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Edición Bilingüe Bilingual Edition

Revista Mexicana de Ciencias Pecuarias Rev. Mex. Cienc. Pecu. Vol. 11 Suplemento 2, pp. 1-145, MARZO-2020

ISSN: 2448-6698

Rev. Mex. Cienc. Pecu. Vol. 11 Suplemento 2 pp. 1-145, MARZO-2020


Alimentación de ganado con ensilado de cerdaza. Granja Buganbilias en San José de Gracia, Michoacán. Fotografía tomada por: Alberto Jorge Galindo Barboza.

REVISTA MEXICANA DE CIENCIAS PECUARIAS Volumen 11 Suplemento 2, Marzo 2020. 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 marzo de 2020.

DIRECTORIO EDITOR EN JEFE Arturo García Fraustro

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

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. Feliciano Milian Suazo, Universidad Autónoma de Querétaro, 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. 11 (Suplemento 2)

MARZO-2020

CONTENIDO ARTÍCULOS

Pág. Efecto de la temperatura del agua sobre la constante de velocidad de reacción de los contaminantes en un humedal construido para el tratamiento de aguas residuales porcícolas Water temperature effect on the reaction rate constant of pollutants in a constructed wetland for the treatment of swine wastewater Celia De La Mora-Orozco, Rubén Alfonso Saucedo-Terán, Irma Julieta González-Acuña, Sergio GómezRosales, Hugo Ernesto FloresLópez………………………………………………………………………………..………………..................................1

Estimación de la producción de metano entérico en ranchos de producción familiar de leche bovina en el sur del estado de Querétaro, México Estimation of enteric methane production in family-run dairy farms in the south of the State of Querétaro, Mexico Sergio Gómez Rosales, María de Lourdes Ángeles. José Luis Romano Muñoz, José Ariel Ruíz Corral……………………………………………………………………………………………………………….……….…18

Efecto del calentamiento global sobre la producción de alfalfa en México Global warming effect on alfalfa production in Mexico Guillermo Medina-García, Francisco Guadalupe Echavarría-Cháirez, José Ariel Ruiz-Corral, Víctor Manuel Rodríguez-Moreno, Jesús Soria-Ruiz, Celia De la Mora-Orozco ................................... 34

Áreas con aptitud ambiental para [Bouteloua curtipendula (Michx.) Torr.] en México por efecto del cambio climático Environmental suitability areas for [Bouteloua curtipendula (Michx.) Torr.] in Mexico due to climate change effect José Ángel Martínez Sifuentes, Noé Durán Puga, José Ariel Ruiz Corral, Diego Raymundo González Eguiarte, Salvador Mena Munguía ...................................................................................................... 49

Efecto en la erosión hídrica del suelo en pastizales y otros tipos de vegetación por cambios en el patrón de lluvias por el calentamiento global en Zacatecas, México Effects of rainfall pattern changes due to global warming on soil water erosion in grasslands and other vegetation types in the state of Zacatecas, Mexico Francisco Guadalupe Echavarría-Cháirez, Guillermo Medina-García, José Ariel Ruiz-Corral .............. 63

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Estimación del factor de transporte del índice de fósforo con climatologías y escenarios de cambio climático en tierras de Jalisco, México Estimation of the transport factor of the phosphorus index in climatology and climate change scenarios in Jalisco, Mexico Hugo Ernesto Flores López, Álvaro Agustín Chávez Durán, José Ariel Ruíz Corral, Celia De La Mora Orozco, Uriel Figueroa Viramontes, Agustín Hernández Anaya .......................................................... 75

Impacto del cambio climático en la distribución potencial de Tithonia diversifolia (Hemsl.) A. Gray en México Impact of climate change on the potential distribution of Tithonia diversifolia (Hemsl.) A. Gray in Mexico Noé Durán Puga, José Lenin Loya Olguín, José Ariel Ruiz Corral, Diego Raymundo González Eguiarte, Juan Diego García Paredes, Sergio Martínez González, Marcos Rafael Crespo González.................... 93

REVISIONES DE LITERATURA

Mitigación y adaptación al cambio climático mediante la implementación de modelos integrados para el manejo y aprovechamiento de los residuos pecuarios. Revisión Mitigation and adaptation to climate change through the implementation of integrated models for the management and use of livestock residues. Review Alberto Jorge Galindo-Barboza, Gerardo Domínguez-Araujo, Ramón Ignacio Arteaga-Garibay, Gerardo Salazar-Gutiérrez .............................................................................................................................. 107

Causas y consecuencias del cambio climático en la producción pecuaria y salud animal. Revisión Causes and consequences of climate change in livestock production and animal health. Review Berenice Sánchez Mendoza, Susana Flores Villalva, Elba Rodríguez Hernández, Ana María Anaya Escalera, Elsa Angélica Contreras Contreras ..................................................................................... 126

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Actualización: marzo, 2020 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.

contener los componentes que a continuación se indican, empezando cada uno de ellos en página aparte. Página del título Resumen en español Resumen en inglés Texto Agradecimientos y conflicto de interés Literatura citada

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.

4.

5.

6.

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 disponibles en el propio sitio oficial de la revista. 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. 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 y ser incluidos en el texto). 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.

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: 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

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iniciales del materno y nombre(s). En caso de 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.

Literatura citada 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. 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.

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.

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).

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

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.

Nombre de los autores, con mayúsculas sólo las iniciales, empezando por el apellido paterno, luego

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

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.

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

i.v. intravenosa (mente) J joule (s) kg kilogramo (s) 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 fotografías e imágenes deberán estar en formato jpg (o compatible) con al menos 300 dpi de resolución. Tanto las fotografías, imágenes, gráficas, cuadros o tablas deberán incluirse en el mismo archivo del 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.

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

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.

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Updated: March, 2020 INSTRUCTIONS FOR AUTHORS Revista Mexicana de Ciencias Pecuarias is a scientific journal published in a bilingual format (Spanish and English) which carries three types of papers: Research Articles, Technical Notes, and Reviews. Authors interested 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 a signed agreement co-author letter should enclosed as complementary file; coauthors at different institutions can mail this form independently. The corresponding author should be indicated together with his address (a post office box will not be accepted), telephone and Email.

4.

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.

5.

Research articles will not exceed 20 double spaced pages, without including Title page and Tables and Figures (8 maximum and be included in the text). 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.

6.

Title page Abstract Text Acknowledgments and conflict of interest Literature cited 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 Literature cited 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.

b) Technical Notes. They should be brief and be

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.

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

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information presented in the sections of t he research article but without section titles.

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.

c) Reviews. The purpose of these papers is to

summarize, analyze and discuss an outstanding topic. The text of these articles should include the following sections: Introduction, and as many sections as 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. Literature cited. 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).

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. 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). 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). 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.

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. 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 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.

Organization, as author VI) The Cardiac Society of Australia and New Zealand. Clinical exercise stress testing. Safety and performance guidelines. Med J Aust 1996;(164):282284.

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

Books and other monographs

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

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.

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.

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.

Thesis

responsables de establecimientos destinados al sacrificio de animales. México. 1996. XVII) AOAC. Official 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.

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. 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. 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. 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 already integrated the corrections and modifications indicated by the Review Committee. The works will have to be elaborated with Microsoft Word. Photographs and images must be in jpg (or compatible) format with at least 300 dpi resolution. Photographs, images, graphs, charts or tables must be included in the same text file. The boxes should not contain any vertical lines, and the horizontal ones only those that delimit the column headings, and the line at the end of the box.

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.

Organization as author 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

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

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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. 17. List of abbreviations: cal cm °C DL50 g ha h i.m. i.v. J kg km L log Mcal MJ m ¾l ¾m

calorie (s) centimeter (s) degree Celsius 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)

mg ml mm min ng

milligram (s) milliliter (s) millimeter (s) minute (s) nanogram (s) P 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.

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https://doi.org/10.22319/rmcp.v11s2.5643

Editorial Climate change is a global phenomenon, but it exerts regional effects due to the synergism that results from the interaction between the causes of climate change (increased concentrations of greenhouse gases) and the causes of regional climate change (changes in the use of soil). Thus, the levels of alteration of the climatic patterns present a spatial variation. Mexico is a territorially vast country, where the interaction of several factors provides a rich climate diversity under which it is possible to develop a great variety of productive activities, agriculture and livestock among them. However, the agroclimate in which these activities take place has been changing noticeably since the second half of the 1980s. The last decade of the 20th century and the first two decades of the 21st century have been the hottest in all the history of instrumental temperature records. These changes in temperature are not alone, the regional patterns of precipitation and evapotranspiration, among others, have also changed in recent decades. Therefore, several studies have focused on evaluating the effect of climate change on the primary sector. However, most of these investigations have centered on the agricultural sector, and the investigations directed to the livestock sector are few. This situation motivated the present Supplement, which is dedicated to the subject of climate change and the livestock sector.

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Rev Mex Cienc Pecu 2020;11(Supl 2)

This Second Supplement includes seven scientific articles and two bibliographic reviews that intend to provide a quantitative idea about the effects of climate change in the diverse aspects of livestock activity. The topics addressed in this Supplement include the effect of climate change on forage availability, the impact of climate change on the distribution of potential forage resources, the interaction between pluvial change and water erosion in rangelands, the effect of increasing temperature on the pollutant dynamics of animal organic residue wetlands, climate change and enteric methane production, climate change and nutrient transport in farming soils, mitigation and adaptation to climate change through the use of livestock waste, and a recapitulative analysis of the effect of climate change on livestock production and animal health. The participants of this Second Supplement intend that their research, presented throughout these manuscripts, be useful to specialists and the general public involved with the subject of climate change and the livestock sector.

Dr. JosĂŠ Ariel Ruiz Corral Universidad de Guadalajara, Department of Environmental Sciences.

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https://doi.org/10.22319/rmcp.v11s2.4681 Article

Water temperature effect on the reaction rate constant of pollutants in a constructed wetland for the treatment of swine wastewater

Celia De La Mora-Orozco a Rubén Alfonso Saucedo-Terán b Irma Julieta González-Acuña c Sergio Gómez-Rosales d Hugo Ernesto Flores-López a*

a

Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias (INIFAP). Campo Experimental Centro-Altos de Jalisco. Tel: 01 800 0882222. AV. Biodiversidad 2470. 47600 Tepatitlán de Morelos. Jalisco, México. b

INIFAP. Sitio Experimental La Campana. Chihuahua, México.

c

INIFAP. Campo Experimental Santiago Ixcuintla. Nayarit, México.

d

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

*Corresponding author: flores.hugo@inifap.gob.mx.

Abstract: Temperature is an important factor in the processes that are carried out in biological systems. In wetlands, the capacity to remove pollutants is limited by environmental factors. The objective was to determine the effect of water temperature on the rate constant for the removal of pollutants in wastewater from pig farms. The evaluation was carried out in a surface flow constructed wetland (SFCW) consisted of a 9 m long and 3 m wide channel covered with a high density geo-membrane (4 mm). The SFCW bed consisted of a 30 cm layer of sand and clay; native vegetation from the study area was used. The hydraulic

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retention time (HRT) was 10 d, and 12 experimental runs were carried out between January 2014 and December 2015. The results showed an average removal rate of the chemical oxygen demand (COD) of approximately 75 and 74 % for 2014 and 2015 respectively; the average removal rate of ammonia nitrogen (NH3-N) of 65 and 69 %, while the average total nitrogen (TN) removal rate was 69 and 63 % and the total phosphorus (TP) removal rate was 75 and 73 % in 2014 and 2015, respectively. The water temperature along the experimental phase ranged from 13 to 22 °C. The removal of NH3-N showed the highest dependence on water temperature with values of R2 = 0.8787 in 2014 and R2 = 0.8957 in 2015. The volumetric reaction constant (kv d-1) in 2014 ranged from 0.041 to 0.185 d-1 with an average temperature in the wetland of 13 to 21 °C. While k presented an average value of 2.60 cm d1 in 2014, and in 2015 the observed value was 3.22 cm d-1. It was evident that the value of kv augmented as the water temperature increased, which indicates that this factor has a direct effect on the removal of the NH3-N. Key words: Temperature, Reaction rate constant, Wetlands, Swine wastewater.

Received: 03/11/2017 Accepted: 25/11/2018

Introduction

In Mexico, the increase in the discharge of wastewater from various activities has caused certain receptor bodies of water to exhibit different types and levels of pollution, generating a strong impact on the reduction of this resource which, unless treated with viable alternatives for its recovery, may cause irreversible damage(1). Approximately 420 m3 of wastewater are generated every second in Mexico; of these, 250 m3 s-1 are municipal, and 170 m3 s-1 are non-municipal; less than 25 % of the latter, the most polluting of which are issued by pig farms, receive no treatment(2). For this reason, special attention has been given to the use of effective, environment-friendly technologies to remove the pollutants present in waste water, especially the nutrients such as nitrogen and phosphorus(3,4). Conventional wastewater treatment technologies are generally effective. However, many of these technologies involve high installation costs and use large amounts of energy.

