors thank the INIFAP for financial support for this study through the project “¿Impacta el peso al nacimiento en la capacidad digestiva y el uso metabólico de nutrientes en cerdos en crecimiento-finalización? (No. SIGI 10145234138).

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Enteric and serological distribution of serotonin and its precursor tryptophan in perinatal low and normal weight piglets. Animal 2014;8:792-799.

34. Chen Y, McCauley SR, Johnson SE, Rhoads RP, El-Kadi SW. Downregulated translation initiation signaling predisposes low-birth-weight neonatal pigs to slower rates of muscle protein synthesis. Front Physiol 2017;8:482.

35. Ramsay TG, Stoll MJ, Shannon AE, Blomberg LA. Metabolomic analysis of longissimus from underperforming piglets relative to piglets with normal preweaning growth. J Anim Sci Biotechnol 2018;9:36.

36. Foxcroft GR, Dixon WT, Novak S, Putman CT, Town SC, Vinsky MDA. The biological basis for prenatal programming of postnatal performance in pigs. J Anim Sci 2014;84(E. Suppl.):E105-E112.

37. Oksbjerg N, Nissen PM, Therkildsen M, Møller HS, Larsen LB, Andersen M, et al.

Meat science and muscle biology symposium: In utero nutrition related to fetal

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development, postnatal performance, and meat quality of pork. J Anim Sci 2013;91:1443-1453.

38. Rehfeldt C, Kuhn G. Consequences of birth weight for postnatal growth performance and carcass quality in pigs as related to myogenesis. J Anim Sci 2014;84(E. Suppl.):E113-E123.

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40. Willemen SA, De Vos M, Huygelen V, Fransen E, Tambuyzer BR, Casteleyn C, et al. Ghrelin in the gastrointestinal tract and blood circulation of perinatal low and normal weight piglets. Animal 2013;7:1978-1984.

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https://doi.org/10.22319/rmcp.v10i4.4839 Article

Regional supply and demand for chicken meat in Mexico, 1996-2016

Eulogio Rebollar Rebollar a Alfredo Rebollar Rebollar b Jaime Mondragón Ancelmo a Germán Gómez Tenorio a*

a

Universidad Autónoma del Estado de México. Centro Universitario UAEM Temascaltepec. Km. 67.5, carretera Toluca-Tejupilco. Colonia Barrio de Santiago s/n. 51300 Temascaltpec. Estado de México, México. b

Universidad Tecnológica del Sur del Estado de México. Estado de México, México.

* Corresponding author: gomte61@yahoo.com

Abstract: Multiple variables can affect meat product supply and demand. An analysis was done of the magnitude of the effect of the main economic and technological variables that influence supply and demand of chicken meat in eight regions in Mexico during the period 1996 to 2016. A multiple linear regression econometric model was formulated for each region, including the main economic and technological variables determining supply and demand. In most of the regions, chicken meat supply reacted directly and elastically to changes in technology (average = 1.7395), directly and inelastically to the price of chicken meat (average = 0.9912), and inversely and inelastically to the prices of pork (average = -0.3686) and feed (-0.1423). In all regions demand behaved elastically in relation to population size (average = 2.0853), and inelastically in relation to the current price of chicken meat (average = -0.1698), per capita income (average = 0.2560) and the current price of beef (average = 0.0272). Population growth had the greatest effect on chicken meat consumption

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in all the regions. All the tested models had overall significance, although not all the predictive variables had a significant effect.

Key words: Chicken meat, Elasticities, Production, Consumption, Econometric models.

Received:03/04/2018 Accepted: 13/07/2018

Introduction In Mexico, poultry farming is the most dynamic livestock production activity. Production and consumption are growing steadily, production and distribution systems are more integrated than in other livestock sectors(1), and it is the principal means of transforming vegetable protein into animal protein(2). Chicken meat is clearly the preferred animal protein source among Mexican consumers(3).

Domestic chicken meat production grew from 1.26 million tons in 1996 to 3.07 million tons in 2016, a 4.55 % average annual growth rate (AAGR). During this period, apparent national consumption increased from 1.39 to 3.84 million tons, at a 5.21 % average annual increase. Indeed, growth in consumption outstripped that in production. The difference between them was covered by imports, which increased by a 7.66 % annual average and, during this period, represented approximately 18 % of chicken meat consumption(3).

This dynamism in poultry production has exhibited disparities between regions over time. For example, in 2016 producers in Mexicoâ&#x20AC;&#x2122;s Central-West (CW) and Central-Eastern (CE) regions achieved strong economic growth, contributing a cumulative 53.37 % to national production, whereas, even when taken together, the Northeast (NE) and Yucatan Peninsula (PE) regions contributed only 7.76 %(4).

During this period, the price of chicken carcasses varied between regions. For example, in 2016 the price per kilogram ($/kg) in the CW region, that with the highest chicken meat production, was $30.53 pesos per kilogram, which was 3.38% lower than the previous year. This decline in production costs responded to improvements in production conditions, control of avian influenza, and decreases in the international prices of the main fodder grains(5). During the same period prices varied from $29.68 / kg in the CE region, to $32.22 / kg in the PE region and $33.37 / kg in the East (ET)(4).

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Sorghum, the principal component of chicken feed(6), also varied widely in price between regions during 2016. For example, compared to 2015 the price of sorghum increased by 10.13 % in the NE, 8.41 % in the North (NT) and 2.84 % in the CE, but decreased by 2.84 % in the PE(4).

Demand by region varied from 1996 to 2016, from 5.15 % AAGR in the CE to 4.95 % in the CW and 4.98 % in the South (ST). In part this behavior can be explained by increases in per capita gross domestic product (GDP) (2.67 % in CE; 4.35 % in CW; and 3.27 % in ST)(7), population growth (1.17 % in CE; 0.99 % in CW; and 1.02 % in ST)(8), consumer preference, number of household members, and income. All these factors positively affect meat consumption probability(9).

Interregional differences in the dynamics of the different economic and technological variables that determine chicken meat supply and demand clearly exist. Econometric models are therefore needed to represent how different regional markets operate, and to generate tools that help guide public policy makers and provide alternatives for designing production support programs based on regional needs.

Chicken meat supply and demand is apparently affected by regional variation in the variables that influence it. The present study objective was to quantify the effect of the main economic and technological variables that influence chicken meat supply and demand in eight regions in Mexico (Northwest, North, Northeast, Central West, Central East, South, East and Yucatan Peninsula) from 1996 to 2016.

Material and methods Regionalization is a methodology, procedure or intervention applied to reorganize a country into smaller territorial units with common characteristics. It is a basic methodological tool in environmental planning since it provides knowledge of regional resources for appropriate management(10). Using this approach Mexico has been divided into eight economic regions to analyze chicken meat supply and demand at the regional level(11) (Table 1).

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Region Northwest (NW) North (NT)

Table 1: Regions of Mexico and the states within them States Baja California, Baja California Sur, Sonora, Sinaloa and Nayarit Chihuahua, Coahuila, Durango San Luis Potosí and Zacatecas

Northeast (NE)

Nuevo León and Tamaulipas

Central-West (CW)

Aguascalientes, Colima, Guanajuato, Jalisco and Michoacán

South (ST)

Mexico City, Hidalgo, Estado de México, Morelos, Puebla, Querétaro and Tlaxcala Chiapas, Guerrero and Oaxaca

East (ET) Yucatan Peninsula (PE)

Tabasco and Veracruz Campeche, Quintana Roo and Yucatán

Central-East (CE)

A multiple linear regression econometric model was developed for chicken carcass supply and demand in each of the regions from 1996 to 2016. Supply variables included: the price of chicken meat; technology (measured as feed efficiency); and input costs (i.e. feed price)(12). Demand variables were the price of chicken meat; financial income; population; and the prices of substitute or additive products(13). The models represent each regional market’s internal behavior.

The data for each variable was obtained from sources such as the Agrifood and Fisheries Information Service (Servicio de Información Agroalimentaria y Pesquera - SIAP), the Agricultural Trusts (Fideicomisos Instituidos en Relación con la Agricultura -FIRA), the National Institute of Statistics, Geography and Data Processing (Instituto Nacional de Estadística, Geografía e Informática - INEGI), the National Council on Population (Consejo Nacional de Población CONAPO) and the National Market Data and Integration System (Sistema Nacional de Información e Integración de Mercados - SNIIM). Feed efficiency (FE) was taken from previous reports for 1996 and 2016 (14), and data for the intervening years estimated with the annual average growth formula: r = (Df / Di)1/n – 1; where Df is final FE data, Di is initial FE data, and r is average annual growth rate (AAGR).

Parameters in the linear models associated with the supply and demand function were estimated with the ordinary least squares (OLS) method(15). This helped to identify the effect of each of the independent variables on the dependent variable, as well as generate the best unbiased linear and minimum variance estimators. These analyses were run with the SAS statistical package(16).

Statistical congruence of the supply and demand models was determined with the coefficient of determination (R2). Calculation of the statistical significance of each equation was done with the F test, and that for the individual significance of each coefficient with the Student t test. Economic 920

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evaluation was done considering the signs and magnitude of the coefficients of the variables of the supply and demand functions. These were interpreted using the fundamentals of economic theory.

In other words, the relationship between chicken carcass supply and price with feed efficiency must be direct, whereas that with the price of pork and feed must be inverse. The relationship between chicken carcass demand and current price must be inverse, while it must be direct with respect to per capita GDP, current price of beef and human population.

In some regions, the variables of chicken meat price, pork (alternative good) price and feed price lagged one to two years behind other regions. Producers in these regions did not immediately decrease production in response to changing prices. Other factors also contributed to this lag such as interregional differences in length of the animal production cycle, degree of investment, production volume and company financial situation.

Calculations were done of the economic elasticities of each explanatory variable affecting chicken meat supply and demand in each region. These were evaluated based on the sign and magnitude of their coefficients, and interpreted following economic theory.

Five econometric models were proposed to calculate chicken meat supply and demand in the eight regional markets: CMSt = β11 + β12 CMRPt + β13 PRPt-2 + β14 RFPt + β15 FEt + £t (NW and NT) CMSt = β21 + β22 CMRPt + β23 PRPt + β24 RFPt + β25 FEt + £t (NE, CW and CE) CMSt = β31 + β32 CMRPt-1 + β33 PRPt -2 + β34 RFPt + β35 FEt + £t (ST and ET) CMSt = β41 + β42 CMRPt + β43 PRPt-2 + β44 RFPt-2 + β45 FEt + £t (PE) CMDt = β51 + β52 CMRPt + β53 RGDPt + β54 BRPt + β55 POPt + £t Where: CMSt): chicken meat (carcass) supply in current period, estimated based on regional chicken carcass production (t); CMRPt: average real weighted regional price of chicken carcass, in current period ($/kg); CMRPt-1: average real weighted regional price of chicken carcass, with a one-year lag ($/kg); PRPt: average real weighted regional price of pork, in current period ($/kg); PRPt-2: average real weighted regional price of pork, with a two-year lag, as alternative product ($/kg); RFPt: average real weighted regional price of chicken feed, in current period, estimated based on price of sorghum as main ingredient ($/kg); RFPt-2: average real weighted regional price of chicken feed, with a two-year lag, estimated based on price of sorghum as main ingredient ($/kg); FEt: feed efficiency; CMDt: volume of chicken carcass demand, in current period, estimated based on apparent regional consumption (thousands of tons); 921

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RGDPt: real regional per capita gross domestic product, current period (thousands of $/person), as a variable for approximating national per capita available income; BRPt: average real weighted regional price of beef ($/kg), as substitute product; POPt: regional population, current period (millions of inhabitants/region).

All monetary variables were deflated based on the National Consumer Price Index (Índice Nacional del Precio al Consumidor – INPC; 2012 baseline = 100).

Model formulation was based on economic theory and empirical evidence. Chicken meat producers in Mexico base decisions on increasing, maintaining or decreasing production, on the price of chicken, the prices of the inputs needed to produce it and alternative products such as pork(17,18).

Feed efficiency (FE) was used to reflect technological progress in chicken meat production volume since it is one of the variables that most influence production in the poultry sector. It is also a factor that has stimulated increased chicken meat production in different regions of Mexico through genetic selection to produce chickens that generate more meat with the same amount of feed.

Poultry farmers can thus continue to supply their product supported by increased productivity(6).

The FE variable integrates technological advances and helps to explain why poultry farmers continue to supply their product in the market, despite a clear downward trend in the price of chicken meat and price increases in sorghum, the main feed input(6). The price of pork as an alternative good was included because some companies produce both chicken and pork(19,20,21), using the same feed inputs(17,22).

Chicken meat and pork prices were calculated using the average real weighted regional price of the product in carcass form. Feed price was considered to be the price of sorghum since it is the main ingredient in both chicken and pig feed(6). All prices were calculated from the weighted average of all the states within each of the eight regions.

Regional chicken meat demand was calculated by considering apparent regional consumption as a variable approximating regional demand. Regional consumption was estimated based on production, plus imports and minus exports, within each region. The result was then multiplied by each region’s population in a given year of the analyzed time series.

Based on economic theory, demand determinants included in the model included average real weighted regional price of chicken carcass [CMRPt]); income (real regional per capita gross domestic product [RGDPt]); price of substitute good (average weighted real regional price of beef [BRPt]) and regional population (POPt). All variables were calculated in the current period(12,13).

Elasticity values for each explanatory variable by region were calculated by multiplying the coefficients of the partial derivatives of the regional equations by the final observed value of each independent variable given the quantities for supply and demand. Since the supply and demand 922

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linear functions contain variable elasticity throughout their range of estimation, elasticity was calculated for the final year of the analyzed period, which is closest to the present(23). The effects established in the functional relationships were quantified in this way.

Results and discussion In most of the regions the chicken carcass supply equations exhibited a high coefficient of determination, the highest value being in the CW (R2 = 98) and the lowest in the NE (R2 = 65) (Tables 2 and 3). In the demand models, the regional coefficients of determination ranged from 0.98 to 0.99. The NW, NT, NE, CE and PE regions exhibited the best fit to the data. The supply and demand model was significant (P<0.05) according to the Fisher’s F test.

Table 2: Estimated coefficients of determination for regional chicken carcass supply in Mexico, 1996-2016 Dependent Region Intercept Explanatory Variables R² Prob>F Variable NW CMSt CMRPt PRPt-2 RFPt FEt 0.97 0.0001 Coefficient -283.530 0.018 -0.084 -3.023 0.696 SE 80.766 7.594 1.474 4.555 0.193 t -3.511 0.002 -0.057 -0.664 3.602 NT CMSt CMRPt PRPt-2 RFPt FEt 0.97 0.0001 Coefficient -429.039 5.467 -2.241 -41.770 1.315 SE 119.379 16.808 2.725 14.331 0.482 t -3.594 0.325 -0.823 -2.915 2.728 NE CMSt CMRPt PRPt RFPt FEt 0.65 0.0024 Coefficient -498.555 17.329 -4.062 -15.540 0.559 SE 374.659 9.015 1.230 5.862 0.255 t -1.331 1.922 -3.301 -2.651 2.191 CW CMSt CMRPt PRPt RFPt FEt 0.98 0.0001 Coefficient -859.623 25.465 -1.955 -2.452 1.449 SE 219.764 18.368 2.105 15.680 0.475 t -3.912 1.386 -0.929 -0.156 3.054 CE CMSt CMRPt PRPt RFPt FEt Coefficient -174.121 36.242 -3.750 -34.724 0.026 0.96 0.0001 SE 173.069 11.621 1.192 10.502 0.252 923

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ST

ET

PE

t CMSt Coefficient SE t CMSt Coefficient SE t CMSt Coefficient SE t

-1.006 -255.419 56.058 -4.556 -69.288 144.104 -0.481 -44.205 18.465 -2.394

3.119 CMRPt 0.595 7.348 0.081 CMRPt 1.798 10.379 0.173 CMRPt 8.873 5.491 1.616

-3.146 PRPt-2 -0.142 0.911 -0.156 PRPt-2 -5.019 3.787 -1.325 PRPt-2 -2.421 0.644 -3.759

-3.306 RFPt -3.699 8.832 -0.419 RFPt -11.709 13.598 -0.861 RFPt-2 -0.218 3.957 -0.055

0.102 FEt 0.97 0.0001 0.598 0.303 1.975 FEt 0.89 0.0001 0.759 0.501 1.515 FEt 0.019 0.94 0.0001 0.210 0.090

SE = Standard error; CMSt: chicken carcass supply in current period; CMRP t: average real weighted regional price of chicken carcass, in current period; CMRP t-1: average real weighted regional price of chicken carcass, with a oneyear lag; PRPt: average real weighted regional price of pork, in current period; PRP t-2: average real weighted regional price of pork, with a two-year lag; RFPt: average real weighted regional price of chicken feed, in current period; RFPt-2: average real weighted regional price of chicken feed, with a two-year lag; FEt = feed efficiency.

Table 3: Estimated coefficients of determination for regional chicken carcass demand in Mexico, 1996-2016 Dependent Region Intercept Explanatory Variables R² Prob>F Variable NW CMDt CMRPt RGDPt BRPt POPt 0.99 0.0001 Coefficient -323.601 -2.783 0.192 0.424 61.174 SE 14.927 0.842 0.183 0.296 3.615 t -21.679 -3.304 1.045 1.433 16.923 NT CMDt CMRPt RGDPt BRPt POPt 0.99 0.0001 Coefficient -496.889 -3.278 0.826 0.350 66.127 SE 64.500 0.835 0.423 0.373 9.670 t -7.704 -3.926 1.955 0.937 6.838 NE CMDt CMRPt RGDPt BRPt POPt 0.99 0.0001 Coefficient -251.052 -1.161 0.304 0.353 54.096 SE 13.831 0.358 0.072 0.125 2.934 t -18.152 -3.247 4.250 2.828 18.440 CW CMDt CMRPt RGDPt BRPt POPt 0.98 0.0001 Coefficient -722.606 -7.026 1.872 0.071 64.197 SE 114.992 2.109 0.516 0.536 10.298 t -6.284 -3.331 3.629 0.133 6.234 924

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CE

ST

ET

PE

CMDt Coefficient SE t CMDt Coefficient SE t CMDt Coefficient SE t CMDt Coefficient SE t

-1538.963 136.262 -11.294 -614.336 60.190 -10.207 -671.124 64.780 -10.360 -96.599 20.881 -4.626

CMRPt -5.887 3.515 -1.675 CMRPt -1.525 0.702 -2.174 CMRPt -3.195 0.475 -6.729 CMRPt -0.444 0.133 -3.332

RGDPt 4.901 1.223 4.009 RGDPt 1.906 0.506 3.765 RGDPt 0.430 0.119 3.610 RGDPt 0.102 0.099 1.024

BRPt 0.297 1.486 0.200 BRPt 0.001 0.453 0.001 BRPt 0.011 0.331 0.033 BRPt 0.000 0.050 0.003

POPt 55.572 8.610 6.455 POPt 72.960 7.604 9.595 POPt 98.815 8.462 11.678 POPt 49.720 8.560 5.808

0.99 0.0001

0.98 0.0001

0.98 0.0001

0.99 0.0001

SE = Standard error; CMDt: volume of chicken carcass demand, in current period; CMRP t: average real weighted regional price of chicken carcass, in current period; RGDP t: real regional per capita gross domestic product, current period; BRPt: average real weighted regional price of beef; POP t: regional population, current period.

The contribution of each of the explanatory variables in both models was evaluated according to their asymptotic t or t-ratio. This must be greater than the unit since this indicates that the estimated parameterâ&#x20AC;&#x2122;s value is greater than its standard error(24). Not all the supply variables were significant in all the regions when using this parameter. The coefficient of the feed efficiency variables was significant in most regions, except the CE and PE. However, the coefficient of the chicken meat price variables was significant only in the NE, CW, CE and PE, but not in the NW, NT, ST and ET. Likewise, the coefficient of the pork price variable was significant in the NE, CE, ET and PE, but not in the NW, NT, CW and ST. In the demand models, the coefficients for chicken meat price, per capita GDP and population were significant (P<0.05) for all eight regions. In contrast, the beef price variable was significant only in the NW and NE, but not in the remaining six regions. Using Kleinâ&#x20AC;&#x2122;s practical rule(25), the present results indicate there to be no multicollinearity between the explanatory variables in the regional supply and demand models.

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Regional elasticities

Coefficients of elasticity were determined by region to measure the magnitude of the dependent variable in response to variations (ceteris paribus) for each explanatory variable in the supply and demand models (Table 4). The magnitudes of supply and demand elasticities considering each independent variable were different in each of the regions; that is, the effect that these produced on chicken meat production varied between them. In most of the regions chicken meat supply was explained directly and elastically by technology (FE), although in the CE and PE this relationship was direct and inelastic. In contrast, chicken meat price was directly and inelastically related for chicken meat price in the NW, NT, CW, ST and ET, but direct and elastic in the NE, CE and PE. For most of the regions this relationship was inverse and inelastic for pork price and feed price, but in the NE it was inverse and elastic.

Table 4: Regional elasticities in supply and demand of chicken carcasses in Mexico, 1996- 2016.

Regions Elasticity NW NT NE CW CE ST ET PE Supply CMRPt 0.0021 0.2824 3.6332 0.8125 1.5794 1.7069 CMRPt-1 0.0941 0.1616 PRPt -1.4563 -0.0913 -0.2024 PRPt-2 -0.0110 -0.1615 -0.0253 -0.4738 -0.5273 RFPt -0.0325 -0.1899 -0.4256 -0.0084 -0.1436 -0.0475 -0.0876 RFPt-2 -0.0034 FEt 2.2151 1.8561 4.6830 1.2548 0.0299 2.2015 1.5884 0.0872 Demand CMRPt RGDPt BRPt POPt

-0.2101 0.0741 0.0656 1.9750

-0.2127 0.2844 0.0506 2.1045

-0.1037 0.2159 0.0817 1.7184

-0.2767 0.3557 0.0063 2.0220

-0.1196 0.5457 0.0122 1.7868

-0.1014 0.2763 0.0001 2.3610

-0.2525 0.1428 0.0017 3.1295

-0.0819 0.1536 0.0001 1.5855

CMRPt: average real weighted regional price of chicken carcass, in current period; CMRP t-1: average real weighted regional price of chicken carcass, with a one-year lag; PRPt: average real weighted regional price of pork, in current period; PRPt-2: average real weighted regional price of pork, with a two-year lag; RFPt: average real weighted regional price of chicken feed, in current period; RFP t-2: average real weighted regional price of chicken feed, with a two-year lag; FEt = Feed efficiency; RGDPt: real regional per capita gross domestic product, current period; BRP t: average real weighted regional price of beef; POPt: regional population, current period.

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The increases recorded in chicken meat production volume in most regions were due to technological change, defined as feed efficiency (FE). This variable exhibited greater elasticities than the others, especially in the NE (4.683), NW (2.215) and ST (2.201), and somewhat lower elasticities in the NT (1.856), ET (1.588) and CW (1.255). Elasticities were below the threshold in the CE (0.030) and PE (0.087), which translates into inelastic response to technological change.

These discrepancies in elasticities were caused by interregional variations in input and chicken meat prices.

Greater technology use in poultry production lowers production costs and improves productivity(6).

For example, if technology use were to increase by 10 % it would cause the chicken carcass supply curve to shift to the right. This would represent production increases of 46.83 % in the NE, 22.21 % in the NW and 22.01 % in the ST. In terms of volume, this would mean raising output (in 2016 terms) from 82,570 to 121,230 t in the NE, from 193,730 to 236,750 t in the NW, and from 190,050 to 231,880 t in the ST. This behavior is consistent with data from 1970-1998(6), during which the elasticity value for technological change related to chicken meat supply in Mexico was 1.972.

Chicken meat supply related to product price at current prices (CMRPt) and with a one-year lag (CMRPt-1) responded elastically in the NE (3.633), CE (1.579) and PE (1.707). This indicates that in response to a one percent rise in the price of chicken meat, the quantity supplied increased by more than one percent in these three regions. In the remaining five regions, this relationship behaved inelastically (NW, 0.002; NT, 0.282; CW, 0.813; ST, 0.094; ET, 0.162), meaning that a one percent rise in the price of chicken meat resulted in not significant increases in supply in these regions. These findings coincide with previous reports of inelastic values for chicken meat supply in relation to price(13,26,27).

Chicken meat supply in response to changes in the price of pork (as an alternative product) at the current price (PRPt), and with a two-year lag (PRPt-2), was inelastic in all regions except the NE, where it was elastic. Under this scenario in the NE region, increases in pork prices caused producers of chicken meat and pork to increase pork production, which could in turn negatively affect chicken meat supply in the region. This response was not significant in the other regions.

Chicken meat supply related to changes in feed price at the current price (RFPt), and with a twoyear lag (RFPt-2) differed between regions. Coefficient magnitude was less inelastic in the NE (-.0144) and NT (-0.190) than in the remaining regions. In other words, chicken meat production volume in the NE and NT responded inversely and more noticeably to variations in feed price. The CW and PE regions were more inelastic (-0.008, -0.003, respectively) than all the other regions; that is, in response to a one percent increase in feed price (RFPt, RFPt-2), chicken meat supply decreased at not significant levels. The coefficient values for the NE and NT were near the -0.164 reported for chicken meat supply in response to the expected price of sorghum (the main ingredient in chicken feed) from 1978-1998 in Mexico(6). Although the magnitude of elasticity may vary 927

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between studies from different periods the inelastic nature of this relationship is apparently constant.

In terms of elasticities of demand, the variable that most affected chicken meat consumption in all the regions was population size, since its behavior was elastic in all of them. The highest elasticities were in the ET (3.1295), ST (2.3610) and NT (2.1045), which represent significant increases in chicken meat consumption given a one percent change in population size. Increases in demand were slightly lower in the CW (2.0220), NW (1.9750) and CE (1.7868). The overall effect of population size on increased chicken meat consumption in the different regions may be explained

by Mexicoâ&#x20AC;&#x2122;s 1.22% average annual population growth during the study period. These results agree with a study in which the behavior of regional supply and demand of pork in Mexico responded elastically in all regions to growth in human population(23).

The elasticity of chicken meat demand related to its current price was inelastic in all the regions, although values varied. They were less inelastic in the PE (-0.0819), ST (-0.1014) and NE (-0.1037). This economic variable had a lesser effect on chicken meat consumption in these regions, probably due to differences in per capita income and substitute product price between the regions. Previous studies have also found inelastic profiles in this relationship (e.g. -0.36, -0.4718, 1.191, -0.2148, -0.1695)(26-29), which differ slightly from the present values because they are from different periods.

For chicken meat demand in relation to per capita gross domestic product (RGDPt) all the regions exhibited some degree of elasticity. The lowest coefficients were in the NW (0.0741), ET (0.1428) and PE (0.1536), and the highest were in the CE (0.5458), CW (0.3557) and NT (0.2844). These higher values indicate chicken meat consumption was explained to a greater extent by increased RGDPt in these regions. Overall elasticity for this relationship (based on available real per capita income) in Mexico from 1970-1998 was 0.3347(6).

Elasticity of chicken meat demand in response to the price of beef (BRPt) varied widely between regions. It was highest in the NE (0.0817), NW (0.0656) and NT (0.0506), and lowest in the ST (0.0001), PE (0.0001) and ET (0.0017). These values indicate that increases in the price of beef had minimal effects on chicken meat demand.

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Conclusions and implications Technological progress (defined as feed efficiency) was the factor that most influenced growth in poultry production in most of the regions in Mexico. Regional demand for chicken meat was elastic in relation to population growth; that is, in all the regions increases in population had the largest influence on increases in chicken meat consumption. The present results explain to what extent the evaluated explanatory variables affect regional supply and demand of chicken meat in Mexico.

Literature cited: 1.

Medina CJC, Rejón AMJ, Valencia, HER. Análisis de rentabilidad de la producción y venta de pollo en canal en el municipio de Acanceh, Yucatán, México. Rev Mex Agroneg 2012;16(30):909-919.

2.

UNA. Unión Nacional de Avicultores.

2018. http://www.una.org.mx/index.php/component/content/article/15-panorama/3-avicultura.

Consultado Feb 05, 2018.

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FIRA. Fideicomisos Instituidos en Relación con la Agricultura. Panorama Agroalimentario.

Dirección de Investigación y Evaluación Económica y Sectorial. Avicultura Carne. 2016. https://www.gob.mx/cms/uploads/attachment/file/200631/Panorama_Agroalimentario_Avic ultura_Carne_2016.pdf. Consultado Nov 14, 2016.

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SIAP. Servicio de Información Agroalimentaria y Pesquera. SAGARPA. Base de datos. 2016. http://www.siap.gob.mx/index.php?option=com_wrapper&view=wrapper&Itemid=371.

Consultado Oct 24, 2016.

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FIRA. Fideicomisos Instituidos en Relación con la Agricultura. Panorama Agroalimentario.

Dirección de Investigación y Evaluación Económica y Sectorial. Avicultura Carne. 2015. https://www.gob.mx/cms/uploads/attachment/file/61946/Panorama_Agroalimentario_Avicul tura_Carne_2015.pdf. Consultado Ago 22, 2016.

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Ramírez JA, García MR, García DG, Matus GJ. Un modelo de ecuaciones simultáneas para el mercado de la carne de pollo en México, 1970-1998. Agrociencia 2003;37(1):73-84.

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INEGI. Instituto Nacional de Estadística y Geografía. Banco de Información Económica. http://www.inegi.org.mx/sistemas/bie/. Consultado Dic 29, 2017.

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CONAPO. Consejo Nacional de población. Indicadores demográficos de México, periodo 1990-2030. https://datos.gob.mx/herramientas/indicadores-demograficos-de-mexicoperiodo-1990-2030?category=web&tag=economia. Consultado Oct 05, 2017

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Téllez DR, Mora FJS, García MR, Martínez DMA. Caracterización del consumidor de carne de pollo en la zona metropolitana del Valle de México. Rev Estudios Soc 2016;48(26):193209.

10. Del Moral BLE, Ramírez GBP, Muñoz, JAR. Crecimiento regional de la producción de carne de cerdo en México 1980-2005. Análisis Económico 2008;52(23):272-290.

11. Bassols BA. Geografía económica de México. Formación de regiones económicas. 1a reimpresión, México, DF: Ed, Trillas; 1992.

12. Salvatore D. Teoría y problemas de microeconomía, México, DF: Ed, McGraw-Hill; 1997.

13. Vázquez AJMP, Martínez DMA. Elasticidades de oferta y demanda de los principales productos agropecuarios de México. Secretaría de Agricultura, Ganadería, Desarrollo Rural, Pesca y Alimentación (SAGARPA). Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias (INIFAP). Centro de Investigación Regional Pacífico Sur. Campo Experimental “Zacatepec”. Zacatepec, Morelos, México. Publicación especial. Num. 51; 2011.

14. Rigolin P. Global poultry director for Alltech. Global Champion of Allzyme SSF, Alltech, Inc., Lexington, Kentucky, USA. Evolución de la conversión alimenticia en pollos de engorde.

2014. http://www.wattagnet.com/articles/17830-conversion-alimenticia-1-1-para-2025-unvistazo-al-futuro-de-la-avicultura 15. Gujarati ND, Porter DC. Econometría. 5ta ed. México, DF: McGraw-Hill Interamericana; 2010.

16. SAS. Statistical Analysis System 2003. Versión 9.1.3 SAS Institute Inc., Cary, NC, USA.

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17. Hall RLM, Lieberman, M. Macroeconomía. Principios y aplicaciones. 3ra ed. Thomson; 2006.

18. SAGARPA. Secretaría de Agricultura, Ganadería, Desarrollo Rural, Pesca y Alimentación.

Situación actual y perspectiva de la producción de carne de pollo en México. 1998. http://www.sagarpa.gob.mx/ganaderia/Publicaciones/Lists/Estudios%20de%20situacin%20a ctual%20y%20perspectiva/Attachments/15/sitpollo97.pdf. Consultado May 19, 2017.

19. Bachoco. https://bachoco.com.mx/el-principio-del-sabor/procesos-del-cerdo/ Consultado Oct 27, 2017.

20. Pilgrims. http://www.pilgrims.com.mx/. Consultado Oct 28, 2017.

21. Tyson (John Tyson, Chaitman). https://www.tysonfoods.com/who-we-are. Consultado Oct 27, 2017.

22. SENASICA. Servicio Nacional de Sanidad, Inocuidad y Calidad Agroalimentaria. Unidades de Producción Avícola Registradas. 2017. https://datos.gob.mx/busca/dataset/unidades-deproduccion-avicola-registradas. Consultado Jul 10, 2017.

23. Rebollar RA, Gómez TG, Hernández MJ, Rebollar RS, González RFJ. Comportamiento de la oferta y demanda regional de carne de cerdo en canal en México, 1994-2012. Rev Mex Cienc Pecu 2014;5(4):377-392.

24. Pérez VFC, García MR, Martínez DMA, Mora FJS, Vaquera HH, González EA. Efecto de las importaciones de carne de porcino en el mercado mexicano, 1961-2007. Rev Mex Cienc Pecu 2010;1(2):115-126.

25. Klein LR. An introduction to econometrics. Prentice Hall, Englewood Cliffs-New York, USA. 1962.

26. Bhati UN. Supply and demand responses for poultry meat in Australia. Australian J Agr Econom 1987;31(3):256-265.

27. Vázquez AJMP, Martínez DMA. Estimación empírica de elasticidades de oferta y demanda.

Rev Mex Cienc Agríc 2015;6(5):955-965.

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28. Ramírez TJ, Martínez DMA, García MR, Hernández GA, Mora FJS. Aplicación de un sistema de demanda casi ideal (AIDS) a cortes de carnes de bovino, porcino, pollo, huevo y tortilla en el periodo 1995-2008. Rev Mex Cienc Pecu 2011;2(1):39-52.

29. González SRF. Estimación de elasticidades de demanda para la carne de res, pollo, cerdo y huevo en México, una aplicación del Sistema de Demanda Casi Ideal. [tesis doctoral]. México: Universidad Autónoma Chapingo; 2001.