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Certain researchers have proposed the use of constructed wetlands (CWs) as a viable alternative for removing nutrients from livestock wastewater(5,6). The processes carried out in these systems are sundry, but the main nutrient removal processes are the growth of the microbial biomass and the adsorption of the nutrients by the vegetation(5,7). The efficiency of the CWs in the removal of pollutants has been widely researched by several authors(8-11). Constructed wetland can reach levels of ammonium removal of 80 to 99 %(12,13,14). The most important factors to be considered in the design of a wetland for its proper operation are the inflow, the load of organic matter, and the hydraulic retention time (HRT)(15,16,17), although temperature is the primordial factor, as biological systems are involved(18,19,20). According to certain authors(6), the efficiency of the wetland is segmented into seasonal cycles, and the effect of the temperature on the biotic reactions is greater at low temperatures (>15 °C) than at high temperatures (<20 °C). However, if the inflow and the concentrations in the wetland also vary seasonally, their affect will blend together with that of temperature(21). The temperature also influences the denitrification process in the wetlands, which occurs under conditions of anoxia in the sediment or in anoxic micro-sites in the film adhered to the substrate or the tissue of plants(21). Through this mechanism, nitrates can be removed in the wetlands(22,23). In shallow wetlands, the process or degree of denitrification can reportedly be increased, due to the proximity of the nutrients in the sediment-water component(24). For this reason, denitrification has been regarded as a very viable method for removing nitrogen from the wetlands(23). In a study carried out in Tennessee, USA, to assess the effect of vegetation and the HRT on pollutant removal capacity in wetlands, the average removal rate was found to be higher when using 6 days of HRT, compared to 2 days. Wetlands with vegetation exhibited favorable results; with 6 days of HRT, removal averages of 67 % were reported for the removal of ammonia nitrogen (NH3-N), and values ranging between 42 and 67%, for the chemical oxygen demand (COD); synthetic wastewater was utilized to simulate agricultural runoffs(25). The capacity to remove total nitrogen (TN), NH3-N, total phosphorous (TP) and COD using a sub-surface wetland in domestic wastewater was assessed in Hong Kong. Two WRTs (5-10 days) and wetlands with and without vegetation were used. The results showed greater removal in wetlands with vegetation using the two retention times. A 68 and 72 % COD removal rate was obtained in wetlands with vegetation; the removal rates were 92 and 95 % for NH3-N, 65 and 62 % for TN, and 67 and 52 % for TP for days 10 and 5, respectively. The quality of the water obtained in the effluent met the standards of Hong Kong for use in recreational parks(26). Another author(27) researched the factors that affect phosphorus retention in a surface flow constructed wetland using wastewater from surface runoffs. The research was performed under cold environmental conditions in Norway. The results showed an average 21 to 44 % reduction of phosphorus. However, the removal percentage was observed to increase with a greater hydraulic load; the statistic results showed that the removal was influenced by different variables, such as the concentration of phosphorus in 3


Rev Mex Cienc Pecu 2020;11(Supl 2):1-17

the influent, the time of the year, the phosphorus content of the suspended solids, and the sedimentation rate of phosphorus. Constructed wetlands have also been utilized to treat wastewater from pig farms. In southern China, 2 SFWs were established using Vetiveria zizanioides in one, and Cyperus alternifolius in the other. The objective was to research the efficiency of the wetlands in the removal of organic matter from the wastewater from pig farms through the seasonal changes during four years. The removal of COD and the biological oxygen demand (BOD5) was 70 and 80 % in the spring, using two days of HRT. The removal rate was also reported to have reached up to 90 % in the summer, and to have dropped to 50 % for COD and 60 % for BOD5 in the fall. No significant differences were found between the two wetlands under experimentation; however, the difference between seasons of the year was significant(28). The availability and quality of water is a primordial need. However, there are risk factors associated to human activities and environmental factors, such as climate change(29-32). The effects of extreme climate events are predicted to be reflected at once, primarily in food safety, as well as the availability of water for the various human activities(29,32,33). The efforts to preserve this resource involve the use of alternative treatments that have a low installation cost and are environment friendly. Studies on the effect of the operational factors on the efficiency of the SFCW are numerous and sundry. However, so far, information about the SFC's pollutants removal efficiency related with the water temperature is scarce. The objective of this study was to determine the reaction rate constant of NH3-N, as well as to identify the seasonal effect of the water temperature on the removal rate of the COD, NH3N, TN and TP contained in wastewater from pig farms through the use of a surface flow constructed wetland at pilot scale, in an effort to determine the functionality of this system under the climate conditions of the Highlands of Jalisco.

Material and methods Characteristics of the farm

The research was carried out at the Santa MarĂ­a farm, located in the municipality of Arandas, in the state of Jalisco, 11 km to the northeast of the city of Arandas. The pig farm has 12 stalls, housing a total population of 12,000 pigs with some variations. For the intake process by the pigs and the cleaning of the pig pens, an average of 120,000 L d-1 of potable water are used and subsequently piped into a biodigester for decomposition of the organic matter; the effluent of the biodigester is then routed to a sedimentation lagoon. 4


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Design of the system

The variables considered in its design and construction were substrate, vegetation and water retention time. The surface flow wetland was built as a 9 m long x 3 m wide canal (Figure 1), with a 30 cm thick layer of a mixture of yellow sand and tezontle (volcanic rock) as support material for the vegetation and an approximately 5 % slope. The wetland was built with a (4 mm thick) high-density polyethylene geo-membrane, metal poles and a mesh as support for the channel. Vegetation (Thypa sp. and Scirpus sp.) from the surroundings of the farm was transplanted into the system and was maintained for a period of two months until it adapted to its new substrate.

Figure 1. Design of the surface flow wetland

Operation of the system The wastewater used in his research was taken from the sedimentation lagoon at the outlet of the anaerobic digester. Because the concentration of the organic matter in the lagoon was approximately 7,160 mg L-1 of COD, it was necessary to dilute it with well water in order to obtain the desired concentration for the research. The water from the lagoon and from the well was pumped into a 2,500 L storage cistern equipped with fluxometers for regulating the amount of water required by each influent and thus obtaining the desired COD concentration. The cistern had an electric mixing motor connected to a pair of blades that were kept in constant motion.

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Experimental design

The organic matter content and the temperature of the water were regarded as independent variables. The dependent variables were: the COD, NH3-N, TN and TP. Twelve experimental runs were carried out with a water retention time of 10 days and an organic load of 835 Âą 64 mg L-1 of COD in 2014 and 774 Âą 26 mg L-1 of COD in 2015.

Sampling and simple analysis

Samples were collected at the inlet and outlet the wetland, at 5-day intervals, and, on a weekly basis, from the sedimentation lagoon. The measured parameters were: Temperature (°C) (NMX-AA-007-SCFI-2000), COD (HACH 800 Method), TN (HACH 10072 Method), NH3N (HACH 10031 Method) y TP (HACH 10127 Method). The equipment used consisted of a HATCH DRB 200 reactor and a HACH DR 2800 spectrophotometer.

Statistical analysis

An ANOVA was performed in order to determine the significant differences between the two years of the study period at a 0.05 confidence interval.

Estimation of the reaction rate constant and the temperature coefficient for NH3-N

The biological reactions occurring in the wetlands are generally described as first-order reactions. The first-order models usually work well in the long term(21). In this study, the monthly averages of the concentrations of the constituents were determined for the assessment of the reaction rates. The following first-order equation for a piston flow was utilized to describe the removal rate of NH3-N: đ??śđ?&#x2018;&#x2019; đ??ś0

â&#x2C6;&#x2019;đ?&#x2018;&#x2DC;đ?&#x2018;Ł

= đ?&#x2018;&#x2019;đ?&#x2018;Ľđ?&#x2018;? (đ??ťđ?&#x2018;&#x2026;đ?&#x2018;&#x2021;)

eq. 1

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Where: Ce is the average NH3-N concentration in the effluent (mg L-1); C0 is the average concentration of the nutrient in the influent (mg L-1), and kv is the volumetric removal rate constant (d-1). The values of kv were estimated using the following equation: đ?&#x2018;&#x2DC;đ?&#x2018;Ł =

đ??ś â&#x2C6;&#x2019;lnâ Ą( đ?&#x2018;&#x2019; ) đ??ś0

đ??ťđ?&#x2018;&#x2026;đ?&#x2018;&#x2021;

eq. 2

According to others authors(30), the areal removal rate constant is expressed as follows: đ?&#x2018;&#x2DC; = đ?&#x2018;&#x2DC;đ?&#x2018;Ł â&#x2C6;&#x2014; â ĄĆ? â&#x2C6;&#x2014; â Ąâ&#x201E;&#x17D;

eq. 3

Where: k is the temperature dependent removal rate constant in a given area (cm d-1); Ć? is the porosity constant of the fraction of space through which water can flow in the wetland (and which has been estimated for the several types of wetlands: 0.75 for the surface flow wetland, and 0.4 for the sub-surface flow wetlands); h is the depth of the system(21,34). Equation 1 can be modified substituting kv by using equation 2, as follows: đ??śđ?&#x2018;&#x2019; đ??ś0

â&#x2C6;&#x2019;đ?&#x2018;&#x2DC;

= exp (đ??ťđ??żđ?&#x2018;&#x2026;)

eq. 4

Where: HLR is the hydraulic load (cm d-1). The effect of temperature on kv or k can be summarized using the Arrhenius equation: đ?&#x2018;&#x2DC;đ?&#x2018;Ł = đ?&#x2018;&#x2DC;đ?&#x2018;Ł20 â&#x2C6;&#x2014; â Ą đ?&#x153;&#x192; (đ?&#x2018;&#x2021;â&#x2C6;&#x2019;20)

eq. 5

Where; Kv20 is the volumetric removal constant at 20 °C (d-1); θ is the temperature coefficient, and T is the temperature of the water (°C). The slope ln(θ) and the intersection of the line ln(kv) were estimated by charting ln(kv) vs (T-20) and through a linear regression analysis. The coefficient of determination (R2) was estimated in order to assess the adjustment of all the regressions.

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Results and discussion The average temperature of the water in the influent of the wetland in the year 2014 was 18.1 ± 2.2 °C, while in 2015 the average temperature was 17.7 ± 2.6 °C. Figure 2 shows the behavior of the monthly average of the temperature. In 2014, the lowest temperature (°C) was observed in December (13.6 °C), while the highest occurred in May (21 °C). The same behavior was observed in 2015, with the lowest temperature occurring in December, with an average value of 13 °C, and the highest temperature was observed in May (21.5 °C). In the year 2014 there was a stage during which the variability remained without significant changes (June – September), and then the temperature descended gradually until reaching the minimum level in the month of December. It may also be observed that the changes in temperature were more drastic in 2015 during the same months (June – September), reaching a minimum in December. Therefore, it is important to consider the seasonality, with the consequent influence of the rainy season on the low temperature of the water –a condition to be expected.

Figure 2: Monthly average temperature at the influent of the wetland in 2014 and 2015

The results of the efficiency of the SFCW for the removal of the organic matter are shown in Figure 3a. As may be observed, the average removal rate of COD in the year 2014 was 75 ± 12 %, while in 2015 the average was slightly lower, with a value of 74 ± 13 %. The minimum removal rate in 2014 (37 %) occurred in January, with an average temperature of the water 8


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of 14 째C, and the maximum removal rate (76 %) was reached in May, with an average temperature of the water of 21 째C. The minimum removal rate in 2015 (54 %) occurred in December, when the average temperature of the water was 13 째C. The maximum value (89 %) was obtained in June, when the average temperature was 19 째C. The removal rate of COD was stable during the years of study. Notably, for instance, in winter the removal rate was approximately 60 %, increasing by approximately 10 % in spring; the system may be observed to have attained its maximum capacity in the summer, with a COD removal rate of approximately 88 %, while in the fall, it ranged between 60 and 70 %.

Figure 3: Removal rates variation of (a) COD, (b) NH3-N, (c) TN, and (d) TP, during 2014 and 2015

There was no significant difference (P>0.05) in the COD removal rate between 2014 and 2015. The regression analysis between the temperature of the water and the COD removal rate yielded a R2 value of 0.661 in the year 2014, and of 0.626 in 2015 (Figure 4a). In general, in the various studied years, the relationship between the COD removal rate and the temperature was moderately strong. The results obtained in this research agree with those of other researches(28); those authors studied the efficiency of a wetland in removing organic 9


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matter and nutrients from an effluent of a pig farm and they determined a COD removal rate of 90 %; however, the levels obtained the rest of the year were lower than those estimated by this research.

Figure 4: Linear regression and determination coefficient between the pollutant removal rate and the water temperature (a) COD, (b) NH3-N, (c) TN and (d) TP in 2014 and 2015

Although the results of the removal rate coincide, there are differences in the HRT, as these authors used only two days, whereas in the present research the HRT was 10 d â&#x20AC;&#x201C;a considerable difference (8 d) in the time of exposure to the nutrient in the system. Another difference in regard to the present research was the COD concentration in the influent; those authors used concentrations of 1,000 to 1,400 mg L-1, whereas in the present research the influent showed little variability, having been 835 Âą 64 mg L-1 in 2014 and 774 Âą 26 mg L-1 in 2015. The authors ascribed the efficiency of the wetland to several factors, such as the season of the year, as the maximum removal rates in the assessed parameters were attained in the summer.

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On the other hand, other techniques have been utilized to try to increase the removal rate of pollutants in wetlands; several authors(32) assessed the effect of the application of oxygen to a surface flow wetland, obtaining an increase of 20 % in the organic matter removal rate. However, other processes, such as nitrification, were also negatively affected, as no significant NH3-N removal rate increase (27-48 %) was obtained in the wetland. The results for NH3-N removal are shown in Figure 3b. In 2014, the mean annual NH3-N removal rate was 65 ± 17 %. The lowest removal rate (34 %) was observed in December, when the average temperature was 14 °C. The maximum NH3-N removal rate (84 %) was obtained in June, with a mean temperature of 20 °C. In 2015, the average removal rate was 69 ± 22 %. The minimum value observed (31 %) occurred in January, with a temperature of 14 °C, while the maximum value (92 %) was obtained in August, with a mean temperature of 19 °C. The variance analysis showed a significant difference (P<0.05) between the NH3N removal rate at different temperatures in 2014. That same year, a significant ratio (P<0.05) was estimated between the temperature and the NH3-N removal efficiency, with a R2 of 0.895. For the year 2015, the linear regression showed a R2 of 0.878 (Figure 4b). NH3-N removal was lower than 70 % when the temperature of the water was below 20 °C. Conversely, when the temperature was equal to or higher than 20 °C, the removal rate increased gradually until reaching levels above 80 % in almost all the cases, amounting to a 10 % increase for each added degree of water temperature. The NH3-N reduction obtained in this research differed from that obtained in other studies. Other researches(14) reported a 52 % removal rate. Other studies(12) obtained a 100% NH3-N removal rate; however, these studies were performed at pilot scale with controlled environmental conditions, which influenced the results. The results obtained in this research were similar to those obtained by other authors(13), as a NH3-N removal rate of approximately 85 % was estimated when assessing the efficiency of a surface flow wetland in southern Texas, USA, during the summer of 2008. These results also agree with those mentioned by different authors(35-38) who highlight the importance of temperature in ammonium removal from the wetlands. According to them, at low temperatures (5–10 °C), biological processes such as denitrification can be drastically inhibited. In general, the processes that take place in the nitrogen cycle are inhibited under cold climate conditions, as the amount of available oxygen diminishes considerably, and therefore the concentration of bacteria also decreases under extreme temperature conditions(38). The arguments mentioned by these authors coincide with the results obtained in this research, as the NH3-N reduction was greater at higher temperatures and dwindled considerably in the cold season. Figure 3c shows variability in the TN removal rate for the years 2014 and 2015. In 2014, the average TN removal rate showed a value of 69 ± 13 %. The minimum removal rate (45 %) occurred in February with a mean temperature of 16 °C. The maximum removal rate (82 %) was observed in June, when the mean temperature was 20 °C. In 2015, an average removal 11


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rate of 63 ± 15 % was obtained. The minimum value was observed in December (42 %), corresponding to a mean temperature of the water of 13 °C, while the highest value (82 %) occurred in May, with an average temperature of 22 °C. The mean TN removal rate obtained in this research is below that reported by other studies(22) where averages of 95 to 98 % TN removal were obtained. However, their experiments used wastewater from aquaculture and from a combination of different types of wetlands. Other factors like the design of the wetland are important and have a direct impact on the efficiency of the system; for example, the results of other studies(20) have suggested that HRT is one of the most important factors for TN removal; they proved that the TN removal rate can increase to up to 99 % with a 6 to 8day HRT. The statistical analysis showed that there are no significant differences (P>0.05) between the TN mean removal rates for the years 2014 and 2015. As for the effect of the temperature on the efficiency of TN removal, in the year 2014 a high determination coefficient (R2=0.758) was obtained, while in 2015 this coefficient decreased slightly (R2=0.656), indicating a moderately strong relationship between the temperature and the TN removal rate, with a 95% confidence interval (Figure 4c). Figure 3d shows the variability of the TP removal rate in the years of the study. The mean TP removal rate in 2014 was 75 ± 13 %, while in 2015 it was estimated in 73 ± 12 %. In the year 2014, the minimum value was obtained in January (52%), with a mean temperature of the water of 14 °C. The maximum removal rate (75 %) was observed in April, with a mean temperature of 18 °C. In 2015, the mean removal rate was 73 ± 17 %. The minimum value (50 %) occurred in December with a mean temperature of 13 °C, and the maximum value (88 %) was obtained in September, with a mean temperature of 20 °C. The statistical analysis showed that there are no significant differences between the two years analyzed in this study (2014 and 2015). Figure 4d shows the linear regression of the obtained data; in the year 2014, the determination coefficient was R2=0.670, indicating a moderately strong relationship between the TP removal rate and the temperature of the water in the wetland. As for the year 2015, the regression analysis yielded a determination coefficient of R2=0.551. The results obtained in this research are above those reported in other studies, where a TP removal rate of 45 % was estimated(39). Another author(27) also reported a lower TP removal rate than the one obtained in the present study: he estimated an acceptable TP removal rate of 21 to 44 % in temperatures below 10 °C. This agrees with the results obtained by various authors, according to whom the P removal rate is less affected by the temperature due to the prevalence of the adsorption processes and sedimentation, as opposed to the biological processes. A large number of wetlands have been proven to operate less efficiently in a cold climate than in temperate climates(38). Studies carried out in China at an ambient temperature of 4 °C and in Norway at 20 °C exhibited an acceptable reduction of pollutants when the wetland was artificially isolated from nature(36).

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Reaction rate constant and coefficient of temperature of ammonia nitrogen Figures 5a and 5b show the relationship between kv and the monthly temperature of the water in the system for NH3-N during the years 2014 and 2015. The volumetric reaction rate constant (kv d -1) in 2014 ranged between 0.041 and 0.185 d-1 at mean temperatures of 13 °C to 21°C in the wetland. It was evident that the kv value increased exponentially with the increased temperature of the water. On the other hand, in 2014 k exhibited a mean value of 2.60 cm d-1, and in the year 2015 the estimated value was 3.22 cm d-1. The results obtained for k in the research were below those reported by another study(11) carried out in Taiwan, where a value of k=6.26 cm d-1 was estimated in a surface flow wetland. However, the NH3N concentration in the influent demonstrated a significant variability during the study (1-26 mg L-1). Conversely, in the present study, the NH3-N concentration remained constant, with little variability (33 ± 2.4 mg L-1 in 2014 and 36 ± 4.5 mg L-1 in 2015). Figure 5: Ratio between kv and monthly temperature for NH3-N during 2014 and 2015

Conclusions and implications This study proved the capacity of a SFCW for the reduction of pollutants like COD, NH3-N, TN and TP in wastewater from a pig farm. During the study period (2014 and 2015), the wetland showed an acceptable efficiency in the reduction of the assessed pollutants; however, in the specific case of NH3-N, the estimated removal rate was less than 60 % in the season with the lowest temperature, and above 75 % in the warm season, evidencing seasonal patterns in the removal rate. Unlike the case of NH3-N, the COD and TP removal rates exhibited little variability during the period in which the system operated. The removal rate 13


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of ammonium exhibited the greatest dependency on the temperature of the water. When the temperature of the water was above 17 °C, the vegetation grew faster, which also increased microbial activity and the NH3-N removal rate, and was significantly higher than when the temperature of the water was below 17 °C. It is recommended to evaluate other parameters such as the air temperature, the precipitation and the evaporation directly at the site in order to assess their effect on the behavior of the wetland.

Literature cited: 1. Rodríguez M, Jácome A, Molina J, Suárez J. Humedal de flujo vertical para tratamiento terciario del efluente físico-químico de una estación depuradora de aguas residuales domésticas. Ing Inv Tec 2013;14(2):223-235. 2. Romo CIA. Evaluación de la remoción de contaminantes procedentes de aguas residuales de origen porcícola mediante un humedal artificial en serie [tesis de licenciatura]. Universidad Politécnica de Pachuca, Zempoala, Hidalgo; 2015. 3. Díaz FJ, O′Geena A, Dahlgrena RA. Agricultural pollutant removal by constructed wetlands: Implications for water management and design. Agr Water Manage 2012;(104):171-183. 4. Jordan TE, Whighama DF, Hofmockel KH, Pittek MA. Nutrient and sediment removal by a restored wetland receiving agricultural runoff. J Environ Qual 2003;(32):1534–1547. 5. Vymazal J. Constructed wetlands for wastewater treatment. Water 2010;2(3):530-549. 6. Kadlec RH, Reddy KR. Temperature effects in treatment wetlands. Water Environ Res 2001;73(5):543-557. 7. Dong X, Reddy GB. Soil bacterial communities in constructed wetlands treated with swine wastewater using PCR-DGGE technique. Bioresource Technol 2010;101(4):1175-1182. 8. Hammer DA. Constructed wetlands for wastewater treatment—Municipal, industrial and agricultural. 1ra ed. Inc, Michigan, USA: Lewis Publishers; 1989. 9. Kadlec RH, Hey DL. Constructed wetlands for river water quality improvement. Water Sci Technol 1994;29(4):159–168. 10. Jing SR, Lin YF, Lee DY, Wang TW. Nutrient removal from polluted river water by using constructed wetlands. Bioresource Technol 2001;76(2):131–135. 11. Jing SR, Lin YF. Seasonal effect on ammonia nitrogen removal by constructed wetlands treating polluted river water in southern Taiwan. Environ Pollut 2004;(127):291-301.

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12. Drizo A, Frost CA, Smith KA, Grace J. Phosphate and ammonium removal by constructed wetlands with horizontal subsurface flow, using shale as a substrate. Water Sci Technol 1997;35(5):95-102. 13. De La Mora-Orozco C. Nutrient Removal Prediction Using Hyperspectral Reflectance Indices and Modeling for a Pilot Constructed Channel Treatment Wetland [doctoral thesis]. Kingsville, Texas, USA: Texas A&M University-Kingsville; 2009. 14. Cameron K, Madramootoo C, Crolla A, Kinsley C. Pollutant removal from municipal sewage lagoon effluents with a free-surface wetland. Water Res 2003;37(12):28032812. 15. Bastian RK, Shanaghan PE, Thompson BP. Use of wetlands for municipal wastewater treatment and disposal—regulatory issues and EPA policies. In: Hammer DA editor. Constructed wetlands for wastewater treatment—Municipal, industrial and agricultural. Chelsea, Michigan USA: Lewis Publishers; 1991. 16. Jing SR, Lin YF, Wang TW, Lee DY. Microcosm wetlands for wastewater treatment with different hydraulic loading rates and macrophytes. J Environ Qual 2002;(31):690-696. 17. Kuschk P, Wiener A, Kappelmeyer U, Weibrodt E, Kästner M, Stottmeister U. Annual cycle of nitrogen removal by a pilot-scale subsurface horizontal flow in a constructed wetland under moderate climate. Water Res 2003;37(17):4236-4242. 18. Hill DT, Payton JD. 1998. Influence of temperature on treatment efficiency of constructed wetlands. Transaction of ASAE 1998;41(2):393–396. 19. Akratos CS, Tsihrintzis VA. Effect of temperature, HRT, vegetation and porous media on removal efficiency of pilot-scale horizontal subsurface flow constructed wetlands. Ecol Eng 2007;(29):173-191. 20. Akratos CS, Papaspyros JNE, Tsihrintzis VA. Total nitrogen and ammonia removal prediction in horizontal subsurface flow constructed wetlands: Use of artificial neural networks and development of a design equation. Bioresource Technol 2009;100(2):586596. 21. IWA. Special Group on Use of Macrophytes in Water Pollution Control, Constructed Wetlands for Pollutant Control. Scientific and Technical Report No 8. London, England. IWA Publishing; 2000. 22. Lin YF, Jing SR, Lee DY, Wang TW. Nutrient removal from aquaculture wastewater using a constructed wetlands system. Aquaculture 2002;(209):169-184.

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23. Tanner CC, Sukias JPS, Headley TR, Yates CR, Stott R. Constructed wetlands and denitrifying bioreactors for on-site and decentralized wastewater treatment: Comparison of five alternative configurations. Ecol Eng 2012;(42):112-123. 24. Almendinger JE. A method to prioritize and monitor wetland restoration for water-quality improvement. Wetl Ecol Manag 1999;(6):241-251. 25. Hunter RG, Combs DL, George DB. Nitrogen, phosphorous, and organic carbon removal in simulated wetland treatment systems. Arch Environ Contam Toxicol. 2001;41(3):274–281. 26. Chung AKC, Wu Y, Tam NFY, Wong MH. Nitrogen and phosphate mass balance in a sub-surface flow constructed wetland for treating municipal wastewater. Ecol Eng 2008;32(1):81–89. 27. Braskerud BC. Factors affecting phosphorus retention in small constructed wetlands treating agricultural non-point source pollution. Ecol Eng 2002;19(1):41-61. 28. Liao X, Luo S. Treatment effect of constructed wetlands on organic matter in wastewater from pig farm. J Appl Ecol 2002;13(1):113-117. 29. IPCC. Intergovernmental Panel on Climate Change (Panel Intergubernamental sobre Cambio Climático). Summary for policymakers of Climate Change, The physical science basis. In: contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge, Cambridge University Press, UK.; 2007. 30. Trenberth KE, Smith L. The mass of the atmosphere: A constraint on global analyses. J Clim 2005;(18):864–875. 31. Karmalkar AV, Taylor MA, Campbell J, Stephenson MT, Centella NA, Benzanilla A, et al. A review of observed and projected changes in climate for the islands in the Caribbean. Atmósfera 2013;26(2):283-309. 32. De La Mora-Orozco C, Ruíz CJA, Flores LHE, Zarazúa VP, Ramírez OG, Medina GG, et al. Climate Change index in the Chiapas Mexico during 1960-2009. Rev Mex Cienc Agr 2016;(13):2523-2534. 33. Kadlec RH, Knight RL. Treatment Wetlands. Boca Raton, Florida, USA. CRC Press, Inc; 1996. 34. Reed SC, Crites RW, Middlebrooks EJ. Natural systems for waste management and treatment, second ed. New York, USA: McGraw-Hill, Inc; 1995.

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Rev Mex Cienc Pecu 2020;11(Supl 2):1-17

35. Sun G, Zhao Y, Allen S. Enhanced removal of organic matter and ammoniacal-nitrogen in a column experiment of tidal flow constructed wetland system. J Biotec 2005;115(2):189-197. 36. Wießner A, Kuschk P, Kästner M, Stottmeister U. Abilities of helophyte species to release oxygen into rhizosphere with varying redox conditions in laboratory scale hydroponic systems. Int J Phytoremediation 2002;(1):1–15. 37. Picard CR, Fraser LH, Steer D. The interacting effects of temperature and plant community type on nutrient removal in wetland microcosms. Bioresour Technol 2005;96(9):1039-47. 38. Spieles DJ, Mitsch WJ. The effects of season and hydrologic and chemical loading on nitrate retention in constructed wetlands: a comparison of low-and high nutrient riverine systems. Ecol Eng 1999;(14):77–91. 39. Prochaska CA, Zouboulis AI. Removal of phosphates by pilot vertical-flow constructed wetlands using a mixture of sand and dolomite as substrate. Ecol Eng 2006;26(3):293– 303.

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https://doi.org/10.22319/rmcp.v11s2.4685 Article

Estimation of enteric methane production in family-run dairy farms in the south of the State of Querétaro, Mexico

Sergio Gómez Rosales a* María de Lourdes Ángeles a José Luis Romano Muñoz a José Ariel Ruíz Corral b

a

Instituto Nacional de Investigaciones Forestales Agrícolas y Pecuarias (INIFAP). Centro Nacional de Investigación Disciplinaria en Fisiología y Mejoramiento Animal. Ajuchitlán, Querétaro, México. b

Universidad de Guadalajara. Centro Universitario de Ciencias Biológicas y Agropecuarias. Camino Ramón Padilla Sánchez No. 2100 Nextipac, 44600, Zapopan, Jalisco, México.