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https://doi.org/10.22319/rmcp.v10i4.4927 Article

Technical optimum milk and meat production levels in dual-purpose cattle systems in tropical Mexico

Yuridia Bautista Martínez a José Antonio Espinosa García b* José Guadalupe Herrera Haro c Francisco Ernesto Martínez Castañeda d Humberto Vaquera Huerta c Benigno Estrada Drouaillet a Lorenzo Danilo Granados Rivera e

a

Universidad Autónoma de Tamaulipas. Facultad de Medicina Veterinaria y Zootecnia.

Carretera Mante Km 5. 87000. Ciudad Victoria Tamaulipas, México. b

Instituto Nacional de Investigaciones Forestales Agrícolas y Pecuarias (INIFAP). Centro Nacional de Investigación Disciplinaria en Fisiología y Mejoramiento Animal, Ajuchitlan Querétaro, México. c

Colegio de Postgraduados, Campus Montecillo. Texcoco. Edo. México, México.

d

Universidad Autónoma del Estado de México. Toluca. Edo. México, México.

e

Campo Experimental General Terán, INIFAP. General Terán. Nuevo León, México.

*Corresponding author: espinosa.jose@inifap.gob.mx

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Abstract: Inputs directly affect profitability in livestock production, although what effects they have vary in response to production system and input type. An analysis was done of the results from a milk and meat production function using data from dual-purpose system (DP) production units in three locations in tropical Mexico. Data were collected through monthly surveys and covered milk production, meat production, income and financial costs over a twelve-month period. The functions were estimated by the indirect linear regression method with transformed data for a Cobb-Douglas function. The milk function showed the feed and cows inputs to explain 91 % of production. Elasticity coefficients were 0.34 for feed and 0.5 for cows. Marginal products were 0.75 for milk and 892.2 for cows, with values of $ 4.03 L for milk and $ 4,800.20 per cow. Both inputs are in stage II of production with diminishing marginal returns. For meat production both the feed and cowsâ&#x20AC;&#x2122; inputs explained 72 % of production, with elasticities of production coefficients of -0.20 for feed and 1.11 for cows.

Feed was in stage III of production with negative marginal returns, but the cows input was in stage I with increasing marginal returns. The sum of the coefficients was less than one for both functions (0.92 for feed, 0.91 for cows), indicating decreasing returns to scale. The optimum technical production levels were 488.97 L milk per day and 10 calves per year. In the studied producers the inputs for milk production were being used rationally, although in meat production feed appears to be overused and should be evaluated.

Key words: Cobb-Douglas, Elasticity, Marginal product, Returns to scale; technical optimum.

Received:07/06/2018 Accepted:29/09/2018

Introduction A total of 11.8 billion liters of milk were produced domestically and 3.7 million liters imported to meet domestic demand in Mexico in 2017. For the same period domestic beef production was 1.85 million tons with 136,000 t imported to meet demand(1). Dairy and beef producers in Mexico clearly do not generate enough product to meet domestic demand, highlighting the need for quantitative analysis of the efficiency of specialized, semispecialized, dual-purpose and family dairy and beef production systems to optimize resource 934

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use. Implemented mainly in tropical regions(2), dual-purpose cattle systems (DP) are characterized by milk production coupled with sale of weaned calves for beef(3). One of its main advantages is that feed costs are reduced since most systems are based on grazing with supplementation for lactating cows(2).

A vital aspect of DPs is the need for efficiency analysis of production units to maximize appropriate use of inputs for production(4). Parametric methodologies have been developed based on estimation of production functions to study the functioning of these systems and manifest cause-and-effect relationships in them. These methodologies identify the relationship between the amounts of different inputs and the quantities of resulting products, as well as associating each input with the maximum production level per period. This data can then be used to formulate productive development strategies for a specific region.

Physiological and non-physiological factors such as feed quantity and quality, fodders, herd size, season, and lactation number and stage, among others(5), influence milk and beef production. It is therefore important to understand the factors that best explain production to facilitate selection of the inputs to be used and make optimal use of them(6). The CobbDouglas function is widely used to identify production functions in livestock systems, and has been applied to estimate milk and beef production in different systems and regions in Mexico(7,8,9). Indicators can be calculated using the properties of Cobb-Douglas type functions and the theory of production. Principal among these is elasticity of production, which is the percentage change in the amount produced relative to the percentage change in input levels(10). Another indicator is marginal return, which describes production decreases or increases in response to addition of an input, and, depending on its behavior (i.e. increase, decrease, zero or negative), can indicate whether the input analyzed is in stage I, II or III of a classic production function. This indicator also allows identification of return types at the livestock production unit level, which helps to explain how production behaves in response to proportional and simultaneous variation of all inputs, which can be increasing, constant or decreasing. Input sales price is used to estimate the indicator marginal product, which is the variation in the quantity produced in response to unit increases in any production input (ceteris paribus) as well as the marginal product value, which is the additional income earned by a livestock company for each additional input unit(11). These data are useful for economic advisers in the livestock sector, extension representatives advising producers and producers themselves. They help in making decisions on rational resource use, and appropriate increases or decreases in inputs for the production process, all aimed at augmenting profits.

The present study objective was to use production functions to analyze data from representative dual-purpose (DP) system milk and beef producers in the Mexican tropics to estimate those inputs that have the greatest influence on production, and calculate the technical optimum levels subject to input price and milk and meat sale price to determine if they are being used rationally.

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Material and methods Study area

The study was done in production units (PU) in three states representative of the tropics in Mexico, and where the DP system predominates. Production units in the state of Tabasco (17°51’ N; 93°23’ W) were at 2 m asl, in an area with a warm humid climate and abundant summer rains, a 26.4 °C average annual temperature and 190.85 mm mean monthly rainfall.

The units in Chiapas (15°41’12” N; 93°12’33” W) were at 57 m asl in an area with a warm subhumid climate, 28 °C average annual temperature, and 80 mm mean monthly rainfall. In Sinaloa (23°14’29” N; 106°24’35” W) the units were at 10 m asl, in an area with 26.0 °C average annual temperature, and 63 mm mean monthly rainfall(12). All PU used Bos indicus x Bos taurus animals. Feeding was based on extensive grazing using supplementation with commercial balanced diets based on net lactation energy and 17 % protein during lactation for tall and medium-height cows. The average number of producing cows among the PU was 39. These were milked once a day using the calf to stimulate milk flow, extracting threequarters of the udder for sale and leaving a quarter to feed the calf. Meat production in all units consisted of the sale of calves weaned at 160 kg average weight.

Input classification and productive variables

Data were collected via monthly producer surveys from June 2012 to July 2013, in 30 PU, 10 per state. Production unit (PU) selection was done by unrestricted random sampling from the PU registered in cooperating local cattle associations. The surveys consisted of forms with sections on herd structure, land use, income from sale of milk and meat, and expenses from supply purchases. The variables used in the production function were based on recommendations for estimating problems when a producer generates multiple products such as livestock and agricultural crops, and when these can change substantially from one region to another; for example, the quantities of concentrate feed, animals, labor and fuel(13). A total of ten variables were recorded: total annual milk production (liters); total annual calf production; total amount of concentrate feed used in the PU per year (kg); number of producing cows; grazing area (hectares); preserved fodder used in PU per year (kg); full-time and seasonal labor (days); number of sires; operating supply costs (electricity, gasoline, diesel).

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Data analysis

Milk and meat production functions were estimated using the indirect method to generate a Cobb-Douglas type function, which consists of a linear regression with the original data transformed to Neperian logarithms of the dependent and independent variables(6).

After each of the variables was converted, the model that best explains milk and meat production was selected using the STEPWISE procedure in the SAS program. This procedure begins by calculating the simple correlation matrix, based on the correlation values; the independent variable (Xi) with the highest correlation to the response variable (Yi) is included in the model. Selection of the variable to include in the model was done using the partial correlation coefficients (R2). At each step the contribution of each variable to the model is examined by applying the partial F test as a criterion; therefore, at each stage all variables are examined for their unique contribution to the model, and those that do not meet a previously established criterion are eliminated.

The estimated specific model for milk is:

ln đ?&#x2018;&#x152;1 = đ?&#x203A;˝0 + đ?&#x203A;˝1 lnđ?&#x2018;&#x2039;1 + đ?&#x203A;˝2 ln đ?&#x2018;&#x2039;2 + đ?&#x203A;˝3 ln đ?&#x2018;&#x2039;3 + đ?&#x203A;˝4 ln đ?&#x2018;&#x2039;4 + đ?&#x203A;˝5 ln đ?&#x2018;&#x2039;5 + đ?&#x203A;˝6 ln đ?&#x2018;&#x2039;6 + đ?&#x203A;˝7 ln đ?&#x2018;&#x2039;7 + đ?&#x153;&#x20AC;

The estimated specific model for meat is:

ln đ?&#x2018;&#x152;2 = đ?&#x203A;˝0 + đ?&#x203A;˝1 lnđ?&#x2018;&#x2039;1 + đ?&#x203A;˝2 ln đ?&#x2018;&#x2039;2 + đ?&#x203A;˝3 ln đ?&#x2018;&#x2039;3 + đ?&#x203A;˝4 ln đ?&#x2018;&#x2039;4 + đ?&#x203A;˝5 ln đ?&#x2018;&#x2039;5 + đ?&#x203A;˝6 ln đ?&#x2018;&#x2039;6 + đ?&#x203A;˝7 ln đ?&#x2018;&#x2039;7 + đ?&#x153;&#x20AC;

Where: Y1 = milk production; Y2 = annual calf production; X1 = concentrated feed used in PU in kg yr-1; X2 = lactating cows; X3 = grazed area in hectares; X4 = preserved forage used in PU in kg yr-1;

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X5 = labor; X6 = sires; X7 = operating supply costs (electricity, gasoline, diesel); βi = parameters to be estimated (i = 0, 1, …,7); ε= residual term.

After estimating the Cobb-Douglas function with the variables that best explain meat and milk production, the input coefficient values were used to calculate the elasticity of production, marginal returns and the production stage of each input. In a Cobb-Douglas function, each input’s coefficient value is equal to its elasticity of production. If this is greater than 1 the input has increasing marginal returns and if it is less than 1 it has diminishing marginal returns. In addition, each input’s elasticity of production indicates the production stage in which it is located: a value βi> 1 indicates stage I; βi <1 is stage II; and βi <0 is stage III(11). The type of returns to scale of the studied livestock producers was identified using the sum of the input coefficients of the milk and meat production functions. Calculations were also done of the marginal product (PMgXi) and the value of the marginal product (VPMgXi) of the inputs derived from the elasticity formula using the means of total milk production and of the inputs, with the following formulas(14).

Ep(b1 ) =

∂Y/Y Xi ∂Y PMgXi = = ∂Xi /Xi Y ∂Xi PPXI

PMgXi = Ep(b1 )* PPXi VPMgXi = PMgXi *PYI Where, PMgXi = Marginal product of input Xi Ep(b1 ) = Elasticity of Yi Yi = Mean of annual milk or calf production Xi = Mean of input used VPMgXi = Value of marginal product Xi PYI = Unit price of Yi PPXi = Average product of input Xi used 938

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The average product of each input was the ratio between mean production (milk, calves) and input average (feed, cows).

Technical optimum milk and meat production levels were estimated by the Lagrange multiplier method, optimizing the milk and meat production functions (objective functions), subject to the prices of the inputs used and product sale price (one liter of milk and one calf).

đ??ż = đ?&#x2018;&#x201C;(đ?&#x2018;&#x2039;1 , đ?&#x2018;&#x2039;2 ) â&#x2C6;&#x2019; đ?&#x153;&#x2020; (đ?&#x2018;&#x192;đ?&#x2018;&#x2039;1 + đ?&#x2018;&#x192;đ?&#x2018;&#x2039;2 + đ?&#x2018;&#x20AC;) Where, L = Lagrange Function đ?&#x153;&#x2020; = Lagrange Multiplier F (X1, X2) = Cobb-Douglas production function for milk and meat PX1, PX2 = Price of variable inputs M = Product unit price

The algebraic procedure consisted of subtracting the constraint from the objective function, and L (first order condition) was partially derived from X1, X2, and Îť. Using the maximization rule, the ratio of partial derivatives was equalized to X1 and X2, which were limited to the input price ratio. The solution provided the values of X1 and X2, which were substituted in the Cobb-Douglas function, thus estimating the technical optimum levels for milk (in liters) and calf production.

Average sale price for a liter of milk was $ 5.38 and that for calves was $ 6,020. The average cost of one kilo feed was $ 4.00. The cost of a producing cow cost was estimated using the capital recovery formula(15), where purchase cost of a replacement heifer was $ 18,000.00, assumed use life was 8 yr in a DP system, annual return rate was 12.5 %, and estimated cost of one cow per year was $ 500.00. Calculation of the technical optimum level in the milk production function was done considering the price of one cow per day; that is the quotient of the price of one cow per year / 365 d, which was $ 1.36.

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Results and discussion Herd structure in the studied PU varied with production intensity and the area available for livestock within the PU (Table 1). Constant movement also occurred due to cow physiological condition (heifer, dry, lactating) or animal purchase and sale(16).

Table 1: Herd structure in double-purpose production units Variable

n

Ě&#x2026;) Mean (đ?&#x2019;&#x161;

SD (S)

CV

Producing cows

30

38.9

15.19

38.81

Dry cows

30

18.97

9.91

52.24

Heifers

30

19.80

11,71

59.14

Bull calf

30

14.22

8.54

60.05

Cow calf

30

12.40

6.73

54.27

Sires

30

2.49

1.49

59.83

n= number of production units; SD= standard deviation; CV= coefficient of variation.

In the milk and meat production function model, the coefficient of determination (R2) indicates that most of the variability in production is explained by the independent variables Feed (91.9 %) and cows (72.4 %) (Table 2). The percentage of unexplained variation in both models can be attributed to differences between PU such as herd management practices or environmental conditions. In milk production systems the feed input explains a greater percentage of variation in milk production than other inputs(15,17). As a result, fresh fodder, preserved fodder and concentrate feed can be used strategically to increase milk production(5,18). Feed handling and quality is clearly important in dairy production systems since it is directly related to production.

Average herd size was 39 producing cows, average annual milk production was 93,678.5 L, and average annual calf production was 14 (Table 3).

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Table 2: Regression models selected for milk and meat production

Milk Intercept InX1 In X2 R2 â&#x2C6;&#x2018; đ?&#x2018;?đ?&#x2018;&#x2013; =0.888 Meat Intercept InX1 In X2 R2 â&#x2C6;&#x2018; đ?&#x2018;?đ?&#x2018;&#x2013; =0.913

Estimated parameter

Standard error

Pr >F

6.099 0.346 0.542 0.919

0.310 0.034 0.095

<.0001 <.0001 <.0001

0.853 -0.205 1.118 0.724

0.430 0.047 0.133

0.0579 0.0002 <.0001

InX1= Neperian logarithm of kilograms feed concentrate; In X2= Neperian logarithm of producing cows; R2 = coefficient of determination; â&#x2C6;&#x2018; đ?&#x2018;?đ?&#x2018;&#x2013; = sum of đ?&#x2018;?đ?&#x2018;&#x2013; coefficients.

Table 3: Means for annual milk and calf production, inputs used in double-purpose system production units Function MPL Milk 93,678.5 Meat ----------

MPB ----------14.22

MIA 45,678.9 45,678.9

MIV 38.9 38.9

PPA 2.05 3.11X10-04

PPV 2,408.18 0.365

MPL= mean milk production kg PU yr-1; MPB= mean calf production heads PU yr-1; MIA= mean feed input kg PU yr-1; MIV= mean cow input head PU yr-1; PPA= average product of feed input; PPV= average product of cow input.

Cobb-Douglas production function for milk

đ??źđ?&#x2018;&#x203A;đ?&#x2018;&#x152;1 = 6.099 + 0.346 đ??źđ?&#x2018;&#x203A;đ?&#x2018;&#x2039;1 + 0.542 đ??źđ?&#x2018;&#x203A;đ?&#x2018;&#x2039;2 (Equation 1) After transformation with antilogarithms: đ?&#x2018;&#x20AC;đ?&#x2018;&#x2013;đ?&#x2018;&#x2122;đ?&#x2018;&#x2DC; = đ?&#x2018;&#x2019; 6.099 đ?&#x2018;&#x2039;1 0.346 đ?&#x2018;&#x2039;2 0.542 (Equation 2) đ?&#x2018;&#x20AC;đ?&#x2018;&#x2013;đ?&#x2018;&#x2122;đ?&#x2018;&#x2DC; = 445.69 đ?&#x2018;&#x2039;1 0.346 đ?&#x2018;&#x2039;2 0.542 (Equation 3)

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Elasticities of production for milk

In the present results a 1% increase in the X1 input (feed) raised total milk production by 0.34 % (ceteris paribus). A 1% increase in the X2 input (cows) raised total milk production by 0.54 %. Increases in either input positively affected milk production, although increasing the number of cows provided a better response in production than increasing feed. This coincides with reports from other dairy production systems in tropical climates similar to those of the study area in which the cow input provided the most elasticity in the studied inputs, with values ranging from 0.40 to 0.60 %, whereas the feed input produced elasticities of 0.15 to 0.30 %(14). Understanding to what degree inputs impact production is vital when analyzing livestock producers since under certain circumstances increases in inputs can decrease production, resulting in negative elasticities(10). For example, dairies in the tropics of India report a 2.4 % decrease in milk production as feed intake increases(19), resulting in financial losses to producers.

Marginal returns and input production stage for milk production

The elasticity coefficients for the milk production function were 0.34 for feed and 0.54 for cows (Equation 1). According to the law of marginal returns both inputs have diminishing marginal returns because their values are less than 1. Also, they are in stage II of a classic production function, meaning that increases in these inputs will increase milk production.

These production increases will become progressively less as input levels increase, until production becomes constant or begins to decrease, beginning stage III of production(10).

Increasing feed availability to cows early on will increase milk production, but when the animals reach maximum feed efficiency (i.e., the amount of feed in kilograms required by the animal to produce a liter of milk)(20) their metabolism will be unable to absorb all the nutrients and translate them into greater milk production. These are then expelled in the urine and feces, representing financial losses for producers in the form of costs for excess feed.

Increasing the number of cows (ceteris paribus) would reduce the availability of resources such as feed, causing a decrease in total milk production.

Returns to scale for milk production

The milk production homogeneous function exhibited decreasing returns to scale since the sum of the coefficients β1 and β2 was 0.888. Therefore a similar percentage increase in all 942

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inputs will cause a percentage increase of smaller magnitude in the product(11). Similar results have been reported in milk production systems in the state of Sinaloa(7) in which this effect is attributed to overuse of producer resources and absence of technology use in the system.

In this scenario large livestock producers experience increasing returns to scale due to specialization in capital and labor(8). Presence of this type of return to scale in PU using DP requires evaluation of input use because increasing feed and cow inputs will not raise income from greater production(21), rather it will cause financial losses due to unnecessary input costs.

Marginal product value for milk production

Milk production marginal product results for the feed input indicated that adding 1 kg of feed would increase milk production by 0.75 L, generating additional income of $ 4.03 per unit of added input (ceteris paribus). Raising the number of cows in a herd would increase milk production by 892.2 L per year, generating additional income of $ 4,800.20. These results are similar to a study of a DP system in Sinaloa in which marginal product values greater than zero for the cow input caused diminishing marginal returns(7). This means that increasing herd size to increase milk production is not the best option for improving efficiency in DP systems. Rather, a better approach is to make optimal use of the inputs that have the greatest impact on production. This is supported by the present marginal product values for the feed and cow inputs: both indicate positive economic benefits in producers, but at values less than 1. The law of marginal returns would classify these as diminishing marginal returns, placing them in stage II of a classic production function. Continued increases in these inputs will therefore cause the marginal product to continue decreasing until reaching zero, eventually becoming negative and leading to financial losses. Examples of this dynamic include production units in the eastern portion of the state of Yucatan, Mexico(5), and tropical dairy production systems in India(9), both of which have negative marginal returns for the feed input, and, even though marginal product values remain positive, milk production no longer increases.

Technical optimum milk production levels đ?&#x2018;&#x152;1 = 445.69 đ?&#x2018;&#x2039;10.346 đ?&#x2018;&#x2039;20.542 subject to 4.0 đ?&#x2018;&#x2039;1 + 1.36 đ?&#x2018;&#x2039;2 = 5.38 Using the Lagrange method: đ??ż = 445.69 đ?&#x2018;&#x2039;10.346 đ?&#x2018;&#x2039;20.542 â&#x2C6;&#x2019; đ?&#x153;&#x2020; (4.0đ?&#x2018;&#x2039;1 + 1.36 đ?&#x2018;&#x2039;2 â&#x2C6;&#x2019; 5.38)

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The partial derivative of L for X1 and X2, under a first order condition: 4.0 đ?&#x153;&#x2020; = 154.257đ?&#x2018;&#x2039;1â&#x2C6;&#x2019;0.653 đ?&#x2018;&#x2039;20.542 1.36 đ?&#x153;&#x2020; = 241.64 đ?&#x2018;&#x2039;10.346 đ?&#x2018;&#x2039;2â&#x2C6;&#x2019;0.457 Equalizing the partial derivatives for X1 and X2 , and substituting X2 in the constraining equation generates the optimum amount for this input.

154.257đ?&#x2018;&#x2039;1â&#x2C6;&#x2019;0.653 đ?&#x2018;&#x2039;20.542 4.0 = 1.36 241.64 đ?&#x2018;&#x2039;10.346 đ?&#x2018;&#x2039;2â&#x2C6;&#x2019;0.457 đ?&#x2018;&#x2039;2 =

4.0đ?&#x2018;&#x2039;1 = 17.41đ?&#x2018;&#x2039;1 0.229

đ?&#x2018;&#x2039;1 =

5.38 = 0.19 27.677

đ?&#x2018;&#x2039;2 = 17.41 (0.19) = 3.38 Substituting the X1 and X2 in the Cobb-Douglas function generates the optimum amount of milk produced. đ?&#x2018;&#x152;1 = 445.69 (0.19) 0.346 (3.380.542 ) = 488.97 đ?&#x2018;&#x2122; Livestock producers in the study area attained an optimal milk production of 488.97 L per day, which is equivalent to producing 12.53 L per day per cow, since on average they had 39 cows in production. In semi-intensive systems, cows must produce 35.38 L per day to achieve optimum milk production using a combination of concentrate feed and fodder inputs(22). The main difference between the DP and semi-intensive systems is that the latter use a larger amount of concentrate.

Cobb-Douglas production function for meat

đ??źđ?&#x2018;&#x203A;đ?&#x2018;&#x152;2 = 0.85366 â&#x2C6;&#x2019; 0.20523đ??źđ?&#x2018;&#x203A;đ?&#x2018;&#x2039;1 + 1.11829đ??źđ?&#x2018;&#x203A;đ?&#x2018;&#x2039;2 (Equation 4) Transformation via antilogarithms results in: đ?&#x2018;&#x152;2 = đ?&#x2018;&#x2019; 0.85366 đ?&#x2018;&#x2039;1 â&#x2C6;&#x2019;0.20523 đ?&#x2018;&#x2039;2 1.11829 (Equation 5) đ?&#x2018;&#x152;2 = 2.348 đ?&#x2018;&#x2039;1 â&#x2C6;&#x2019;0.20523 đ?&#x2018;&#x2039;21.11829 (Equation 6)

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Elasticities of production for meat

The meat production function (Equation 6) shows that a 1% change in the number of cows would increase calf production by 1.11 %, while the same change in the feed input would decrease it by 0.20 % (ceteris paribus). In DP systems, calf feeding is based on controlled lactation in the form of one quarter of the milk in the udder at milking and the quantity and quality of forage consumed when grazing(23). Nutritional supplementation of calves with good quality diets does improve weaning weight in DP systems, but this does not increase the prices paid to the producer for calves. Producers therefore search for alternatives to reduce the weaning period through supplementation with alternative forages (e.g. forage trees and bushes) that improve pre- and post-weaning calf development(24). The present results indicate that the amount of concentrate feed included in calf diets should be reduced because it does not improve overall production performance. Use of concentrate feed generally increases productive variables in livestock production systems(25), although any improvements will depend on feed quantity and quality, since lack of data on the appropriate amount of feed can generate unnecessary costs and cause financial losses for producers.

Marginal returns and input production stage for meat production

The elasticity coefficient for the meat production function is negative for the feed input (X1), but greater than one for the cow input (X2) (Equation 4). Values greater than one indicate increasing marginal returns and that the input is in stage I of a classical production function(10). Therefore, increasing the cows input would increase milk production at this stage, making it unadvisable for the producer to lower this input and consequently slow or stop production. Similar behavior has been reported in grazing systems in the State of Mexico and Yucatan(8,9). In other words, increasing herd size within the resources available to the studied PU would raise yield as represented by production variables. In contrast, the feed input exhibited an elasticity of less than zero, representing negative marginal returns and placing it in stage III(10). That is, increasing the amount of feed does not benefit calf production, and indeed could decrease it. A similar effect has been reported for beef production Yucatan, where increases in the amount of concentrate feed did not improve production(9). Rather, a more effective way of increasing calf weaning weight was to properly manage existing pastures by using high quality forages that meet animal nutritional requirements. If the feed input is in stage III of production, the livestock producer is not economically viable because it is spending money on an input that does not increase income from increased production.

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Returns to scale for meat production

The meat production functions exhibited diminishing returns to scale because â&#x2C6;&#x2018; đ?&#x2018;?1 is less than one. That is, increasing all inputs in the same proportion would not increase total production, an effect similar to that reported in small- and medium-sized producers in the State of Mexico(8). Large producers, in contrast, attain increasing returns to scale through genetic improvement (capital) and management efficiency strategies (labor)(8). An alternative for improving returns to scale for small and medium producers using DP would be to increase adoption of technology to improve efficiencies.

Marginal product and marginal product value for meat production

The marginal product of the feed input for meat production was less than zero (Table 4), indicating that total calf production would no longer increase, and that maximum production was attained with a smaller amount of feed. In the studied DP systems this input was used excessively, generating losses of 0.38 cents for each additional unit of feed. In contrast, increasing the number of cows in production by one unit generates $ 2,460.93 incomes, and because it is in stage I of the production function, the marginal product would continue to increase, as would its value. The same trend has been reported in PU in the state of Yucatan in which increases of one animal unit raised meat production to 980.7 kg, which is attributed to their production being less than maximum due to the limited use of breeding programs and genetic improvement(9). Considering this, the producers studied here need not stagnate calf production by maintaining the cows input unchanged since by increasing this input they could enter stage II of production.

Table 4: Marginal product and marginal product value of inputs used in a double-purpose milk and meat production system Function Milk Meat

Unit Price $ Milk Calves 5.38 ---------------6020

Feed PMg 0.75 -6.38exp-05

VPMg 4.03 -0.38

Cows PMg VPMg 892.2 4800.2 0.408 2460.93

PMg= marginal product; VPMg= value of marginal product.

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Technical optimum meat production level đ?&#x2018;&#x152;2 = 2.34 đ?&#x2018;&#x2039;1â&#x2C6;&#x2019;0.205 đ?&#x2018;&#x2039;21.118 subject to 4.0 đ?&#x2018;&#x2039;1 + 500 đ?&#x2018;&#x2039;2 = 6,020 By the Lagrange method: đ??ż = đ?&#x2018;&#x2039;1â&#x2C6;&#x2019;0.205 đ?&#x2018;&#x2039;21.118 â&#x2C6;&#x2019; đ?&#x153;&#x2020; (4.0đ?&#x2018;&#x2039;1 + 500 đ?&#x2018;&#x2039;2 â&#x2C6;&#x2019; 6,020) Partial derivative of L for X1 and X2, under first order condition: 4.0 đ?&#x153;&#x2020; = 0.481đ?&#x2018;&#x2039;1â&#x2C6;&#x2019;1.205 đ?&#x2018;&#x2039;21.188 500 đ?&#x153;&#x2020; = 2.625 đ?&#x2018;&#x2039;1â&#x2C6;&#x2019;0.205 đ?&#x2018;&#x2039;2â&#x2C6;&#x2019;0.188 Equalizing partial derivatives for X1 and X2 0.481đ?&#x2018;&#x2039;1â&#x2C6;&#x2019;1.205 đ?&#x2018;&#x2039;21.188 4.0 = 2.625 đ?&#x2018;&#x2039;1â&#x2C6;&#x2019;0.205 đ?&#x2018;&#x2039;2â&#x2C6;&#x2019;0.188 500 đ?&#x2018;&#x2039;2 =

4.0đ?&#x2018;&#x2039;1 = 0.043 đ?&#x2018;&#x2039;1 91.61

Substituting X2 in the constraining equation produces the optimum level for this input, and substituting the X1 and X2 values in the Cobb-Douglas function produces the optimum amount of milk produced. đ?&#x2018;&#x2039;1 =

6,020 = 236.07 25.5

đ?&#x2018;&#x2039;2 = 0.043 (233.07) = 10.151 đ?&#x2018;&#x152;2 = 2.348 (236.07) â&#x2C6;&#x2019;0.205 (10.151)1.118 = 10.22 đ?&#x2018;?đ?&#x2018;&#x17D;đ?&#x2018;&#x2122;đ?&#x2018;Łđ?&#x2018;&#x2019;đ?&#x2018; The technical optimum meat production level in the studied PU was 10.22 calves annually.

Combining the X1=236.07 and X2=10.15 inputs maximizes the calf production isoquant.

Conclusions and implications Feed and cows are the inputs that best explained milk and meat production in dual-purpose livestock producers in the studied areas. Producers need to place more emphasis on their use of these inputs since irrational use can decrease production variables. The elasticities of production indicated that, ceteris paribus, increasing these inputs in milk and meat production raises total production, except for the feed input in meat production. In milk 947

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production both inputs exhibited diminishing returns to scale, and were in stage II of production, the stage during which emphasis is needed on production. In meat production, the feed input had a negative marginal product value, placing it in stage III of a production function, and generating financial losses; operating in this stage will cause losses. Although in milk production both inputs had positive marginal product values they should not be increased since they are operating within the law of diminishing marginal returns. The studied livestock producers have generally diminishing returns to scale. One alternative for improving these returns is to increase the use of technology in different areas (feed, pasture management, forage conservation, reproduction, technical training) with the purpose of specializing capital and labor in these systems.

Literature cited: 1. SIAP. Servicio de Información Agroalimentaria y Pesquera. Resumen Nacional Pecuario.

2018. México.

2. Rojo RR, Vázquez AJF, Pérez P, Mendoza MGD, Salem AZM, Albarrán PB, et al. Dualpurpose cattle production in Mexico. Trop Anim Health Prod 2009;41(5):715–721.

3. Orantes ZMÁ, Platas RD, Córdova AV, Santos LD, Carmen M, Córdova AA. Caracterización de la ganadería de doble propósito en una región de Chiapas, México.

Ecosistemas y recursos agropecuarios 2014;1(1):49–58.

4. Albarrán PB, Rebollar RS, García MA, Rojo RR, Avilés NF, Arriaga JCM. Socioeconomic and productive characterization of dual-purpose farms oriented to milk production in a subtropical region of Mexico. Trop Anim Health Prod 2015; 47(3):519–523.

5. Vishnoi S, Pramendra, Gupta V, Pooniya R. Milk production function and resource use efficiency in Jaipur District of Rajasthan. Afr J Agric Res 2015;10-32:3200-3205.

6. Bravo UBE, Rieger L. Alternative production frontier methodologies and dairy farm efficiency. JAE 1990;41(2):215–26.

7. Cuevas RV, Loiza MA, Astego CH, Moreno GT, Borja BM, Reyes JJE, et al. Análisis de la función de producción de leche en el sistema bovinos doble propósito en Ahome, Sinaloa. Rev Mex Cienc Pecu 2018;9(2):376-386.

8. Morales HJL, Gonzales RFDJ, Hernández MJ. 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.

9. Pech MV, Santos FJ, Montes PR. Función de producción de la ganadería de doble propósito de la zona oriente del estado de Yucatán, México. Téc Pecu Méx 2002;40(2):187-192.

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10. Debertin DL. Agricultural production economics. 2nd ed. New York, USA: MacMillan; 1982.

11. Vargas BBE. La Función de producción Cobb - Douglas. Revista de Difusión Cultural y Científica de la Universidad La Salle en Bolivia 2014;8(8):67–74.

12. CNA. Comisión nacional del agua. Normales climatológicas. México; 2017.

13. Griliches Z. Estimates of the Aggregate Agricultural Production Function from crosssectional data. J Farm Econom 1963;45(2):419–28.

14. Ghebremariam WK, Ortmann GF, Nsahlai IV. A production function analysis of commercial dairy farms in the Highlands of Eritrea using ridge regression. JAE 2006; 45(2):225-241.

15. Monke EA, Pearson SR. The policy analysis matrix for agricultural development. Policy Anal; 1989.

16. Quiroz J, Granados L, Barrón M, Espejel A, Espinosa JA. Estructura de los hatos bovinos en Tabasco. AICA 2014;(4):252-253.

17. Mahajan S. Economic analysis of rural and pere urban farm in Ludhiana District of Punjab [M.Sc.Thesis. Karnal Haryana, India. Deemed University; 2008.

18. Sinhg S. Economic analysis of milk production in Varanasi District of Utta Pradesh.

[M.Sc.Thesis].Karnal, Haryana, India: Deemed University; 2008.

19. Ashish C, Bhadauria A. Production function analysis on member dairy cooperative society for milch cow in Distric Etawah (U.P). IJEAB 2017; 2(1):23-29.

20. Mackle TR, Parr CR, Stakelum GK, Bryant AM, MacMillan KL. Feed conversion efficiency, daily pasture intake, and milk production of primiparous Friesian and Jersey cows calved at two different liveweights. N Z J Agric Res 1996; 39(3):357–70.

21. Cursack AM, Castignani MI, Osan O, Castignani H. Función de producción en sistemas lecheros de alta producción de la Cuenca Central Santafesina, Argentina. 11º Congreso Panamericano de la Leche. Bello Horizonte, Brasil; 2010.

22. Rebollar RS. La función Cobb-Douglas de la producción semi -intensiva de leche en el sur del Estado de México. Análisis económico. Repositorio Institucional UNAM. 2018;(82) vol. XXXIII. 23. Pérez HP, Becerril PCM, Lamothe ZC, Torres HG, López OS, Gallegos SJ. Efecto del amamantamiento retrasado en la actividad posparto de las vacas y en los becerros de doble propósito. Interciencia 2006;31:748-752.