*Corresponding author: gomez.sergio@inifap.gob.mx

Abstract: The objective was to estimate the emission factor (EF) of methane (CH4) and the daily losses of gross energy (GE) converted to CH4 (LCH4), by means of prediction equations of the Level 2 method of the Intergovermental Panel of Climate Change (IPCC) or based on technical information from the family-run dairy farming system. The study was carried out in 10 farms, obtaining technical information on the type and quantity of the ingredients offered to the herd during three visits in different periods of the year. The technical information —body weight, milk production and amount of each ingredient consumed, together with laboratory analysis of dry matter (DM) and GE content of the sampled ingredients— was used to calculate the DM and GE intake; the EF and the PCH4 were estimated using the IPCC methodology. The same variables were estimated using the prediction equations of the IPCC. In cows in milking conditions, the EF (81 and 70, kg CH4 yr-1) and the LCH4 (2.95 and 2.56, Mcal d-1) obtained using the IPCC equations were similar to those obtained through the observations in the 18


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farms; the weighted EF per farm was similar (49.06 and 54.09, kg CH4 yr-1), but the LCH4 estimated using the IPCC equations was lower than that obtained through farm observations (1.11 and 1.97, Mcal d-1; P<0.01, respectively). In general, the use of technical information from the farms, made it possible to estimate the EF and to show a higher LCH4 per farm, and, consequently, a lower energetic efficiency, compared to the IPCC methodology. Key words: Stable, Subsistence production, Ruminal fermentation, Environmental contamination, Energy loss.

Received: 18/11/2017 Accepted: 27/08/2019

Introduction Dairy farming is very important for meeting the demand for high quality food for human consumption; however, it also contributes to the emission of greenhouse gases (GHGs) such as methane (CH4), which is produced by enteric fermentation and is eliminated mostly through the burp(1,2). CH4 is one of the main GHGs emitted by the systems of production of bovine cattle, which has been associated with the global warming and climate change, as evidenced by comparison with the pre-industrial era(1,2). The issued CH4 also contributes to the energy leaks in the livestock production systems, as it amounts to the loss of 6 to 12% of the total gross energy (GE) consumed by the dairy producing livestock(2). An important reference for estimating the CH4 production is the methodology proposed by the Intergovernmental Panel on Climate Change (IPCC)(3). This methodology is used in different countries in order to generate the inventories of GHGs, and applies the corresponding procedures depending on the information available at levels 1, 2 and 3. Level 1 (Tier 1) is applied to Mexico and other developing countries; it is based on the use of an emission factor (EF) of CH4 yr-1 animal-1 that is multiplied by the national inventory within each category of livestock. A criticism made to this methodology is that it employs an EF that in many situations does not represent the reality of the particular conditions of production, particularly with regard to the characteristics of the food consumed by the dairy cattle raised in different regions of the country, which varies according to the agro-ecological, environmental, economic and health characteristics(4.5).

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CH4 emissions have also been estimated for dairy cattle in Mexico, according to the methodology of Level 2, which requires calculating the requirement of GE and using a conversion factor of CH4 by default, which on average is 6.5 %, amounting to the percentage of the GE of the food turned into CH4. Based on this method, an EF of 166 and 182 kg CH4 yr-1 has been estimated in primiparous and multiparous cows in milking conditions of herds registered at the Holstein Association of Mexico(6), and 115 kg CH4 yr-1, for the total dairy cattle of Mexico(7). The variability in the EF obtained in these studies is probably related to factors within each production system, such as the degree of technification, genetic capacity of animals for milk production and environmental influences. Due to the diversity of factors that may influence the EF, it is recommend having an EF for each type of system, and within these, an EF for each subtype, taking into account the particular characteristics of production and, in particular, the power schemes, including the type, concentration and quality of inputs used in the formulation of rations(3). In Mexico, there are various dairy production systems, the most predominant of which is intensive production, characterized by the use of modern, efficient technologies at all stages of the production process(6). At the other extreme is the backyard or family-run production system, which contributes 10 % of the national milk production, and which is based mainly on traditional schemes of production and is characterized by a wide range of subsystems, and varying degrees of modernization and of productive efficiency(5,8,9). In general, the backyard dairy farming systems have been little studied, and EFs have not been generated for this category of dairy production; these are relevant for estimating the GHGs corrected for each type of system, for determining the degree of impact of this kind of farming on emissions and energy losses in the form of CH4, and for designing and implementing strategies for the research, transfer of technologies and innovations to improve the sustainability of backyard dairy farming systems. Therefore, the aim of the present study was to estimate the EF and the PCH4, using the predictor equations of the level 2 method of the IPCC and the technical information gathered on family-run dairy farming units.

Material and methods The study was carried out at farms in the municipalities of El Marqués, Pedro Escobedo and San Juan del Río, in the state of Querétaro. All three municipalities are nestled in the Neovolcanic Axis, which is characterized by geomorphological contrasts between hills situated at an latitude between 2 and 3 thousand meters above sea level and valleys located at 800 to 900 m asl; temperate subhumid climates are prevalent in them, with a mean annual temperature of 17.3 ºC and a mean annual precipitation of 542.9 mm. Visits were made to 20 stables of the family system belonging to the Livestock Producer Groups for Technology 20


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Validation and Transfer of the State of Querétaro, with low level of modernization and in the process of adopting new technologies. The owners were interviewed in order to obtain technical information; for this purpose, at least three visits per producer were carried out in different months of the year during the period from April to August. The body weight of the adult animals was estimated based on the thoracic perimeter, and that of the young animals was estimated subjectively by the producers themselves; the milk production was calculated using a plastic bucket with a scale in liters. The data analysis included only 10 stables, since those participating farms that did not provide complete information or whose data were not considered to be reliable were discarded. The structure of the herd was divided, according to the terminology used by the producers interviewed, as follows: lactating or milk-producing cows; gestating cows with a 210-270 days’ pregnancy that were not being milked; heifers weighing 200-400 kg; calves weighing 100-200 kg; stud bulls, and young bulls in fattening. Table 1 shows the number of animals per category. Table 1: Inventory of animals and milk production of lactating cows No. Average±SD Minimum Maximum Inventory of animals Cows in milking condition 10 17.90±7.4 5 27 Pregnant cows 9 4.60±2.6 0 9 Heifers 8 6.50±5.42 0 18 Calves 9 6.70±4.88 0 17 Bulls 5 0.60±0.7 0 2 Young bulls in fattening 4 1.60±2.27 0 6 Total 10 39.10±19.73 5 55 Milk production of lactating cows Milk production, kg/d Duration of the lactation, days Milk production, kg/lactation Milk production, kg/yr

8.90±3.84 253.10±131.77 2130±1620.2 3231±1401.2

3 180 669 1253

14 420 5546 5263

CV 41.34 63.45 83.4 72.8 116.53 141.91 50.45

43.36 52.06 76.08 43.36

No.= number of farms that had animals in each category; SD= standard deviation; CV= coefficient of variation.

Samples of the food ingredients were obtained from all the stables, and the type and amount of the ingredients offered to the animals in the different phases of production were recorded. With this information, it was calculated the levels of inclusion of each ingredient in kilograms of fresh matter for each category of animals. Table 2 lists the food ingredients utilized and 21


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the number of samples taken. In the laboratory, the ingredients were dried in a forced air oven at a temperature of 55°C for a period of 24 to 72 h, depending on the moisture content, and ground with a Wiley type mill through a 2 mm sieve. The content of dry matter (DM) was determined in accordance with the AOAC official method 930.15(10), while the GE content was determined by combustion of the samples in an adiabatic calorimetric bomb. Table 2: List of food ingredients, number of samples, dry matter content and of gross energy content in a dry base Gross energy Kcal/kg Dry matter, Ingredients No. % Average ± SD Minimum Maximum CV Concentrate 14 90.76 3927±288.53 3206 4649 7.35 Medicago sativa L., 13 33.11 3602±298.76 2787 3824 8.29 green forage Medicago sativa L., 14 84.69 3724±302.26 2963 4090 8.12 hay Zea mayz, grain 15 92.80 4068±669.22 2395 5741 16.45 Zea mayz, ensiled 15 32.46 3694±181.81 3239 4148 4.92 Zea mayz, stubble 15 92.12 3713±311.79 2933 4492 8.40 Avena sativa, hay 9 92.32 3790±236.74 3198 4382 6.25 Lolium perenne, green 1 91.87 4412±0 4412 4412 0 forage Brassica oleracea, 1 21.85 3407±0 3407 3407 0 green forage Mixture of pasture, 4 74.34 3787±321.74 3020 4554 8.49 hay Chicken droppings 5 77.12 3375±769.33 1452 5298 22.79 Number of samples analyzed; SD= standard deviation; C= coefficient of variation.

Estimation of the CH4 using the information of the production units DM consumption (DMC) and of GE (GEC) animal-1 d-1 was estimated based on the consumption of each ingredient and the laboratory results for DM and GE. The interviewed owners provided the total amount of each ingredient offered on a wet basis to each category of animals in the herd. This value was divided by the number of animals per category in order to estimate the average consumption in kilograms per animal. The EF was calculated for each category of animal, using equation (1) recommended for level 2(3): EF = ⟨GEC ∗ (|Ym/100| ∗ 365)⟩/55.65 22


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Where: EF= Emission Factor, kg CH4 head-1 yr-1; GEC= Gross energy consumption MJ head1 yr-1; Ym= CH4 conversion factor as a percentage of the gross energy of the food converted into CH4 (6.5 %). The factor 55.65 (MJ/kg of CH4) is the energy content of CH4.

Estimation of the CH4 production using the IPCC methodology Based on the technical information recorded in each production unit, such as body weight, milk production and weight gain, and on the use of predictor equations(3), the following variables were obtained: Net energy for maintenance (equation 2: ENm)(11): ENm = Cfi ∗ (Body weight)0.75 Where: ENm= EN required by the animal for its maintenance, MJ day-1; Cfi= a coefficient that varies for each category of animals: lactating cows = 0,386; non-lactating cows = 0,322; other bovines and bulls = 0,370 MJ day-1 kg-1(12). Body weight = Weight of live animal, kg. Net energy for growth (equation 3: ENg)(12): ENg = 22.02 ∗ [PCm/(C ∗ PChm)0.75 ] ∗ GDP1.097 Where: ENg= EN for growth, MJ day-1; MBW= mean body weight of the animals of the population, kg; C= coefficient with a value of 0.8 to for females, 1.0 for oxen, and 1.2 for bulls; BWmf= body weight a mature female in moderate body condition, kg; ADG= average daily gain of the animals of the population, kg d-1. Net energy for lactation (equation 4: ENl)(13): ENl Milk ∗ (1.47 + 0.40 ∗ Fat) Where: ENl= EN for lactation, MJ d-1; Milk= Amount of milk produced, kg milk d-1; Fat= fat content of milk, %. The fat content in milk is taken from a previous work that analyzed the chemical composition of milk from stables of the family-run farming system in municipalities in the state of Querétaro(14). Net energy for pregnancy (equation 5: ENp): ENp = Cpregnancy*ENm Where: ENp= EN for pregnancy, MJ d-1; Cpregnancy= coefficient of pregnancy. For cows, it is 0.10. ENm= EN required by the animal for their maintenance, MJ d-1.

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The relationship between the available EN in the diet for maintenance and the digestible energy (DE) consumed was estimated with equation 6(15): REM = [1.123 – (4.092 ∗ 10−3 ) + {1.126 ∗ 10−5 ∗ (DE%2 )} − (25.4/DE%)] Where: REM= relationship between available EN in a diet for maintaining and DE consumed DE%= DE expressed as a percentage of the GE. The relationship between the available EN in a diet for growth and DE consumed was estimated with equation 7(14): REG = [1.164 – (5.160 ∗ 10−3 ∗ DE%) + {1.308 ∗ 10−5 ∗ (DE%)2 } − (37.4/DE%)] Where: REG= relationship between the EN available in the diet for growth and DE consumed DE%= DE expressed as a percentage of the gross energy. The requirement of GE was derived on the basis of the sum of the requirements of EN and the characteristics of availability of energy from food. For lactating cows were used the requirements of: ENm, ENl and ENg; in gestating cows: ENm, ENp and ENg; and, in growing animals: ENm and ENg. The general equation (8) was the following: GE = [{(ENm + ENl + ENp)/REM} + (ENg/REG)]/(DE%/100) Where: GE= gross energy, MJ d-1; ENm= EN required by the animal for their maintenance, MJ d-1; ENl= IN for lactation, MJ d-1; ENp= EN required for pregnancy, MJ d-1; REM= relationship between available EN in a diet for maintaining and DE consumed; ENg= EN for growth, MJ d-1; REG= relationship between available EN in a diet for growth and the DE consumed; DE%= DE expressed as a percentage of the GE. The estimated requirement of GE was utilized as the equivalent of the GEC in order to calculate the EF using the equation recommended for level 2, for each category of animals. The DMC per day was also estimated, by dividing the requirement of GE by the density of energy from food, using a default value of 6.45 MJ kg-1 of DM(3). In addition to the estimation of the EF, we calculated the daily loss of GE in the form of CH4 for each animal category using the equation 9: PCH4 = [GE ∗ (Ym/100)]/0.236 Where: PCH4= daily loss of GE in the form of CH4, Mcal animal-1. The 0.236 factor was used to convert the energy values from MJ to Mcal. Subsequently, the weighted averages of EF and PCH4 were estimated for each farm, considering all phases of production. The weighted average of the loss of GE kg milk -1 d-1 (ML) was also estimated using the equation 10: ML PCH4 Milk −1 Where: ML= loss of GE in the form of CH4, Mcal kg milk-1 d-1; PCH4= daily loss of GE in the form of CH4, Mcal animal-1 d-1; Milk= milk production, kg d-1. 24


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The inventories of animals, milk production of lactating cows and the GE concentration of the ingredients were subjected the PROC MEANS procedure of SAS(16) in order to calculate the means and the minimum and maximum values. In addition, the coefficient of variation (CV) was estimated according to the following formula: CV= (standard deviation รท average) * 100. The results of DMC, GEC, EF, HCP4 and ML using the equations of the IPCC were compared with the results of DMC, GEC, EF, HCP4 and ML using the technical data of the farms with variance analyses and the GLM procedures of SAS(16). It was used a completely random model with 10 repetitions per treatment. For these variables, the results tables show the means of the least-squares and the standard error of the mean. In the case of statistical significance, the differences between means were compared with the Minimum Significant Difference test.