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24. Pérez HP, Solarís MF, García WM, Osorio AM. Gallegos SJ. Comportamiento productivo y reproductivo de vacas de doble propósito en dos sistemas de amamantamiento en trópico. Arch Latinoam Prod Anim 2001;9:79-85.

25. Camacho VJH, Cervantes EF, Palacios RMI, Rosales NF, Vargas CJM. Factores determinantes del rendimiento en unidades de producción de lechería familiar. Rev Mex Cienc Pecu 2017;8(1):23-29.

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https://doi.org/10.22319/rmcp.v10i4.4470 Article

Productive and socioeconomic characterization of a sheep production system in a natural protected area in Mexico

Daniel Hernández Valenzuela a Ernesto Sánchez Vera b* William Gómez Demetrio b Carlos Galdino Martínez García b

a

Universidad Autónoma de Guerrero, Facultad de Ciencias Agropecuarias y Ambientales, Guerrero, México. b

Universidad Autónoma del Estado México, Instituto de Ciencias Agropecuarias y Rurales, Estado de México, México. Campus “El Cerrillo Piedras Blancas”, 50090, Toluca, Estado de México. México.

* Corresponding author: esanchezv@uaemex.mx

Abstract: Natural protected areas experience pressure from increased human presence and productive activities. Agricultural, socioeconomic and grazing resource use data were used to characterize a sheep production system in the Nevado de Toluca Flora and Fauna Protection Area, Mexico. Based on sheep producer (n= 162) interviews, 25 variables were analyzed with multivariate and univariate statistics. A principal components analysis identified six factors explaining 71 % of variance. A cluster analysis identified three groups of producers [small (28 %), intermediate (35 %) and capitalized (6%)] differentiated by the number of animals, cultivated area and income (P<0.05). Overall, lamb mortality was generally high (23 %), forage oats (Avena sativa) were planted on 50 % of cultivated area, and maize (Zea mays) on variable percentages. Head of household age and schooling did not differ between groups (P>0.05), and sheep were found to contribute less than 30 % to household income.

Rotational grazing in the forest was used by 58 % of producers, but 60 % used a semi-stabling 951

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approach. The Nevada de Toluca sheep production system does not depend on producer capitalization, but sheep are essential to the family economy. Management practices are compatible with conservation efforts in the natural protected area.

Key words: Small ruminants, rotational grazing, Nevado de Toluca, Natural resources, Silvopastoral systems.

Received: 25/04/2017 Accepted: 30/08/2018

Introduction Livestock systems occupy about 45 % of the planetâ&#x20AC;&#x2122;s terrestrial surface area(1). They occur in many forms, from extensive mixed grazing, which integrates agricultural and livestock production, to highly mechanized, market-oriented systems(2). Extensive small ruminant systems tend to use native vegetation for grazing(3) as part of an intricate relationship between agriculture, livestock and natural resources on which many households depend(4). This relationship has been discussed in terms of its environmental impacts(5), which are particularly salient when these systems are located in and/or use natural protected areas (NPAs). Grazing of livestock in NPAs poses a dilemma between exploitation and use restrictions on natural resources(4).

Production systems are understood as a population of units similar in terms of resource base, livelihoods and limitations(6). They can be characterized by their structural components, and technical-productive and economic indicators. This allows integration of complex and diverse elements in an analysis(7), as well as development of strategies and recommendations aimed at achieving greater production system efficiency and profitability. This applies to characterization of livestock production in protected areas(8), in which grazing is treated as the main element of interaction between livestock and natural resources(5,9,10). No research has yet been done on sheep production systems (SPSs) in NPAs in Mexico that incorporates socioeconomic aspects, grazing dynamics and productive results. The present study objective was to characterize the productive, agricultural and socioeconomic aspects of the sheep production system in the Nevado de Toluca Flora and Fauna Protection Area (NT), and

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analyze use of grazing resources to better understand this system and identify opportunities for its improvement.

Material and methods Study area

The study area was the Nevado de Toluca Flora and Fauna Protection Area, in the State of Mexico, Mexico. This NPA has elevations ranging from 3,000 to 4,660 m asl. Climate is subhumid semi-cold (CEh), with average annual temperatures ranging from -2 to 7 °C, and annual precipitation from 1,000 to 1,400 mm. Overall NPA area is 53,987 ha, divided among 61 agricultural nuclei. Within the NPA are twenty towns and a total of approximately 10,000 inhabitants(11). These communities have about 191,000 head of livestock of which 60 % are sheep. Grazing is done in forest and grasslands inside communal use areas(11). Originally a national park, NT was reclassified as a Flora and Fauna Protection Area in 2013, a change which allows people living in the NPA to maintain ownership of their land and continue some productive activities, without changing land use(12).

Producer identification and data collection

During 2015, a total of 162 questionnaires were applied to sheep producers who had been chosen by convenience sampling. This is the same method used in previous studies, although it does not allow building of a reliable sampling framework(13). The questionnaire had three sections: (i) livestock, which recorded data on number of animals, feeding and grazing, reproduction, health and technical practices; (ii) agricultural activity, which recorded the number of crops, cultivated area and machinery; and (iii) socioeconomic characteristics, which covered head of household age and education level, income sources, family participation in labor, experience and training. Producers had no records on technical and economic information, which is common in family production systems(7,14,15). As a result, the collected data was supplemented by direct observation during visits to production units, as well as forty in-depth interviews with sheep producers held during grazing periods.

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Statistical analysis

A principal component analysis (PCA) was done using 25 variables to provide an initial approach to the variables describing SPS in the study area. The PCA produced small groups of linear combinations (components or factors) which explained as much variance as possible in the original data with minimal data loss(16). Parsimony of the principal components (PC) was verified with the Kaiser-Meyer-Olkin (KMO) test, and sample suitability confirmed with the Bartlett test of sphericity(16). Orthogonal varimax rotation was applied to the PCs to improve interpretation(6,16).

Linear PCA combinations were introduced into the hierarchical cluster analysis (CA) to form groups of producers and characterize the SPS. Case clustering was done following Wardâ&#x20AC;&#x2122;s method, and the squared Euclidean distance used as a measure of similarity. A dendrogram analysis and cluster coefficient were applied to identify the number of groups (6). Because group size was not homogeneous, differences between groups were identified with an analysis of variance (quantitative variables) by comparing Hochberg means(17). Categorical variables were analyzed with contingency tables and a Ď&#x2021;2 test(6). Statistical analysis results were triangulated with field observations and interviews. All statistical analyses were run with the SPSS ver. 22.0 program.

Results and discussion Sheep production system in the Nevado de Toluca

The SPS in the NT is a low-tech family-run system closely linked to agriculture, and which provides financial security, much like a previously described SPS(18). Most of the sheep herds were fed by grazing, and supplemented in stables. Breeds were mainly Suffolk, Hampshire and crosses thereof, chosen for weight and ease of handling. Reproduction is continuous although females exhibit marked seasonality. Mating occurs most frequently in the summer with births between November and February. This is the coldest time of the year, which may contribute to high lamb mortality due to respiratory diseases.

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Factors characterizing SPS

The PCA identified six principal components (PC) which explained 71% of the variance.

Results for the KMO test (0.61) and Bartlettâ&#x20AC;&#x2122;s test of sphericity (P<0.001) confirmed analysis trustworthiness. Sixty percent (60%) of the analyzed variables were retained in the PC (Table 1), which coincides with previous reports of a 64% retention of variables(7,13). This suggests that characterizing these production systems may only require from 10 to 20 variables, and that production unit size (i.e. number of hectares and animals) has the highest relevance in classification(19). Variables with communality values less than 0.5 were excluded from the PCA because this indicates they had low associations with the selected PC(16,17).

Table 1: Principal components with associated variables, correlation coefficients and explained variance per component VariableExplained Component Variables Factor Variance* Correlation Animals .770 20.2 Parturitions, % .768 1 (20.2) Cultivated surface, ha .767 Age, years .903 14.3 Education level, years -.712 2 (34.5) Experience, years 808 Mortality (adults), % .746 11.2 3 Mortality (lambs), % .739 (45.7) Cultivated species .678 9.4 Family participation, # .654 4 (55.1) Machinery, # .792 Forest grazing, % .779 8.9 5 Distance to grazing, km .816 (64) Weaning, % .830 6.9 6 Deparasitization, % .717 (71) * Cumulative variance indicated in parentheses.

Principal component one (PC 1, capitalization) incorporated variables relating to system assets (Table 1), which determine the investment capacity in technical practices (20). The second one (PC 2, human capital) showed the inverse relationship between head of household education level, age and experience. This reflects these adultsâ&#x20AC;&#x2122; limited access to 955

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education, due in part to the lack of formal employment opportunities and nonexistent infrastructure for access to educational services. These are associated with the nature of the NPA and led these adults to enter productive activities at an early age(21).

The third component (PC 3, animal health) highlighted adult animal mortality (11%) and lamb mortality (23%), both of which were higher than values reported for other SPSs(22).

Animal health management is therefore an area of opportunity for improving system productive and financial efficiency. The fourth component (PC 4, agriculture) associated crop diversity with family participation and use of machinery. This is characteristic of mixed production systems, in which exploitation of agricultural and livestock resources depends on working as a family(19).

The fifth component (PC 5, rotational grazing) linked forest grazing with the distance traveled to grazing. In this technique animals are allowed to graze freely on a surface for short periods (about 10 min) along relatively long routes (2 to 4 km), much like the grazing circuits used in the French Mediterranean(9). Finally, PC 6 (technical practices) associated deparasitization with weaning, which occurs at time of sale and without prior weight gain regimes that could improve producer income(14). Deparasitization was implemented in 70 % of the studied herds, a rate higher than the 58% reported for other SPSs(23).

Sheep production groups in the NT

The cluster analysis identified three producer groups, differing mainly in terms of quantity of animals, cultivated agricultural area and income from sheep and agricultural. As has been done in other studies(8,24), these differences were used to classify the groups as small producers (Group 1), intermediate producers (Group 2) and capitalized producers (Group 3).

Following are descriptions of the productive, socioeconomic and grazing resources use aspects of each producer group.

Sheep production

Data on the SPS show small producers (28 %) had the fewest animals, and weaned and deparasitized at lower rates, probably due to their lower training levels (Table 2).

Intermediate producers (35 %) had an intermediate number of animals, but the highest percentages of deparasitization and weaning. Capitalized producers (6 %) had the largest number of animals with herds about 120 % larger than in other regions of Mexico(18,22), but 956

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20 % smaller than herds in Europe(3,8). Despite the differences in capitalization, parturition and lamb mortality rates did not differ (P>0.05) between the three groups. Clearly in this SPS the resource base is not reflected in productivity(19), probably due to lack of inadequate training.

Table 2: Quantitative (mean and SE) and qualitative (%) characteristics of sheep production system by producer groups based on cluster analysis Group 1 (n=74)

Variables Quantitative variables: Number of animals Parturitions, % Mortality (adults), % Mortality (lambs), % Qualitative variables, % producers: Weaning Deparasitization Training Sale of animals: Lambs Grown animals Waste Self-supply Wool abc

Group 2 (n=70)

Group 3 (n=18)

P*

16.2±1.4a 84.4±2.7 16.5±2.3a 24.1±3.2

24.6±2.3b 83.0±2.4 9.6±1.2b 22.8±2.2

71.7±7.2c 85.7±3.7 7.5±1.4b 23.4±3.6

.000 .861 .002 .942

4 51 27

57 86 31

22 83 50

.000 .000& .170

65 54 38 7 20

64 59 47 3 9

89 56 72 11 22

.117 .860 .030 .327 & .106 &

Different letter superscripts in the same row indicate significant difference (P<0.05). * P value in ANOVA and χ2. &More than 20% of squares with counts less than five.

Sheep product marketing data showed that the small and intermediate producers sold some lambs when young (4 to 5 months of age) and the remainder throughout the rest of the year, and less than half discarded unproductive animals (Table 2). This confirms financial security as one of the main functions of livestock in this system since animals are sold in response to financial need(18,23). In contrast, the larger, capitalized producers sold more lambs and waste animals because their facilities and limited labor did not allow increases in herd size. This finding suggests that herd size self-regulates and that animal load therefore remains stable, leading to improved herd productivity without increased size(15), largely through use of reproductive management techniques and health treatments. Sale of wool was almost null since only about 20 % of producers sheared their sheep and these just discarded the wool due “to its low price” ($1.00/kg); the market therefore limits development of this system(14).

Agricultural production Small producers had less agricultural surface, fewer crops and less machinery. Most agricultural production was used for subsistence, and 12 % of these producers owned no 957

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farmland. Intermediate producers had greater crop diversity and more machinery use, although this was not owned by them. They tended to sow higher percentages of forage oats (Avena sativa), fava bean (Vicia faba), pea (Pisum sativum) and common bean (Phaseolus vulgaris) (P>0.05). While they sold a higher percentage of potato (Solanum tuberosum) than the small producers, a third sowed small amounts (<0.3 ha) for family use (Table 3). The capitalized producers had more cultivated area and machinery use, grew potato (S. tuberosum) on 18 % of their land and maize (Zea mays) on 26 %; in contrast, the small and intermediate producers planted S. tuberosum on only 9% of their land and Z. mays on 41 %.

The proportions of crops and their commercial purpose depended on producer capitalization level. In general, all three groups grew oats on 50 % of their land. They allocated Z. mays for subsistence, and fodders and crop waste to feed horses and ruminants. These in turn provided fertilizer for crops in a complementary management system between agriculture and livestock production like that reported in other SPSs(18,22). This highlights that NT sheep producers have traditionally developed a comprehensive resource use strategy.

Table 3: Quantitative (mean and SE) and qualitative (%) variables of agricultural production in Nevado de Toluca sheep producer groups Variables Quantitative variables: Crops cultivated Surface cultivated, ha Machinery # Qualitative variables, % producers: Crops: Oats (Avena sativa) Maize (Zea mays) Potato (Solanum tuberosum) Others: Subsistence crops Maize (Zea mays) Potato (Solanum tuberosum) Others

Group 1

Group 2

Group 3

P*

1.9±0.1a 1.9±0.2a 1.9±0.2a

2.5±0.1 b 3.1±0.3a 3.3±0.2b

1.9±0.3ab 5.3±1.7b 2.7±0.4ab

.004 .000 .000

70 64 28 19

86 79 30 29

83 50 39 11

.069 .030 .683 .130

98 52 86

100 29 81

100 14 50

.503f .115f .483f

abc

Different letter superscripts in the same row indicate significant difference (P<0.05). * P value in ANOVA and χ2. #Total possible: vehicle, yoke of oxen, tractor, chainsaw, forage shredder, forage packer. f More than 20% of squares with counts less than five. Others= fava bean (Vicia faba), pea (Pisum sativum) and common bean (Phaseolus vulgaris).

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Socioeconomic aspects

The families (5 members) of small producers participated less in agricultural activities because at least one member took non-agricultural jobs outside the NPA, consequently reducing dependence on natural resources(2). Intermediate producers had larger families (6 members) and these were responsible for a larger share of agricultural activities. However, they also had more income sources since different members could service the livestock after other activities (e.g. housework, jobs, or school) (Table 4). The present data support engagement in multiple activities as a strategy for increasing income and ensuring the flow of financial resources to the household(10). The capitalized producers were younger, had finished elementary school and their families (5 members) covered all livestock care needs.

These results coincide with other studies done in rural Mexico(25), but contrast with results for European countries where 30% of producers have a high school or university education(20) and family members contribute from 33 to 74% of labor(8,13).

Table 4: Socioeconomic aspects of producer groups formed in cluster analysis P* Variables Group 1 Group 2 Group 3 Age, head of household (years) 52.6±1.6 53.6±1.4 48.7±2.4 .247 Education level, head of household (years) 4.7±0.4 3.8±0.3 5.6±0.8 .061 # a b ab Family participation 2.2±0.4 3.0±0.2 2.6±0.3 .004 ab b a Income sources 5.3±0.2 5.8±0.2 4.6±0.5 .006 Annual income, sheep, $ 5,699.2± 17,554.0± 101,790.3± a 790 4651ª 51,257b .000 Sheep contribution to income, % 13.3±2.3a 17.3±2.2a 30.6±7.8b .009 Annual agricultural 11,123.5± 33,291.9± 133,943.3± a a income, $ 3,051.4 12,218.2 70032.5b .001 abc

Different letter superscripts in the same row indicate significant difference (P<0.05). * P values in ANOVA. #Number of members participating in agricultural and livestock activities.

Income

Incomes from agriculture and livestock did not differ (P>0.05) between the small and intermediate producers (Table 4). The ANOVA detected no differences due to high 959

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intragroup variability. This phenomenon has been observed in studies including socioeconomic aspects(6,13). In addition, income flow from sheep was not continuous because producers kept animals for sale later when they needed money, leading to productive cycles exhibiting various high and low sales periods; for example, only 16% of the studied households sold animals during the study period. Income from sheep was highest among the capitalized producers, although sale of sheep provided only a third of overall household income. The NT SPS is clearly a complement to family income(14,24), in addition to employment outside the NPA, remittances and government subsidies.

Use of grazing resources

Most (97%) of the surveyed producers grazed their herds in the grazing-only or semi-stabled modalities (grazing-stabled). Of these, 58% grazed in the forest, although this proportion increased among the capitalized producers, who used grazing-only to avoid raising costs from feed purchases(22). The small and intermediate producers used semi-stabled (Table 5), because their herds required less feed volume, allowing them to reduce grazing and channel their labor into higher-income activities(2).

Table 5. Sheep grazing and feed management by producer group (%) Group 1 Group 2 Group 3 P* Variables (n=74) (n=70) (n=18) Feed management: Grazing 26 26 44 .060 Semi-stabled 73 64 44 .055 Stabled 1 10 11 .060& Forest grazing: 57 51 67 .490 Feeds used: Hayed forage 84 96 89 .065 Balanced feed 18 31 39 .068 Mineral salt 64 84 100 .001 *

P value in Ď&#x2021;2. &More than 20% of squares had counts less than five.

Forest grazing circuits were frequently changed and differed between producers, who modified their routes year-round based on their perception of vegetation availability(5,9). This suggests that pressure on grazing resources is regulated by apparent fodder availability, previous grazing cycles and in situ agroecological characteristics. Management of this SPS may therefore be compatible with conservation efforts if animal load is adjusted by developing methodologies appropriate to rotational grazing and adequate knowledge is shared occurs between producers.

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Sheep herds are grazed in the NT forest mainly in the dry season (March to May) when pasture quantity and quality decreases. During the rainy season (June to October) the preference is for grasslands containing the genera Vulpia, Nassella, Trisetum, Muhlenbergia, Potentilla(26), and forest grazing decreases. After local crops are harvested (November to February) field stubble and roadsides are grazed. Producers in this SPS are clearly adapting to year-round resource availability(22,26). Their experience in grazing area use could be incorporated into zoning plans within the new Flora and Fauna Protection Area designation, essentially integrating local knowledge into management plan design(27).

Feeding

Most producers (88%) provided feed concentrate (commercial and empirically-processed homemade mixtures) to their herd, confirming that traditional grazing management is transforming into a semi-stabled system, with day-grazing followed by stabling and supplementary feed in the evening. This arises from producer interest in intensifying production(1,5) and adaptation strategies responding to restrictions on natural resource use(4).

Some producers (22 %) were intensifying their strategy by stabling weaned lambs to promote weight gain and consequently higher sales prices (Table 5). However, producers need to be careful that intensification does not undermine profitability due to the need for input purchase(3).

The technological transition observed in the NT has been reported for different production systems(5,7,14). It also suggests that use of grazing resources in NPA should be incorporated into more complex feeding strategies that include commercial feed and cultivated fodder.

Grazing-based systems that adequately integrate their resources can be financially efficient and environmentally friendly(3,15), since grazing can contribute to maintaining biodiversity(10) and avoids accumulation of combustible material(13,28).

The main feeds used in the study area were hayed oats and corn stover (Table 5); both are low cost because they are by-products of crops grown by producers. A very few producers fed their sheep mixtures of wheat bran or soybean (13 %) or potato waste (2 %), although use of this resource has not been documented. Feed management practices in the NT are similar to those used in other SPS(22,23). They also approximate traditional agrosilvopastoral management in which resources are used in an integrated manner, although different plant strata do not necessarily share the same space(27). This differs from agroecological designs in which trees are combined with different vegetation strata to provide animal feed(28).

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Conclusions and implications Three groups of sheep producers were identified within the Nevada de Toluca Flora and Fauna Preservation Area. These were distinguished by their level of capitalization in the form of quantity of animals, cultivated owned and income generated. Capitalization was not reflected in productivity since all three groups had similar parturition and lamb mortality rates, highlighting the need for increased training to raise production and income levels.

Agricultural production did not correlate with producer capitalization because those with more productive assets devoted more area to commercial crops. The crop-to-livestock ratio in the studied system agrees with conservation strategies, since, for example, use of manure decreases application of chemical fertilizers. Social aspects such as head of household age and education level did not affect sheep system productivity, although system economic efficiency depended on family labor. Sheep production was not the main income source among the studied producers but was essential to the family economy because it provided the financial safety not available from other economic components. Any management plan for the study area needs to consider that this sheep production system is in transition from an extensive to a semi-stabled grazing strategy. It therefore requires improved feeding strategies to reduce grazing within the natural protected area, and definition, where appropriate, of action plans for sustainable use based on level of grazing area deterioration and its relationship to animal load. Adoption of agrosilvopastoral management strategies can help to make sheep production compatible with conservation efforts. However, the area’s biological and socioeconomic characteristics mean achieving a technically viable model requires interaction and cooperation among multiple actors with interests in the natural protected area.

Acknowledgements The research reported here was financed by the Universidad Autónoma del Estado de México, through the project “Ganadería Modos de vida y paisaje del Área de Protección de Flora y Fauna Nevado de Toluca. Interacciones y co-evolución desde inicios del siglo XX”.

Thanks are due the producers of the Nevado de Toluca for their trust and time.

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2. Babulo B, Muys B, Nega F, Tollens E, Nyssen J, Deckers J, et al. Household livelihood strategies and forest dependence in the highlands of Tigray, Northern Ethiopia. Agric Syst 2008;(98):147–155.

3. Pérez JP, Gil JM, Sierra I. Technical efficiency of meat sheep production systems in Spain.

Technical note. Small Ruminant Res 2007;(69):237–241.

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6. Madry W, Mena Y, Roszkowska-Madra B, Gozdowski D, Hryniewski R, Castel JM. An overview of farming system typology methodologies and its use in the study of pasturebased farming system: a review. Spanish J Agric Res 2013;11(2):316-326.

7. Toro-Mujica P, Aguilar C, Vera R, Rivas J, García A. Sheep production systems in the semi-arid zone: Changes and simulated bio-economic performances in a case study in Central Chile. Livestock Sci 2015;(180):209-219.

8. Gaspar P, Escribano M, Mesías FJ, Rodriguez LA, Pulido F. Sheep farms in the Spanish rangelands (dehesas): Typologies according to livestock management and economic indicators. Small Ruminant Res 2008;(74):52–63.

9. Lasseur J. Sheep farming systems and nature management of rangeland in French Mediterranean mountain areas. Livest Prod Sci 2005;(96):87-95.

10. Riedel L, Casasús I, Bernués A. Sheep farming intensification and utilization of natural resources in a Mediterranean pastoral agro-ecosystem. Livestock Sci 2007;(111):153– 163.

11. CONANP. Comisión Nacional de Áreas Naturales Protegidas. Estudio Previo Justificativo para la Modificación de la Declaratoria del Parque Nacional Nevado de Toluca, ubicada en el Estado de México, México. 2013.

12. DOF. Diario Oficial de la Federación. Decreto que reforma, deroga y adiciona diversas disposiciones del diverso publicado el 25 de enero de 1936, por el que se declaró Parque Nacional la montaña denominada "Nevado de Toluca".

2013. http://www.nevadodetoluca.conanp.gob.mx/decreto.php#.WAefVPl96M8. Consultado: 19 Oct, 2015.

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13. Toro-Mujica P, García A, Gómez-Castro A, Perea J, Rodríguez-Estévez V, Angón E. et al. Organic dairy sheep farms in south-central Spain: Typologies according to livestock management and economic variables. Small Ruminant Res 2012;(104):28-36.

14. Raineri C, Nunes BCP, Gameiro AH. Technological characterization of sheep production systems in Brazil. Anim Sci J 2015;(86):476–485.

15. Al-Khalidi KM, Alassaf AA, Al-Shudiefat MF, Al-Tabini RJ. Economic performance of small ruminant production in a protected area: a case study from Tell Ar-Rumman, a Mediterranean ecosystem in Jordan. Agric Food Econom 2013;1:8.

16. Hair JF, Black WC, Tatham RL, Anderson RE. Multivariate Data Analysis. 7th ed.

London, United Kingdom. Prentice Hall International; 2010.

17. Field, A. Discovering statistics using IBM SPSS statistics. 4th ed. Great Britain: SAGE Publications; 2013.

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Tipología de explotaciones ovinas en la sierra norte del estado de Puebla. Téc Pecu Méx 2009;47(4):357-369.

19. Cortez-Arriola J, Rossing AHW, Améndola MRD, Scholberg MSJ, Groot JCJ, Tittonell P. Leverages for on-farm innovation from farm typologies? An illustration for familybased dairy farms in north-west Michoacán, Mexico. Agric Syst 2015;(135):66–76.

20. Rivas J, García A, Toro-Mujica P, Angón E, Perea J, Morantes M, et al. Caracterización técnica, social y comercial de las explotaciones ovinas manchegas, centro-sur de España.

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22. Galaviz-Rodríguez JR, Vargas-López S, Zaragoza-Ramírez JL, Bustamante-González A, Ramírez-Bribiesca E, Guerrero-Rodríguez JD, et al. Evaluación territorial de los sistemas de producción ovina en la región nor-poniente de Tlaxcala. Rev Mex Cienc Pecu 2011;2(1):53-68.

23. Kosgey IS, Rowlands GJ, Van-Arendonk JAM, Baker RL. Small ruminant production in smallholder and pastoral/extensive farming systems in Kenya. Small Ruminant Res 2008;(77):11-24.

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25. Ordaz JL. México: capital humano e ingresos. Retornos a la educación, 1994-2005. En: CEPAL - Serie Estudios y perspectivas – México. 2007;(90).

26. Martínez HJ, Arriaga JCM, González RIC, Rosa GR, Hernández LGB, Valdés RJ, et al.

La acumulación neta de fitomasa y calidad nutritiva de pastizales en el Área de Protección de Flora y Fauna Nevado de Toluca para la producción ovina. Reunión Científica de la Sociedad Española para el Estudio de los Pastos. Lugo-A Coruña, España 2016:381-386.

27. Choocharoen C, Neef A, Preechapanya P, Hoffmann V. Agrosilvopastoral Systems in Northern Thailand and Northern Laos: Minority Peoples’ Knowledge versus Government Policy. Land 2014;(3):414-436.

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Comparing silvopastoral systems and prospects in eight regions of the world. Agroforest Syst 2012;(86):303–314.

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https://doi.org/10.22319/rmcp.v10i4.4799 Article

Comparison of the direct and indirect routes of human values’ influence on consumption of two traditional cheeses from Chiapas, Mexico

Carolina Illescas-Marin a Arturo Hernández-Montes a* Esaú Estrada-Estrada a Rolando Murguía-Cozar a Anastacio Espejel-García a Armando Santos-Moreno a

a

Universidad Autónoma Chapingo. Departamento de Ingeniería Agroindustrial. Km 38.5 Carretera México-Texcoco. 56230, Chapingo, Estado de México, México.

* Corresponding author: sensorial@prodigy.net.mx

Abstract: Product preference can be influenced by specific human values and individual beliefs, among other factors. An analysis was done to identify the direct and indirect influence of human values, as mediated by tangible attributes, on acceptance of Ocosingo Bola and Chiapas cream cheeses. Surveys were applied to samples (n1= 200; n2= 230) of Chiapanecan consumers of each cheese variety. Correlation coefficients (R1) were generated from selected factor regressions from a factorial analysis of tangible attributes and consumption frequency. Calculations were done of R2 for the consumption regressions and selected human values factors, along with residual factors of tangible attributes. Human value-only factors and cheese consumption provided R3. The direct influence of human values on cheese consumption was determined by subtracting R2 from R1 (change in R), and the indirect influence was the difference between the R3 coefficient and the change in R. Direct influence was not significant in Ocosingo Bola cheese, but did affect preference for Chiapas cream cheese. The values for the latter cheese therefore predicted its consumption at a level beyond that of importance of 966

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tangible attributes. The most important motivational values in both consumer samples were Benevolence and Security. Chiapas cream cheese consumers also manifested Power and Universalism as supplementary values, while among Ocosingo Bola cheese consumers Hedonism was a supplementary value.

Key words: Chiapas, Human values, Intangible attributes.

Received: 06/03/2018 Accepted: 29/08/2018

Introduction

Among the myriad traditional cheeses made in Mexico are two raw milk cheeses made in the state of Chiapas: Chiapas cream cheese and Ocosingo Bola cheese(1,2). Traditional foods are defined as “a product frequently consumed or linked to a celebration or season, normally transmitted from one generation to another, made in a specific way based on culinary heritage, with or without processing, recognized and known due to its sensory properties, and associated with a certain area, region or country”(3). Artisanal cheeses are produced using methods that are unique, non-industrial, small-scale, and minimally mechanized(4). Thirty-one traditional artisanal cheeses have been described in Mexico(2).

These are associated with small-scale animal production systems, and are often produced by farmers as a way of saving money and ensuring family well-being(5,6).

Products are a collection of tangible and intangible attributes. The tangible refers to objectively verifiable elements of a product, and the intangible to that which does not alter a product’s physical form but helps construct its symbolic meaning(7). Thomson et al(8) noted that when processing sensory information consumers obtain functional, emotional and abstract conceptualizations. However, they also seek to build symbols, and manifest more favorable attitudes towards products when these symbolize the values consumers endorse(9,10). Values and beliefs are the units with which attitudes are built in consumers(11). Human values are desirable purposes that transcend situations, vary in importance, and serve as guiding principles in an individual’s life or other social entity(12,13). They have been used in the study of food consumption behavior(14,15) in different cultures, such as India(16), China(17) and Europe(18).

The expectation-value model has been used to explain attitudes towards the consumption behavior of a product, be it food in general(19) or a specific product such as meat(20, 21). It

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uses tangible attributes as mediators of the influence of human values on preference.

Lindberg, et al(22) found that preference for a product was a function of beliefs that the product should completely reinforce human values and their importance. Keaveney and Hunt(23) argued that although the perception of intangible attributes is embedded in the set of perceptions of tangible ones, perception of intangible attributes is greater than the sum of perceptions of tangibles. They state that the distinction between these should be the consequence of the influence of human values, mediating between both kinds of attributes, to influence product preference. When consumers evaluate the utilitarian meaning of a product, human values can influence the importance of its tangible attributes, which in turn indirectly influence product preference. But when consumers assess symbolic meaning through affective holistic judgment, human values can directly influence product preference, employing tangible attributes as mediators. In the indirect route the product serves an instrumental psychological function and in the direct route it serves an expressive function(24). Under these assumptions, when choosing some traditional cheese varieties native cheese consumers use intangible attributes more than tangible ones to express their consumer behavior. For other cheese varieties, however, consumers will only be able to use tangible attributes to make their decision. The present study objective was to identify the influence route of human values on consumption frequency of Chiapas cream cheese and Ocosingo Bola cheese.

Material and methods

Descriptive closed-response surveys were used to collect data(19,25), which was processed with the Microsoft Access 2016 program (Microsoft Corporation, USA). For Chiapas cream cheese, a sample of 230 consumers was used which included natives of three municipalities in the state of Chiapas: Pijijiapan, Tuxtla Gutiérrez and Comitán de Domínguez. For Ocosingo Bola cheese, a sample of 200 consumers was used who were natives of the municipality of Ocosingo, Chiapas. In both cases, respondents were consumers over the age of 40 and equally balanced between genders. Sampling was convenience type for an infinite population, and sample size was calculated using maximum variance, with 95 % reliability and a 7 % margin of error. The surveys included three sections. In the first section for Chiapas cream cheese each respondent was presented with a list of ten tangible attributes (the color yellow, the color white, firmness, moistness, crumbliness, milk aroma, soar milk aroma, fat aroma, acid flavor, salty flavor) and the separate attribute “affordable price”. For Ocosingo Bola cheese the first section also addressed “affordable price” plus fourteen tangible attributes: two for the rind (yellow color and moisture), twelve for cheese filling (cream color, moisture, milk aroma, soar milk aroma, fat aroma, acid flavor, salty flavor, bitter flavor, spreadability, softness, 968

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crumbliness, and graininess). Attributes were evaluated on a ten-point scale (1 “not important” and 10 “very important”) based on the importance assigned to each attribute by the respondent when deciding to buy the cheese.

For both cheese types the second section was intended to identify the importance respondents assigned to human values. Participants first read a printed sheet with a list of forty human values and their meanings (Table 1); any doubts were resolved by the pollster. Initially they were asked to select the thirteen most important human values, and then to select the thirteen least important values. A worth of 3 was assigned to the values chosen as the most important, a worth of 1 to those deemed least important, and a worth of 2 to those not chosen by the respondent. This methodology is based on a study by Schwartz(12) involving people from twenty countries (n ≥ 200 per country), including all the continents, thirteen languages, and eight religions, as well as atheists. The study used eleven motivational values and 56 human values, all previously agreed to by experts in the discipline. The results produced recommendations for the use of forty human values associated with ten motivational values, and proposed a model for the structure of relationships between types of motivational values which is widely used in human values research(14-18). In the third section of each survey consumers were asked how often they had consumed either of the two studied cheeses in the previous three days.

Table 1: Motivational values, human values and their meanings as used in the survey of traditional cheese consumers in the state of Chiapas.

Motivational values

Human values

Self-direction Self-direction Self-direction Self-direction Self-direction Benevolence Benevolence Benevolence Benevolence Benevolence Benevolence Benevolence Benevolence Conformity Conformity

Choosing own goals Self-respect Creativity Independent Freedom True friendship Mature love Caring Honest Indulgence Responsible A spiritual life Helpful Self-disciplined Politeness

Universalism

Equality

Definition Ability to determine one’s own destiny Self-esteem Bold, creative Self-confident, self-sufficient Independence, free choice Close Friends Spiritual and sexual intimacy Affectionate, tender Sincere, believable Desire to pardon others Worthy of confidence, trustworthy Saved, eternal life Work for the good of others Restrained Courteous, Good manners Each person gratified according to the amount s/he has done

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Conformity Stimulation Stimulation Hedonism Hedonism Hedonism Hedonism Achievement Achievement Achievement Achievement Achievement Power Power Security Security Security Tradition Universalism Universalism Universalism Universalism Universalism Universalism

Obedient An exciting life Daring Cheerful Happiness Pleasure A comfortable life Ambitious Capable Intelligent Logical A sense of attainment Social power Social recognition Clean Family security National security Respect for tradition Inner harmony Equality Social justice Broadminded Wisdom A world at peace

Has obligations, respectable A stimulating, active life Stands by convictions Not serious To feel content Agreeable, relaxed life A prosperous life Hard work, aspire Competent, effective Intelligent, thoughtful Consistent, rational Lasting contribution Position of authority and importance Respect, admiration Orderly, neat Care for loved ones Protection from attack Acceptance and commitment to customs Free of inner conflicts Community, equal opportunity for all Rectitude, non discrimination Open-minded Mature understanding of life Free of war and conflict

Source: Schwartz(12).

Statistical analysis

Analyses were run to identify any direct and/or indirect influence of human values on the frequency of consumption of Chiapas cream cheese and Ocosingo Bola cheese. The data for each cheese type was analyzed separately. The first step in each case was to apply one factorial analysis without rotation, using the principal components method, for human values and another for tangible attributes with the goal of reducing the number of variables (forty human values and cheese tangible attributes). Chosen factors were had eigenvalues equal to or greater than one (â&#x2030;Ľ1). Subsequently, a multiple regression (block one) was run using the selected factors for the tangible attributes (independent variables) and the frequency of cheese consumption (dependent variable). A second regression (block two) was done using the frequency of cheese consumption and selected human value factors, along with the residual factors of tangible attributes (i.e. those with 970

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eigenvalues less than one); these were treated as independent variables. A third and final multiple regression was run with the selected human values factors and frequency of cheese consumption, called the human values-only regression. Multiple regressions were done using the step-by-step method with an α= 0.1 to both include and exclude independent variables. Variance analyses and multiple regression coefficients (R) were done for each regression and each cheese. All analyses were done with the XLSTAT ver.

2014 program (Addinsoft, USA).

The direct influence of human values on cheese consumption frequency, with tangible attributes as mediators, was quantified by subtracting the R of the block one regression from the R of the block two regression (i.e. change in R). The significance of the change in R was evaluated by transforming the regression coefficients to Fisher’s z (z')(26), then applying a z-test to evaluate the null hypothesis that an R is equal to a specific value (1= k). These analyses were run with the SAS ver. 9.4 program (SAS Institute Inc., Cary, NC). The indirect influence of human values on the consumption frequency (using tangible attributes as mediators) of each cheese was calculated from the difference between the human values-only regression coefficient and the change in R(19,24).

Results

Factorial analysis of cheese tangible attributes and human values

For Chiapas cream cheese the factorial analysis for tangible attributes produced three factors (eigenvalues ≥1), which explained 49.86 % of total variation in the data. For Ocosingo Bola cheese five factors (eigenvalues ≥1) were identified that explained 62.04 % of total variation in the data.

The factorial analysis of human values in consumers of Chiapas cream cheese identified fourteen factors that explained 60.4 % of total variability. For Ocosingo Bola cheese, fifteen factors were identified which explained 64.2 % of total variation in the human values data.

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Direct and indirect influence of human values on consumption of Chiapas cream cheese and Ocosingo Bola cheese

A Kolmogorov-Smirnov test of normality(27) was applied to the observation coordinates of each selected tangible attribute factor for each cheese variety and of each selected human value factor of the two consumer samples; in all cases the hypothesis of an assumption of normal distribution (P>0.05) was not rejected. Of the three multiple regressions for Chiapas cream cheese (Table 2), the three-factor regression including tangible attributes and consumption frequency identified factor two (T2) as a significant variable (P<0.1). The regression using fourteen human value factors, the residuals of intangible factors (RT4 â&#x20AC;&#x201C; RT11) and consumption frequency as a dependent variable provided four significant variables (P<0.1), three human value factors (V3, V4, and V5) and a residual factor of tangible attributes (RT8). The block three multiple regression, including the fourteen human value factors and cheese consumption, identified three variables or factors (V3, V4 and V5). Of the Ocosingo Bola cheese multiple regressions (Table 3), the five-factor regression containing tangible attributes and consumption frequency identified three significant variables (P<0.1), which were T1, T2 and T3. The regression including the fifteen human value factors plus the intangible factor residuals (RT6 â&#x20AC;&#x201C; RT13) and consumption frequency as a dependent variable provided three significant variables (P<0.1), two human value factors (V7 and V13) and a residual factor of tangible attributes (RT8). Finally, the multiple regression that included the fifteen human value factors and cheese consumption as a dependent variable identified two significant variables or factors (V7 and V13).

Table 2: Multiple regressions using consumer human values data and tangible attributes of Chiapas cream cheese

Regression of tangible attribute factors and cheese consumption

Source

Coefficient value (B)

Standard error

Intersection T1 T2 T3

3.530 0.000 0.268 0.000

0.111 0.000 0.084 0.000

Pr > |t|

31.708

< 0.0001

3.200

0.002

Consumption = 3.530+0.268*T2

Regression equation (R1)

Regression of tangible attribute residuals factors plus

t

Intersection

3.530

0.109

RT4

0.000

0.000

RT5

0.000

0.000

972

32.428

< 0.0001

Rev Mex Cienc Pecu 2019;10(4):966-985 the human values and cheese consumption factors

RT6

0.000

0.000

RT7

0.000

0.000

RT8

-0.505

0.136

RT9

0.000

0.000

RT10

0.000

0.000

RT11

0.000

0.000

V1

0.000

0.000

V2

0.000

0.000

V3

-0.124

V4

-0.135

V5

-3.710

0.000

0.073

-1.703

0.090

0.077

-1.758

0.080

0.159

0.081

1.953

0.052

V6

0.000

0.000

V7

0.000

0.000

V8

0.000

0.000

V9

0.000

0.000

V10

0.000

0.000

V11

0.000

0.000

V12

0.000

0.000

V13

0.000

0.000

V14

0.000 0.000 Consumption = 3.530-0.505*RT8-0.124*V30.135*V4+0.158*V5

Regression equation (R2)

Regression of human values and consumption factors

Regression equation (R3)

Intersection

3.530

0.112

V1

0.000

0.000

31.549

< 0.0001

V2

0.000

0.000

V3

-0.127

0.075

-1.689

0.093

V4 V5

-0.132

0.079

-1.667

0.097

0.171

0.083

2.054

0.041

V6

0.000

0.000

V7

0.000

0.000

V8

0.000

0.000

V9

0.000

0.000

V10

0.000

0.000

V11

0.000

0.000

V12

0.000

0.000

V13

0.000

0.000

V14

0.000

0.000

Consumption = 3.530-0.126*V3-0.131*V4+0.171*V5

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Table 3: Multiple regressions run using human values data for consumers and tangible attributes of Ocosingo Bola cheese

Regression of tangible attribute factors and cheese consumption

Source

Coefficient value (B)

Standard error

t

Pr > |t|

Intersection

3.1100

0.1262

24.6481

< 0.0001

T1

0.0000

0.0000

T2

0.0000

0.0000

T3

-0.3282

0.0988

-3.3216

0.0011

24.8671

< 0.0001

2.0334

0.0434

2.9989

0.0031

-2.0696

0.0398

Consumption = 3.110-0.328*T3

Regression equation (R1) Intersection

3.1100

0.1251

V1

0.0000

0.0000

V2

0.0000

0.0000

V3

0.0000

0.0000 V4

Regression of tangible attribute residuals factors plus the human values and cheese consumption factors

Regression equation (R2)

Regression of human values and consumption factors

V5

0.0000

0.0000

V6

0.0000

0.0000

V7

0.2024

0.0995

V8

0.0000

0.0000

V9

0.0000

0.0000

V10

0.0000

0.0000

V11

0.0000

0.0000

V12

0.0000

0.0000

V13

0.3544

0.1182

V14

0.0000

0.0000

V15

0.0000

0.0000

RT6

0.0000

0.0000

RT7

0.0000

0.0000

RT8

-0.3209

0.1551

RT9

0.0000

0.0000

RT10

0.0000

0.0000

RT11

0.0000

0.0000

RT12

0.0000

0.0000

RT13

0.0000

0.0000

Consumption = 3.110+0.202*V7+0.354*V13-0.320*RT8 Intersecciรณn

3.1100

0.1261

V1

0.0000

0.0000

V2

0.0000

0.0000

974

24.6625

< 0.0001

Rev Mex Cienc Pecu 2019;10(4):966-985

Regression equation (R3)

V3

0.0000

0.0000

V4

0.0000

0.0000

V5

0.0000

0.0000

V6

0.0000

0.0000

V7

0.1867

0.1001

V8

0.0000

0.0000

V9

0.0000

0.0000

V10

0.0000

0.0000

V11

0.0000

0.0000

V12

0.0000

0.0000

V13

0.3534

0.1191

V14

0.0000

0.0000

V15

0.0000

0.0000

1.8656

0.0636

2.9660

0.0034

Consumption = 3.110+0.186*V7+0.353*V13

An analysis of variance of the three regressions for each cheese variety showed the models to be significant (correlation coefficients in Tables 4 and 5). For Chiapas cream cheese the difference (change in R) between the block two and block one regression correlation coefficients was 0.104 (P<0.05). This indicates the degree to which human values predicted consumer acceptance of Chiapas cream cheese. Because it goes beyond the influence of tangible attributes, it represents the direct influence of the human values expressed through the importance of intangible attributes. For Ocosingo Bola cheese, the difference (change in R) between the block two and block one regression correlation coefficients was 0.051 (P>0.05), indicating the degree to which human values (direct route) did not predict consumer acceptance of Ocosingo Bola cheese.

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Table 4: Results for regressions of the importance of tangible attributes and human values, and the residuals of tangible attributes in consumption of Chiapas cream cheese and Ocosingo Bola cheese Cheese Variety

Block 1 Tangible attributes Introduced Factors a

Chiapas cream cheese

Block 2 Human values plus residuals for tangible attributes

T2 (yellow color, milk aroma and fat aroma)b

Coefficients Mutliple R () 0.268

0.207 F = 10.24 g.l.=1, 228

T3 (affordable price)

-0.229

RT8 (salty)b V3 (hedonism, universalism)c V4 (benevolence, security)c V5 (hedonism, power) c

(p=0.002)

Ocosingo Bola cheese

Introduced Factors a

0.229 F = 11.03 g.l.=1, 198

RT8 (softness)c V7 (benevolence)c V13 (benevolence)c

(p=0.001)

Coefficients Mutliple R () -0.505

0.311 F = 6.04 g.l.=4, 225

-0.124

(p=0.002)

Change in R 0.104 z = 1.68 p= 0.046

-0.135 0.159

-0.142

0.280 F = 5.58 g.l.=3, 196

0.139

(p=0.001)

0.051 z = 0.76 p= 0.223

0.205

= includes only significant factors (p  0.1) b = Tangible attributes. c = Motivational values.

Block 1 = importance of tangible attributes over product acceptability.

Block 2 = human values over remnants of product acceptability, not considered in importance of tangible attributes. a

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Table 5: Human values-only regression for consumption of Chiapas cream cheese and Ocosingo Bola cheese Human values Cheese variety

Introduced factorsa V3

Chiapas cream cheese

Coefficients (ď ˘) -0.127

(hedonism, universalism)b V4 (benevolence, security)b V5 (hedonism, power)b

Ocosingo Bola cheese

Multiple R 0.204 F = 3.28 g.l.=3, 226 (p=0.022)

Indirect route 0.100

-0.132 0.171

V7 (benevolence)b

0.186

V13 (benevolence)b

0.353

0.242 F = 6.13 g.l.= 2, 197(0.002)

0.191

a

= only includes significant factors (p < 0.1). b = Motivational value.

Indirect route: regression coefficient (R) only of value minus the change in R.

Analyses of the variances in human values-only and cheese consumption found them to be significant (P<0.05) (Table 5). For Chiapas cream cheese, the regression correlation coefficient for human values-only (0.204) minus the change in R (0.104) resulted in a score of 0.10. This represents the influence of human values, via the importance of tangible attributes (indirect route), on cheese preference. The regression correlation coefficient for human values-only and consumption frequency of Ocosingo Bola cheese was 0.242; when the change in R (0.051) was subtracted the representation of indirect influence was 0.191.

In the block one regression (Chiapas cream cheese), the tangible attributes (Table 6) with the highest positive correlations to the variable (factor T2) were yellow color, sour milk aroma and fat aroma, while the highest negative correlation was for white color. In the block two regression, the tangible attributes residual variable eight (RT8) was positively correlated with acid flavor, affordable price and moisture, but negatively correlated to salty flavor. The motivational values (Table 7) with the highest correlations to variable three belonged to Power (social power) and Universalism (social justice), while those with the highest negative correlations belonged to Hedonism (joyful) and Universalism (broadminded). Variable four was positively correlated to the motivational values of Benevolence (useful) and Security (clean), and negatively correlated to Achievement (intellectual). Variable five was positively correlated with Hedonism (pleasant) and Power (social power).

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Table 6. Correlations of Chiapas cream cheese tangible attributes to the variables (factors) selected in the multiple regression analysis

Yellow color White color Firmness Crumbliness Moisture Milk aroma Sour milk aroma Fat aroma Acid flavor Salty flavor Affordable price

T2

RT8

0.815 -0.658 0.109 -0.005 -0.167 0.119 0.547 0.485 0.120 -0.041 -0.248

-0.049 -0.026 -0.202 -0.171 0.241 0.108 0.062 0.092 0.296 -0.572 0.266

Table 7: Correlations of human values of Chiapas cream cheese consumers to the variables (factors) selected in the multiple regression analysis Motivational value

Human value

V3

V4

V5

Stimulation Benevolence Stimulation Hedonism Achievement Universalism Conformity Segurity Segurity Benevolence Universalism Benevolence Power Power Hedonism Conformity Universalism Achievement Security Self-direction Universalism Achievement

Daring Helpful An exciting life A comfortable life A sense of attainment A world at peace Self-disciplined National security Family segurity A spiritual life Wisdom Responsible Social recognition Social power Pleasure Obedient Broadminded Logical Clean Freedom Social justice Intelligent

0.280 0.125 0.207 0.181 0.168 0.173 -0.046 -0.047 -0.063 -0.270 -0.385 -0.056 0.304 0.358 -0.047 0.099 -0.474 -0.205 -0.372 -0.325 0.384 -0.035

0.245 0.403 0.064 0.248 -0.078 0.276 -0.251 -0.242 0.250 0.200 -0.130 0.206 -0.088 -0.270 -0.053 0.322 -0.187 -0.305 0.384 -0.257 -0.183 -0.386

-0.084 -0.051 -0.074 -0.230 0.018 -0.149 -0.083 -0.149 -0.340 0.041 0.032 0.012 0.398 0.423 0.437 0.347 0.256 -0.213 -0.025 0.103 -0.021 -0.243

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Benevolence Self-direction Self-direction Universalism Benevolence Hedonism Universalism Conformity Benevolence Achievement Self-direction Self-direction Tradition Universalism Benevolence Benevolence Achievement Hedonism

Indulgence Independent Creativity Equality Honest Happiness Equality Politeness Caring Capable Self-respect Choosing own goals Respect for tradition Inner harmony Mature love True friendship Ambitious Cheerful

0.125 -0.027 -0.261 0.373 -0.028 0.046 0.257 0.023 -0.140 0.188 0.034 0.023 -0.209 0.142 -0.360 0.142 0.238 -0.497

-0.167 -0.296 0.143 -0.087 0.283 0.329 -0.271 0.234 0.138 -0.041 -0.035 -0.287 0.077 -0.064 -0.170 0.160 -0.098 0.021

0.289 0.272 -0.353 -0.022 0.146 0.136 -0.228 0.046 0.156 -0.159 -0.093 -0.193 0.155 0.084 -0.211 -0.029 -0.418 0.067

In the block one regression (Table 8), the highest positive correlations of the tangible attributes of Ocosingo Bola cheese with variable three (factor T3) were affordable price and white color, while the highest negative correlation was with bitter flavor. In the block two regression, variable eight of the residual tangible attributes (RT8) was positively correlated with acid flavor and negatively with white color (Table 8). The motivational values with the highest correlation to variable seven (Table 9) were Benevolence (responsible and honest) and Security (national security), and those with the highest negative correlations were Universalism (wisdom) and Self-direction (creativity).

Variable thirteen was positively correlated with Hedonism (joyful) and negatively with Achievement (intelligent) and Power (social recognition).

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Table 8: Correlations of tangible attributes of Ocosingo Bola cheese with variables (factors) selected in multiple regression analysis

Yellow color on rind Rind moisture Cream color Moisture Milk aroma Sour milk aroma Fat aroma Acid flavor Salty flavor Sour flavor Spreadability Softness Graininess Crumbliness Affordable price

T3

RT8

0.0838 0.0180 0.2442 0.1246 -0.2932 -0.2383 -0.4330 0.2464 0.0405 -0.6424 0.2650 0.4221 -0.2349 -0.3181 0.5815

-0.0310 -0.0315 -0.1415 0.1670 0.2590 -0.0386 -0.2053 0.4768 0.1229 -0.0203 -0.0957 -0.4709 0.0111 -0.1116 0.0868

Table 9: Correlations of human values of Ocosingo Bola cheese consumers with variables (factors) selected in multiple regression analysis Motivational values

Human values

V7

V13

Stimulation

Daring

0.0117

-0.0244

Benevolence Stimulation

Helpful An exciting life

-0.2558 0.1042

0.3224 0.2111

Hedonism Achievement

A comfortable life

-0.2390 0.0953

0.1227 0.1028

Universalism Conformity Security Security Benevolence

A world at peace Self-disciplined National security Family security A spiritual life

-0.0084 -0.2979

-0.0828 -0.2645 0.1018 0.1015 -0.0757

Universalism Benevolence Power Power

Wisdom Responsible Social recognition Social power

-0.4311 0.3627 0.0245 0.0602

-0.3019 0.0260

Hedonism Conformity Universalism

Pleasant Obedient Broadminded

0.0289 0.0532 -0.0529

-0.0965 -0.0380 0.0797

A sense of attainment

980

0.3739 -0.0940 0.2474

0.0762 0.0568

Rev Mex Cienc Pecu 2019;10(4):966-985

Achievement Security Self-direction

Logical Clean Freedom

-0.0754 0.0109 0.0721

-0.2071 -0.0740 -0.0385

Universalism Achievement Benevolence Self-direction

Social justice Intelligent Indulgence Independent

0.0882 -0.1513 0.2883 0.2518

-0.1271

Self-direction Universalism Benevolence Hedonism

Creative Equality Honest Happiness

-0.3254 0.0050 0.3191 0.1565

0.0974 -0.0330 -0.3177 0.0689

Universalism Conformity Benevolence Achievement Self-direction Self-direction Tradition Universalism

Equality Politeness Caring Capable Self-respect Choosing own goals Respect for tradition Inner harmony

-0.0023 0.1183 0.2397 0.0149 -0.2436 -0.1547 -0.0396 0.1170

-0.1587 -0.0745 0.0480 0.1555 0.1822 -0.1493 0.2495 0.0689

Benevolence Benevolence Achievement Hedonism

Mature love True friendship Ambitious Cheerful

-0.2948 0.1880 -0.1301 -0.2977

0.0550 0.0277 0.2475

-0.4160 -0.0279 0.1944

0.5414

Discussion

In contrast to Ocosingo Bola cheese, Chiapas cream cheese exhibited a direct influence of human values on consumption. This indicates that consumers of Chiapas cream cheese used intangible as well as tangible attributes to express the influence of values on cheese consumption, with tangible attributes functioning as mediators. The motivational values most involved in affecting preference were those of Power, Universalism, Benevolence, Hedonism, and Security. In Schwartzâ&#x20AC;&#x2122;s scheme the values of Universalism and Benevolence are adjacent those of Tradition and Conformity, while Hedonism and Power are somewhat opposed to them; this arrangement is to be expected in a traditional community(12). These results coincide with previous studies of red meat which show that human consumers use intangible attributes (symbolic and affective) when making choices(21). In Brazil, for instance, red meat was found to symbolize social hierarchy and

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its consumption was linked to the values of Power(20). However, in Australia meat consumption was reported to be linked to values associated with Universalism, Power and Security in that people who had a positive attitude towards red meat consumption expressed values related to Power and Security as being important(21). In a study done in China of healthy beverages the motivational values identified in a consumer sample were Security, Hedonism, Benevolence and Achievement(17). Comparisons can be made of the structures of human and motivational values between consumers from different cultures.

For example, consumers who give priority to the values of Conservation and Tradition normally place Achievement and Hedonism in opposition, while consumers who prefer red meat prioritize the values of Power and Achievement over Benevolence and Universalism(16,17,20,21).

For Ocosingo Bola cheese the influence of consumer human values fully employed tangible attributes to manifest preference. The main motivational values included in this indirect route were Benevolence, Safety and Hedonism; the first two are adjacent to the values of Tradition and Conformity whereas the third is their opposite(12,13). Ocosingo Bola cheese exhibited some similarities with Chapingo cheese(28), in that both types have Benevolence as an important motivational value in the selected regression variables, and they manifest the same (indirect) route of the influence of human values on cheese consumption.

Conclusions and implications

Benevolence and Security were the most important motivational values in common between the two consumer samples. Chiapas cream cheese consumers also manifested the values of Power and Universalism, while Ocosingo Bola cheese consumers manifested Hedonism as a supplementary value. The influence of the human values of Chiapanecan consumers on preference for Chiapas cream cheese was direct, meaning they used intangible attributes (affective and symbolic), in addition to tangible ones, in a holistic evaluation of cheese consumption. For Ocosingo Bola cheese, the influence of human values on cheese preference was indirect, indicating that consumers valued only its tangible attributes and gave it a utilitarian meaning. These results imply that during consumption of different varieties of traditional Mexican cheeses the identification of direct or indirect routes can provide information on cheese preference based on parameters beyond mere tangible attributes. This presents the possibility of differentiating and characterizing traditional cheeses based on the type of influence human values exert on consumer preference. These findings can be applied in marketing of cheeses by incorporating distinctive symbols on product labels and planning marketing strategies for

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sociodemographically differentiated consumer groups to promote greater affective engagement with regional cheeses. In addition to evaluating cheese tangible attributes, identification of the symbolic meanings manifested by consumers towards traditional Mexican cheeses needs to be done using an integrative and interdisciplinary approach.

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https://doi.org/10.22319/rmcp.v10i4.4959 Article

Endoparasites in captive Odocoileus virginianus and Mazama temama in Veracruz, Mexico

Cristina Salmorán-Gómez a Ricardo Serna-Lagunes a* Norma Mora Collado a Dora Romero-Salas b Dulce María Ávila-Nájera c Pedro Zetina-Córdoba c

a

Universidad Veracruzana, Facultad de Ciencias Biológicas y Agropecuarias, región Orizaba-Córdoba, Unidad de Manejo y Conservación de Recursos Genéticos. Josefa Ortiz de Domínguez S/N, Col. Centro, Peñuela. 94945 Amatlán de los Reyes, Veracruz, México. b

Universidad Veracruzana, Facultad de Medicina Veterinaria y Zootecnia, Veracruz, México. c

Universidad Politécnica de Huatusco. Unidad Académica de Biotecnología y Agroindustrial. Huatusco, Veracruz, México.

* Corresponding author: rserna@uv.mx

Abstract: Parasitosis in commercially important captive wild species can cause losses due to decreased productivity, increased veterinary expenses, secondary infections and animal mortality. An analysis was done to quantify endoparasite prevalence and abundance in the cervids Odocoileus virginianus and Mazama temama in captivity. Fecal samples (n= 60) were collected during the rainy and dry seasons from six O. virginianus and four M. temama of different ages and sexes. Endoparasites were extracted using the flotation technique with a saturated sugar solution, and the parasites identified by anatomical comparison. Seven parasite genera were identified: Ascaris sp.; Eimeria sp.; Estrongilido 986

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sp.; Strongyloides sp.; Parascaris sp.; Paragonimus sp.; and Taenia sp. In both cervid species Ascaris sp. and Eimeria sp. exhibited the highest abundance. Males and females of each cervid species exhibited different parasite prevalences. Parascaris sp. and Paragonimus sp. were found only in O. virginianus. No differences (P>0.05) were present in parasite abundance between the rainy and dry seasons. The genus Ascaris was generally more abundant than the other parasite genera (P<0.05). These results will be useful in the control and prevention of parasites in captive ungulates.

Key words: Ascaris sp., Cervids, Parasitosis, Flotation technique, Zoonosis.

Received: 28/06/2018 Accepted: 05/10/2018

Introduction

The ecology of diseases and parasitosis in wildlife has been studied for over a century, with special emphasis on species used for hunting and eating(1,2). An animal health approach has been used to address this issue in recent decades because zoonotic diseases can occur that affect domestic animals and humans, leading to death in both wild and captive animal populations(3,4,5,6).

White-tailed deer (Odocoileus virginianus) is in high demand for hunting and other uses(7,8). The diversity of parasites that cause infectious diseases in this cervid have been described(5). These can affect behavior, reproduction and even morbidity and mortality(2,9,10). The Central American red brocket (Mazama temama) is distributed from southeast Mexico to northern Columbia. Very little study has been done of its parasitosis(11), and only minimal data is available on the conditions needed to maintain it in captivity and conserve its populations.

Protozoa, helminths, arthropods and pentastomides are the most abundant parasites in domestic animals(12). In cervids the most common diseases are caused by viruses, bacteria, infectious conditions and parasitosis(2). Gastrointestinal parasitosis is a major pathology in deer and is mainly caused by helminths and protozoa(13). Factors such as climate(14), the presence or absence of intermediate hosts, soil composition, vegetation type and water quality are principal factors influencing parasite prevalence(12). Mortality in wild O. virginianus populations due to gastrointestinal parasites is approximately 2.7 %(15).

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Wildlife Conservation Management Units (UMA) have been implemented in Mexico as a management strategy for wildlife conservation and exploitation. When intensive management of wild animals in UMAs involves inadequate animal health protocols, losses can be incurred due to decreased reproduction and productivity(16), and higher incidences of secondary infections, increased digestive tract lesions, anaphylactic reactions, anemia, and even death. Mismanagement can also increase the chance of these conditions becoming zoonotic diseases, and inadequate prevention and mitigation measures raise the risk of contagion between wildlife and livestock(3,17,18). Cervid management programs need to consider the prevention and control of the most common infectious and parasitic diseases to ensure population viability and reproductive success(2).

Research and data on parasitosis in captive wildlife is scarce(15,19,20), and management plans for successful in situ and ex situ production of cervids in UMA have not met expectations(21). Greater knowledge is needed on the parasites that affect the health of ex situ cervid populations. The present study objective was to quantify parasite prevalence, abundance, and endoparasite diversity in a captive population of O. virginianus and M. temama.

Material and methods

Study area

The study was done at El Pochote, an intensive mode UMA registered with the Ministry of Environment and Natural Resources (SEMARNAT; UMA-IN-CR-0122-VER/og).

Located in the municipality of Ixtaczoquitlán, in the state of Veracruz, Mexico (18°52’13.70” N; 97°02’59.97” W) it is at 1,137 m asl. Regional climate is semi-warm humid (Cwa) with abundant summer rains, an annual temperature ranging from 18 to 24 °C and annual average rainfall from 1,900 to 2,600 mm. Vegetation around the El Pochote UMA is mainly evergreen tropical and second-growth forests(22). The main objective of this UMA is conservation and reproduction of O. virginianus and M. temama.

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Cervid specimens

The experimental animals were six O. virginianus (three females [2, 3 and 5 yr of age] and three males [3, 4 and 9 yr of age]) and four M. temama (two females [2 and 4 yr of age] and two males [both 3 yr of age]. All were apparently healthy and had good body condition. One year before sampling began all animals were administered the Hemoplex® supplement (2 ml per 10 kg weight) and the Catosal® metabolic stimulant, both by intramuscular injection. The two cervid species were kept in separate corrals (30 m long by 12.5 m wide) surrounded by deer fence and with 50 m distance between corrals. Each corral was equipped with two drinking bottles, was roofed, and an 80 % shade mesh placed at head height (1.2 to 60 cm above ground surface) to avoid eye contact between the species. Feces were cleaned daily. The animals were fed daily at 0800 h with alfalfa (20% ~ 2 kg per animal) and a balanced feed for sheep (80% ~ 4 kg per animal) containing crude protein (34%), fat (2%), crude fiber (5%), ash (17%) and moisture (13%). Water was freely available.

Feces samples

Parasite incidence and abundance can vary between seasons(23). Fecal samples were therefore collected during two seasons: rainy (September-November) and dry (MarchMay). In each sampling period, feces were collected from all animals once a month. At the first spontaneous defecation, approximately between 0600 and 0900 h, the portion of excreta not in direct contact with the ground was collected manually using latex gloves, placed in a sealed, marked plastic bag, and stored at 4 °C in a cooler. For analysis the samples were transported to the Optical Microscopy Laboratory of the Faculty of Biological and Agricultural Sciences, Orizaba-Córdoba region, Universidad Veracruzana. A total of 60 stool samples were collected from each species, 30 during the rainy season (2016) and 30 during the dry season (2017).

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Feces analysis

Parasites and their eggs were extracted using flotation in a saturated sugar solution, based on the separation of particles of greater and lesser density. Eggs and whole individual parasites were collected and fixed on slides for later morphological identification(24).

Endoparasite morphological identification and abundance

The extracted parasites were identified by comparison of anatomical characteristics with those reported in the Parasitological Catalogs(25), and books on parasitology and the parasite diseases of domestic animals(12). Oocyst taxon genus was identified based on the number of sporozoites present(26). Abundance was considered the number of endoparasites recorded in each cervid host, since this is an indirect measure of prevalence(20,27).

Statistical analysis

Cervid species (O. virginianus and M. temama), sex (males and females) and collection season (dry and rainy) were treated as sources of variation. The response variable was parasite abundance in each cervid host since this is considered an indicator of nematode parasite infection(27). Prevalence (%) by sex, and cervid species was calculated, and descriptive statistics of abundance generated for each source of variation. A KruskallWallis test was applied together with the Fisherâ&#x20AC;&#x2122;s LSD (least significant difference) means comparison test (Îą= 0.05) to identify which parasite species was most abundant in each cervid species, sex and season (rainy or dry). A Ď&#x2021;2 test was run to determine the association of parasite abundance to cervid species and sex.

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Results

Seven endoparasite genera were identified among the two studied cervid species: Ascaris sp., Eimeria sp., Estrongilido sp., Paragonimus sp., Parascaris sp., Strongyloides sp. and Taenia sp. Abundances varied between the seasons and cervid species (Figure 1; Table 1). All (100%) the experimental animals exhibited endoparasites. The gastrointestinal parasite genera Parascaris sp., Paragonimus sp., and Taenia sp. were prevalent in O. virginianus and absent in M. temama.

Figure 1: Endoparasite abundance in O. virginianus and M. temama in the El Pochote UMA. Different lowercase letters above the bars indicate significant difference (P â&#x2030;¤ 0.05)

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Average abundance

SD

M. temama

Ascaris sp.

Eimeria sp.

Estrongilido sp.

Strongyloides sp.

Taenia sp.

2.00 1.67 1.67 0.67 0.33

1.4 1.7 1.2 1.1 0.8

O. virginianus

Ascaris sp.

Eimeria sp.

Paragonimus sp.

Parascaris sp.

2.33 1.67 0.33 0.33

2.0 1.2 0.8 0.8

M. temama

Rainy

Season Cervid

Dry

Parasite Species

Ascaris sp.

Eimeria sp.

Estrongilido sp.

Strongyloides sp.

3.00 0.33 0.33 0.33

0.0 0.8 0.8 0.8

O. virginianus

Table 1: Endoparasite genera identified in O. virginianus and M. temama, and their average abundance during the rainy and dry seasons at El Pochote UMA, Veracruz, Mexico

Ascaris sp.

Estrongilido sp.

Parascaris sp.

Strongyloides sp.

1.33 1.67 0.67 0.33

1.1 1.7 0.8 0.8

SD= standard deviation.

No significant effect (P>0.05) on parasite abundance was observed for season or cervid species (Table 2). Differences (P<0.05) in abundance were identified between the endoparasite genera, with Ascaris sp. being the most abundant genus (Table 3).

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Table 2: Effect of season (dry vs. rainy), host cervid species (O. virginianus and M. temama) and endoparasite genus

Model Seasons Cervid species Parasite

SS 13.06 0.14 1.07 11.86

DF 8 1 1 6

MS 1.63 0.14 1.07 1.98

Error Total

31.2 44.26

57 65

0.55

P-valor 0.007 0.619 0.167 0.004*

F 2.98 25 1.95 3.61

SS= sum of squares; DF= degrees of freedom; MS= mean squared; F= table value; P-valor = significance value.

Table 3: Average abundance by endoparasite genus in the two studied cervid species Endoparasite Taenia sp.

Paragonimus sp.

Strongyloides sp.

Parascaris sp.

Estrongilido sp.

Eimeria sp.

Ascaris sp.

N 6 6 12 6 12 12 12

Average 0.17 0.17 0.33 0.5 0.92 0.92 2.17

SE 0.57 0.57 0.4 0.57 0.4 0.4 0.4

Differences a a a a a a b*

N= sample size; SE= standard error; *differences at Îą = 0.05.