Results and discussion Table 1 shows the number of animals by category and milk production of cows in milking condition. The total number of cows in milking condition and pregnant cows averaged 22.5, and the heifers amounted to 13.2 animals, amounting to 57.5 and 33.8 % of the total number of animals; the young bulls added up to merely 4 and 1.5 % of the total herd, respectively. The total number of animals was 39.1, with a minimum of 5 and a maximum of 55. Due to the fact that cows in milking condition were found in all the farms, but bulls in fattening were found only in four, since the rest of the producers sold to intermediaries; this was reflected in a lower and a higher CV in these two groups of animals. Only one farm had five cows in milking condition, while the rest of the units had 8 to 27 cows in milking condition and a total of 23 to 55 animals. The size of the herd in the present study was within the range reported in the family-run dairy farms in various states of the Republic(5,8,9). Eight farms had only Holstein animals, and Holstein and Brown Swiss American animals were found in two farms. The average, minimum and maximum milk productions were 8.9, 3.4 and 14.4 kg d-1, respectively. The duration of lactation was on average 253.1 d, with a minimum and a maximum of 180 and 420 d, respectively. The average, minimum, and maximum production of milk lactation-1 kg-1 was 2.130, 670 and 5.546 kg. Milk production in kg-1 yr averaged 3.231, with minimum and maximum values of 1.253 and 5.263 kg. The milk production reported in other assessments of family systems was 10.7, 11 and 17.3 kg of milk cow-1 d-1(5,8,9), which is greater than that reported in this paper. At the same time, milk production in kg yr-1 was lower than that reported for family systems of milk production with traditional management (3.417 kg of milk yr-1) or improved through a program of validation and transfer of technology (4.632 kg of milk yr-1)(5). 25


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The average content, minimum and maximum values, and CV of the GE are presented in Table 2. In general, the average values and CV found in this study agree with those of other reports and reflect the natural variation of the ingredients available for the animals(12,17). The minimum and maximum CV occurred in corn silage (4.9 %) and chicken dung (22.8 %). Various agro-ecological and management conditions reportedly influence the content of GE at different times of the year, depending on the weather, on whether the production is seasonal or utilizes irrigation, and on the doses of fertilization. Probably, the greatest extremes in terms of availability and quality of the ingredients occur between the dry and rainy season; in the present work it was tried to minimize this effect when carrying out the samplings between spring and summer. Other factors that influence the GE content are the variety of plants, the harvest time, the conditions of post-harvest handling and storage, and the type of processing(12,17,18). During visits to the farms it was noted that various alfalfas occurred in adjoining plots, but the majority of the producers had no knowledge of the seed variety, the age of the crop, the number of cuts or the age of the forage at the time of the cutting; some alfalfas were offered fresh, sugar-coated or dried. Other producers who bought alfalfa in bales, usually dehydrated, were unaware of the information mentioned before and did not know for how long the bales had been stored before acquiring them. This lack of information was observed in most of the sampled ingredients. Table 3 shows the DMC, GEC, EF and PCH4 are shown by productive phase 3. In cows in milking condition, the DMC (11.28 and 10.42, kg d-1), GEC (45.42 and 39.41, Mcal d-1), EF (81 and 70, kg CH4 yr-1) and PCH4 (2.95 and 2.56, Mcal d-1) estimated with IPCC equations of the IPCC were similar to the estimates of the observations carried out at the farms. This indicates that, in cows in milking condition, the results of the IPCC equations constitute a good point of comparison or verification for the prediction of these variables with the use of procedures recommended for level 2. Concentrates were included in the food of the cows in milking condition in all stables, accounting for the largest portion of the ration, in an average proportion of 33 %; in order of frequency, they were followed by the alfalfa and corn silage. According to the majority of the interviewed producers interviewed, the family-run dairy production systems follow similar feeding schemes of cows in milking condition to that of cows in intensive systems within the study area. This is probably the reason why there were no differences in the variables evaluated at the farms and those estimated with the IPCC.

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Table 3: Dry matter intake and gross energy consumption, methane emission factor and loss of energy in the form of methane by productive phase, using the equations of the IPCC or the technical data of the farms IPCC Farms SEM P< -1 Dry matter intake, kg d Lactating cows* 11.28 10.42 0.757 0.43 c d Pregnant cows** 11.47 9.28 0.456 0.01 Heifers 7.09 6.93 0.545 0.84 Calves 4.51 4.49 0.367 0.98 e f Bulls 14.88 9.75 1.217 0.02 Young bulls 6.95 7.4 0.819 0.75 Gross energy intake, Mcal d-1 Lactating cows Pregnant cows Heifers Calves Bulls Young bulls

45.42 26.58c 14.63c 10.57c 34.26 15.55e

39.41 34.97d 25.96d 16.77d 37.11 22.88f

3.896 1.662 2.271 1.385 4.505 2.191

0.28 0.01 0.01 0.01 0.46 0.05

Emission factor (kg of CH4 head-1 yr) Lactating cows 81.02 Pregnant cows 47.41e Heifers 26.10e Calves 18.86e Bulls 61.12 Young bulls 27.74g

70.3 62.36f 46.29f 29.91f 66.17 40.82h

6.95 2.964 4.049 2.469 8.033 3.908

0.28 0.01 0.01 0.01 0.46 0.05

Loss of energy in the form of methane, Mcal d-1 Lactating cows 2.95 Pregnant cows 1.73c Heifers 0.95c Calves 0.69c Bulls 2.23 Young bulls 1.01g

2.56 2.27d 1.69d 1.09d 2.41 1.49h

0.253 0.108 0.148 0.09 0.293 0.136

0.28 0.01 0.01 0.01 0.46 0.05

IPCC = using the equations recommended by the IPCC; Farms= using the technical information of the farms; SEM= standard error of the mean. *Means without a superscript in the same row are not statistically different (P>0.05). **Means with a different superscript in the same row are statistically different (P<0.05). 27


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The EF values of the cows in milking condition were between 41.6 and 65.8 % lower than those found in milk-producing cows in other studies conducted in Mexico in which the procedures recommended for level 2 were also utilized. The EF in primiparous and multiparous cows of 16 herds registered at the Holstein Association of Mexico was 166 and 182 kg CH4 yr-1, in lactations of 305 d, respectively(6); in order to calculate the EF, the DE content of the diet was estimated using a default value and the content of total digestible nutrients (TDN), and subsequently converted to GE using another default value, while an EF of 115 kg CH4 yr-1 was estimated for the total milk-producing herd of Mexico, with a lactation period of 305 d(7). The EF was calculated based on an estimate of the GE contents of five diets reported in studies of dairy cattle in Mexico published between 1971-2009. The lower EF estimated in the present work can have two explanations: 1) differences in the methodologies used to estimate the GEC in relation to the previous work, which emphasize that the content of DM and GE in the ingredients or food consumed by animals included in the studies was not analyzed in a laboratory, and 2) the differences in the levels of milk production, which directly affect the level of consumption of nutrients, and the GEC in the case of the estimates based on the IPCC equations. The conversion factor of CH4 (Ym) is a value that represents a fixed percentage of the GE (6.5%), which is converted to CH4 (Equation 1) and, therefore, the higher the GEC, the greater the production of CH4(3). In the present work, the production of milk per lactation adjusted to 305 d was 2.715 kg; for the Holstein cows, it was 9.985 kg(6) and for the milk-producing herd of Mexico, it was 3.795 kg(7). Thus, the EF is greater in the cows with higher milk production, intermediate in the cows with intermediate production of milk, and lower in the cows in milking condition included in this study. This same trend is observed in several reports where the EF ranged between 102 and 128 kg CH4 year-1 in cows in milking condition with an annual milk production of 5.365 to 8.270 kg, respectively(19.20). In pregnant cows, the observed DMC was 19 % lower (P<0.01), but the observed GEC, EF and the PCH4 were 32 % higher (P<0.01) than those estimated with the equations of the IPCC (Table 3). These results indicate that, in gestating cows, the use of the equations of the IPCC overestimated the DMC and underestimated the GEC, the EF and the PCH4 and therefore they do not constitute a good point of comparison or verification for the prediction of these variables using the level 2 method. The difference in the DMC was 2.2 kg d-1 below the expected value, and the GEC was 8.0 Mcal kg-1 above the expected value. In gestating cows, the most frequently utilized forages were stubble and oats hay, in a higher concentration than those used for cows in milking condition. Probably, these ingredients caused a greater filling of the rumen, which, associated with the pressure of the fetus, reduced the DMC, but at the same time contributed to a greater GEC.

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The estimated and observed EF was of 47 and 62 kg CH4 yr-1 in pregnant cows. There are few researches on the CH4 emissions by these animals. A report found that the EF was 55.15 kg CH4 yr-1 in heifers aged over one year until delivery, with a body weight of 310-520 kg(19). In the present study, the weight of the pregnant cows averaged 475 kg, which is within the range mentioned above; however, the requirement of GE for non-pregnant heifers and pregnant cows was calculated separately because, at the same weight, pregnant cows have a higher requirement of GE. This is due to the use of the ENp (equation 5), and in cows of between 210-270 d of gestation, the ENc increases in parallel with the growth of the fetus and also depends on the expected weight of the calf at birth(21). The DMC was similar in the heifers and the calves, but the GEC, EF and the PCH4 estimates were lower (P<0.01) with the equations of the IPCC than those observed in farms (Table 3). The observed GEC, EF and the PCH4 were 56 % higher in the heifers and 63 % higher in calves with respect to the corresponding estimated values. In most of the farms, corn stalks were the ingredient utilized most frequently and in the greatest proportion (on average, 50 % of the ration) and provided the largest amount of the GE in growing females. For calves up to one year of age, with body weight of 43 to 320 kg, the reported EF was 34 to 35 kg CH4 yr-1, and in females aged one to two years, with weights of 310 to 530 kg, the EF was 49 kg CH4 yr-1(19). In heifers of 499 kg of weight and kept in solitary confinement or grazing, the EF was of 77 and 67 kg CH4 yr-1, respectively(22). The EF of heifers (estimated= 26 and observed= 46 kg CH4 yr-1) and calves (estimated= 19 and observed= 30 kg CH4 yr-1) were lower than those reported on previous occasions. This is likely due to differences in body weight and has an impact on the requirement of ENm and ENc and on the DMC(3,12). In bulls, the estimated DMC was 53 % higher (9.32 vs 9.75 kg d-1; P<0.02) than that observed (Table 3). The estimated and observed values of GEC, EF, and PCH4 were similar, despite the large difference in the DMC. For bulls aged over two years, a DMC of 8.6-9.2 kg d-1, a GEC of 37.95 Mcal d-1 and an EF of 62.18 kg CH4 yr-1(19) were estimated; these values are consistent with those observed in the present study. In four of five farms whose herds included bulls, these were fed food concentrates and, in general, the composition of the rations for bulls was more similar to that for cows in milking condition. Probably, the inclusion of concentrates and more digestible ingredients caused the observed DMC to be lower than expected, and the GEC to be similar to the expected value. In young bulls in fattening, the DMC (6.95 and 7.40, kg d-1) was similar, but the GEC (15.55 and 22.88, Mcal d-1), EF (27.74 and 40.82, kg CH4 yr-1) and the PCH4 (1.01 and 1.49, Mcal) estimates were 69 % lower (P<0.05) than those observed in the farms (Table 3). In young bulls aged one to two years, a DMC, 8.3 kg d-1, GEC 36.0 Mcal d-1 and an EF of 60.1 kg CH4 yr-1(19) were estimated. These values are greater than those found in the present study, probably due to the greater weight of the growing bulls (on average, 540 kg) and quality of 29


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the diet in the above-mentioned study. In the present work, the average weight of the growing bulls was 290 kg, and in three of four farms, corn stalks were the predominant ingredient in the food. Table 4 shows the weighted averages of the EF, HCP4 and ML farm-1. The EF (49.06 and 54.09, kg CH4 yr-1) was similar, but the PCH4 (1.11 and 1.97, Mcal d-1; P<0.01) and ML (0.13 and 0.27, Mcal kg milk-1; P<0.03) estimates were 44 and 52 % lower, respectively, than those observed in the farms. The lack of difference in the weighted EF between the estimated and the observed values coincide with the results observed in cows in milking condition; the estimates appear to be appropriate when applied to the herd as a whole, probably due to the specific weight of lactating cows, when the IPCC methodology is used. The PCH4 and ML are not included in the estimates of the IPCC; however, they make it possible to know the impact of nutritional practices on the inefficiency of energy use and can be used to estimate economic inefficiencies associated with these losses, with additional information on input costs. The strength of these variables is that their estimation takes into account both the productive and the non-productive animals in the herd, in addition to the cows in milking condition. The results suggest that, using the technical information of the farms, it was possible to demonstrate a greater PCH4 and ML farm-1, and, consequently, a lower efficiency, with respect to the IPCC methodology, probably due to the greater PCH4 observed in pregnant cows, heifers, calves and young bulls (Table 3). Table 4: Emission Factor and energy losses in the form of methane per unit of production using the equations of the IPCC or the technical data of the farms IPCC Farms SEM P< -1 CH4 Emission Factor, kg yr * 49.06 54.09 3.889 0.37 -1 c d Loss of GE in the form of CH4, Mcal d ** 1.11 1.97 0.161 0.01 -1 e f Daily loss of GE, Mcal kg milk 0.13 0.27 0.043 0.03 IPCC= using the equations recommended by the IPCC; Farms= using the technical information of the farms; SEM = standard error of the mean. *Means without a superscript in the same row are not statistically different (P>0.05). **Means with a different superscript in the same row are statistically different (P<0.05).

The findings suggest that the estimates of DMC, GEC, EF AND PCH4 with the level 2 methodology recommended by the IPCC were similar only to those observed in cows in milking condition, but there were contrasting results in the rest of the animals of the herd. This is probably due to the fact that most of the case studies where they have been derived from the equations have been conducted in cows in milking condition, but there is little work done on animals at other stages of production. The largest PCH4 and ML observed in the farms may be due to the fact that predictor equations have been derived from studies on animals with higher levels of production and in experimental conditions with adequate 30


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nutritional, environmental and sanitary control; while the visited farms used the ingredients available according to the time of year, they do not apply a proper balancing of rations, their facilities are poor, and they do not implement appropriate preventive actions(5,8,9).

Conclusions and implications The estimates of DMC, GEC, EF and PCH4 using the procedures proposed by the IPCC level 2 method agreed with the observed values only in cows in milking condition; however, the results were inconsistent when applied to the rest of the animals in the herd. In general, the use of technical information of the farms made it possible to estimate the EF and demonstrate a greater PCH4 and ML farm-1, and, consequently, a lower efficiency, with respect to the IPCC methodology. There are several nutritional factors that influence the degradability of food, the patterns of enteric fermentation and ruminal production of CH 4 —such as the amount and type of starches, the type and solubility of proteins, the type and concentration of the fractions of fiber, among others—, that should be considered in future work to improve the precision of the estimates of CH4. Other limitations of the study were that the producers did not have systematic records of information; part of the technical data were estimated on the basis of the subjective perception of producers, and there was no means of verification.

Acknowledgements The authors wish to express their gratitude for the funding of the Project (FORDECyT Code 143064): "Establishment of modules for validation and transfer of farming technology to boost actions for mitigating climate change and taking care of the environment".

Literature cited: 1.

Steinfeld H, Gerber P, Wassenaar T, Castel V, Rosales M, de Haan C. Livestock’s Long Shadow: environmental issues and options. Food and Agriculture Organization of the United Nations (FAO), Rome, Italy. 2006.

2.

Gerber PJ, Steinfeld H, Henderson B, Mottet A, Opio C, Dijkman J, Falcucci A, Tempio G. Tackling climate change through livestock – A global assessment of emissions and mitigation opportunities. Food and Agriculture Organization of the United Nations (FAO), Rome. 2013.

31


Rev Mex Cienc Pecu 2020;11(Supl 2):18-33

3.

IPCC. Intergovernmental Panel on Climate Change. 2006 IPCC Guidelines for national greenhouse gas inventories. Hayama, Japan: Institute for Global Environmental Strategies. 2006.

4.

Opio C, Gerber P, Mottet A, Falcucci A, Tempio G, MacLeod M, Vellinga T, Henderson B, Steinfeld H. Greenhouse gas emissions from ruminant supply chain– A global life cycle assessment. Food and Agriculture Organization of the United Nations (FAO), Rome. 2013.

5.

Espinosa GJA, Wiggins S, González OAT, Aguilar BU. Sustentabilidad económica a nivel de empresa: aplicación a unidades familiares de producción de leche en México. Tec Pecu Mex 2004;42:55-70.

6.

Morante LD, Guevara EA, Suzán AH, Lemus RV, Sosa FCF. Estimación Tier II de emisión de metano entérico en hatos de vacas lactantes en Querétaro, México. Rev Mex Cienc Pecu 2016;7:293-308.

7.

Rendón-Huerta JA, Pinos-Rodríguez JM, García López JC, Yáñez-Estrada LG, Kebreab E. Trends in greenhouse gas emissions from dairy cattle in Mexico between 1970 and 2010. Anim Prod Sci 2013;54:292-298.

8.

Sánchez GLG, Solorio RJL, Santos FJ. Factores limitativos al desarrollo del sistema familiar de producción de leche, en Michoacán, México. Cuad Des Rural 2008;5:133146.

9.

Álvarez-Fuentes G, Herrera-Haro JG, Alonso-Bastida G, Barreras-Serrano A. Calidad de la leche cruda en unidades de producción familiar del sur de Ciudad de México. Arch Med Vet 2012;44;237-242.

10. AOAC. Official methods of analysis. 17th ed. Association of Official Analytical Chemists. Arlington, VA. 2002. 11. Jurgen MH. Animal feeding and nutrition. Sixth ed, Iowa, USA: Kendall/Hunt Publishing Company; 1988. 12. NRC (National Research Council). Nutrient Requirements of Beef Cattle, National Academy Press, Washington, DC. USA. 1996. 13. NRC (National Research Council). Nutrient Requirements of Dairy Cattle, National Academy Press, Washington, DC. USA. 1989. 14. Escobar RMC, Hernández AL, Alvarado IA, Gómez RS, Ángeles ML. Diagnóstico y control de microorganismos patógenos en establos de lechería familiar. Centro Nacional 32


Rev Mex Cienc Pecu 2020;11(Supl 2):18-33

de Investigación Disciplinaria en Fisiología y Mejoramiento Animal, INIFAPSAGARPA. Libro Técnico No. 3, Colón, Querétaro. 2012. 15. Gibbs MJ, Johnson DE. "Livestock Emissions." In: International Methane Emissions, US. Environmental Protection Agency, Climate Change Division, Washington, DC, USA. 1993. 16. SAS. SAS User´s Guide; Versión 9.0: SAS Institute Inc. Cary, NC (USA). 2002. 17. NRC (National Research Council). Nutrient Requirements of Dairy Cattle. National Academy Press, Washington, DC, USA. 2001. 18. Ali M, Cone JW, Hendriks WH, Struik PC. Starch degradation in rumen fluid as influenced by genotype, climatic conditions and maturity stage of maize, grown under controlled conditions. Anim Feed Sci Technol 2014;193:58-70. 19. Smink W, Pellikaan WF, van der Kolk LJ, van der Hoek KW. Methane production as a result from rumen fermentation in cattle calculated by using the IPCC-GPG Tier 2 method. Feed Innovation Services Report FS 04 12 E, Utrecht, The Netherlands: National Institute for Public Health and the Environment. 2004. 20. Bannink A, van Schijndel MW, Dijkstra J. A model of enteric fermentation in dairy cows to estimate methane emission for the Dutch National Inventory Report using the IPCC Tier 3 approach. Anim Feed Sci Technol 2011;166-167:603-618. 21. Bell AW, Slepetis G, Ehrhardt RA. Growth and accretion of energy and protein in the gravid uterus during late pregnancy in Holstein cows. J Dairy Sci 1995;78:1954–1961. 22. Ominski KH, Boadi DA, Wittenberg KM, Fulawka DL, Basarab JA. Estimates of enteric methane emissions from cattle in Canada using the IPCC Tier-2 methodology. Can J Anim Sci 2007;87:459–467.

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https://doi.org/10.22319/rmcp.v11s2.4686 Article

Global warming effect on alfalfa production in Mexico

Guillermo Medina-García a* Francisco Guadalupe Echavarría-Cháirez a José Ariel Ruiz-Corral b Víctor Manuel Rodríguez-Moreno c Jesús Soria-Ruiz d Celia De la Mora-Orozco b

a

Instituto Nacional de Investigaciones Forestales Agrícolas y Pecuarias (INIFAP). Campo Experimental Zacatecas, Km 24.5 Carretera Zacatecas-Fresnillo, Calera, Zac., México. b

Universidad de Guadalajara. Centro Universitario de Ciencias Biológicas y Agropecuarias. Camino Ramón Padilla Sánchez No. 2100 Nextipac, 44600, Zapopan, Jalisco, México. c

INIFAP. Campo Experimental Pabellón, Carretera Aguascalientes-Zacatecas km 32.5, Pabellón de Arteaga, Ags., México. d

INIFAP. Sitio Experimental Metepec, km. 4.5 Carretera Toluca-Zitácuaro, Vialidad Adolfo López Mateos s/n, Zinacantepec, Edo. Méx., México.

*Corresponding author: medina.guillermo@inifap.gob.mx

Abstract: Alfalfa is the main forage crop in Mexico in terms of sown surface area, with 583,561 ha, representing 57.1 % of total forage, while forage crops of maize, oats and sorghum account for 42.9 %. The aim of this study was to estimate the impact of global warming as a result of climate change, under the basis of future climate scenarios over alfalfa production in potential irrigation areas of Mexico. Anomalies of temperature and precipitation for the 2021-2080

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period were estimated through an ensemble of 11 general circulation models. Potential surface areas for alfalfa production were determined by considering reference climate and future climate projections focused on two Representative Concentration Pathways (RCP) of greenhouse gases (GHG), Results suggest an increasing temperature and its influence upon the reduction of areas with a high productive potential, as progress is made towards the future, with a reduction of 24.7% in 2070 in the RCP 4.5 with respect to the reference climate. Similar results, but with greater decrease of surface areas with productive potential â&#x20AC;&#x201D;a situation that becomes even worse with timeâ&#x20AC;&#x201D;, were estimated under the basis of the RCP 8.5. A differential effect was estimated depending on the harvesting region. Given its high water demand, alfalfa may be replaced by other crops with lower water requirements, such as maize. These results could be used in the design of strategies to adapt the crop to the effects of climate change in alfalfa producing areas. Key words: Medicago sativa, Climate change, Productive potential, RCP, Mexico.

Received: 08/11/2017 Accepted: 12/07/2018

Introduction Under irrigation conditions, alfalfa is the main forage crop in Mexico in terms of land planted, with 583,561 ha (2006-2015), amounting to 57.1 %, while maize, oats and sorghum forage crops make up the remaining 42.9 %(1). This crop demands great water consumption(2), with water requirements ranging between 1,200 and 1,800 mm, approximately, per year(3,4,5) which makes it dependent on the availability of irrigation water. In addition to the vulnerability to weather conditions, the influence of climate change on the performance and production of this legume is uncertain in the future. Currently, climate change causes changes in climatic patterns and, therefore, in the climate related to the management of agricultural activities. The increase in temperature, caused by the increase in the atmospheric concentration of greenhouse gases (GHG)(6), leads to the desiccation of many regions due to the increase in evaporation(7) and the modification of rainfall patterns(8). The effects of climate change in the future are estimated using climate scenarios, which are representations of the future climate consistent with the assumptions about future emissions 35


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of greenhouse gases and other pollutants and, with the understanding that the effect of increasing atmospheric concentrations of these gases in the global climate serve as the basis for taking adaptation and emission reduction actions(9,10). It is important to recognize that there is uncertainty in the results of these scenarios. The general circulation models allow to project the future climate, but there is no single model that is the most convenient; therefore, ensembles of several models are used to reduce uncertainty(11). Recent studies have shown that the temperature in the agricultural surface areas of Mexico has increased markedly since 1990(12,13). This increase in temperature brings about modifications in agroclimatic variables such as the accumulation of cold hours in the winter period(14). As in other countries, in Mexico there is a concern about climate change and its possible impacts on the primary productive sector. On the other hand, as a result of the increase in GHGs in the atmosphere, the increase in temperature can have both positive and negative effects on crop production. An increase in temperature accelerates the process of maturity of the crops, reduces the duration of the leaf surface area and, thus, the total water requirement until the maturity of the crop(15,16). Various studies have been developed to identify surface areas where crop production could be carried out with the greatest probability of success and profitability. These surface areas are also called surface areas with productive potential(17-20). However, the effect of climate change on crops in surface areas with productive potential has been little studied. Changes in climate patterns have profound effects on plant growth and productivity in the short term(21). In Mexico, studies have been carried out on the theme of climate change and its impact on agriculture, but few have analyzed in detail the effects on product systems in particular, which limits the design of crop adaptation strategies to climate change(22). Alfalfa is a species that has a wide range of adaptability. The belief is that depending on the environment where it develops, climate change can influence it positively or negatively. Several studies have demonstrated the great variability of alfalfa's response to climate change(23-27). Alfalfa is a crop with intensive water demand. Its profitability depends largely on the availability of water and its costs. It is possible to obtain greater alfalfa production by increasing irrigation in the growth period(2). Water deficiency affects plant growth and climate change is expected to increase water stress in crops in some parts of the United States of America(6). Large reductions in alfalfa cultivation surface area in the northern plains in the United States of America have been observed due to the expansion of more profitable crops such as maize and soybeans, as well as the decrease in irrigation water(28).

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The aim of this study was to estimate the impact of global warming as an effect of climate change on future climate scenarios on potential surface areas of irrigation alfalfa in Mexico.

Material and methods An ensemble model was integrated from the value of the median of 11 general circulation models (MCG) reduced in scale and calibrated (29) and belonging to CMIP5 (Coupled Model Intercomparison Project Phase 5) reported in the 5th IPCC delivery: (BCC-CSM1-1, CCSM4, GISS-E2-R, HadGEM2-AO, HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM, MIROC-ESM, MIROC5, MRI-CGCM3, NorESM1-M), which were obtained from the information of the data portal of Global Change of World Clim. The ensemble was generated considering two representative routes of concentration (RCP) of greenhouse gases, that is, for this purpose an intermediate emission RCP (4.5) was used consistently with a future with relatively ambitious emission reductions, and a high emission RCP (8.5), consistently with a future without policy changes to reduce emissions(10). The monthly values of the ensemble of the 11 models of the maximum temperature, minimum temperature and precipitation variables of the years 2021 to 2080, for the scenarios 2021-2040, 2041-2060 and 2061-2080, hereinafter referred to as climates for the years 2030, 2050 and 2070, respectively, were used. The base or reference climate based on the same variables from the 1961-2010 period of the INIFAP climate information system(30) was considered. Thematic raster images were generated with a resolution of 30â&#x20AC;? arc, corresponding to the monthly values of the three variables of the base climate and the scenarios. In studies related to agriculture, including that of productive potential, it is convenient to use a good resolution for the application of the results of the MCGs with scale reduction. Therefore, the INIFAP climate information system uses a resolution of 90 m, so that the results of the productive potential have sufficient detail to be applied in the decision making of long-term plans. The second part of the study consisted of the estimation of the productive potential, which is based on the agroecological requirements of the plant species(31). The surface areas with productive potential for alfalfa cultivation under irrigation conditions were obtained. Potential surface areas were estimated for the base climate and the three climate scenarios in RCP 4.5 and 8.5. The surface areas with productive potential were calculated based on temperature and precipitation information from INIFAP, land use information series 5, and 37


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the edaphological cartography scale 1: 250,000 from INEGI. For this purpose, the geographic information systems IDRISI Selva and ArcGis Ver. 10.1 were utilized. Finally, according to the results obtained, some adaptation actions are proposed for alfalfa cultivation in the face of climate change scenarios.