The parasite genus Eimeria sp. was associated with male M. temama and female O. virginianus (X2= 8.57, d.f. 1; P= 0.0034). Taenia sp. was present in one male M. temama and Paragonumus sp. in one female O. virginianus (X2, P<0.05). The genera Parascaris sp., Ascaris sp., Estrongilido sp., and Strongyloides sp. exhibited no association (X2, P>0.05) to sex or cervid species.

Discussion

Seven endoparasite genera were identified in O. virginianus and M. temama. This study constitutes the first report of these endoparasites in UMAs in situ or ex situ in the state of Veracruz. The genera Ascaris sp. and Eimeria sp. were recorded in both seasons and both

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cervid species, whereas Taenia sp. was present only during the rainy season (P>0.05) in M. temama.

In various studies analyzing different sample sizes (20 to 200 feces samples) at different times of year (dry, transition and rainy seasons), eight parasite genera have been described of which Eimeria sp. and Strongyloides sp. had the highest abundances(20,27,28); both these genera were also recorded in the present results. Another study of approximately 1,000 feces samples from O. virginianus collected from three corrals during a one-year period identified seven endoparasite genera(15). These included Eimeria sp. and Strongyloides sp., both of which were reported in previous studies and the present results.

In the present study Ascaris sp. was the most prevalent genus in both cervid species, with levels significantly higher than the other identified parasite genera. Ascarididae Family parasites are present throughout the animal kingdom, and are commonly found in the intestines of fish, amphibians, reptiles, birds and mammals. However, they tend to cause the most damage in domestic species such as pigs, horses, cattle, poultry, dogs and cats, but can also be found in wild mammals such as foxes(27). Ascaris sp. nematode eggs have been identified in the primates Alouatta fusca and A. seniculus, most probably via anthropozoonotic contamination(29), that is, cross-contamination from caregivers. Species belonging to the genus Eimeria sp. mainly parasitize mammals, and are common parasites of the host digestive canal where they take root in the epithelial cells and destroy them, causing the disease known as coccidiosis(25).

Compared to wild populations, captive ungulates have a higher number of endoparasites(13). This may be due to an increased risk of parasite transmission from their general dependence on feed prepared by humans, often without proper sanitary protocols, and excess moisture in corrals from puddles and water leaking from drinking bottles(30).

In addition, the stress of captivity can reduce immunological capacity, and promote parasitosis and greater parasite diversity and abundance(31).

The lack of inter-seasonal differences in parasite types and abundance observed in the present results coincide with previous studies(20,28). A favorable climate for parasite transmission in both of the seasons is the most probable reason for this lack of difference.

All the identified parasites utilize intermediate hosts, meaning greater or lesser parasite frequency in the studied cervids would depend on the presence of these hosts(32).

Wild animals are hosts to a variety of parasites but are normally able to keep their parasite communities in balance, preventing disease symptoms from appearing(33). Factors that can weaken a hostâ&#x20AC;&#x2122;s immune system include age, malnutrition and stress, among others, all of which can increase the risk of excessive parasitization(34). The spread of endoparasites between wild and domestic species can be dangerous(35). Variations in parasite abundance and richness between cervid species can be related to habitat, coexistence with other species, enclosure size and characteristics, and population density(36,37). Future research will need to consider the characteristics of enclosures at UMAs to detect the risk factors associated with parasitosis.

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Parasite dynamics over time may be influenced by host sex since parasite prevalence by host sex is linked to individual traits such as age and body condition(38). For example, adult Alces alces have a higher parasite load than sub adults during the mating season(39), whereas in O. virginianus this occurs outside the mating season(40). In the present study Eimeria sp. and Paragonumus sp. were prevalent in O. virginianus females during the mating season, possibly due to infection by males. Of note is that the presence of Taenia sp. in one male M. temama was not necessarily sex-dependent but more probably due to high humidity in the enclosure, tree leaves falling into the corral and/or ingestion of plant sprouts. Further study is needed on the parasitology of M. temama to strengthen management programs for captive populations, and contribute to their conservation, as has been done successfully with other ungulates (e.g. Gazella gazella)(41).

Conclusions and implications

The analyses reported here of gastrointestinal parasite prevalence and diversity in captive O. virginianus and M. temama identified seven parasite genera among the two cervid species. Both species can be treated with specific deparasitization treatments to prevent excessive parasite load, which can cause host morbidity or mortality. Based on the present data the dry season would be the best season in which to apply deparasitization treatments.

Ascaris sp. was the most abundant in both cervids, followed by Estrongilido sp. and Eimeria sp. These findings highlight the importance of identifying parasitosis risk factors in captive wildlife to optimize prevention and mitigation strategies. The present results have implications for the conservation and management of captive O. virginianus and M. temama, as well as for prevention of zoonotic diseases that can affect wild and domestic animal populations, with possible financial impacts for producers.

Acknowledgements

The authors thank Arantxa Penagos de la Llave for technical assistance and access to El Pochote UMA; Carlos Manuel Galรกn Pรกez for laboratory assistance; and the Genetic Resources Management and Conservation Unit of the Faculty of Biological and Livestock Sciences for bioinformatic analyses.

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https://doi.org/10.22319/rmcp.v10i4.4703 Review

Supplementation of ascorbic acid to improve fertility in dairy cattle. Review

Juan González-Maldonado a Raymundo Rangel-Santos a* Raymundo Rodríguez-de Lara a Gustavo Ramírez-Valverde b J. Efrén Ramírez Bribiesca c J. Cruz Monreal-Díaz a

a

Universidad Autónoma Chapingo. Departamento de Zootecnia. Estado de México, 56230, México. b

Colegio de Postgraduados. Departamento de Estadística. Estado de México, México.

c

Colegio de Postgraduados. Departamento de Ganadería. Estado de México, México.

*Corresponding author: rangelsr@outlook.com

Abstract: Ascorbic acid (vitamin C: VC) is an antioxidant that participates in the regulatory processes involved in the development of ovarian structures and fertility. However, supplementation of VC to dairy cattle to improve fertility has received little attention. However, reduced fertility in dairy cattle associated with high genetic merit for milk production and heat stress, which also diminish blood VC concentrations, suggest a potentially beneficial role for VC supplementation. The objectives of this review are to contribute to the current knowledge regarding the relationship between VC and fertility and to share many experiences that support the relevance of VC supplementation to improve dairy cattle reproductive performance.

Key words: Antioxidants, Ascorbic acid, Reproduction.

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Received: 18/10/2017 Accepted: 11/07/2018

Introduction

The economic gains of a dairy farm increase as cattle reproductive efficiency improves. However, the historical decline in fertility of Holstein dairy cows hampers profitability, but at the same time offers a challenge to develop strategies to enhance reproductive performance. The cause of low fertility in modern Holstein dairy cattle is multifactorial. The main associated are the improvement in genetic merit to milk production, the inability to meet nutritional requirements, the adverse environmental conditions and the susceptibility to diseases that compromise oocyte and embryo viability(1).

The exact cause of low fertility is unknown, but oxidative stress could be implicated. Oxidative stress results when free radicals exceed the organismâ&#x20AC;&#x2122;s antioxidant capacity(2). Free radicals are molecules with an unpaired electron, highly reactive and normally produced in living aerobic organisms(3). At a controlled production rate, they serve as molecular signals, but over production may result in a pathological process(4). Sources of free radicals that may surpass the cowâ&#x20AC;&#x2122;s antioxidant capacity include milk production yield and heat stress. High milk producers have higher blood concentrations of oxidative stress markers than those that produce less milk(5), and are also more susceptible to heat stress(6). This is relevant because heat stress produces oxidative stress in dairy cattle(7). Oxidative stress creates unfavorable intraoviductal conditions(8) that result in embryo death(9).

Oxidative stress is counteracted by antioxidants, which suppress the deleterious effect of free radicals by giving them one electron. One antioxidant that is relevant to mammalian reproduction is water soluble ascorbic acid (vitamin C, here after referred to as VC) (10). The chemistry and biological functions of VC in cattle have been reviewed by others(11), and will therefore be not further addressed here. However, the impact of VC supplementation on dairy cattle fertility has been poorly studied, probably because bovines can synthetize their own VC in the liver from glucose(11), and thus have no need for external supplementation(12). Nevertheless, the same factors that are blamed for disrupting fertility (high milk yield and heat stress) decreased blood VC concentration in dairy cattle(13,14). It might be suspected that if VC is necessary for reproduction, a 1001

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diminished supply could affect fertility. Previous research has shown that supplementation of VC is advantageous to improving reproductive performance of repeat breeder cows(15) and dairy cattle under heat stress conditions(16). It is important to consider that VC supplementation and impacts on dairy cattle fertility deserve more attention.

The objective of this review is to contribute to the current knowledge regarding the relationship between VC and fertility, and to share the experiences on the relevance of VC supplementation to improve dairy cattle reproductive performance.

Ovarian follicle and corpus luteum development Vitamin C deficiency increases the number of atretic follicles(17). However, supplementation attenuates follicular cell apoptosis(18), promotes primordial follicle activation(19), increases the population of growing follicles(20) and reduces those in atretic state(21). These findings suggest that VC supports the development of healthy ovarian follicles.

The ovarian follicle is under constant structural remodeling. Its diameter increases up to 475 times from the primordial to the ovulatory size(22,23). This increase in size implies a constant remodeling of the follicular basal lamina(24) and changing intrafollicular concentrations of VC, which are higher in smaller follicles(25). The follicular basal lamina gives the follicle stability and serves as a molecule filter(24), but it needs increasing amounts of collagen as it increases in size(26). Since VC is a cofactor in collagen synthesis(27), it is logical to assume that VC would be required in higher quantities in developing follicles. In fact, supplementation of VC improves follicle survival and increases the odds of a follicle reaching preovulatory size(28). This could be explained by VC preventing follicular cell death and maintaining base membrane integrity as the follicle grows(18,29).

Under an environment with a regressing corpus luteum, the dominant follicle will reach the preovulatory state. At this stage, VC is needed for normal follicular steroidogenesis(30), which is accomplished by promoting the expression of key enzymes involved in steroidogenesis such as aromatase and P450 cholesterol side-chain cleavage(31). However, as the follicle grows there is a reduction in the concentration of VC. Preovulatory follicle has lower intrafollicular concentrations of VC than large follicles from other stages of the estrus cycle(32). This reduction may result from a higher intrafollicular concentration of IGF-I, which induces the uptake of VC by granulosa cells(33). The LH surge also causes a reduction in VC concentrations(34), probably by increasing intrafollicular reactive oxygen species (ROS) concentrations(35).

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The reduced intrafollicular concentrations of VC at the preovulatory stage may be part of the mechanism controlling ovulation. The collagen in the follicular basal lamina is reduced as the follicle grows, which makes it more expandable and easier to remodel(36). The reduced intrafollicular concentrations of VC, together with degradation of collagen in preovulatory follicles, results in the weakening and rupture of the basal lamina, which are crucial events that can lead to preovulatory follicle rupture(37,38).

The number of pregnant women with luteal phase defects increases after supplementing VC, which likely worked by increasing corpus luteum progesterone(39). Corpus luteum diameter(32) and concentration of progesterone(40) has been related to VC concentration. In addition, the content of VC is higher during the early stages of corpus luteum development(41), reaching the highest concentration, at least in bovines, on d 12 of the estrous cycle(42). Furthermore, one key element in the relationship between VC and the corpus luteum is that this vitamin is required, as mentioned previously, for the synthesis of collagen, which is essential for corpus luteum development(43).

Vitamin C and fertility

Vitamin C improved fertility(44). The enhancement in oocyte and embryo development could explain these results(45,46). Unfortunately, the limited information available on this topic has been obtained mostly under in vitro conditions. In contrary, high doses of VC might harm both the oocyte (750 µM mL-1) and embryo development (˃200 µM in culture medium)(47,48), possibly resulting from a pro-oxidant effect of VC. The VC at low concentrations can act as an antioxidant while the opposite occurs at high concentrations, which may depend on the concentration of metal ions (iron)(49). A pro-oxidant effect of VC could be expected as the concentration of metal ions increases(50). The latter may be true under in vitro conditions, but it is unlikely to occur in living organisms(51).

Relationship between vitamin C and vitamin E

Vitamin C may control follicular development by interacting with other elements known to affect fertility. It is well accepted that after vitamin E fulfills its antioxidant activity, it can be reactivated

by VC(52), which increases its availability(53). Vitamin E deficiency disrupt follicle development, produces estrous cycle abnormalities and pregnancies loss(54). It is not known exactly the blood concentration at which vitamin E can be considered as adequate or deficient in cattle. Vitamin E blood concentrations ˃1 µg mL-1 can considered as adequate, but there is not agreement on this topic(55). In addition, it is unaware of any vitamin E recommendation for optimal reproduction 1003

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performance in cattle. However, previous work (see next section of this manuscript and reference 16), have shown that supplementation of 3,000 IU of vitamin E during a synchronized estrus is advantageous to improve fertility in dairy cattle.

The relationship between vitamins C and E in reproductive issues has received little attention. An antioxidant system, that includes vitamins C and E, is activated during ovarian steroidogenesis(42).

The supplementation with vitamin C (125 mg kg-1 d-1) and E (75 mg kg-1 d-1) to rats increases blood concentrations of testosterone, FSH and LH(56). These higher concentrations of gonadotropins are in agreement with the fact that VC stimulates its secretion from pituitary(57). Studies in vitro have shown a positive effect of vitamin C and E on oocyte quality and embryo development when supplemented separately, but not together(53,58,59). Addition of vitamin C and E to the maturation medium impairs blastocyst occurrence rate by preventing the formation of the amount of ROS necessary for oocyte developmental competence(53). This is acceptable because a tonic supply of ROS has proved to interrupt oocyte meiotic arrest(60). However, it is unlikely that the situation described by Dalvit et al(53) also occur in vivo because supplementation of both vitamins has resulted in more pregnancies in dairy cattle (see next section of this manuscript and reference 16).

In addition, an improvement in embryo quality after injecting superovulated cattle donors before estrus with two antioxidants, β-carotene and vitamin E, has been reported(61).

In vitro studies resemble conditions found under physiological conditions. However, contrary to in vivo conditions, in vitro systems are static, where metabolic activity, nutrient adsorption and storage, as well as waste disposal are limited by time and medium culture conditions. In addition, adaptation to changing conditions is faster in in vivo systems. Therefore, when supplementing vitamin C concomitant with vitamin E, the living organism choose between to storage, to excrete or to distribute them to where they are needed. This avoid possible harmful effects on cell biological process such as those affecting oocyte quality and embryo development.

Experiences supplementing vitamin C to dairy cattle

The evidence presented here supports a prominent role of VC on fertility. The first approach to evaluating the effect of VC on dairy cattle fertility was carried out on cows under heat stress conditions(16). The results of this study revealed that injecting both vitamin C and E results in more pregnant cows than administering one or the other separately. In addition, no effect of vitamins supplementation was found on preovulatory follicle and corpus luteum size. These findings led to assume that the increased number of pregnant cows, obtained after supplementing both vitamins, was the result of cows carrying a healthier follicle, which eventually becomes a corpus luteum that produces more progesterone than that carried by non-supplemented cows. To prove this assumption a second trial was carried out (T2).

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The general procedure, as well as justification of the doses and time of vitamins injections used in T2 is explained in detail elsewhere(16). Briefly, the follicular wave of the cows was synchronized with a device containing 1.0 g of progesterone (Sincrogest®, Ourofino Agronegocio), inserted intravaginally for 8 d, and an intramuscular (i.m.) injection of 250 µg of GnRH analogue (GnRH, Sanfer). Estrus behavior was induced by an i.m. injection of 500 µg of cloprostenol (Celosil, MSD, Animal Health) at intravaginal device removal. Once the intravaginal device was withdrawn, the animals were constantly monitored by direct observation for signs of standing estrus. The cows were artificially inseminated 12 h after standing estrus with a single dose (approximately 20 x 106 spermatozoa) of semen from a single bull of proven fertility. Cows that received vitamins (n=32.

Control group, n=28) were injected with a single i.m. injection of 3,000 IU of vitamin E ((±) αtocopherol, Sigma-Aldrich)) on d-5 (day 0 is the day of intravaginal device removal) and subcutaneous (s.c.) injections with a total dose of 3,000 mg of VC (ascorbic acid, Q.P., Reasol) on d-5, immediately after estrus detection and 2 d after artificial insemination.

As depicted in Table 1, vitamin supplementation did not affect preovulatory follicle or corpus luteum size. In addition, no effect was noted on blood estradiol and progesterone production.

However, in agreement with previous findings(16), pregnancy rate was higher (P=0.06) in cows injected with vitamins 45 d after artificial insemination (Figure 1).

Table 1: Least square means (±SE) for the effect of injecting vitamin C and E on ovarian structures size, estrus presentation and hormone concentrations in Holstein dairy cows

Variable Time to estrus, h Diameter of the preovulatory follicle, mm Estradiol concentration, pg mL-1 Area of the corpus luteum, cm2 Progesterone concentration, ng mL-1

Treatment Control (n=28) 57.1±4.89 18.3±0.57 45.1±3.12 6.9±0.39 10.8±1.60

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Vitamin Cand E (n=32) 58.4±4.57 17.2±0.60 46.8±3.26 6.7±0.37 12.5±1.60

P-value 0.67 0.21 0.71 0.74 0.26

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Figure 1: Pregnancy rate 35 and 45 days after AI in control group (black bars, n=28) and Holstein dairy cows injected with vitamins C and E (grey bars, n=32)

Pregnancy rate (%)

80 70 60 50 40 30 20 10 0

35

45 Days after AI

Estrus synchronization is a reproductive tool used in dairy cattle to improve fertility because it makes possible to control the onset of estrus. However, most technicians prefer to use fixed-time artificial insemination because it avoids the need for estrus detection. In addition, it is very convenient because all the cows are scheduled to be inseminated at the same time. Based on previous findings, it was decided to incorporate vitamin C and E injections to a fixed-time artificial insemination protocol (T3) to increase the number of pregnant cows. Briefly, cows were injected i.m. with 250 µg of GnRH analogue on d 0, 7 days after administering an i.m. injection of 500 µg of cloprostenol. A second dose of GnRH was given to cows 48 h after injecting cloprostenol.

Insemination was performed 14 to 16 h after the second injection of GnRH. Injections of vitamins C and E were carried out as mentioned in T2, but the first injection of vitamin C and E was given 3 d after the first injection of GnRH. The second and third injections of VC were administered just after the second injection of GnRH and 2 d after artificial insemination.

The effect of vitamin C and E injections on preovulatory follicle diameter (16.8 ± 0.70 vs 16.2 ± 0.77 mm, for control group and cows injected with vitamins) and area of the corpus luteum (5.4 ± 0.48 vs 6.1 ± 0.50 cm2, for control group and cows injected with vitamins) were not significant.

Similar to previous results(20) and with T2, a greater percentage of cows were found to be pregnant 30 and 45 d after artificial insemination in the group of cows supplemented with vitamins than those in the control. However, the differences are not significant, most likely because of the small sample size used in T3 (cows injected with vitamins, n=16. Control group, n=17), Figure 2.

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Figure 2: Pregnancy rate 30 and 45 days after AI in control group (black bars, n=17) and Holstein dairy cows injected with vitamins C and E (grey bars, n=16) 80

Pregnancy rate (%)

70 60 50 40

30 20 10 0 30

45 Days after AI

The results obtained show that VC injections in combination with vitamin E are a feasible way to improve dairy cattle fertility. This effect is not mediated by changes in preovulatory or corpus luteum size, nor by affecting estradiol or progesterone production. A likely explanation for the increased pregnancy rate in dairy cattle injected with vitamins C and E is that cows supplemented with vitamins produce better quality oocytes and embryos than those not supplemented.

Conclusions In conclusion, contrary to current thought, evidence suggest that supplementation of vitamin C to dairy cattle improve fertility. However, there is a need to investigate the optimal dose and time of vitamin C supplementation to improve dairy cattle reproductive performance.

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32. Serpek B, Baspinar N, Haliloglu S, Erdem H. The relationship between ascorbic acid, oestradiol 17β and progesterone in plasma and in ovaries during the sexual cycle in cattle. Rev Med Vet 2001;152:253-260.

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34. Guarnaccia MM, Takami M, Jones EE, Preston SL, Behrman HR. Luteinizing hormone depletes ascorbic acid in preovulatory follicles. Fertil Steril 2000;74:969-963.

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54. Martin AJP, Moore T. Some effects of prolonged vitamin-E deficiency in the rat. J Hyg 1939;39:643-650.

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https://doi.org/10.22319/rmcp.v10i4.4575 Technical note

Effect of Moringa oleifera intake on productive and toxicological parameters in broiler chickens

Martha K. Fuentes Esparza a Teódulo Quezada Tristán a* Salvador H. Guzmán Maldonado b Arturo G. Valdivia-Flores a Raúl Ortíz-Martínez a

a

Universidad Autónoma de Aguascalientes. Centro de Ciencias Agropecuarias, Departamento de Clínica Veterinaria, Av. Universidad N° 904, 20131 Aguascalientes, Aguascalientes, México. b

Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias. Laboratorio de Alimentos Funcionales, Guanajuato. México.

*Corresponding author: tquezada@correo.uaa.mx

Abstract: Poultry farming is common in rural Mexico in part because it provides extra household income. Alternative protein sources for poultry feed are needed for these systems. An evaluation was done of how 10% inclusion of Moringa oleifera leaf in diets for broilers affected productive and blood parameters. A proximal analysis, and Fe, Ca and tannins contents were quantified for moringa leaf flour. Four productive and six blood parameters (two proteins and four enzymes) were evaluated in Ross-308 broilers. Histopathological analyses were done of liver and kidney tissue. The moringa leaf flour contained 33.4% protein, and had high iron (19.7 mg/100 g) and calcium (2593.3 mg/100 g) contents, as well as low tannins content (24.4 mg CE/100 g). Compared to a control, daily weight gain decreased 12 % in the moringa leaf treatment while productivity index values decreased 20%. No differences were observed between the control and treatment in terms of protein content, and alanine aminotransferase and aspartate aminotransferase activities.

However, differences in albumin, alkaline phosphatase and the glutamyl transpeptidase 1013

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levels suggest possible liver and renal damage in the moringa leaf treatment. These results were not confirmed in the histological analysis. Further research is needed using lower moringa leaf flour inclusion levels to compare to the present results and better define possible inclusion levels in poultry diets.

Key words: Broilers, Moringa, Productivity, Serology, Histopathology.

Received: 14/08/2017 Accepted: 20/02/2018

Commercial poultry farming is a major industry in Mexico(1). Poultry feed can include pastes from cottonseed, peanuts, safflower seeds, sesame seeds and coconut as sources of protein and other nutrients, although these are all lysine deficient. Soybean paste, in contrast, is a protein source with an optimal amino acids balance for animal feed(2).

Poultry farming, including growing of backyard chickens, is important in the countryâ&#x20AC;&#x2122;s rural communities since it provides families with meat and eggs as well as surplus birds for sale(3). Greater research is needed on protein sources from locally available resources which can be added to the poultry feeds used in rural areas such as corn, food scraps and other materials.

The tree Moringa oleifera has been studied as a protein source for animals(4,5) and humans(6). Native to India and Pakistan, it was introduced to Mexico(7). The moringa leaf contains 20 to 30 % protein, 5.0 to 7.5 % fat and 25 to 31 % fiber(8,9). It is also a good source of iron, calcium and vitamin C(9). Due to the other phytochemicals it contains, moringa leaf has also been recommended for treating gastrointestinal ulcers(10), lowering cholesterol levels and as a source of antioxidants(11).

Moringa leaf has been used in feed for rabbits(12), goats(5), sheep(13) and cows(14), among other domestic species. Only minimal research has been done on the use of moringa leaf as an ingredient in feed for broilers. Studies have been done evaluating its effect on intestine morphology and the condition of other internal organs(15,16), breast fatty acids content(17), tibia strength and mineral content(18), animal weight, hematology and immune response(4,19). Only two of these studies have evaluated moringa leafâ&#x20AC;&#x2122;s effect on weight gain(4,19), and one used Moringa stenopetala(4), a different species in the same genus.

The present study objective was to characterize nutritional content and vitamin C and condensed tannins levels in Moringa oleifera leaf flour, and evaluate its effect on productive and toxicological parameters when included in diets for broiler chickens.

Leaves were harvested from two-year-old M. oleifera trees in an orchard in the municipality of Gasca, in the state of Guanajuato, Mexico. Immediately after harvest the leaves were washed and disinfected with a 0.1% sodium hypochlorite solution followed 1014

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by sterile distilled water, and dried in the shade. The dried leaves were milled with a hammer mill (2.0-3.0 mm sieve) and stored at -20 °C until analysis and inclusion in broiler diets. Experimental animals were 180 ROSS-308 line broilers (39 ± 1.5 g initial weight), mixed, which were one-day old at experiment outset.

The animal trial was carried out at the Poultry Unit of the Livestock Area of the Zootechnical Station of the Autonomous University of Aguascalientes, in the municipality of Jesús María, in the state of Aguascalientes. The chickens were randomly selected to form two groups with three repetitions of thirty birds each. They were fed either a conventional feed (T1) or a conventional feed plus 10% moringa leaf flour (MF).

Both diets were isoproteic and isoenergetic(20) (Table 1). Feed and water were freely available throughout the 42-day experimental period.

Table 1: Experimental diet ingredients and nutritional composition Moringa oleifera leaf flour (%) Ingredients (g/kg)

0 10 Broilers 1-21 days

0 10 Broilers 22-42 days

Sorghum (8.5%)

600

560

685

640

Soy paste (46%) Moringa leaf flour (33.4%) Vegetable oil Micro pollo eng 1a Micro pollo eng 2b

335 0.0 25 40 0.0

280 100 20 40 0.0

250 0.0 25 0.0 40

200 100 20 0.0 40

3150 21.0 0.52 0.81 0.73 0.23 0.13 4.0 0.884 0.213 1.236 1.137 0.350

3150 21.0 0.54 0.83 0.75 0.24 0.14 4.4 0.902 0.213 1.263 1.163 0.451

3200 18.0 0.48 0.75 0.66 0.20 0.11 3.5 0.824 0.205 1.115 1.029 0.311

3200 18.0 0.50 0.75 0.68 0.21 0.12 4.0 0.411 0.205 1.139 1.050 0.824

Calculatedc Metabolic energy/bird, kcal/kg Crude protein, % Total lysine, % Digestible lysine, % Digestible methionine, % Digestible methionine+cysteine, % Digestible threonine, % Digestible tryptophan, % Digestible arginine, % Calcium, % Available phosphorous, % Na, % Crudeber, %

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Rev Mex Cienc Pecu 2019;10(4):1013-1026 a Initiation feed supplement (g/kg product): 12.6 L-lysine HCl; 56.6 methionine; 10.2 60% choline chloride (National);274 21% orthophosphate; 10 25% copper sulfate; 50 ground salt (Roche); 10 mineral oil 90 NF (Petroblanc) ; 10 Bacitracin B.M.D. 110; 169.4 soybean paste; 40 baking soda; 7.6 Ronozyme VP; 10 Sacox; 268 M-20 calcium; 60 V/init broiler plus NF/3*Ton; 7.5 Vip/Prem/A/Prem Lucantin yellow at 10; 4 Invivo/P/AYC/Prem Ronozyme Hiphos GT/1*Ton. b

Mineral, vitamin and amino acid supplement (kg product): 10,000,000 IU vit A; 3,000,000 IU vit D3; 7,000 IU vit E; 1.5 g vit K; 4.4 g riboflavin;15 mg vit B12; 5.5 g pantothenic acid; 25 g niacin; 25 mg biotin; 1 g thiamine; 0.5 g folic acid; 32.5 g iron; 50 g manganese; 50 g zinc; 4.5 g copper; 150 mg selenium; 450 mg iodine; 150 g choline; 50 g antioxidant. Vehicle: 3 Kg.

a

Finishing feed supplement (g/kg product): 25 L-lysine HCl; 1.8 L-threonine; 64.8 methionine; 64.8 60% choline chloride (National); 186 21% orthophosphate; 10 25% copper sulfate; 50 ground salt (Roche); 10 90 NF mineral oil (Petroblanc); 10 Bacitracin B.M.D. 110; 235.6 soybean paste; 30 baking soda; 5 Ronozyme VP; 11 Sacox; 5 Progen 20; 244 M-20calcium. 64 V/init broiler plus NF/3* Ton; 37.5 Vip/Prem/A/Prem Lucantin yellow at 10. b

Mineral, vitamin and amino acid supplement (kg product): 10,000,000 IU vit A; 3,000,000 IU vit D3; 7,000 IU vit E; 1.5 g vit K; 4.4 g riboflavin;15 mg vit B12; 5.5 g pantothenic acid; 25 g niacin; 25 mg biotin; 1 g thiamine; 0.5 g folic acid; 32.5 g iron; 50 g manganese; 50 g zinc; 4.5 g copper; 150 mg selenium; 450 mg iodine; 150 g choline; 50 g antioxidant. c

Calculated according to NRC (1994).

Proximal analysis of the moringa flour was done with established methods for protein (960.52), fiber (991.43 G, H), fat (920.85) and ash (923.03)(21). Carbohydrate content was calculated by the percentage difference compared to the previous analyses. Iron and calcium content were determined by atomic absorption spectrometry(22), and vitamin C by high performance liquid chromatography (HPLC)(23). Total phenols(24) and condensed tannins(25) contents were also determined.

Daily records were kept of feed provided and consumed. At the end of the experiment calculations were done of average weight (AW), daily weight gain (DWG), feed conversion rate (FCR) and the productivity index (PI)(26).

After 42 d blood samples were taken to quantify total proteins (TP) and albumin (ALB); as well as blood levels of alanine amino transferase (ALT), aspartate amino transferase (AST), alkaline phosphatase (ALP) and gamma glutamyl transpeptidase (GGT)(27).

Blood samples were taken from three chickens selected from each of the three replicates of the two groups originally formed. These were killed by cervical dislocation following the Ethical Management and Humanitarian Sacrifice Guide of the Autonomous University of Aguascalientes. Samples (1 cm3) of liver and kidney were taken for histopathological analysis(28). In the liver, evaluations were done of the condition of the histo-architecture of the hepatic parenchyma, presence or absence of inflammation cells, the Mรถll space, the Disse space, the hepatic sinusoids and the bile ducts. In the hepatocytes evaluations were done of the nucleus-cytoplasm relationship, cell nucleus characteristics (pyknosis, karyolysis and karyorrhexis) and presence or absence of lipid vacuoles in the cytoplasm.

The data were processed with an analysis of variance (ANOVA) and Student t test with repeated measurements and a P<0.05. All statistical analyses were run with the Statistical Analysis System statistical package (SAS, 2001)(29).

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The moringa leaf flour in the present study had a higher protein content (33.4%) than reported elsewhere for this species (20.0 to 29.0 %)(8,9,30) (Table 2). The higher protein content in the present results was probably because the reported data were from other moringa species and/or local environmental conditions affected protein content(9). The remaining compounds were within ranges reported in the literature(8,9,30). Moringa is known to be a good source of iron and calcium (Ca= 2.6 g; Fe= 19 mg/100g)(9), which is consistent with the present results (Table 2)(31). Moringa may therefore contribute substantial amounts of calcium when added to animal feed. Its high iron content may assist in synthesis when added to broiler diets, and its vitamin C content could promote metabolism(32); indeed, the combination of these compounds could contribute to this phenomenon.

Table 2: Proximate analysis (%), and mineral and vitamin C (mg/100 g, DM) and condensed tannins (mg CE/100 g, DM) contents in Moringa oleifera leaf Compound

Content

Proximate chemical analysis (%) Protein Fiber Fat Ash Carbohydrates

33.4 ± 0.72 8.8 ± 0.70 8.1 ± 0.41 2.3 ± 0.46 47.4 ± 0.52

Minerals (mg/100 g DM) Iron Calcium Vitamin C Tannins (mg CE/100 g, DM)

19.7 ± 1.07 2593.3 ± 121 63.5 ± 1.63 24.4 ± 0.92

CE= catechin equivalents, DM= dry matter..

Use of sorghum with high tannin content (1,360 mg/100 g) in broiler diets is reported to reduce weight gain, feed intake and absorption of iron and calcium, among other minerals(33). The moringa leaf flour used in the experiment had a very low tannin content, suggesting it did not have any antinutritional effect in the experimental animals.

Average final weight in the MF treatment was 414 g less than those in the control group (Figure 1A). This weight loss reflects the lower feed intake in the MF treatment (Table 3). Average total feed intake per replicate in each treatment was 123.35 kg in the control group and 107.19 kg in the MF group. This 16.16 kg difference (P<0.05) in the MF treatment can be attributed to higher dietary fiber intake. This contrasts with a study in which broilers were fed diets containing from 5 to 14 % flour from M. stenopetala in which the 5 to 11 % M. stenopetala diets exhibited no weight difference compared to the 1017

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control(4). The difference between this study and the present results may be attributed to use of different moringa species in each. The difference in average weight in the present results was also reflected in the DWG results throughout the study (Figure 1B); at no time did DWG in the MF treatment exceed that in the control group.

Figure 1: Production parameters in the control and moringa flour treatment: A) Average weight at 42 d (g); B) Daily weight gain (g/d); C) feed conversion index (feed intake/weight); D) productivity index (daily weight gain) (viability)/(FCx10).

Asterisks indicate signficant difference (Student t, P<0.05, n=63).

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Table 3: Average feed intake (kg) in control and moringa flour treatment (n=30) Age (days)

Control (kg)

Moringa (kg)

7 14 21 28 35 42

4.42 ± 0.425 11.42 ± 0.915 18.00 ± 0.740 24.45 ± 1.198 30.90 ± 1.255 34.16 ±1.322 123.35 ± 7.255

4.04 ± 0.327 9.13 ± 0.721 12.49 ± 0.951 16.77 ± 1.114 27.65 ± 1.300 37.11 ± 1.625 107.19 ± 6.234

The lower the FCR the more efficiently a feed increases animal weight(26). In the present results the FCR in the two treatments was similar only in the second and third weeks (Figure 1C). In a study conducted with M. stenopetala, no differences in FCR compared to the control were observed in broilers fed diets containing from 5 to 8 % moringa leaf(4).

However, at higher moringa inclusion levels the FCR increased, with the 14 % moringa leaf diet resulting in the lowest weight. Productivity index (PI) values were 21% higher in the control group than in the MF treatment, probably because average weight, DWG and FCR results for the latter treatment were lower (Figure 2D).

Total protein (TP) content did not differ between the treatments (Figure 2A), although albumin content was higher in the MF treatment (Figure 2B). However, in both cases TP levels were lower than the reported reference values for poultry (3.1 to 5.05 g/dl)(34). The low globulin levels observed in both treatments in the present study may be associated with pathologies in the liver and/or kidney, or intrinsic factors typical of the studied animals.

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Figure 2: Blood protein and enzyme activity levels in the control and moringa flour treatment: A) total proteins (g/dl); B) albumin (g/dl); C) alanine amino transferase (IU/L); D) aspartate amino transferase (IU/L); E) alkaline phosphatase (IU/L); and F) gamma glutamyl transferase (IU/L).

ab

Different lowercase letters in the same parameter indicate significant difference (P<0.05 n=90).

No differences (P>0.05) were observed in ALT and AST activities (Figure 2C and 2D).

However, in the MF treatment the ALT level was 35.1 IU/L, near the reference maximum for this enzyme (9.5 to 37.2 IU/L)(35). Values above this maximum for ALT suggest hepatic and renal damage(36). The lack of a difference in AST is to be expected since chronic liver damage normally only causes subtle cell rupture, which manifests as normal or even decreasing AST levels(37), as was observed in the MF treatment (Figure 2D).

Values for AST activity above 275 IU/L may be related to liver or muscle disturbances, while values above 800 IU/L strongly suggest severe liver damage(38); this was not the case in the present study.

Activities for ALP and GGT differed between the treatments (Figure 2E and 2F). The reference ALP activity value is 600 IU/L(4), which is higher than occurred in both groups (Control and MF). Increases in ALP activity suggest bone growth, osteomyelitis and neoplasms(38), although it is not known if these problems arise at levels higher than the reference. The present results do not allow verification of whether this problem occurred in the MF treatment.

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The membrane enzyme GGT is associated with a protein and linked to the level of amino acid metabolism. Increased GGT activity can be caused by inflammation, biliary cholestasis and bile duct hyperplasia(39). A 25% increase in GGT activity was observed in the MF treatment (Figure 2F). Normal GGT values are between 0-10 IU/L(40). Based on the reference values, both groups of birds apparently exhibited active liver lesion(41).

No micromorphological differences were apparent between the groups (Figure 3A and 3B) since the evaluated structures exhibited no congestion, inflammation, scar tissue, liquefaction, lipid vacuoles, pyknosis, karyolysis or karyorrhexis.

Figure 3: Histopathologies in the liver of the control (A) and the MF treatment (B), and in the kidneys of the control (C) and the MF treatment (B)

A) (a) histological architecture of parenchyma; (b) basophilic cells; (c) MĂśll space; (d) sinusoids, (e) bile ducts; and (f) hepatocytes.

B) (a) parenchyma; (b) sinusoids; and (c) centrilobular vein.

C) (a) renal glomeruli; (b) proximal tubule; (c) Bowman capsule; (d) glomerular visceral layer.

D) (a) renal glomeruli; (b) reduced urinary space; (c) filtration barrier; (d) apparently normal macula densa; (e) whole proximal tubule.

Histopathological analysis of the kidneys (Figure 3C and D) examined the glomeruli, Bowmanâ&#x20AC;&#x2122;s capsule, the urinary space, podocytes, the vascular and urinary poles, the filtration barrier, the proximal tubules, the Henle loop, the distal tubules, the juxtaglomerular apparatus, the collecting tubules and the renal interstitium. No micromorphological differences were apparent between the two groups since the evaluated structures showed no congestion or inflammation and their structures were complete.

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Leaves from Moringa oleifera are a good source of protein and minerals. Addition of 10 % moringa flour to a broiler diet reduced production parameter values during the study period. Differences in protein and enzyme levels were detected although no visible toxic effects were apparent, which was confirmed in the histopathological studies. The higher GGT and ALP activity levels in the moringa flour treatment may be due to certain inflammatory processes in the bile ducts which occurred in both groups and were caused by factors other than consumption of moringa and feed. Establishing the proper inclusion levels of moringa leaf in poultry diets requires further research to compare with the present results.

The research reported here was financially supported by the INIFAP through the project “Potencial agronómico, nutricional y nutracéutico de la moringa (Moringa oleífera) y usos alternativos de la hoja y la pasta de la semilla” (No. Preci 11163719248). MKFE received a master’s scholarship from the CONACYT.

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18. Nkukwana TT, Muchenje V, Masika PJ, Hoffman LC, Dzama K, The effect of Moringa oleifera leaf meal supplementation on tibia strength, morphology y inorganic content of broiler chickens. South African J Anim Sci 2014; 44:228-239.

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29. Statiscal Analysis System (SAS). General Linear Models (GLM). North Caroline State Universality, Raleigh, North Caroline, USA: 2001.

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31. Ross B, Suplemento de nutrición del pollo de engorda. http://es.aviagen.com/assets/Tech_Center/BB_Foreign_Language_Docs/Spanish_T echDocs/RossBroilerHandbook2014-ES.pdf.

32. Nagórna-Stasiak B, Lazuga-Adamczyk A. The effect of iron on metabolism of vitamin C in chickens. Arch Vet Pol 1994;34(1-2):99-106.

33. Hassan IAG, Elzubeir EA, Tinay AH. Growth and apparent absorption of minerals in broiler chicks fed diets with low and high tannin contents. Trop Anim Health Prod 2003;35:189-196.

34. Meluzzi A, Primiceri G, Giordani R, Fabris G. Determination of blood constituents reference values in broilers. Poult Sci 1992;71:337-345.

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36. Landeros P, Reyes W, De Lucas E, Albarrán E, López Y, Quezada T. Evaluación de dos adsorbentes (manano oligosacáridos y clinoptilolita) en dietas de pollos de engorde contaminadas con fumonisina B1. Rev Salud Anim 2008;30:50-58.

37. Werner LL, Laboratory medicine - Avian and exotic pets. Vet Clin Pathol 2001;30(1):2-46.

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Philadelphia, Lippincott, USA: Wiley-Blackwell; 2004:479-492.

39. Fernandez A, Verde MT, Gascon M, Ramos J, Gomez J, Luco DF, et al. Variations of clinical biocheT1mical parameters of laying hens y broiler chickens fed aflatoxin‐ containing feed. Avian Pathol 1994;23:37-47.

40. Lumeij JT. Avian clinical biochemistry. In: Kaneko JJ, Harvey JW, Bruss ML editors. Clinical biochemistry of domestic animals. 5th ed. San Diego, California, USA: Academic Press; 1995:308-335.

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41. Fudge AM. Avian liver and gastrointestinal testing. In: Fudge AM editor. Laboratory Medicine â&#x20AC;&#x201C; Avian and Exotic Pets. 1rst ed. St. Louis, Missouri, USA: WB Saunders Co; 2000:47-55.

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https://doi.org/10.22319/rmcp.v10i4.4932 Technical note

Productive evaluation and cost:benefit analysis of lactating sows fed a diet containing nopal (Opuntia ficus-indica)

Gerardo Ordaz Ochoa a Aureliano Juárez Caratachea a Liberato Portillo Martínez b Rosa Elena Pérez Sánchez c* Ruy Ortiz Rodríguez d

a

Universidad Michoacana de San Nicolás de Hidalgo. Instituto de Investigaciones Agropecuarias y Forestales. México. b

Universidad de Guadalajara, Centro Universitario de Ciencias Biológicas y Agropecuarias. México. c

Universidad Michoacana de San Nicolás de Hidalgo, Facultad de Químico Farmacobiología. México. d

Universidad Michoacana de San Nicolás de Hidalgo. Facultad de Medicina Veterinaria y Zootecnia. México.

*

Corresponding author: rosa_elenap@yahoo.com

Abstract: Postpartum sows can suffer from hypophagia, which negatively effects productive and reproductive performance. An evaluation was done of productivity, production costs and the cost:benefit ratio in lactating hybrid sows administered one of two feeding regimes (FR): 1) conventional feed (CFR); and, 2) conventional feed with added cladodes of nopal Opuntia ficus-indica (OFR). A total of 116 parturitions were evaluated: 58 in the CFR (n= 17 sows), and 58 in the OFR (n= 17 sows). Seven variables were recorded: blood glucose (BG); daily feed intake (FId-1); body weight loss (BWL), weaning-estrus interval (WEI); repeated services percentage (RSP); non-productive days (NPD); and subsequent litter size (LS). Statistical analysis was done with the Fixed Effects Models (MIXED 1027

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SAS®) and economic evaluation with the cost-benefit analysis methodology. Compared to the CFR, sows in the OFR performed better (P<0.05) in terms of having lower preprandial BG (55.5 x 2.31 mg dL-1), higher FId-1 (5.3 x 0.17 kg d-1); lower BWL (6.0 %), WE (144 h), RSP (12.4 %) and NPD (36.0 d), and higher LS (11.2 piglets).

Values in the WEI sows were preprandial BG (70.0 x 2.31 mg dL-1); FId-1 (4.7 x 0.17 kg d-1); BWL (11.7 %); RSP (17.1%); NPD (50.0 d); and LS (9.8 piglets). The production cost weaned piglet-1 was $539.02 MXN in the OFR vs $590.81 MXN in the CFR, while profit sold piglet-1 was $216.68 MXN in the OFR vs $168.88 MXN. Inclusion of nopal in the diet of lactating sows reduced blood glucose levels and increased daily feed intake, thus lowering body weight loss in this stage and generating greater sow productivity and economic efficiency.

Key words: Cladode, Piglet, Glucose, Profitability.

Received: 08/06/2018 Accepted: 07/09/2018

A vital objective in livestock production is to minimize production costs and therefore maximize revenue per unit produced(1), but the indicators that most impact production costs must be identified if they are to be reduced(2). In swine production, the production cost from piglet to weaning has the greatest impact (≥31 %) on the cost per kilogram of pork(3). Consequently, a primary strategy for minimizing this cost is to raise sow prolificity(4). However, increasing this variable alone does not guarantee lower production costs since higher prolificity results in larger percentages of stillborn piglets(4), lower piglet weight at birth and weaning(5), and greater sow physiological fatigue when nursing larger litters(6). The latter is particularly notable in lean and hyperprolific sows(2).

During the peripartum-lactation transition period, high amounts of glucose(6) are required to support exponential growth of fetuses and mammary gland development(7,8); an insulin resistance physiological mechanism is implemented for this purpose(6). This phenomenon can cause the postpartum sow to begin lactation with a negative energy balance and to reduce her feed intake (known as hypophagia)(7,9). This in turn encourages mobilization of the sow’s body reserves to meet its nutritional requirements and produce milk(8,9).

Lactational hypophagia hinders sow productive efficiency(10) and therefore proves a challenge to reducing piglet-to-weaning production costs. Regulation of the metabolic hunger centers in lactating sows is crucial to augmenting feed intake and productivity(11).

This effect can be triggered by increasing fiber intake through addition of ingredients such as prickly pear nopal (Opuntia ficus-indica) to sow feed(12-16). The present study

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objective was to evaluate the effect of addition of nopal (Opuntia ficus-indica) to the feed of lactating sows on voluntary feed intake, and its impact on productivity, production costs and the cost:benefit ratio.

The trial was done at the swine production system of the Zootechnical Post of the Faculty of Veterinary Medicine and Zootechny of the Universidad Michoacana de San Nicolás de Hidalgo (FMVZ-UMSNH), in Tarímbaro Municipality, Michoacán state, Mexico. An evaluation was done of 116 births from 34 parturition sows (Yorkshire x Landrace x Pietrain) over a 24-mo period. The sows were selected by self-replacement from the FMVZ-UMSNH’s reproductive herd. Beginning at first estrus (82 ± 9.4 kg), they were monitored for three consecutive cycles to assess their reproductive viability based on estrus cyclicity (20 ± 2 d between estruses) up to time of first service (117.7 ± 12.4 kg).

Serviced sows with a positive gestation diagnosis were housed as a group (n= 7) in pens (16 m2) and given 2.0 kg commercial feed sow-1 day-1 (Table 1) during the first two thirds of gestation, and 2.5 kg sow-1 d-1 (in two portions at 0800 and 1400 h) during the final third (up to 108 d gestation). Water was freely available via an automatic drinking bottle.

One week prior to probable parturition date, the sows were randomly selected and assigned to one of two postpartum feeding regimes (FR): 1) conventional feeding regime, or CFR (n= 58 piglets from 17 sows); and, 2) the CFR plus nopal (O. ficus-indica), or OFR (n= 58 piglets from 17 sows). Once assigned, the sows were moved to the parturition room where they were fed a conventional diet for lactating sows at 2.5 kg sow-1 (Table 1). Postpartum, the sows in both groups were fed ad libitum during the 21 d of lactation with the designated diet. The nopal in the OFR treatment was administered on a fresh base at 1% of total feed amount (calculated based on prepartum sow body weight). Before feeding, nopal cladodes were cut into pieces (approx. 3x2 cm), immediately added at the corresponding commercial feed proportion ratio in the OFR, and fed to the sows at 0800 h.

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Table 1: Feed ingredients and nutritional composition Gestation Ingredients, g kg

-1

Sorghum Soy paste Canola paste Orthophosphates Calcium carbonate Soy oil Lysine Salt Vitamin and mineral premixa

824.0 60.0 61.5 11.8 14.0 22.0 1.2 4.0 2.0

Lactation* CFR

OFR

649.7 100.0 185.3 5.4 12.4 38.5 2.5 4.0 2.5

649.7 100.0 185.3 5.4 12.4 38.5 2.5 4.0 2.5

O. ficus-indica nutritional compositionb Crude protein, % Crude fat, % Fiber, % Moisture, % Ash, % Nitrogen free elements, % Mucilage, g 300 g-1 dry base

5.6 0.2 28.8 88.6 24.5 40.8 2.6

Nutritional compositionc Metabolizable energy, Mcal/kgd Crude protein, % Crude fat, % Fiber, % Moisture, % Ash, % Calcium, %d Phosphorous, %d Lysine, %d Met-Cyst, %d

2.3 12.5 3.7 3.1 12.0 10.0 0.75 0.60 0.52 0.43

2.3 17.5 4.5 4.3 12.0 10.0 0.75 0.60 0.95 0.59

2.3 17.4 4.4 4.5 12.8 9.9 0.75 0.59 0.94 0.59

*

CFR = conventional feed regime; OFR = conventional feed regime plus nopal Proportion kg-1 of diet: Cu 30 mg; Fe 160 mg; Zn 160 mg; Mn 55 mg; Se 0.5; Cr 0.2 mg; Vitamin A 14,200 IU; Vitamin D3 2800 IU; Vitamin E 125 mg; Vitamin K3 5 mg; Vitamin B1 2.4 mg; Vitamin B2 8.7 mg; Vitamin B6 4.5 mg; Vitamin B12 0.05 mg; pantothenic acid 35 mg; folic acid 6 mg. b Nopal provided only in morning in fresh base. Addition rate was 1% based on prepartum sow body weight. c Nutritional composition of diet containing 1% added nopal determined after addition of nopal in a dry base. a

Seven variables were evaluated per sow-1 FR-1. Pre- and postprandial blood glucose (BG) was measured according to an established protocol(17). Daily feed intake (FId-1) was calculated based on the feed supplied and feed rejected sow-1 d-1. Feed was weighed on a digital scale (DibatecÂŽ; 40 kg capacity, 5 g accuracy). Feed rejected per sow-1 d-1 was

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weighed daily in the morning, before feeding. Body weight loss (BWL) was calculated by subtracting prepartum sow live weight (d 110 gestation) from sow live weight at weaning (21 d postpartum). The variables used for calculation of BWL were expressed as a percentage using the following equation:

);

đ?&#x2018;&#x160;đ?&#x2018;&#x2019;đ?&#x2018;&#x2013;đ?&#x2018;&#x201D;â&#x201E;&#x17D;đ?&#x2018;Ą đ?&#x2018;&#x17D;đ?&#x2018;Ą đ?&#x2018;¤đ?&#x2018;&#x2019;đ?&#x2018;&#x17D;đ?&#x2018;&#x203A;đ?&#x2018;&#x2013;đ?&#x2018;&#x203A;đ?&#x2018;&#x201D; â&#x2C6;&#x2014; 100

đ??ľđ?&#x2018;&#x160;đ??ż% = 100 â&#x2C6;&#x2019; (

đ?&#x2018;&#x160;đ?&#x2018;&#x2019;đ?&#x2018;&#x2013;đ?&#x2018;&#x201D;â&#x201E;&#x17D;đ?&#x2018;Ą đ?&#x2018;&#x17D;đ?&#x2018;Ą đ?&#x2018;?đ?&#x2018;&#x153;đ?&#x2018; đ?&#x2018;Ąđ?&#x2018;?đ?&#x2018;&#x17D;đ?&#x2018;&#x;đ?&#x2018;Ąđ?&#x2018;˘đ?&#x2018;&#x161;

The weaning-estrus interval (WEI) was the time in hours from the moment of weaning to appearance of estrus. A sowâ&#x20AC;&#x2122;s non-productive days (NPD) were estimated with the following equation: đ?&#x2018; đ?&#x2018;&#x192;đ??ˇ = 365 â&#x2C6;&#x2019; [đ?&#x2018;&#x192;đ?&#x2018;&#x2020;đ?&#x2018;&#x152; â&#x2C6;&#x2014; (đ??ˇđ??ż + đ??ˇđ??ş ): Where PSY= parturitions sow-1 year-1; DL= days in lactation; DG= days of gestation; đ?&#x2018;&#x192;đ?&#x2018;&#x2020;đ?&#x2018;&#x152; = 365 /đ??źđ?&#x2018;&#x192;đ??ź.

Where IPI= inter-parturition interval; đ??źđ?&#x2018;&#x192;đ??ź = đ??ˇđ??ş + đ??ˇđ??ż + đ?&#x2018;&#x160;đ??¸đ??ź + đ?&#x2018;&#x2026;đ?&#x2018;&#x2020;đ?&#x2018;&#x192;đ?&#x2018;&#x2018;

Where WEI= weaning-estrus interval; RSPd= repeated services percentage in days. The repeated services percentage (RSP) was estimated with the equation: đ?&#x2018;&#x2026;đ?&#x2018;&#x2020;đ?&#x2018;&#x192; = 100 â&#x2C6;&#x2019; [(đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;â&#x2C6;&#x2019;đ?&#x2018;&#x2020;đ?&#x2018;&#x2026;đ??¸) â&#x2C6;&#x2014; 100] ; đ?&#x2018;&#x2026;đ?&#x2018;&#x2020;đ?&#x2018;&#x192;đ?&#x2018;&#x2018; = ( đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;

đ?&#x2018;&#x2026;đ?&#x2018;&#x2020;đ?&#x2018;&#x192;â&#x2C6;&#x2014;đ?&#x2018;&#x2020;đ?&#x2018;&#x2020; 100

) â&#x2C6;&#x2014; 21 .

Where SS= serviced sows; SRE= sows returned to estrus. Productivity in the subsequent parturition was calculated using litter size (LS), piglet live births (LB) and weaned piglets (WP).

The data were analyzed with the fixed effects methodology (MIXED) (SAS Inst. Inc., Cary, NC, USA). Data for sow BG and FId-1 were analyzed with the repeated measurements methodology using sow as the object of the random effect of time (days in lactation) and three fixed effects: FR, parturition number (PN) and the nesting of PN within FR:

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đ?&#x2019;&#x20AC;đ?&#x2019;&#x160;đ?&#x2019;&#x2039;đ?&#x2019;&#x152;đ?&#x2019;? = đ?? + đ?&#x2018;­đ?&#x2018;šđ?&#x2019;&#x160; + đ?&#x2018;Ş(đ?&#x2018;­đ?&#x2018;š)đ?&#x2019;&#x2039;(đ?&#x2019;&#x160;) + đ?&#x2018;ˇđ?&#x2018;ľđ?&#x2019;&#x152; + đ?&#x2018;ˇđ?&#x2018;ľ(đ?&#x2018;­đ?&#x2018;š)đ?&#x2019;&#x152;(đ?&#x2019;&#x160;) + Ć?đ?&#x2019;&#x160;đ?&#x2019;&#x2039;đ?&#x2019;&#x152;đ?&#x2019;? .

Body weight loss (BWL), WEI, RSP, NPD, LS, LB and WP were estimated using FR, PN and the nesting of PN within FR with the model: đ?&#x2019;&#x20AC;đ?&#x2019;&#x160;đ?&#x2019;&#x2039;đ?&#x2019;&#x152; = đ?? + đ?&#x2018;­đ?&#x2018;šđ?&#x2019;&#x160; + đ?&#x2018;ˇđ?&#x2018;ľđ?&#x2019;&#x2039; + đ?&#x2018;ˇđ?&#x2018;ľ(đ?&#x2018;­đ?&#x2018;š)đ?&#x2019;&#x2039;(đ?&#x2019;&#x160;) + Ć?đ?&#x2019;&#x160;đ?&#x2019;&#x2039;đ?&#x2019;&#x152; .

Where: Yijkl = response variable: BG, FId-1; Âľ = constant characterizing population; FRi = fixed effect of i-th feed regime with i= CFR and OFR; C(G)j(i)= random effect j-th sow, nested within i-th feeding regime; PNk = fixed effect k-th parturition number with k= 1, 2, 3 and 4; N PN(FR)k(i) = fixed effect of nesting of k-th parturition number inside i-th feeding regime; Ć?ijklmn = random error associated with each observation (~NID=0, ď ł2e).

Differences between the means were identified using the least mean square (LsMeans) method with an Îą= 0.05. Values in tables and text are presented as least mean square Âą standard error (SE). Identification of relationships between FRs (CFR vs OFR) and reproductive and productive indicators was done using Pearson correlations of these indicators within each FR using the correlation procedure in the SASÂŽ statistical program.

The economic analysis was run using numerical data for the variables described for both FRs (CFR and OFR) and applying the methodology proposed by Rouco and MuĂąoz(18), as modified by Bobadilla et al(19).

Strategies have been implemented to mitigate the effects of lactational physiological hypophagia(20,21). These have failed either because they are economically unviable or because they do not resolve the source of the problem: regulation of postpartum blood glucose (BG)(6). In the present results pre- and postprandial BG levels in the OFR treatment were lower (P<0.05) (postprandial 63.5 to 67.5 mg dL-1) than in the CFR (postprandial 75.7 to 80.3 mg dL-1) (Table 2). In the CFR, it was the 1st and 4th parturition sows that exhibited the highest BG levels (P<0.05).

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Table 2: Least mean squares for prepartum and weaning sow weight, blood glucose, and feed intake by feeding regime.

General mean Indicator Prepartum sow weight, kg

Preprandial BG, mg dL-1

Postprandial BG, mg dL-1

Feed intake (commercial feed), kg

Nopal intake FB/DB, kg

Nopal rejection FB/DB, kg

Weaning weight sow, kg

Body weight loss, %

FR

Mean

CFR

213.21

OFR

207.21

CFR

70.01

OFR

55.52

CFR

75.91

OFR

65.32

CFR

4.71

OFR

5.32

CFR

--

SE 6.10

1.27

1.29

0.10 --

Parturition number 3

P-value

1

2

4

175.9ª1

201.9b1

218.7c1 236.2d1

189.5ª1

195.3b1

211.1c1 232.9d1

71.5a1

66.2b1

66.5b1

75.6a1

53.8a2

54.6a2

56.0a2

58.1a2

76.5a1

80.3a1

79.4a1

75.7a1

63.5b2

66.0b2

65.0b2

67.5b2

3.7a1

4.7b1

4.3c1

4.8b1

5.1a2

5.2a2

5.3a2

5.5a2

--

--

--

--

1.7/0.20

0.03

1.3/0.15a

1.7/0.20b

CFR

--

--

--

--

--

OFR

0.3/0.04

0.01

0.5/0.07

0.2/0.03

0.3/0.03

CFR

187.01

157.8a1

176.1b1

1895c1 213.6b1

OFR

192.62

178.7a2

184.5b2

201.3b2 213.7c2

CFR

11.71

11.3a1

12.0a1

13.8a1

9.8b1

OFR

6.02

6.5ab2

5.2a2

4.9a2

7.4b2

0.31

--

FR

PN

PN(FR)

6.21 0.5185 <0.001 <0.001

2.31 <0.001

0.031

0.011

2.43 <0.001

0.531

0.021

0.17 <0.001 <0.001 <0.001 --

1.8/0.21b 1.9/0.2 0.07 c 4c

OFR

1.13

SE

--

0.4/0.0 0.02 5

--

--

--

--

<0.001

--

--

--

--

--

<0.001

--

2.22 <0.001 <0.001 <0.001

0.57 <0.001

0.330

<0.001

FR= feeding regime; CFR= conventional feeding regime; OFR= conventional feeding regime plus nopal; PN= parturition number; BG= blood glucose; FB= fresh base; DB= dry base. a, b, …, e 1, 2

Different letter superscripts in the same row indicate significant difference (P<0.05).

Different numerical superscripts in the same column indicate significant difference (P<0.05) between CFR and OFR within each indicator.

The observed decrease in BG in the OFR treatment coincides with previous research indicating that it is due to the effect (mechanical pathway) of the pectins and mucilage present in the soluble fiber of the nopal(15,22,23); these components increase feed viscosity, slowing its transit and increasing glucose absorption(22). However, the non-fermentable dietary fiber in nopal may also lead to an increase in intestinal release of the GLP-1 protein, which inhibits glucagon release, consequently slowing glucose synthesis(13,24).

This protein also causes increased insulin synthesis(16,25).

Daily feed intake (FId-1) was higher (11.3 %) in the OFR than the CFR (P<0.05) (Table 2). Parturition number (PN) did not affect FId-1 in the OFR (P>0.05) but did affect it in the CFR (P<0.05). The feed intake of lactating sows is mainly affected by sow age (PN) and metabolic physiology(7), both of which are difficult to manipulate(6). The lack of an effect (P>0.05) of PN on FId-1 in the OFR treatment suggests that addition of nopal to the diet of lactating sows in this treatment counteracted the negative effects of lactational hypophagia.

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Body weight loss (BWL) was higher in the CFR than in the OFR (P<0.05; Table 2).

Within the CFR treatment BWL was higher in third parturition sows (13.8 %) than in the other PN categories (P<0.05). In the OFR fourth parturition sows (7.4 %) had higher BWL than the other categories (P<0.05). A possible explanation for this discrepancy between treatments is that addition of nopal to the diet improved colon fermentation processes, leading to a higher concentration of volatile fatty acids (VFAs)(26,27). This increase in VFAs can be channeled into the organismâ&#x20AC;&#x2122;s energy expenditure thus inhibiting catabolism and BWL during lactation(27).

A BWL greater than 10 % at lactation cessation has been linked to inadequate restoration of ovarian function and reproductive failure(28). Compared to those the CFR, sows in the OFR exhibited a number of positive responses as a result of their better BWL: improved restoration of ovarian function (P<0.05); fewer reproductive failures (P<0.05); shorter weaning-estrus intervals (WEI) (122.4 h); a lower repeated services percentage (RSP) (12.4 %); and fewer non-productive days (NPD) (36.0 d)(Figure 1).

Figure 1: Least squares means for sow reproductive and productive variables by feeding regime

a, b, â&#x20AC;Ś, e

1, 2

Different letters over columns indicate significant difference (P<0.05).

Different numbers over the general mean columns indicate significant difference (P<0.05).

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Greater efficiency in neuroendocrine regulation in sows during and after the lactation period improves post-weaning reproductive indicators, raises prolificity and productivity and reduces non-productive days(6,7). The present results coincide with this report (Figures 1 and 2). Increased insulin synthesis caused by nopal consumption(22), also positively affected post-lactation reproductive indicators (Figure 1). This occurs because insulin influences regulation of sow reproductive processes through increased insulin synthesis and IGF-1 release, which regulate production of follicle stimulating hormone and luteinizing hormone(28).

Figure 2: Pearson correlations and conceptualization of the effect of added nopal in diets for lactating sows on productive and reproductive indicators

Litter size (piglets born live and weaned piglets) in primiparous sows did not differ (P>0.05) between treatments because they were given the same feed during gestation.

However, in the following parturition, litter size increased (P<0.05) in the OFR (Figure 1).

The present results agree with a previous study indicating that addition of nopal to the diet of lactating sows has no effect on sow milk nutritional components (protein, fat and lactose) or quantity, meaning piglet development during lactation is unaffected(29).

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The sow productivity analysis (Figure 1 and 2) showed that weaned piglets sow-1 yr-1 in OFR (22.5 WP sow-1 yr-1) was higher than in CFR (18.8 WP sow-1 yr-1). Feed still represented the largest portion of production costs in both regimes: 73.97 % in OFR and 74.42 % in CFR (Table 3). Amortization sow-1 was higher (4.2 %) in the OFR. This indicator is based on the tangible fixed asset (sow)(30), which exhibited variation in its use life within each FR. However, bore amortization did not differ between the treatments since its initial value and use life were similar in each FR.

Table 3: Production costs structure (MXN and %) by feed regime CFR Concept Amortization sow-1

MXN 1,672.09

Amortization boar

OFR % 1.18

MXN 1,745.02

% 1.12

928.58

0.65

928.58

0.59

Sow feed

105,762.44

74.42

115,685.33

73.97

Boar feed

3,859.22

2.72

3,859.22

2.47

Piglet feed

6,169.53

4.34

7,458.35

4.77

Medication

16,409.81

11.55

18,668.72

11.94

Opportunity cost

7,319.74

5.15

8,055.06

5.15

CFR = conventional feeding regime; OFR = conventional feeding regime plus nopal.

In the CFR production costs (WP-1) were $590.81 MXN, which generated profits of $168.88 MXN piglet-1 sold. In the OFR, production costs (WP-1) were $539.02 MXN and profits were $216.68 MXN piglet-1 sold. The marginal cost was therefore higher (8.7 %) in the CFR. Based on the number of WP, the break-even point or threshold of profitability was lower in OFR (Table 4). The cost:benefit ratio indicated that for each peso invested FR-1 profits were 41 ¢ in OFR and 30 ¢ in CFR.

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Table 4: Analysis of production costs, income and profits (MXN) per weaned piglet by feed regime CFR 168.88 443.82 590.81 759.51 168.88 318.73 149.16 1.30

Concept Fixed costs Variable costs Total costs Total income Net profit Marginal cost Break even (NÂş. weaned piglets) Cost/Benefit ratio

OFR 121.48 417.55 539.02 759.51 216.68 349.19 137.72 1.41

Difference -47.41 -26.28 -51.60 -51.60 23.23 -11.44 0.11

CFR= conventional feeding regime; OFR= conventional feeding regime plus nopal.

Pig feed is the item that most affects production costs, ranging from 65 to 95 % of the total(1), which coincides with the present results. Compared to the CFR sows, the higher productive efficiency (weaned piglets yr-1) of the sows in the OFR generated: (i) a lower cost per weaned piglet; (ii) a reduction in production costs; and (iii) higher net revenue vs. productivity. As a result the cost:benefit ratio in the OFR was 1.41 % while that for the CFR was 1.30 %. Both figures are within reported ranges (1.04 to 2.11 %)(31,32), although a swine production system is considered profitable when its cost:benefit ratio is â&#x2030;Ľ1.15 %(18,19). Nonetheless, numerous variables (in addition to those included in the present study) affect profitability(30,32), including sales price policies in effect at a given time, which cannot be controlled by producers, and production system structural variation, both technical and financial.

Supplementation of commercial feed with cladodes of prickly pear nopal Opuntia ficusindica on a fresh basis in the diet of lactating sows had positive effects on the piglet production system. It mitigated lactational physiological hypophagia and body weight loss, and improved sow productivity by increasing the number of piglets weaned sow-1 yr-1. These contributed to lowering production costs per weaned piglet, consequently improving system profitability.

The research reported here was financed by the CONACYT. Thanks are due the Facultad de Medicina Veterinaria y Zootecnia-UMSNH for access to facilities.

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20. Lovise ST, Helen AG, Petter NK, Hetland H, Framstad T. Pea starch meal as a substitute for cereal grain in diets for lactating sows: the effect on sow and litter performance. Livest Sci 2013;157:210-217.

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21. Huang FR, Liu HB, Sun HQ, Peng J. Effects of lysine and protein intake over two consecutive lactations on lactation and subsequent reproductive performance in multiparous sows. Livest Sci 2013;157:482-489 22. Halmi BS, Benlaksira B, Bechtarzi K, Berouel K, Serakta M, Richi F, et al.

Pharmaco-toxicological study of Opuntia ficus indica L. aqueous extract in experimental animal. Inter. J Med Arom Plants 2013;3(3):375-381.

23. Shapiro K, Gong WC. Natural products used for diabetes. J Am Pharm Assoc 2002;42(2):217-26.

24. Nuñez LMA, Paredes LO, Reynoso CR. Functional and hypoglycemic properties of nopal cladodes (O. ficus-indica) at different maturity stages using in vitro and in vivo tests. J Agric Food Chem 2013;61:10981−10986.

25. Alarcón AFJ, Valdez AA, Xolalpa MS, Banderas DT, Jiménez EM, Hernández GE, Román RR. Hypoglycemic activity of two polysaccharides isolated from Opuntia ficusindica and Opuntia streptacantha. Proc Western Pharmacol Soc 2003;46:139-142.

26. Molist F, Gómez A, Gasa J, Hermes RG, Manzanilla EG, Anguita M, Pérez JF. Effects of the insoluble and soluble dietary fibre on the physicochemical properties of digesta and the microbial activity in early weaned piglets. Anim Feed Sci Technol 2009;149:346–353.

27. Chen XB, Mao LQ, Che B, Yu J, He J, Yu GQ, Han ZQ, Huang P, Zheng D, Chen W. Impact of fiber types on gut microbiota, gut environment and gut function in fattening pigs. Anim Feed Sci Technol 2014;195:101–111.

28. Soede NM, Langendijkb P, Kempa B. Reproductive cycles in pigs. Anim Reprod Sci 2011;124:251–258.

29. Ortiz RR, Orozco GA, Val AD, Portillo ML, Pérez SER. Efecto de la adición de nopal (Opuntia ficus-indica) a la dieta de cerdas lactantes sobre la producción y calidad de la leche. Nova Scientia 2017;18(9):290-312.