Results and discussion Table 1 shows the surface areas with alfalfa production potential under irrigation conditions, in current climatic conditions and for climates 2030, 2050 and 2070, in two representative routes of concentration of greenhouse gases. The potential surface area obtained is independent of the current use of agricultural land, i.e. it does not necessarily imply that surface area is available for sowing alfalfa under irrigation conditions. Table 1: Surface area with high and medium production potential of irrigation alfalfa as a perennial crop under current climatic conditions and in the 2030, 2050 and 2070 climate scenarios in RCP 4.5 and 8.5 Productive potential RCP Climate scenario High Medium Current 5’389,719 3’160,165 2030 4’940,739 2’754,646 4.5 2050 4’294,163 2’626,868 2070 4’058,779 2’267,566 2030 4’735,023 2’586,661 8.5 2050 4’006,668 2’079,239 2070 3’126,862 1’962,538 This Table shows how the surface area of high potential for alfalfa irrigation decreases as progress is made towards the future in the years 2030, 2050 and 2070 in the RCP 4.5, with respect to the average or current climatic conditions, from 5’389,719 ha in the current climate at 4’058,779 ha in the year 2070. Similarly, the average productive potential decreases towards the future, from 3,160,165 ha in the current scenario to 2,267,566 ha in the year 2070 (Figure 1).

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Figure 1: Productive potential of irrigated alfalfa for the current average climatic conditions and the projected for 2030, 2050 and 2070 by the RCP 4.5

1

A similar behavior occurs in RCP 8.5, except that the decrease in surfaces is greater compared to the current scenario, with the higher productive potential diminishing from 5’389,719 to 3’126,862 ha, a reduction of 42.0 % of the surface area, and the mean potential cutting back its surface area from 3’160,165 to 1’962,538 ha, i.e. 37.9 %, in the year 2070 (Figure 2). Figure 2: Productive potential of irrigated alfalfa for the current average climatic conditions and the projected for 2030, 2050 and 2070 by the RCP 8.5

1

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The reduction of the surface area with high and medium productive potential may be basically due to the increase in the annual mean temperature in the different climatic scenarios, for a rise in the temperature can reduce the production of alfalfa, as has been found in other studies in Mexico in the warm environments where this species is grown(32). This is shown in Table 2, where the average temperature of the current scenario in high potential areas is 19.9 °C, while in the first two climates of RCP 4.5 it is 20.9 and 21.9 ° C —i.e. there is an estimated increment of 1.0 and 2.0 ° C—, respectively. The increase in the third climate may be 2.5 °C with respect to the reference climate, which can result in less than optimal conditions for the development of alfalfa. Table 2: Temperature and annual mean precipitation in the highly productive potential areas of irrigation alfalfa, based on the current potential surface area in the different RCP and scenarios Temperature (°C) Precipitation RCP Scenario Mean Mean DEA DCS (%) (mm) Current 19.9 436.6 2030 20.9 1.0 425.8 2.5 4.5 2050 21.9 2.0 414.9 5.0 2070 22.4 2.5 413.8 5.2 2030 21.2 1.3 416.3 4.7 8.5 2050 22.5 2.6 395.9 9.3 2070 23.7 3.8 383.4 12.2 DCS=Difference in relation to the current scenario.

Figure 3A shows the surface area with highly productive potential for the different climatic scenarios, in some of the main alfalfa producing states in the country. The possible effect of climate change is not the same in the different regions of the country for the production of alfalfa irrigation, as has been reported by other authors(26), according to whom the yield varied by municipality from -10 to 14% in scenarios B1 and A2 in the state of California, USA. In general, and at the country level, the trend is towards less surface area with a high potential. However, in current temperate regions the surface with potential may increase in the future, as in the case of the state of Chihuahua State, similarly to the results obtained in other studies(27), while in other states, such as Guanajuato and Hidalgo, it will remain stable, similarly to what occurred in another study in California, USA, where the yield remained unchanged until 2050 in scenarios B1 and A2(24). On the other hand, in the states with a warm climate, the surface areas with high productive potential have a tendency to decrease significantly, as in the states of Baja California and Sonora and at the La Laguna region of the states of Coahuila and Durango, similarly to what was found with high temperatures without any yield increase(25). In the average potential there is a differentiated trend between the states, but, in general, where the surface area with high potential decreases, the medium 40


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potential increases, since that surface area will pass from high to medium potential. In Sonora State, the mean potential increases first and then decrease (Figure 3B). A similar trend has been found in the surface areas producing maize(22) and beans(13) in Mexico. In RCP 8.5 (Figure 4A and 4B), a similar behavior is observed in the high potential areas, but the tendency to decrease in the yield is more noticeable. Figure 3: Surface area of high (A) and medium (B) productive potential of perennial cycle irrigation alfalfa in the mean climatic conditions and climates of RCP 4.5, in different states of the country A)

B)

1

Figure 4: Surface area of high (A) and medium (B) productive potential of perennial cycle irrigation alfalfa in the average climatic conditions and climates of RCP 8.5, in different states of the country

1

In the short term (2030), only in the hottest region where alfalfa is grown (i.e. the states of Baja California and Sonora) will the area with high productive potential decrease by 17.8 and 60.0 % in RCP 4.5, respectively, and 40.1 and 69.7 % in RCP 8.5, respectively (Figures 3 and 4).

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Although the estimation of the surface areas with potential for alfalfa production does not consider precipitation, these potential areas may be affected by the low availability of irrigation water, either dam or pumping water from aquifers. Precipitation in future climate scenarios with respect to the current climate will suffer a reduction (Table 3). In areas of high potential in RCP 4.5, there is a reduction of 22.8 mm by 2070, while in RCP 8.5 there is a greater reduction in precipitation of up to 53.2 mm in 2070 in areas of high potential and 59.8 mm in those of medium potential. Table 3: Sown area and water balance for alfalfa production in the main alfalfa producing states in Mexico PP SS RH SS Deficit SS SS AP WR State %WR AP 3 (ha) (mm) (mm) (million m ) Chihuahua 77,144 410 1,473 316.1 1,136.3 820.2 27.8 Baja California 29,388 103 1,822 30.3 535.5 505.2 5.7 Sonora 29,038 276 1,636 80.3 475.1 394.8 16.9 Durango 28,267 274 1,601 77.6 452.6 375.0 17.1 Guanajuato 52,397 620 1,333 324.8 698.6 373.8 46.5 Coahuila 21,308 251 1,566 53.4 333.7 280.3 16.0 Hidalgo 47,686 537 1,054 255.9 502.5 246.6 50.9 San Luis Potosí 13,809 394 1,345 54.4 185.7 131.3 29.3 Zacatecas 11,104 414 1,205 46.0 133.8 87.8 34.4 Jalisco 9,680 612 1,450 59.2 140.3 81.1 42.2 Puebla 18,205 701 1,100 127.7 200.3 72.7 63.7 Querétaro 8,108 545 1,182 44.2 95.8 51.6 46.1 Aguascalientes 6,339 493 1,205 31.2 76.4 45.2 40.9 México 8,247 691 1,019 57.0 84.0 27.0 67.8 SS=Sown surface area, AP= annual precipitation, WR= alfalfa’s water requirements. %WR AP= percentage of water requirement covered by precipitation.

Alfalfa cultivation is very demanding of water(2). Table 3 shows the consumptive use or water requirement in each of the main alfalfa producing states, which ranges between 1,019 in the state of Mexico and 1,822 in the state of Baja California. The water requirements of the entire area were estimated based on this datum and the average sown area (2006-2015. On the other hand, the average volume of water contributed by precipitation on the same sown area by state was also estimated. The water deficit in the planted area was calculated based on these data. Table 3 also shows that the water deficit in the area sown with alfalfa varies greatly between one state and another, ranging between 27.0 and 820.2 million m3, corresponding to an average deficit of 63.9% with respect to the water requirement. This deficit is greatest in the northern state of the country, because the sown area there is greater, and the precipitation is 42


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lower. Those states are Chihuahua, Baja California and Sonora (Figure 5), i.e., in the states in the center of the country with a greater precipitation, the water deficit is lower. Figure 5: Water requirements in the area sown with alfalfa in each state and the volume contributed by precipitation on the same surface area 1,200 1,000

Millions m3

800 600 400 200 0

Water requirements in planted area

Annual rainfall in planted area

The decrease in rainfall and the increase in temperature in future years may cause higher levels of evapotranspiration, due to which the crops will suffer more due to the lack of moisture in their water balance (6.22). Alfalfa crops demand much more water â&#x20AC;&#x201D;an average of 1,350 mm per yearâ&#x20AC;&#x201D; than other forage crops such as maize, which has a water demand of 550 mm throughout the cycle. In the above conditions, it is expected that in the years to come, the cultivation of alfalfa will gradually be replaced by other less water-demanding crops, as is happening in the plains of the northern United States of America, alfalfa is being replaced to a large extent by other crops, such as corn and soybeans, which require less water for irrigation(28). The results obtained could be used in the planning or in the design of strategies to face climate change in alfalfa producing areas, such as the search for new varieties of alfalfa that adapt to higher temperature conditions and are tolerant to low humidity conditions.

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Conclusions and implications Global warming as an effect of climate change in the 21st century, can have a negative effect upon the viability of alfalfa cultivation in agricultural irrigation surface areas of Mexico, since the surface area with a high potential for this species is expected to decline steadily between 2030 and 2070, in both the RCP 4.5 and RCP 8.5 scenarios. However, if greenhouse gas emission patterns evolve towards RCP 8.5, the feasibility of alfalfa cultivation may be more strongly affected, since the reduction of high potential surface area would be greater than in the RCP 4.5 scenario. The most negative scenario is foreseen for the year 2070 in RCP 8.5, where the reduction of the high potential surface area may reach 42 %. As we move from a nationwide view to a state-based perspective, a differentiated effect of climate change is to be expected; the states where the potential surface area for alfalfa cultivation could be most negatively affected in the future are Baja California, Sonora and the La Laguna region in Coahuila and Durango; while other states in the center of the country, such as Guanajuato and Hidalgo, may experience virtually no negative effects; while an increase in the potential surface area for alfalfa cultivation may even be expected in Chihuahua. Alfalfa, a species with very high demand of water, is grown in irrigation conditions with an average deficit of 63.9 % with respect to the water requirement of the planted surface area. A mean reduction in precipitation of 7.2 % is expected to occur by 2050; therefore, the deficit of available water may increase, leading to the replacement in the near future of alfalfa by other less waterdemanding crops like maize. The results of this study can serve as a basis for the design of strategies to face climate change in those areas of Mexico where irrigation alfalfa is produced, including the generation of new varieties that adapt to higher temperature and evapotranspiration or the design of a new composition of forage cropping patterns in the country's irrigation areas.

Literature cited: 1. SIACON. Sistema de Información Agropecuaria de Consulta 1980-2014. SAGARPA. México, D. F. 2015. http://www.siap.gob.mx/siacon .Consultado 16 Sep, 2016. 2. Russo C, Green R, Howitt R. Estimation of Supply and Demand Elasticities of California Commodities. Department of Agricultural and Resource Economics. University of California, Davis. Working Paper No. 08-001. 2008. 3. Villanueva DJ; Loredo OC, Hernández RA. Requerimientos hídricos de especies anuales y perenes en las zonas media y altiplano de San Luis Potosí. Centro de Investigación Regional del Noreste. Campo Experimental Palma de la Cruz. Folleto Técnico No. 12. 2001. 44


Rev Mex Cienc Pecu 2020;11(Supl 2):34-48

4. Maciel PLH, Hernández DFJ, Macías VLM. Requerimientos hídricos de cultivos forrajeros en la unidad de riego El Niágara, Aguascalientes. Centro de Investigación Regional Norte-Centro. Campo Experimental Pabellón. Folleto Técnico No. 30. 2007. 5. Guzmán RSC, Valenzuela SC, Felix VP, Jiménez TA, Ruiz CS. Necesidades hídricas de los principales cultivos en el estado de Baja California. Centro de Investigación Regional del Noroeste. Campo Experimental Valle de Mexicali. Folleto Técnico No. 13. 2008. 6. IPCC. Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, R.K. Pachauri and L.A. Meyer (eds.)]. IPCC, Geneva, Switzerland. 2014. 7. Woodhouse CA, Meko DM, MacDonald GM, Stahle DW, Cook ERA. 1,200 year perspective of 21st century drought in southwestern North America. Proc Natl Acad Sci USA 2010;(107):21283–21288. 8. Durán PN, Ruiz CJA, González EDR, Ramírez OG. Impact of climate change on grasses cultivation potential of three altitudinal strata‐ agricultural lands of México. AJAR. 2014;9(18):1396-1406. 9. IPCC-TGCIA. Guidelines on the Use of Scenario Data for Climate Impact and Adaptation Assessment. Version 1. Prepared by Carter, TR, Hulme M, Lal M. Intergovernmental Panel on Climate Change, Task Group on Scenarios for Climate Impact Assessment. 1999. 10. Van Vuuren, DP, Edmonds J, Kainuma M, Riahi K, Thomson A, Hibbard K, Hurtt GC, et al. The representative concentration pathways: an overview. Climatic Change: 2011;109(1):5-31. 11. Montero-Martínez MJ, Ojeda-Bustamante W, Santana-Sepúlveda JS, Prieto-González R, Lobato-Sánchez R. Sistema de consulta de proyecciones regionalizadas de cambio climático para México. Tecnología y Ciencias del Agua. 2013;4(2):113-128. 12. Ruiz CJA, Medina GG, Manríquez OJD, Ramírez DJL. Evaluación de la vulnerabilidad y propuestas de medidas de adaptación a nivel regional de algunos cultivos básicos y frutales ante escenarios de cambio climático. Informe Final de Proyecto INIFAP-INE. Guadalajara, Jalisco. 2010.