30. Bobadilla SEE, Martínez CFE. Porcicultura Mexicana: Auge y crisis de un sector.

Universidad Autónoma del Estado de México. Capitulo 5: Variables macroeconómicas y su relación con la demanda y oferta del cerdo en México. 2013;5:101-119.

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31. De-Caro A. Efecto de los factores técnico-económicos sobre el resultado de la empresa porcina. Avances Tecnol Porcina 2004;1:53-60.

32. Bobadilla SEE, Rebollar RS, Rouco YA, Martínez CFE, Determinación de costos de producción en granjas productoras de lechón. Rev Mex Agronegocios 2013;32:268-279.

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https://doi.org/10.22319/rmcp.v10i4.4660 Technical note

Dry matter yield and nutritional values of four herbaceous legumes in a humid tropical environment in Hueytamalco, Puebla, Mexico

Sergio Alberto Lagunes Rivera a Juan de Dios Guerrero-Rodríguez a* Josafath Omar Hernández-Vélez b José de Jesús Mario Ramírez-González b Dulce Violeta García-Bonilla a Antonio Alatorre-Hernández a

a

Colegio de Postgraduados, Campus Puebla. Boulevard Forjadores de Puebla, No. 205 Santiago Momoxpan, 72760 San Pedro Cholula, Puebla, México. b

Instituto Nacional de Investigaciones Forestales Agrícolas y Pecuarias. México.

* Corresponding author: rjuan@colpos.mx

Abstract: Dry matter yield and nutritional data are needed before forage legumes can be proposed for inclusion in livestock systems. An evaluation was done of the dry matter (DM) yield, concentrations of neutral and acid detergent fiber (NDF and ADF) and crude protein (CP), and in vitro DM digestibility (IVDMD) of the forage legumes Stylosanthes guianensis (SG), Centrosema macrocarpum (CM), Pueraria phaseoloides (PP) and Arachis pintoi (AP). During an eleven-month period six cuts were made of these legumes at 56-d intervals. Experimental plots were 3 x 7 m, and analyses were done using a completely random block design with four replicates. Overall DM yield was higher (P≤0.05) in SG (19,410 kg DM ha-1) and CM (17,462 kg DM ha-1), than in PP (14,704 kg DM ha-1) and AP (12,466 kg DM ha-1). Arachis pintoi had the lowest NDF (60%) and ADF (35 %) contents (P≤0.05). Digestibility was highest in AP (74 %), followed by SG (62%), PP (57 %) and CM (55 %). Crude protein content was highest (P≤0.05) in AP and CM 1042

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(21 %) followed by SG (19 %) and PP (17.8 %). Although DM yield was highest in SG and CM, and digestibility and crude protein content were highest in AP, all four forage legume species exhibited sufficiently high nutritional values and DM production to make them alternative protein sources for ruminants with low-nutrient diets.

Key words: Nutritional quality, Yield, Herbaceous legumes.

Received: 10/10/2017 Accepted: 09/08/2018

In tropical Mexico, ruminant livestock production depends largely on the grazing of native and introduced grasses. These usually require high levels of nitrogen fertilization and experience substantial declines in protein content and digestibility as they mature(1,2).

Indeed, depending on age, in some grass species protein content can be as little as 7 %, which can prove limiting to ruminant production(3).

One way of improving the diet of grazing ruminants is to include herbaceous legume forage species, which, compared to grasses, maintain a high nutritional value throughout their biological cycle(1,4). Depending on the species, tropical herbaceous legumes can attain crude protein contents ranging from 19 to 22 %(5), and dry matter digestibility values of 58 to 72 %(6). When associated with grasses their ability to fix nitrogen in the soil can increase grass dry matter yield and nutritional quality(7,8). They can also be used in monoculture or as protein banks. The legumes Stylosanthes guianensis, Centrosema macrocarpum, Pueraria phaseoloides and Arachis pintoi are proven to be important forage resources in the development of livestock production systems in wet and dry tropical regions(9,10,11). Stylosanthes guianensis is a semi-erect evergreen herbaceous legume(9) similar to C. macrocarpum, although the latter is less woody than the former.

Pueraria phaseoloides and A. pintoi are long-stemmed vines and with a creeping growth habit(12,13). Both can adapt to soils ranging from clay to sandy, usually with an organic matter content above 3 %(12,14).

Introduction of these species to a given region requires prior knowledge of their productive behavior, and any possible environmental factors that could influence dry matter yield and nutritional value. Production data is available for each species but few comparisons have been made between them under the same edaphoclimatic conditions.

This is vital when deciding which of them to plant. Evaluation of species dry matter yield and nutritional value is basic to developing supplementation strategies and optimizing use of available fodder(4). The present study objective was to evaluate the dry matter yield

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and nutritional value of four herbaceous legume forage species in a warm humid tropical area of the state of Puebla, Mexico.

The experiment was run at the Las Margaritas Experimental Station of the National Institute of Agricultural and Livestock Forest Research (Instituto Nacional de Investigaciones Forestales Agrícolas y Pecuarias), in the Municipality of Hueytamalco, Puebla, under seasonal conditions. The station is located at 20°00’ N and 97°18’ W, and an altitude of 450 m asl. Regional climate is Af(c), with a 21 °C average annual temperature, 3,000 mm average annual rainfall and 90 % average relative humidity(15).

During the study period, accumulated rainfall was 1200.7 mm, maximum temperature ranged from 14.9 to 33.6 °C, and minimum temperature from 7.6 to 21 °C (Figure 1). Soil in the study area was clayey with a pH of 4.4, EC of 0.26 mmhos cm-1 and organic matter content of 5.2 %. Nutrient content was poor in N (0.004 %), P (4.06 ppm), B (0.166 ppm) and Zn (4.66 ppm), rich in K (150 ppm), Ca (570 ppm) and Cu (520 ppm), and had average Mg levels (73.33 pm). The four legume species were evaluated for eleven months (March 2009 to February 2010). After an initial cut 45 d post-planting to create a homogenous height, successive cuts were done every 56 d, for a total of six cuts (Figure 1). Cut dates were 16 May, 12 July, 6 September, 2 November, and 28 December 2009, and 24 February 2010.

Figure 1: Temperature and rainfall during experimental period (March 2009 to February 2010). Arrows indicate cut dates of four forage legumes Precipitation (mm) T° min

350

T° max

40 35 30

250

25 200 20 150 15 100 50

Uniformity harvest

10 H

H 2

H 3

H 4

H 5

H 6

0

5 0

March

April

May

June

July

August

September October

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November December January

February

Temperature (°C)

Precipitation (mm)

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Soils were prepared by plowing and raking. Fertilizer (00-80-60 N-P-K ha-1) in the form of tricalcium superphosphate and potassium chloride was applied once at the time of planting. Seeds of three of the legumes [Styloshanthes guianensis (Aubl.) Sw. (cv Ubon); Centrosema macrocarpum Benth (CIAT 5713); and Pueraria phaseoloides (Roxb.)

Benth (cv Jarocha)], were scarified with water at 75 °C for 3 min. They were then sown by aerial spreading in experimental units of 21 m2 (3 x 7 m) at a density of 35 kg seed ha-1; the seeds of these species have physical latency, and they were 3 yr-old prior to the trial. Arachis pintoi Krapov and W. C. Greg. (CIAT 17434) was planted using vegetative material (stems with roots of 2 to 4 nodules) with 10 cm between plants.

Vegetal material samples were collected every 56 d by randomly placing a 0.25 x 0.25 m metal frame inside each experimental unit. Within this quadrant all aerial biomass was removed from each plant(11); for A. pintoi this was done at 10 cm above soil surface, and for S. guianensis, C. macrocarpum and P. phaseoloides at 15 cm above soil surface. The collected matter was weighed fresh and stored in marked paper bags. The leaf-to-stem ratio of each species was measured by separately weighing the leaf and stem fractions of each plant and dividing leaf weight by stem weight. All samples were dried in a forcedair stove at 55 °C to constant weight.

Dried samples were ground in a cyclone mill until passing through 1 mm mesh. Neutral detergent fiber (NDF) and acid detergent fiber (ADF) were measured sequentially and in duplicate with a fiber analyzer (ANKOM 200/220) following manufacturer protocols(16).

Crude protein (CP) was measured in duplicate with the Kjeldahl method, multiplying nitrogen percentage by 6.25(17). In vitro dry matter digestibility (IVDMD) was measured in duplicate following the pepsin-cellulase enzymatic method(18,19).

Because the experimental parcel was sloped, a completely random block design with four repetitions was used; each legume species constituted a treatment. Data were analyzed with a divided plots arrangement in which the largest plots were each legume species and the smallest plots were each cut. An analysis of variance was run and the means compared with the Tukey test. All statistical analyses were done with the SAS ver. 9.0 statistical package(20).

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Dry matter (DM) production differed (P≤0.05) between the four studied legume species and between cuts. In most of the cuts AP had the lowest yields, although it did not differ from PP. Overall, the four legumes tended to increase DM production from cut 2 to cut 3 (Figure 2), but from cut 4 to cut 6 production decreased substantially. Total DM production during the study period was highest in SG (19,410 kg DM ha-1) and CM (17,462 kg DM ha-1), and lowest in PP (14,704 kg DM ha-1) and AP (12,466 kg DM ha1 ).

Dry matter yield (kg ha-1)

Figure 2: Dry matter (DM) yield (kg ha-1) of the herbaceous legumes Stylosanthes guianensis, Centrosema macrocarpum, Arachis pintoi and Pueraria phaseoloides Stylosanthes guianensis Centrosema macrocarpum Pueraria phaseoloides Arachis pintoi

9000 8000 7000 6000 5000

4000 3000 2000 1000 0 16-may-09

12-jul-09

06-sep-09 02-nov-09 Date of harvest

28-dic-09 24--Feb-10

Average leaf production was higher in SG, CM and PP than in AP (P≤0.05), while stem production was highest in SG (P≤0.05). The leaf-to-stem ratio was highest in PP, followed by CM (P≤0.05), and was consistently the lowest in SG (Table 1).

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Table 1: Average yield for dry matter(DM) per cut, leaves and stems, and leaf-to-stem ratio values for Stylosanthes guianensis (SG), Centrosema macrocarpum (CM), Pueraria phaseoloides (PP) and Arachis pintoi (AP) Variable DM, kg ha-1 -1

SG

CM

PP

AP

SEM

3235.3a

2910.3a

2450.6b

2077.8c

a

a

1467.5

a

1171.5

b

99.4

196.0

Leaf, kg ha

1601.5

1641.2

Stem, kg ha-1

1633.6a

1269.1b

983.2c

906.1c

95.5

1.07c

1.38ab

1.57a

1.45b

0.048

Leaf:stem a,b,c

SEM= standard error of the mean.

Different letter superscripts in the same row indicate significant difference (P≤0.05).

Average neutral detergent fiber (NDF) content ranged from 61 to 66 % and differed (P≤0.05) between all four species. Acid detergent fiber (ADF) content ranged from 35 to 46 %, and again differed (P≤0.05) between all four species. The lowest fiber content in all but one of the cuts was in AP (Figure 3).

Figure 3. Neutral detergent fiber (NDF), acid detergent fiber (ADF), crude protein and in vitro dry matter digestibility (IVDMD) in Stylosanthes guianensis (SG), Centrosema macrocarpum (CM), Pueraria phaseoloides (PP) and Arachis pintoi (AP)

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Crude protein (CP) content varied from 18 to 21 %, and was highest in AP and CM (P≤0.05) in most cuts (Figure 3; Table 2). Average IVDMD ranged from 55 to 74 %, with AP consistently having the highest levels (P≤0.05).

Table 2. Average NDF, ADF, CP and IVDMD percentages (n=24) for Stylosanthes guianensis (SG), Centrosema macrocarpum (CM), Pueraria phaseoloides (PP) and Arachis pintoi (AP) Variable NDF ADF CP IVDMD a,b,c,d

SG 65a 44b 19b 62b

CM 66a 46a 21a 55d

PP 65a 44b 18c 57c

AP 61b 35c 21a 74a

SEM 0.591 0.575 0.300 0.945

SEM= standard error of the mean.

Different letter superscripts in the same row indicate significant difference (P≤0.05).

Throughout the six cuts the four legume species exhibited differential DM yield patterns in response to environmental temperature and humidity. Yields were highest when minimum temperatures exceeded 18 °C and humidity was high, but as these two environmental variables declined so did DM accumulation. This is similar to the results reported in a study of S. guianenesis, A. pintoi, P. phaseoloides and Clitoria ternatea which found that during a regrowth period of 21 to 84 days DM increase was higher (1.17 to 6.52 t ha-1) during the rainy season than the dry season (0.749 to 4.37 t ha-1)(9). These results are interpreted to indicate that these legumes’ high forage potential depended on favorable humidity and temperature conditions. In the present case, the lower DM yield from November to February may have been due to the studied species inability to adapt to the cold season. The lowest average temperature during these months (NovemberFebruary) was 12.9 °C. These months also coincide with the last three cuts, in which the four species exhibited the lowest DM yield. However, average DM yields per cut for the studied legumes were similar to those reported in other studies for Stylosanthes macrocephala (2,701 kg MS ha-1), P. phaseoloides (2,404 kg), A. pintoi (1,470 kg) and Centroscema pubescens (2,172 kg)(9,11,21).

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Leaf yield did not differ between SG, CM and PP, although SG and CM had higher stem production. The latter two species have a semi-erect growth habit which apparently provided them with higher cumulative DM production than the creeping vines AP and PP. The highest leaf-to-stem ratio was observed in PP. This agrees with a study of P. phaseoloides in which it was found to have a higher leaf-stem ratio than S. macrocephala and Macrotyloma axillare(11). Leaves apparently provide a greater portion of DM production in PP, which directly influences its advantageous composition for livestock feed. The lowest leaf-to-stem ratio values among the studied species were observed in SG. Similar observations have been made in a study of S. guianensis which found it to have a lower leaf-to-stem ratio than A. pintoi, P. phaseoloides and C. ternatea largely due to its semi-erect, semi-woody growth habit and short branches(9).

The lower NDF and ADF contents in AP compared to the other species were probably due to differences in growth habits. Semi-erect legumes such as CM and SG generally accumulate more structural carbohydrates in stems to support foliage than do creepers such as AP(22). Of note is that PP has the same growth habit as AP but its NDF and ADF contents did not differ from SG and CM. This indicates that, despite having a creeping growth habit and a higher leaf-to-stem ratio, PP contains more fibrous components than AP. The NDF and ADF values observed here for SG and CM are comparable to those reported for Stylosanthes scabra (NDF= 50%; ADF= 40%) and C. pubescens (NDF= 53 %; ADF= 48%)(10).

Given its lower NDF and ADF contents the high CP content of AP is to be expected. In another report with the same observation this higher CP content was attributed to the stable leaf-to-stem ratio of AP throughout its growth period and its consequently higher cellular content(9). The leaves of AP accumulate a higher proportion of N derived from reserves in the roots and mature stems(23). This may explain the similar CP content in AP and CM since the latter has a larger proportion of leaves than AP but they still have very similar leaf-to-stem ratio values. In contrast, PP had more leaves and a higher leaf-tostem ratio than AP but had the lowest CP content; even SG, which had the highest stem proportion, had a higher CP content than PP. There is evidence that PP has a low CP content even though it produces extensive foliage during growth(9). Despite the differences in CP content between the studied legumes all their levels surpassed the estimated 7 % CP minimum maintenance requirements for ruminants(3).

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Throughout all six cuts IVDMD remained highest in AP, which is directly related to its lower NDF and ADF contents during growth. In contrast, PP and CM had the lowest IVDMD values due to their high fiber contents. The low digestibility of these species (PP, CM) may also be due to high lignin concentration (not evaluated here) since this can considerably reduce forage IVDMD(3). Further supporting this possibility is the low digestibility reported for C. pubescens and S. scabra due partially to high NDF and ADF contents, but primarily caused by these speciesâ&#x20AC;&#x2122; high lignin concentration (17 and 18 %, respectively)(10). However, a forage plantâ&#x20AC;&#x2122;s nutritional value can vary in response to factors such as species, climatic conditions, sampling site and vegetative stage(24).

Dry matter production was variable in the four studied legumes and responded mainly to rainfall and temperatures at the experimental site. Inter-species variation in chemical composition was affected mainly by differences between species. Stylosanthes guianensis and Centrosema macrocarpum had the highest dry matter production, but Arachis pintoi exhibited the highest nutritional quality due mainly to its high crude protein content and digestibility. Although they differed in terms of the evaluated variables, the four studied legumes had high nutritional value, are promising alternative forage sources and apt for use as complements to low nutritional quality diets for ruminants in the study region.

Literature cited: 1.

Dewhurst RJ, Delaby RJ, Moloney A, Boland T, Lewis E. Nutritive value of forage legumes used for grazing and silage. Irish J Agric Food Res 2009;48:167-187.

2.

Enriquez-Hidalgo D, Hennessy D, Gilliland T, Egan M, Mee JF, Lewis E. Effect of rotationally grazing perennial ryegrass white clover or perennial ryegrass only swards on dairy cow feeding behavior, rumen characteristics and sward depletion patterns. Livest Sci 2014;169:48-62.

3.

Nunes ATD, Cabral LV, Amorim ELC, dos Santos MVF, Albuquerque UP. Plants used to feed ruminants in semi-arid Brazil: a study of nutritional composition guided by local ecological knowledge. J Arid Environ 2016;135:96-103.

4.

Geleti D, Hailemariam M, Mengistu A, Tolera A. Nutritive value of selected browse and herbaceous forage legumes adapted to medium altitude subhumid areas of western Oromia, Ethiopia. Glob Vet 2013;11(6):809-816.

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5.

Phengsavanh P, Frankow-Linberg BE. Effect of harvesting interval on biomass yield and nutritive value of five tropical forage legumes (Aeschynomene histrix ‘BRA 9690’, Canavalia brasiliensis ‘CIAT 17009’, Stylosanthes guianensis ‘CIAT 184’ and ‘Composite’ and Vigna unguiculata ‘CIAT 1088‐4’) in Lao PDR. Grassl Sci 2013;59(2):80-26.

6.

Muamba IT, Ignatius VN, Mangeye HK, Hornick JL. Nutritive value of Adenodolichos rhomboideus leaves compared with Leucaena leucocephala and Stylosanthes guianensis forages in indigenous goats in Lubumbashi (DR Congo).

Biotechnol, Agrono, Soc Environ 2014;18(2):165-173.

7.

Njoka-Njiru EN, Njarui MG, Abdulrazak SA, Mureithi JG. Effect of intercropping herbaceous legumes with Napier grass on dry matter yield and nutritive value of the feedstuffs in semi-arid region of eastern Kenya. Agric Trop Subtrop 2006;39(4):255267.

8.

Njarui DMG, Njoka EN, Abdulrazak SA, Mureithi JG. Effect of planting patterns of two herbaceous forage legumes in fodder grasses on productivity of grass/legume mixture in semi-arid tropical Kenya. Trop Subtrop Agroecosyst 2007;7(2):73-85.

9.

García-Ferrer L, Bolaños-Aguilar ED, Ramos-Juárez J, Osorio-Arce M, LagunesEspinoza LC. Yield and nutritive value of forage legumes in two seasons and four regrowth stages. Rev Mex Cienc Pecu 2015;6(4):453-468.

10. Musco N, Koura IB, Tudisco R, Awadjihè G, Adjolohoun S, Cutrignelli MI, Mollica MP, Houinato M, Infascelli F, Calabró S. Nutritional characteristics of forage grown in south of Benin. Asian-Austr J Anim Sci 2016;29:51-61.

11. Araújo SAC, da Silva TO, Rocha NS, Ortêncio MO. Growing tropical forage legumes in full sun and silvopastoral system. Act Scient Anim Sci 2017;39:27-34.

12. Di Palma MVL, Méndez AC. Leguminosa forrajera Maní mejorador Arachis pintoi CIAT 17434. Una alternativa para la ganadería. Costa Rica. Boletín divulgativo 1994;1-18.

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13. Pozo LA, Alvarado AA, Carrera MB, Pilalola DW. Evaluación de distintas densidades de siembra de Kudzú tropical (Pueraria phaseoloides) como alternativa de cobertura vegetal en plantaciones de cacao en la zona agrícola del cantón El Triunfo, provincia del Guayas. Mis Agro 2016;(11):1-20.

14. Ramírez-Bahena MH, Chahbohune R, Velázquez E, Gómez-Moriano A, Mora E, Peix A, Toro M. Centrosema is a promiscuous legume nodulated by several new putative species and symbiovars of Bradyrhizobium in various American countries.

Syst Appl Microbiol 2013;392-400.

15. COTECOCA, Comisión técnico consultiva para la determinación regional de los coeficientes de agostadero, SAGARPA, Delegación en el Estado de Puebla, Subdelegación Agropecuaria. 2001:1-2.

16. Ankom Technology. Operator's manual. Ankom Technology, Macedon, New York 2006.

17. AOAC. Official Methods of Analysis of Association of Official Analytical Chemists.

17th. ed. Washington, USA. 2000.

18. Jones DIH, Hayward MV. The effect of pepsin pretreatment of herbage on the prediction of dry matter digestibility from solubility in fungal cellulase solutions. J Sci Food Agric 1975;26:711-718.

19. Clarke T, Flinn PC, Mcgowan AA. Low-cost pepsin-cellulase assays for prediction of digestibility of herbage. Grass Forage Sci 1982;37:147-150.

20. SAS (Statistical Analysis System). User's Guide: Statistics, version 9.0. SAS Institute Inc., Cary, North Caroline, USA; 2002.

21. Adjolohoun S, Buldgen A, Adandedjan C, Decruyenaere V, Dardenne P. Yield and nutritive value of herbaceous and browse forage legumes in the Borgou region of Benin. Trop Grassl 2008;42(2):104-111.

22. Mupangwa JF, Ngongoni NT, Hamudikuwanda H. The effect of stage of growth and method of drying fresh herbage on chemical composition of three tropical herbaceous forage legumes. Trop Subtrop Agroecosyst 2006;6:23-30.

23. Black AD, Laidlaw AS, Moot DJ, O’Kiely P. Comparative growth and management of white and red clovers. Irish J Agric Food Res 2009;48:149-166.

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24. Adjolohoun S, Mahamadou D, Claude A, Soumanou TS, Valentin K, Brice S. Evaluation of biomass production and nutritive value of Panicum maximum ecotypes in Central region of Benin. Afr J Agric Res 2013;8:1661-1668.

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https://doi.org/10.22319/rmcp.v10i4.5008 Technical note

Abortion outbreak caused by Campylobacter fetus subspecies venerealis and Neospora caninum in a bovine dairy herd

Melissa Macías-Rioseco a Rubén D. Caffarena a,b Martín Fraga a Caroline Silveira a Federico Giannitti a,c Germán Cantón d Yanina P. Hecker e Alejandra Suanes f Franklin Riet-Correa a*

* Corresponding author: frcorrea@inia.org.uy

a

Instituto Nacional de Investigación Agropecuaria (INIA). Plataforma de Investigación en Salud Animal. Ruta 50 km 11, 70000, La Estanzuela, Uruguay. b

Universidad de la República, Facultad de Veterinaria, Montevideo, Uruguay.

c

University of Minnesota, Veterinary Population Medicine Department, MN, USA.

d

Instituto Nacional de Tecnología Agropecuaria. Balcarce, Argentina.

e

Consejo Nacional de Investigaciones Científicas y Técnicas. Balcarce, Argentina.

f

Ministerio de Ganadería Agricultura y Pesca. Montevideo. Dirección de Laboratorios Veterinarios, Uruguay.

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Abstract: In November 2015, an abortion outbreak occurred in a commercial dairy herd of 650 Holstein cows in Florida department, Uruguay. Forty-five (45) cows aborted within 3 wk. Five fetuses were subjected to gross and microscopic pathologic examination, and microbiological testing. One fetus had fibrinous epicarditis and peritonitis, and neutrophilic bronchopneumonia. Campylobacter fetus subsp. venerealis was detected by direct immunofluorescence, isolated and identified by PCR and sequencing of the 16S rDNA in the abomasal fluid and/or lung. Histologic examination of two other fetuses revealed nonsuppurative necrotizing encephalitis, lymphohistiocytic myositis and myocarditis, and lymphocytic interstitial nephritis. In these fetuses, N. caninum antigen was detected intralesionally by immunohistochemistry, and N. caninum DNA was amplified by PCR on formalin-fixed paraffin-embedded brain. Antibodies against N. caninum were detected by indirect immunofluorescence in 10 of 27 cows, with titers ranging from 1/200 to 1/3200. The results indicate that two abortigenic microorganisms may coexist and cause contemporaneous abortion in a herd. It is relevant to highlight the importance of performing multiple diagnostic tests in various aborted dams and fetuses from the same herd for the etiologic confirmation of bovine abortion syndrome.

Key words: Bovine abortion, Campylobacter fetus subsp. venerealis, Neospora caninum, Diagnosis of abortion.

Received: 02/08/2018 Accepted: 28/08/2018

Campylobacter fetus subspecies venerealis is the causal agent of bovine genital campylobacteriosis(1). Bulls can carry the bacterium asymptomatically in the prepuce for indefinite time and transmit the agent to females at mating. Infected females can develop infertility, embryonic death or abortion. Abortion can occur at any gestational age, but it is more commonly diagnosed in the fourth to sixth month of gestation(2). Lesions caused by C. fetus venerealis include endometritis, placentitis, fetal serositis, hepatitis and pneumonia(1).

The protozoan Neospora caninum is an important cause of abortion in beef and dairy cattle in South America(3). Members of the Canidae family are definitive hosts and shed oocysts in feces(4). Cattle are intermediate hosts and get infected with N. caninum by ingestion of oocysts or by transplacental transmission. The definitive hosts acquire the infection by ingesting bradyzoites that are encysted in the tissues of the intermediate hosts. Depending on the gestational age at the time of infection, fetal death with either abortion or mummification 1055

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can occur. If the infection takes place within the first 100 d of gestation, the chances of fetal survival are low because there is incomplete development of the fetal immune system(4).

Abortion due to N. caninum frequently occurs during the second or third trimester. If the fetus develops an immune reaction against N. caninum, it is born as seropositive calf.

However, the birth of seronegative calves from seropositive dams can occasionally occur(4).

Necropsy findings in aborted fetuses are scant, fetuses can be severely autolytic or mummified. Grossly, the placenta can show necrosis of the cotyledons with no changes of the intercotyledonary region. The fetal heart and skeletal muscles can have gray to whitish foci, that microscopically are characterized by necrosis and inflammation. The main microscopic lesions in the fetus are multifocal non-suppurative necrotizing encephalitis with gliosis, myocarditis and myositis, which are highly specific of this protozoon(4).

This report describes a bovine abortion outbreak in a commercial dairy farm caused by the concurrent action of two different pathogens. The importance of performing multiple diagnostic tests in various fetuses and serological studies in the cows is highlighted.

The outbreak occurred in a brucellosis free dairy herd with 650 milking Holstein cows in a semi-extensive system with periods of confinement of variable lengths depending on pasture availability. The farm was located in Florida department, Uruguay. The average daily milk production was approximately 20 L/cow. Calving was scheduled in autumn-winter, and artificial insemination was performed from May to October, followed by natural breeding with bulls. The affected farm worked with a second dairy farm where they received cows for insemination that have calved at least two months before. After being inseminated, these cows remained in the farm during lactation. A total of 45 cows aborted during a period of 3 wk in November 2015. Five fetuses (cases 1-5) were necropsied, the gestational age was estimated based on the crown-to-rump length and other gross characteristics of the fetuses(5).

There was no placenta submitted for examination in any of the cases. For histology, fetal tissues were fixed in 10 % neutral buffered formalin, embedded in paraffin, sectioned at 4 to 6 Âľm, and stained with hematoxylin and eosin. Immunohistochemistry (IHC) was performed in sections of brain for N. caninum, sections of kidney and liver for Leptospira spp., and in liver, heart, and lung for bovine viral diarrhea virus (BVDV)(6-8).

Titers of antibodies against Leptospira spp. were determined by microagglutination test (MAT) on samples of pericardial/thoracic fluid from the five fetuses, with a cutoff point >1/10. Samples of abomasal fluid, and liver from the five aborted fetuses were spiked in blood Agar in microaerophilic conditions.

The molecular identification of N. caninum was done from formalin-fixed paraffinembedded sections of brain in those fetuses with microscopic brain lesions typical of neosporosis. The DNA was isolated using a commercially available kit (DNeasy Tissue Kit, QIAGEN Group, Germany) according to the manufacturerâ&#x20AC;&#x2122;s recommendations, and DNA concentration was measured using an Epoch micro-volume spectrophotometer system (Epoc, 1056

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Bioteck® Instruments, Inc., Vermont, USA). Neospora caninum DNA was assessed by a nested-PCR targeting the internal transcribed spacer one (ITS1) region with four oligonucleotides, as previously described(9).

The diagnosis of Campylobacter infection was done by bacterial culture, inoculating samples of fetal abomasal fluid, lung, and liver in Skirrow agar. Incubation was performed for 48 h at 37 ºC in an AnaeroJar™ (Oxoid) with a microaerobic environment (approximately 5 to 10% O2, 5 to 10% CO2) generated with CampyGen™ sachets (Oxoid)(10). Direct immunofluorescence was done on smears of fetal abomasal fluid (20 µL) fixed in acetone at 20 0C for 30 min, using a commercially available fluorescein-isothiocyanate conjugated antiserum (FITC) against Campylobacter fetus (Biotandil, Argentina), with appropriate positive and negative controls provided with the kit. Incubation was performed inside a humid chamber at 37 ºC for 30 min, slides were then visualized under an AXI0 Lab A.1 microscope with a FITC filter and 470 nm UV light.

For the molecular identification of the isolates, DNA was extracted using the Gene bacterial genomic DNA extraction kit (Sigma-Aldrich, USA), following by two separate multiplex PCR protocols that amplify specific regions of the C. fetus genome that discriminate between C. fetus subspecies(11,12). Additionally, the almost complete gen that codifies the 16S rRNA was amplified using the universal primers 27F and 1492R(13). The PCR products were purified and sequenced at Macrogen Inc., Seoul, South Korea. The obtained sequences were compared with sequences from public databases using the "Classifier” tool from the Ribosomal Database Project and BLASTn from the National Center for Biotechnology Information(14,15).

Indirect fluorescent antibody test for the detection of anti-Neospora caninum antibodies was done in serum of 27 cows from the affected herd at the Division of Veterinary Laboratory of the Uruguayan Ministry of Agriculture, Livestock and Fishery, following their standard protocols.

All five necropsied fetuses (cases 1-5) had approximate gestational ages of 180 d. Grossly, case 1 had diffuse fibrinous epicarditis (Figure 1) and peritonitis. Histologically in this fetus, there was neutrophilic bronchopneumonia and epicarditis. Campylobacter fetus was detected by direct immunofluorescence and isolated from abomasal fluid and lung. Identification of the isolate was further confirmed by PCR, which yielded amplification products of sizes corresponding to those described for C. fetus subsp. venerealis(11,12). Additionally, the 16S rDNA gene sequence of the isolates was compatible with C. fetus. No Campylobacter spp. were isolated from samples of cases 2-5.

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Figure 1: Case 1, aborted by Campylobacter fetus subsp. venerealis. The epicardium is covered by moderate to large amount of fibrinous material along with serosanguinous fluid

No significant gross lesions were observed in cases 2 to 5, and no microscopic lesions were observed in cases 2 to 3. However, in cases 4 and 5, histology revealed multifocal necrotizing non-suppurative encephalitis (Figure 2), lymphocytic and histiocytic myocarditis and myositis, and lymphocytic interstitial nephritis. Neospora caninum antigen was detected intralesionally in the brain of these two fetuses by IHC (Figure 2 inset) antibody titers to N. caninum ranged from 1/200 to 1/3200 in 10 of the 27 examined cows. Additionally, PCR for N. caninum DNA was positive in both cases. BVDV IHC was negative in liver, heart, and lung in cases 2, 4 and 5. Lastly, the IHC for Leptospira spp. was negative in kidney and liver from all fetuses. No antibody titers against Leptospira spp. were detected in any of the five fetuses. Other abortigenic pathogens were ruled out, such as Brucella spp. Their negativity was based on negative isolation of the pathogen and the absence of the pathogen in association with the compatible lesions.

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Figure 2: Fetal brain from case 4 aborted by Neospora caninum

The neuropil is disrupted by necrotizing non-suppurative multifocal encephalitis. Hematoxylin and eosin stain.

Inset. There is immunoreaction against Neospora caninum antigen within the affected sections of brain.

The etiologic diagnosis of bovine abortion is complex because multiple potential causes can be involved, and fetal autolysis can preclude the identification of the etiologic agent. In this outbreak, the identification of two abortigenic pathogens in 3/5 fetuses suggests that the examination of various fetuses is recommended. While coinfection by multiple abortifacients has been reported(16,17), little has been discussed about the concurrent detection of different pathogens in different aborted fetuses and in outbreaks of abortion in dairy farms. Abortion outbreaks can be caused by different infectious agents contemporaneously.

The main identified causes of bovine abortion in dairy and beef cattle in South America are infectious(18-22). In one study from Uruguay, the most frequent cause of bovine abortion identified in laboratory submissions was leptospirosis (41 % of 241 cases with diagnosis), followed by neosporosis (36 %) and campylobacteriosis (12 %)(20). In Argentina, leptospirosis was the third most frequently detected cause (7.3 %) (21) while in Brazil it was diagnosed in 0.6 % of the abortions(22). Such differences may be due to the different frequencies of leptospirosis in these countries, but also to the different laboratory techniques used for the diagnosis and interpretation of the results. The diagnosis in the Uruguayan study was based on the presence of high titers of antibodies in aborted cows (>1/800) and/or fetuses (>1/10), whereas in Argentina and Brazil the etiologic diagnosis was based on detection of 1059

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Leptospira spp. by PCR, immunofluorescence, immunohistochemistry, and/or Warthin Starry stain in fetal samples(19,22). Tests that aim at detecting the agent in the aborted fetuses are more suitable for the confirmatory diagnosis of abortion by Leptospira spp. than serologic tests performed on the dam´s serum of fetal fluids.

The microbiologic and pathologic evaluation of the placenta in cases of abortion is key to increase the chances of reaching a diagnosis. For some diseases, such as coxiellosis or chlamydiosis, it is difficult to arrive to an etiologic diagnosis if the placenta of aborted cows is not evaluated. In this outbreak, no placentas were examined, and this could have been a limitation for the determination of the diagnosis in two of the five fetuses. Previous reports in the USA show that placentitis can result in abortion, in the absence of fetal lesions(23).

In this report, the diagnosis of C. fetus subsp. venerealis was confirmed in one of the fetuses.

The affected herd practiced artificial insemination followed by natural breeding. A national survey including 340 farmers indicated that only 21 % of the dairy farms used artificial insemination and 29 % used natural breeding after artificial insemination(24). Half of the farms used only natural breeding(22). These data suggest that bovine campylobacteriosis, diagnosed for the first time in Uruguay in 1970 in dairy cows(25), is still a health problem in dairy farms in the country. Nevertheless, natural breeding maintains the risk of bovine genital campylobacteriosis and should be avoided when possible. Neosporosis was diagnosed in two fetuses and about 37 % of the examined cows had titers against N. caninum. A serologic survey done in beef cattle in Uruguay showed that in 2006, neosporosis was present in 69.2 % of 229 farms, and that 14.3 % of the cows and 12.9 % of the heifers were seropositive(26) proving that N. caninum is endemic in the Uruguayan bovine population, including dairy bovines(27).

In this outbreak, antibody titers against L. interrogans serovars were detected in serum of 15/18 cows examined by MAT (data not shown). Unfortunately, the vaccination status of the herd unknown, and whether these dams had aborted or not was not recorded. The antibodies detected were against serovars Pomona (13 cows), Hardjo-prajitno (9 cows), Wolfii (9 cows) and Hadjo-bovis (seven cows), with titers ranging from 1/200 to 1/3200. In the absence of fetal lesions compatible with leptospirosis coupled with negative IHC results, and the negative MAT results in the thoracic/cavitary fetal fluids, it is possible to conclude that none of the five examined fetuses were infected with Leptospira spp.

Abortion outbreaks can be caused by different infectious agents contemporaneously in the same herd. In such cases, it is necessary to perform necropsies in many fetuses using the specific techniques for each agent and, if possible, to evaluate the placenta, along with the serum of the dams.

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The authors thank Cecilia Monesiglio, Anderson Saravia, Yisell Perdomo and all graduate students of the animal health platform at INIA. This work was funded by grant FSSA_X_2014_1_105696 of the Uruguayan “Agencia Nacional de Investigación e Innovación” (ANII).

Literature cited: 1. Michi AN, Favetto PH, Kastelic J, Cobo ER. A review of sexually transmitted bovine trichomoniasis and campylobacteriosis affecting cattle reproductive health.

Theriogenology 2016;85(5):781-791.

2. de Vargas AC, Costa MM, Vainstein MH, Kreutz LC, Neves JP. Campylobacter fetus subspecies venerealis surface array protein from bovine isolates in Brazil. Curr Microbiol 2002;45(2):111-114.

3. Moore DP. Neosporosis in South America. Vet Parasitol 2005;127(2):87-97.

4. Donahoe SL, Lindsay SA, Krockenberger M, Phalen D, Šlapeta J. A review of neosporosis and pathologic findings of Neospora caninum infection in wildlife. Int J Parasitol Parasites Wildlife 2015;24(2):216-238.

5. Roberts SJ. Veterinary obstetrics and genital diseases (Theriogenology). Woodstock, VT: David & Charles Inc., 1986.

6. Bradd B, Rowe JD, Sverlow KW, BonDurant RH, Ardans AA, Oliver MN et al.

Experimental reproduction of bovine fetal Neospora infection and death with a bovine Neospora isolate. J Vet Diagn Invest 1994;6(2):207-215.

7. Baszler TV, Evermann JF, Kaylor PS, Byington TC, Dilbeck PM. Diagnosis of naturally occurring bovine viral diarrhea virus infections in ruminants using monoclonal antibodybased immunohistochemistry. Vet Pathol 1995;32(6):609-618.

8. Szeredi L, Haake DA. Immunohistochemical identification and pathologic findings in natural cases of equine abortion caused by leptospiral infection. Vet Pathol 2006;43(5):755–761.

9. Buxton D, Maley SW, Wright S, Thomson KM, Rae AG, Innes EA. The pathogenesis of experimental neosporosis in pregnant sheep. J Comp Pathol 1998;118(4):267-279.

10.

OIE. Bovine genital campylobacteriosis.

2017: Chap.

2.4.4. http://www.oie.int/fileadmin/Home/fr/Health_standards/tahm/2.04.04_BGC.pdf.

Accessed July 12, 2018.

11. Hum S, Quinn K, Brunner J, On SL. Evaluation of a PCR assay for identification and differentiation of Campylobacter fetus subspecies. Aust Vet J 1997;(75): 827-831.

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12. Iraola G, Hernández M, Calleros L, Paolicchi F, Silveyra S, Velilla A, et al. Application of a multiplex PCR assay for Campylobacter fetus detection and subspecies differentiation in uncultured samples of aborted bovine fetuses. J Vet Sci 2012;(13):371376.

13. Linton D, Owen R, Stanley J. Rapid identification by PCR of the genus Campylobacter and of five Campylobacter species enteropathogenic for man and animals. Res Microbiol 1996;(147):707-718.

14. Wang Q, Garrity GM, Tiedje JM, Cole JR. Naïve Bayesian Classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol 2007;(73):5261-5267.

15. Altschul S, Madden T, Schäfer A, Zhang J, Miller W, Lipman D. Grapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 1997;(25):3389-3402.

16. Björkman C, Alenius S, Manuelsson U, Uggla A. Neospora caninum and bovine virus diarrhoea virus infections in Swedish dairy cows in relation to abortion. Vet J 2000;159(2):201-206.

17. Quinn HE, Windsor PA, Kirkland PD, Ellis JT. An outbreak of abortion in a dairy herd associated with Neospora caninum and bovine pestivirus infection. Aust Vet J 2004;82(1-2):99-101.

18. Bove R, López F, Perera C, Carracelas B, Torres-Dini D, De Souza G, et al. Diagnóstico de Campylobacter fetus venerealis por PCR, en un aborto bovino espontáneo. Vete Montevideo 2013; 49 (192):20-28.

19. Campero CM, Moore DP, Odeón AC, Cipolla AL, Odriozola E. Aetiology of bovine abortion in Argentina. Vet Res Commun 2003;27(5):359-369.

20. Easton C. Estudio patológico de las principales causas infecciosas en el aborto bovino en Uruguay [tesis Maestría]. Montevideo, Uruguay: Universidad de la República; 2006.

21. Morrell E. Caracterización diagnóstica de las causas infecciosas del aborto bovino [Tesis doctorado]. Ciudad de la Plata, Argentina: Universidad Nacional de La Plata; 2010.

22. Antoniassi NAB, Juffo GD, Santos AS, Pescador CA, Corbellini, LG, Driemeier D. Causas de aborto bovino diagnosticadas no Setor de Patologia Veterinária da UFRGS de 2003 a 2011. Pesquisa Veterinária Brasileira 2013;33(2):155-160.

23. Clothier K, Anderson M. Evaluation of bovine abortion cases and tissue suitability for identification of infectious agents in California diagnostic laboratory cases from 2007 to 2012. Theriogenology 2016;85(5):933-938.

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24. Instituto Nacional de Investigación Agropecuaria. Encuesta Lechera INALE 2014. http://www.inale.org/innovaportal/file/4086/1/encuesta-lechera-2014--presentacionresultados-preliminares-foro.pdf. Accessed July 12, 2018.

25. Stella JL, Canabez F. El diagnóstico de la vibriosis genital de los bovinos del Uruguay [resumen]. Congreso Latinoamericano de Microbiología. Punta del Este, Uruguay.

1971:121.

26. Banales P, Fernandez L, Repiso M, Gil A, Dargatz D, Osawa T. A nationwide survey on seroprevalence of Neospora caninum infection in beef cattle in Uruguay. Vet Parasitol 2006;139(1-3):15-20.

27. Kashiwazaki Y, Gianneechini RE, Lust M, Gil J. Seroepidemiology of neosporosis in dairy cattle in Uruguay. Vet Parasitol 2004;120(1-2):139-144.

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https://doi.org/10.22319/rmcp.v10i4.4763 Technical note

Polycyclic aromatic hydrocarbons (PAHs) in four milk brands sold in Mexico City: evaluating three fat extraction methods

Javier Chay Rincón a José Jesús Pérez González a Beatriz Sofía Schettino Bermúdez a Rey Gutiérrez Tolentino a Dayana Sosa Pacheco b Arturo Escobar Medina a,b* Salvador Vega y León a

a

Universidad Autónoma Metropolitana. Departamento de Producción Agrícola y Animal.

Calzada del Hueso 1100, Col. Villa Quietud, Delegación Coyoacán, 04960, Ciudad de México, México. b

Centro Nacional de Sanidad Agropecuaria (CENSA), San José de las Lajas, Mayabeque, Cuba.

*Corresponding author: arturo_c_escobar2002@yahoo.com

Abstract: Polycyclic aromatic hydrocarbons (PAHs) are recognized as emerging pollutants in milk due to their risk to human health. Identification and quantification of PAHs requires analytical methods that allow more accurate and complete estimates. An analysis was done of the sixteen PAHs considered priority by the U.S. Environmental Protection Agency in whole milk from Mexico City, and this used to compare three milk fat extraction procedures. Of the four milk brands analyzed, three were ultrapasteurized (UHT) and one was pasteurized (HTST). The milk was acquired from March-June 2016. Three extraction methods were 1064

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tested: saponification (method A); detergent solution extraction (method B); and liquid-liquid extraction (method C). The PAH profiles from each method were generated by gas chromatography with a flame ionization detector. Three of the four milk brands (75 %) were positive for at least one of the sixteen analyzed PAHs. Profiles differed by extraction method with only low molecular weight compounds in method A, both low and high molecular weight compounds in method B, and higher recovery rates of low and high molecular weight compounds in method C. This method produced better recovery rates for low (58.7-12.3) and high molecular weight PAHs (81.8-8.0) than in method B (low molecular weight = 15.0-8.0, high molecular weight = 58.0-21.0).

Key words: Polycyclic aromatic hydrocarbons, Extraction methods, Milk, Gas chromatography.

Received: 04/02/2018 Accepted: 21/09/2018

Polycyclic aromatic hydrocarbons (PAHs) include over one hundred different chemicals formed during incomplete combustion of organic matter and released into the environment in large quantities(1,2). Due to their persistence and toxicity, the U.S. Environmental Protection Agency (EPA) has included sixteen PAHs (Table 1) in its list of persistent organic pollutants(3).

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Table 1. The sixteen polycyclic aromatic hydrocarbons (PAHs) listed as pollutants by the US EPA, by molecular weight PAHs Low molecular weight (LMW): Naphthalene Acenaphthene Acenaftilene Fluorene Phenanthrene Anthracene High molecular weight (HMW): Fluoranthene Pyrene Benzo(a)anthracene Chrysene Benzo(b)fluoranethane Benzo(k)fluoranethane Benzo(a)pyrene Benzo(g,h,i)perylene Indeno (1,2,3-cd)pyrene Dibenzo(a,h)-anthracene

Abbreviation

Molecular weight (g/mol)

NAP ANA ANY FLU PHE ANT

128 154 152 166 178 178

FLT PYR BaA CHR BbF BkF BaP BPE IPY DBA

202 202 228 228 252 252 252 276 276 278

EPA, 1998(3).

These compounds occur worldwide as particulate matter in the air(4), and can accumulate in soils and grasses(5,6). If lactating cows eat fodder containing PAHs, these can then be detected in milk and derived dairy products(7-11). Contamination of milk with PAHs depends on environmental factors such as exposure source, cow lactation stage, animal health status and breeding system(12,13).

Consumption of milk containing PAHs poses a risk to human health. The European Union (EU) has consequently established maximum residue levels of 1 to 35 Âľg kg fat in different foods for benzo(a)pyrene (BaP) and the combination of BaP, benzo(a)anthracene (BaA) , benzo(b)fluoranthene (BbF) and chrysene (CHR)(14).

No official method exists for quantification of PAHs in milk, but two methodologies are currently in use: 1) gas chromatography with an ionizing flame detector and mass spectrometry(15,16); and 2) high-resolution liquid chromatography with a fluorescence detector(7,8,17). Various procedures have been used for sample preparation, including saponification, liquid-liquid extraction (LLE), and cleaning by column chromatography, or 1066

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more recently, solid phase extraction (SPE)(18,19,20). However, their results can differ. For example, direct identification and quantification of PAHs in milk by saponification with subsequent extraction, or by fat extraction followed by purification, produce different PAH profiles, and tend to identify phenanthrene (PHE), anthracene (ANT), fluorene (FLU), pyrene (PYR), BaA and CHR. The present study objective was to evaluate the efficacy of three fat extraction methods in the identification and quantification of the presence of PAHs in four brands of milk.

Four brands of whole milk (three ultrapasteurized [UHT] and 1 pasteurized [HTST]) were randomly selected. Three samples were collected for each brand (n= 12) during March-June 2016 in supermarkets in the Coyoacán delegation of Mexico City, Mexico. All samples were stored for no more than 5 d after purchase in the Instrument Analysis Laboratory of the Metropolitan Autonomous University-Xochimilco (Universidad Autónoma Metropolitana).

The UHT samples were stored in a cool, dry place, and the pasteurized sample under refrigeration (5 °C). Before beginning the extraction process samples were homogenized in a water bath (40 °C) for 30 min, manually stirring every 5 min. The samples were processed with one of three extraction methods: Method A: Saponification. This was done following an established method(17), with modifications. Briefly, 8 ml 0.4 M sodium hydroxide solution in ethanol was added to 4 ml (4 g) milk. The mixture was homogenized for one minute in a vortex and placed in a thermal bath at 40 °C until almost dry (1 ml). It was completely dried under a nitrogen flow, reconstituted in 1,000 µl isooctane and stored at -20 °C until analysis.

Method B: Detergent solution extraction. Sample (250 ml) and 250 ml detergent solution (50 g sodium hexametaphosphate in 24 ml Triton X -100 dissolved in 1 l water) were added to a 500 ml flask. The flask was vigorously stirred by being placing in a water bath at 90 °C, and inverting every 15 min until fatty matter had separated out in the neck of the flask. The fat was removed from the flask, filtered at 50 °C through No. 4 Whatman filter paper in the presence of anhydrous sodium sulfate and stored in glass tubes at -20 °C until analysis(21).

Method C: Liquid-liquid extraction (AOAC 989.05). Sample (150 ml) and 0.5 g ethylenediaminetetraacetic acid (EDTA) were added to a separation funnel, stirred for one minute and allowed to sit for 2 min. Methanol (50 ml) was added to the funnel and the solution stirred again for 1 min. This operation was repeated, adding 50 ml diethyl ether and 50 ml petroleum ether. It was set aside to allow separation of the organic phase (supernatant).

The lower layer was drained off and the supernatant passed through No. 1 Whatman filter paper, adding 5 g anhydrous sodium sulfate. The organic phase was rotory evaporated at 40 °C, transferred to a 5 ml bottle and stored at -20 °C until analysis.

The saponified fat sample was slowly deposited in a column containing 6 g silica gel in its inferior portion and 1 g anhydrous sodium sulfate. Hexane (20 ml) was added and the organic 1067

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phase marked as F1. Using a different flask, 30 ml 9:1 hexane-dichloromethane (v/v) were added and allowed to flow in slowly. When it arrived at the level of sodium sulfate, 20 ml 1:1 hexane-dichloromethane (v/v) were added. The entire organic phase was collected in a single flask and marked as F2 (recovered PAHs). This phase was rotary evaporated at 40 °C until almost dry (1 ml), transferred to an amber vial and completely dried under a nitrogen flow. It was reconstituted in 250 µl isooctane and stored at -20 °C until analysis(19).

A high-resolution digital gas chromatographer with self-sampler (Shimadzu GC 2010) was used with a PTV injector at 250 °C in Splitless mode with a 1 min sampling time, 5.0 ml min-1 purge flow, and 5 ml min-1 septum purge. Nitrogen was the vehicle gas and was used at a 9.8 ml min-1 flow rate. The column was an HP5-MS (30 m length x 0.025 mm ID x 0.25 mm thickness). The temperature sequence was as follows: initial temperature 40 °C for 3 min; increased to 50 °C at 2 °C/min; increased to 160 °C at 3 °C/min; increased to 210 °C at 5 °C/min; increased to 255 °C at 7 °C/min; increased to 265 °C at 4 °C/min; increased to 300 °C at 5 °C/min; 300 °C for 5 min. Chromatographic analysis was done with the GG solution software.

Sample extract (1 µl) was injected into the column of a chromatographer (Agilent GC 5890).

A capillary column (30 m length x 0.25 mm ID x 0.25 mm thickness) (Rtx-5Sil MS, Restek Bellafonte, PA, USA) was used along with a precolumn (2 m length x 0.53 mm ID) (Siltek, Restek). The vehicle gas was helium at a constant flow rate of 1 ml/min. Injector temperature was set at 3 °C above device temperature at all times. The run temperature sequence was as follows: 1 min at 100 °C; increased to 300 °C at 5 °C/min; 15 min at 300 °C. Analyte detection was done with a mass spectrometer (Agilent MS 5972) in electron impact mode at 70 eV ionization energy, and using single ion monitoring(22) (Figure 1).

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Figure 1: Mass spectrometry chromatogram identifying sample peak to determine correspondence to native compound

Internal standard: orthoterphenyl (peak 7); 6 ethyl chrysene (peak 12); indeno[1,2,3-cd]fluoranthene (peak 16).

Chemicals were reagent quality and solvents were HPLC quality; all were acquired from J.T. Baker Chemical, USA. Analyte identification and quantification were done with a mixture of sixteen PAH compounds recommended in the method EPA 610 (Chemicalservice, USA): naphthalene (NAP); acenaphthalene (ALC); acenaphthylene (ACY); fluorene (FLU); phenanthrene (PHE); anthracene (ANT); fluoroanthracene (PMA); pyrene (PYR); benzo(a)anthracene (BaA); chrysene (CHR); benzo(b)fluoranthene (BbF); benzo(k)fluoranthene (BkF); benzo(a)pyrene (BaP); dibenzo(ab)anthracene (DBA); benzo(ghi)perylene (BGP) and indeno(cd)pyrene (IcdPy) (Table 1).

Extraction by saponification (Method A)(17) identified only LMW PAHs. Extraction with the detergent solution (Method B)(19) identified both LMW and HMW PAHs; 66.66 % were LMW and 33.33 % were HMW (Table 2).

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Table 2: Polycyclic aromatic hydrocarbons (PAHs) concentration (Âľg g-1) in milk determined with methods A and B PAHs

Method A

Method B

NAP ALC ACY FLU PHE ANT FLT PYR BaA CHR BbF BkF BaP DBA BGP IcdPy Sum of 16 PAHs Sum of 4 PAHs Sum of LMW PAHs Sum of HMW PAHs

0.066 0.200 0.066 Nd Nd 5.385 Nd Nd Nd Nd Nd Nd Nd Nd Nd Nd 5.717 0.0 5.717 (100 %) 0.00

Nd 0.372 Nd 0.915 7.153 14.924 Nd 3.773 0-.056 0.044 1.264 0.750 0-.114 4.061 Nd 1.641 35.067 1.478 23.365 (66.6 %) 11.702 (33.4 %)

Extraction methods= A: saponification and direct extraction; B: detergent solution extraction.

Nd= not determined; Âľg g-1: microgram PAH per gram milk fat.

Extraction of PAHs from milk by saponification (Procedure A) produced a PAH profile different from previous studies which report a predominance of HMW PAHs with higher concentrations of PHE and ANT, as well as a LMW PAHs proportion of 50 to 68% of total PAHs(17,23). Absence of HMW compounds when using Method A may be due to low sample concentrations, as observed elsewhere(24). However, milk sample size (4 ml) was not enough to exceed PAH detection limits under the present conditions (flame ionization detector).

Low HMW PAH concentrations have been reported in infant dairy formulas(18,25), and whole and UHT milk(17). Low molecular weight (LMW) PAHs (2 and 3 rings), particularly NAP, ACE and ACY, have not been reported in various studies(9,17,23), or were recovered at percentages less than 50%, possibly due to their high volatility(17). Saponification time and temperature play an important role in recovery rates. For example, in the present study 1070

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detection temperature was 40 째C, similar to the 60 째C saponification temperature(24), and various LMW compounds were detected. In a previous study saponification was done at 80 째C and only PHE and ANT were detected(23). This suggests that saponification temperature is a critical factor when extracting PAHs(9).

In Method B the milk fat was not saponified and was run through a purification column, allowing identification of 66.6 % LMW PAHs and 33.4 % HMW PAHs. This profile is similar to the 75.5 % LMW and 24.5 % HMW proportions reported for 31 milk samples from Brazil and Argentina(7). The LMW PAH proportion is within the 40 to 69 % range reported for fresh milk from farms near an industrial area(19). Differences between these studies may be due to milk fat extraction method since one study used organic solvents(7) and another a detergent solution(19).

Most studies using direct saponification of samples have employed mass-coupled or fluorescent detectors, which allow quantification of low PAH concentrations(17,18,24).

However, when using gas chromatography with flame ionization detection, a larger amount of milk fat is needed to achieve adequate sensitivity. Extraction with a detergent solution produces sufficient amounts of fat although PAHs may be lost due to the temperature (90 째C) to which samples are subjected.

Recovery rates in methods B and C, as confirmed by GC-MS, were highly variable, with higher rates of HMW PAHs recovered (Table 3). This variability among LMW and HMW PAHs was probably due to fat extraction method and rotary evaporation temperature. Under the evaluated conditions the most appropriate method was C since it attained recovery rates ranging from 45.3 to 95.1 %. These are similar to those reported in another study using organic solvents for fat extraction in which recovery rates ranged from 40 to 125 %, although individual PAH compounds were not identified(26). Rates in a study of human milk varied from 42 to 101 %, using the boiling point, with an R2 of 0.779(27). Particularly high recovery rates (95 to 98 %) have been reported for powdered milk when using an ultrasound bath and subsequent column purification(15), and when using a solid phase microextraction system (87.6 to 112 %)(28).

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Table 3: Recovery rates of polycyclic aromatic hydrocarbons (PAHs) in milk using two extraction methods (mean ± standard error) PAHs

Method B

Method C

NAP ALC ACY FLU PHE ANT FLT PYR BaA CHR BbF BkF BaP DBA BGP IcdPy Sum of LMW PAHs Sum of HMW PAHs

Nd 15.2±7.3 10.8±9.1 23.8±4.8 28.3±10.7 30.4±15.3 48.0±10.9 44.7±1467 70.9±16.7 59.3±15.5 93.5±21.1 45.4±11.1 127.0±35.0 78.4±17.9 64.5±15.7 66.3±14.7 15 ± 8 % 58 ± 21 %

Nd 45.3±19.0 46.5±14.7 72.3±20.9 67.6±22.6 61.6±16.9 77.5±24.8 72.0±25.9 80.0±14.4 95.1±27.5 80.9±11.3 72.7±22.4 85.6±7.0 92.1±18.3 86.9±21.9 75.0±15.7 58.7±12.3 81.8±8.0

Methods= B: detergent solution extraction; C: liquid-liquid extraction.

In milk samples, fat extraction method has a substantial effect on which PAHs can be identified. Recovery rates with Method C agreed with those reported for environmental pollutants in biological matrices at concentrations less than 1 µg kg-1, where rates can range from -50 to +20%(29). This recovery rate allows accurate assessment of the presence of PAHs in milk samples.

Of the four analyzed milk brands one (A) contained no detectable PAHs, whereas in the remaining three brands at least one of the sixteen compounds was detected (Table 4); that is, 75 % of samples were positive for PAHs. The compounds PHE and ANT had the highest incidence (54.5 %), followed by FLUO and DBA (45.5 %). The highest concentration was of ANT (341 µg g-1), followed by PHE (20 µg g-1) and DBA (12.3 µg g-1). These results coincide with previous reports in which LMW PAHs occur with more frequency at higher concentrations(17,23).

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Table 4: Presence of polycyclic aromatic hydrocarbons (PAHs) in milk samples (n=12) ACE % Inc 36.4 Sum 1.2 Min 0.2 Max 0.6

FLUO 45.5 5.6 0.3 3.6

PHE 54.5 20.0 0.4 7.5

ANT 54.5 341.0 0.0 155.0

PYR 9.1 3.8 3.8 3.8

BaA 27.3 0.2 0.0 0.1

CHR 27.3 0.2 0.0 0.1

BbF 27.3 2.3 0.2 1.3

BkF 18.2 1.1 0.3 0.7

BaP 9.1 0.1 0.1 0.1

IND 27.3 6.3 0.7 3.0

DBA 45.5 12.3 0.1 5.6

Inc= incidence, Min= minimum, Max= maximum.

Of the four analyzed milk brands, D had the largest mean sum of four PAHs (Table 5). This concentration exceeds EU guidelines for nursing formulas (1 µg kg-1)(14), indicating it poses a risk to human health. Perhaps the higher concentration in this brand was due to the vegetable fat included in its formulation, which is absent in the other three milk brands.

Table 5: Mean sum of sixteen and four polycyclic aromatic hydrocarbons (PAHs) in four milk brands from Mexico City Brands

Ʃ 16 PAHs µg kg-1

Ʃ 4PAHs µg kg-1

A B C D

Nd 47.56 93.95 51.49

Nd 0.23 1.14 4.04

Nd= Not detected.

When extracting fat from milk samples for identification and quantification of polycyclic aromatic hydrocarbons, methods B and C preserved variable percentages of low and high molecular weight compounds. Method C exhibited the best recovery rate, although Method B could be an alternative when using gas chromatography-mass spectrometry. Three of the four (75 %) milk brands were positive for polycyclic aromatic hydrocarbons, and two brands exceeded maximum levels recommended by the European Union.

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Edición Bilingüe Bilingual Edition

Rev. Mex. Cienc. Pecu. Vol. 10 Núm. 4, pp. 801-1076, OCTUBRE-DICIEMBRE-2019

ISSN: 2448-6698

Pags.

Diversidad y estructura genética de una población de cabras criollas negras de tres municipios del estado de Querétaro, México

Genetic diversity and structure in Criolla Negra goats in Queretero, Mexico Juan Carlos Silva-Jarquin, Héctor Mario Andrade-Montemayor, Héctor Raymundo Vera-Ávila, Marina Durán-Aguilar, Sergio Iván Román-Ponce, Vincenzo Landi, Amparo Martínez-Martínez, Juan Vicente Delgado-Bermejo, Consorcio BioGoat.......................................................................................................801

Estudio biométrico del bovino criollo de Santa Elena (Ecuador)

Biometric study of Criollo Santa Elena Peninsula cattle (Ecuador) Ronald Roberto Cabezas-Congo, Cecilio Jose Barba-Capote, Ana María González-Martínez, Orly Fernando Cevallos-Falquez, José Manuel León-Jurado, José Manuel Aguilar-Reyes, Antón Rafael García-Martínez……………………...................................................................................…819

Efecto de la suplementación con minerales de fuentes queladas o inorgánicas y vitamina E en la calidad y estabilidad oxidativa de la carne de bovinos Effect of supplementation with vitamin E and chelated or inorganic minerals on beef quality and oxidative stability Manuel Andrés González-Toimil, Pedro Garcés-Yépez, Luis Humberto López-Hernández, Diego Braña-Varela, Everardo González-Padilla............................................................................837

Productive and economic response to concentrate supplementation by grazing dairy cows at high stocking

Respuesta productiva y económica a la suplementación con concentrados de vacas lecheras en pastoreo con alta carga animal Benito Albarran-Portillo, Felipe López-González, Miguel Ruiz-Albarrán, Carlos Manuel Arriaga-Jordán..................................................................................................................................................855

Comportamiento productivo e ingestivo de ovinos en crecimiento en sistemas silvopastoriles y de engorda en confinamiento

Productive and ingestive behavior in growing hair sheep in silvopastoral and stabled weight-gain systems Carlos Ricardo Villanueva-Partida, Víctor Francisco Díaz-Echeverría, Alfonso Juventino Chay-Canul, Luis Ramírez Avilés, Fernando Casanova-Lugo, Iván Oros-Ortega....................870

Indicadores de estrés en bovinos por el uso de prácticas de manejo en el embarque, transporte y desembarque

Stress indicators in cattle in response to loading, transport and unloading practices Silvia Larios-Cueto, Rodolfo Ramírez-Valverde, Gilberto Aranda-Osorio, María Esther Ortega-Cerrilla, Juan Carlos García-Ortiz.................................................................................................885

Impacto del peso al nacimiento del lechón sobre los balances de nitrógeno y energía en la fase de crecimiento

Impact of piglet birth weight on nitrogen and energy balances in the growth phase Enrique Vázquez-Mandujano, Tércia Cesária Reis-de-Souza, Ericka Ramírez-Rodríguez, Gerardo Mariscal-Landín............................................................................................................................903

Oferta y demanda regional de carne de pollo en México, 1996-2016

Regional supply and demand for chicken meat in Mexico, 1996-2016 Eulogio Rebollar-Rebollar, Alfredo Rebollar-Rebollar, Jaime Mondragón-Ancelmo, Germán Gómez-Tenorio......................................................................................................................................917

Óptimos técnicos para la producción de leche y carne en el sistema bovino de doble propósito del trópico mexicano

Technical optimum milk and meat production levels in dual-purpose cattle systems in tropical Mexico Yuridia Bautista-Martínez, José Antonio Espinosa-García, José Guadalupe Herrera-Haro, Francisco Ernesto Martínez-Castañeda, Humberto Vaquera-Huerta, Benigno Estrada-Drouaillet, Lorenzo Danilo Granados-Rivera...........................................................................................933

Caracterización productiva y socioeconómica del sistema de producción ovina, en un área natural protegida de México

Productive and socioeconomic characterization of a sheep production system in a natural protected area in Mexico Daniel Hernández-Valenzuela, Ernesto Sánchez-Vera, William Gómez-Demetrio, Carlos Galdino Martínez-García............................................................................................................................951

Influencia de los valores humanos en el consumo de quesos tradicionales chiapanecos: una comparación de las rutas directa e indirecta

Comparison of the direct and indirect routes of human values’ influence on consumption of two traditional cheeses from Chiapas, Mexico Carolina Illescas-Marín, Arturo Hernández-Montes, Esaú Estrada-Estrada, Rolando Murguía-Cozar, Anastacio Espejel-García, Armando Santos-Moreno..............................................966

Endoparásitos de Odocoileus virginianus y Mazama temama bajo cautiverio en Veracruz, México

Endoparasites in captive Odocoileus virginianus and Mazama temama in Veracruz, Mexico Cristina Salmorán-Gómez, Ricardo Serna-Lagunes, Norma Mora-Collado, Dora Romero-Salas, Dulce María Ávila-Nájera, Pedro Zetina-Córdoba.............................................................986

REVISIONES DE LITERATURA Supplementation of ascorbic acid to improve fertility in dairy cattle. Review

Suplementación con ácido ascórbico para mejorar la fertilidad del ganado lechero. Revisión Juan González-Maldonado, Raymundo Rangel-Santos, Raymundo Rodríguez-de Lara, Gustavo Ramírez-Valverde, J. Efrén Ramírez-Bribiesca, José Cruz Monreal-Díaz................1000

NOTAS DE INVESTIGACIÓN Efecto del consumo de moringa sobre parámetros productivos y toxicológicos en pollos de engorda

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Evaluación productiva y análisis costo-beneficio de cerdas alimentadas con una dieta adicionada con nopal (Opuntia ficus-indica) durante la lactancia

Productive evaluation and cost:benefit analysis of lactating sows fed a diet containing nopal (Opuntia ficus-indica) Gerardo Ordaz-Ochoa, Aureliano Juárez-Caratachea, Liberato Portillo-Martínez, Rosa Elena Pérez-Sánchez, Ruy Ortiz-Rodríguez.........................................................................................1027

Rendimiento de materia seca y valor nutritivo de cuatro leguminosas herbáceas en la zona tropical de Hueytamalco, Puebla, México

Dry matter yield and nutritional values of four herbaceous legumes in a humid tropical environment in Hueytamalco, Puebla, Mexico Sergio Alberto Lagunes-Rivera, Juan De Dios Guerrero-Rodríguez, Josafath Omar Hernández-Velez, José de Jesús Mario Ramírez-González, Dulce Violeta García-Bonilla, Antonio Alatorre-Hernández.......................................................................................................................................................1042

Abortion outbreak caused by Campylobacter fetus subspecies venerealis and Neospora caninum in a bovine dairy herd

Brote de abortos causado por Campylobacter fetus subespecie venerealis y Neospora caninum en un hato bovino lechero Melissa Macías-Rioseco, Rubén D. Caffarena, Martín Fraga, Caroline Silveira, Federico Giannitti, Germán Cantón, Yanina P. Hecker, Alejandra Suanes, Franklin Riet-Correa.......1054

Presencia de hidrocarburos aromáticos policíclicos (HAP) en leche comercializada en la Ciudad de México, evaluando diferentes métodos de extracción Polycyclic aromatic hydrocarbons (PAHs) in four milk brands sold in Mexico City: evaluating three fat extraction methods Javier Chay-Rincón, José Jesús Pérez-González, Beatriz Sofía Schettino-Bermúdez, Rey Gutiérrez-Tolentino, Dayana Sosa-Pacheco, Arturo Escobar-Medina, Salvador Vega-y-León............................................................................................................................................................1064

Revista Mexicana de Ciencias Pecuarias Rev. Mex. Cienc. Pecu. Vol. 10 Núm. 4, pp. 801-1076, OCTUBRE-DICIEMBRE-2019

CONTENIDO CONTENTS

Rev. Mex. Cienc. Pecu. Vol. 10 Núm. 4, pp. 801-1076, OCTUBRE-DICIEMBRE-2019