45


Rev Mex Cienc Pecu 2020;11(Supl 2):34-48

13. Medina-García G, Ruiz-Corral JA, Rodríguez-Moreno VM, Soria-Ruiz J, Díaz-Padilla G, Zarazúa-Villaseñor P. Efecto del cambio climático en el potencial productivo del frijol en México. Rev Mex Cienc Agríc 2016;(Pub. Esp. Núm. 13):2465-2474 14. Medina-García G, Ruiz-Corral JA, Ramírez-Legarreta MR, Díaz-Padilla G. Efecto del cambio climático en la acumulación de frío en la región manzanera de Chihuahua. Rev Mex Cienc Agríc 2011;(Pub. Esp. Núm. 2):195-207. 15. Ojeda-Bustamante W, Sifuentes-Ibarra E, Íñiguez-Covarrubias M, Montero-Martínez M.J. Impacto del cambio climático en el desarrollo y requerimientos hídricos de los cultivos. Agrociencia 2011;45(1):1-11. 16. Hatfield JL, Boote KJ, Kimball BA, Ziska LH, Izaurralde RC, Ort D, Thomson AM, Wolfe D. Climate impacts on agriculture: Implications for crop production. Agron J 2011;(103):351-370. 17. Medina GG, Zegbe DJA, Mena CJ, Gutiérrez LR, Reveles HM, Zandate HR, Ruiz CJA, Díaz PG; Luna FM. Potencial productivo de especies agrícolas en el Distrito de Desarrollo Rural Zacatecas, Zacatecas. Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias, Centro de Investigación Regional Norte Centro, Campo Experimental Zacatecas, Calera de V. R., Zacatecas., México. Publicación Técnica No. 3. 2009. 18. Liu D, Wan F, Guo R, Li F, Cao H, Suna G. GIS-based modeling of potential yield distributions for different oat varieties in China. Mathematical and Computer Modelling 2011;(54):869–876. 19. Aguilar RN, Galindo MG, Fortanelli MJ, Contreras SC. Evaluación multicriterio y aptitud agroclimática del cultivo de caña de azúcar en la región de Huasteca (México). Ciencia y Tecnología Agropecuaria 2010;11(2):144-154. 20. Díaz PG; Medina GG; Ruiz CJA, Serrano AV. Potencial productivo del cultivo de canola (Brassica napus L.) en México. Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias, Centro de Investigación Regional Golfo Centro, Campo Experimental Cotaxtla, Veracruz, México. Publicación Técnica No. 2. 2008. 21. Attipalli RR, Girish KR, Agepati SR. The impact of global elevated CO2 concentration on photosynthesis and plant productivity. Curr Science 2010;99(1):46-57.

46


Rev Mex Cienc Pecu 2020;11(Supl 2):34-48

22. Ruiz CJA, Medina GG, Ramírez DJL, Flores LHE, Ramírez OG, Manríquez OJD, Zarazúa VP, González EDR, Díaz PG, Mora OC. Cambio climático y sus implicaciones en cinco zonas productoras de maíz en México. Rev. Mex. Cienc. Agríc. 2011;(Pub. Esp. Núm. 2):309-323. 23. Izaurralde RC, Thomson AM, Morgan JA, Fay PA, Polley HW, Hatfield JL. Climate Impacts on Agriculture: Implications for Forage and Rangeland Production. Agron J 2011;(103):371–381. doi:10.2134/agronj2010.0304 24. Jackson LE, Wheeler SM, Hollander AD. Case study on potential agricultural responses to climate change in a California landscape. Climatic Change 2011;(109):407. doi:10.1007/s10584-011-0306-3. 25. Hunink JE, Droogers P. Climate Change Impact Assessment on Crop Production in Uzbekistan. World Bank Study on Reducing Vulnerability to Climate Change in Europe and Central Asia (ECA) Agricultural Systems. Report FutureWater: 106. FutureWater. Costerweg 1G. 6702 AA Wageningen. The Netherlands. 2011. 26. Lee J, De Gryze S, Six J. Effect of climate change on field crop production in California’s Central Valley. Climatic Change 2011;(109):335. doi:10.1007/s10584-011-0305-4. 27. Erice G, Sanz-Sáez A, Aranjuelo I, Irigoyen JJ, Aguirreolea J, Avice JC, Sánchez-Díaz M. Photosynthesis, N2 fixation and taproot reserves during the cutting regrowth cycle of alfalfa under elevated CO2 and temperature. J Plant Physiol 2011;(168):2007-2014. 28. Derner J, Joyce L, Guerrero R, Steele R. Northern Plains Regional Climate Hub Assessment of Climate Change Vulnerability and Adaptation and Mitigation Strategies, Anderson T, editor. United States Department of Agriculture. 2015. 29. Walton D, Meyerson J, Neelin JD. Accessing, downloading, and viewing CMIP5 data. Earth System Grid Federation. 2013. 30. Ruiz-Corral JA, Medina-García G, Rodríguez-Moreno VM, Sánchez-González JJ, Villavicencio GR, Durán PN, Grageda GJ, García RJE. Regionalización del cambio climático en México. Rev Mex Cienc Agríc 2016;(Pub. Esp. Núm. 13):2451-2464. 31. Medina GG, Ruiz CJA, Martínez PRA, Ortiz VM. Metodología para la determinación del potencial productivo de especies vegetales. Agr Téc Méx. 1997;23(1):69-90.

47


Rev Mex Cienc Pecu 2020;11(Supl 2):34-48

32. Santamaría CJ, Núñez HG, Medina GG, Ruiz CJA, Tiscareño LM, Quiroga GMH. 2000. Evaluación del modelo EPIC para estimar el potencial productivo de alfalfa (Medicago sativa L.) en diferentes ambientes ecológicos de México. Téc Pecu Méx 2000;38(2):151161

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https://doi.org/10.22319/rmcp.v11s2.4693 Article

Environmental suitability areas for [Bouteloua curtipendula (Michx.) Torr.] in Mexico due to climate change effect

José Ángel Martínez Sifuentes a Noé Durán Puga a* José Ariel Ruiz Corral a Diego Raymundo González Eguiarte a Salvador Mena Munguía a

a

Universidad de Guadalajara. Centro Universitario de Ciencias Biológicas y Agropecuarias. Camino Ramón Padilla Sánchez No. 2100 Nextipac, 44600, Zapopan, Jalisco, México.

*Corresponding author: noe.duran@cucba.udg.mx

Abstract: The grasslands are exposed to climate change effects that will be observed along the next decades. This will change the plant communities, modifying in turn the services and products supplied by these areas. The influence of the climate as a primary productivity determinant for ecosystems has led to research on the impact of climate change on plant communities with the use of simulation models. The species of Bouteloua genus are among the most important ones in Mexico’s grasslands due to their quality as forage for livestock and their ecological characteristics —the most prominent being the sideoats gramma [Bouteloua curtipendula (Michx.) Torr.]—. The objective was to analyze the areas with environmental suitability for B. curtipendula as an effect of climate change in Mexico. The reference and the future climate were analyzed through the General Circulation Models (GCM) HadGEM and GFDL, with the RCP4.5 and RCP8.5 for the period 2041-2060 and 2061-2080; for the niches of potential distribution modelling, georeferences from 407 collection sites and 29 environment variables were used with the MaxEnt model. Both GCMs predict that the potential area for B. curtipendula will experience an initial decrease of 3.1 to 14.4 %, 49


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although later it will recover and even reach an increase of 1.4 %. The annual temperature, the May to October precipitation, and the December to April moisture index, were the main environmental variables accounting for the potential distribution of the species. Key words: Bouteloua curtipendula, Environmental suitability, Descriptors, Ecological niche, MaxEnt, General circulation models, Climate change.

Received: 12/11/2017 Accepted: 12/07/2018

Introduction The grazing area in Mexico covers more than 45 % of the national territory, with a larger proportion in the northern region of the country where it reaches 70 %(1). However, these grassland areas are exposed to the effects of changing climate conditions that will occur in the following decades according to various studies, bringing about changes in plant communities, as well as the products and services they provide(2,3). The influence of climate as a determinant of primary productivity of ecosystems has led to studies in order to assess the impact of climate change on plant communities using simulation models. Some studies carried out in Mexico have shown that the ambient temperature will increase between 1.8 and 4.5 °C during the period 2040-2100, while the precipitation will decrease from 2 to 12 %(4,5,6). The species of the genus Bouteloua are some of the most important ones in the grasslands of Mexico, due to their forage quality and their ecological characteristics, among which the grass known as sideoats gramma [Bouteloua curtipendula (Michx.) Torr.] is prominent; this species has been included in some programs for the improvement of pastures, due to its outstanding features as fodder, and improved varieties have been released in Mexico(7,8) and in the United States(9). In addition, B. curtipendula has a wide variability of the polymorphism that could give it an advantage in terms of adaptability to the effects of climate change(10). Some studies have reported that the structure of the plant communities depends largely on the weather conditions, among which the precipitation and the temperature stand out as determinants(11,12,13); at the same time, it has been found that chronic drought strongly reduces the coverage of grasses while it increases the coverage of shrub species in some areas of the Chihuahuan Desert(13).

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Other results(14) show that there is little evidence that changes in precipitation will influence the competitive effects of individual plants in a grassland area with dominance of Bouteloua curtipendula, Bouteloua hirsuta and Schizachyrium scoparium, and that their intraspecific and interspecific interactions are what can modify the colonization of spaces with inadequate agro-ecological characteristics. The objective of this research was to analyze the areas with environmental suitability for B. curtipendula by effect of climate change in Mexico; using two general circulation models (GCM) under the representative concentration pathway of greenhouse gases (RCP4.5 RCP8.5) for the periods 2041-2060 and 2061-2080.

Material and methods Databases and environmental information systems The research was based on the analysis of data of the baseline climate and modeling of future climates, obtained from the Global Climate Data portal of WorldClim.org. The data for the 1950-2000 period were used for the reference climate, and the data utilized for the future climate were those corresponding to the periods 2041-2060 and 2061-2080, henceforth referred to as periods 2050 and 2070, respectively, with a spatial resolution of 30 arc seconds(15). The HadGEM-IS and GFDL-CM3 GCMs were utilized; the first was selected because one of the variables it considers is the vegetation type and includes the native grassland as part of the vegetation cover of the planet(16); the second, because version CM3 not only includes emerging issues of climate change but also has an improved spatial resolution and pays special attention to the simulation of precipitation in tropical areas(17). The simulation utilized representative concentration pathways (RCP) of greenhouse gases(18) 4.5 and 8.5 to analyze a low and a high scenario; the RCP2.6 was not used because the trends show that this scenario is hard to achieve(19). The weather data were processed by the ArcGIS software; maps with climate and bioclimate variables were subsequently generated using the Idrisi Selva software and were utilized to analyze the areas with environmental suitability for B. curtipendula with Maximum Entropy Species Distribution Modeling (MaxEnt).

Potential distribution of B. curtipendula The following adjustments were made in order to run the MaxEnt model: use of 25 % of the data for testing, the test 10 replications with cross-validation, and a maximum of 2,000 iterations. These adjustments used 410 geo-referenced data obtained from four sources: (a) direct collection, (b) collection from other researchers, (c) data contained in the herbarium 51


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specimens of the Biology Institute of the University Center for Biological and Agricultural Sciences of the University of Guadalajara, and (d) collectionsâ&#x20AC;&#x2122; data published on the website of the Global Biodiversity Information Facility(20). The environmental variables utilized were: annual precipitation, May-October precipitation, November and April precipitation, December-February precipitation, precipitation in the wettest month, precipitation in the driest month, maximum annual temperature, maximum temperature May-October, maximum temperature between November and April, mean annual temperature, average temperature in May-October, average temperature in November-April, average temperature of the warmest month, average temperature of the coldest month, annual minimum temperature, minimum temperature in May-October, minimum temperature in November-April, May-October photoperiod, November-April photoperiod, annual humidity index, May-October humidity rate (estimated as the ratio of precipitation to evapotranspiration), November-April humidity rate, December-February humidity rate, annual thermal oscillation, May-October thermal oscillation, November-April thermal oscillation, December-February thermal oscillation, and soil texture.

Areas with probability of environmental suitability The model for the prediction of areas with environmental suitability for B. curtipendula obtained with MaxEnt was used with the Idrisi system 17.0(21) with the generated a map with the threshold values corresponding to the 10th percentile(22). For the calculation of the surface with environmental suitability of the species, the areas occupied by the bodies of water and urban centers were not considered; these thematic layers were obtained by means of the use of the soil chart and the vegetation(23).

Fit of the model The ecological niche model employed by MaxEnt predicts the rate of occurrence (Receiver operating characteristic, ROC) of the species as a function of the environmental predictors in each locality(24) represented by each cell of the mesh of approximately 900 x 900 m in the geographic scale of 30 arc sec.; in turn, the area under the curve (AUC) can be interpreted as the fit of the model, in which a value of 1.0 would be a perfect classifier and a random classifier would have a value of 0.5(25); therefore, the values close to 1.0 show greater fit of the model to the data.

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Results and discussion Analysis of the potential distribution niche The average AUC values obtained for baseline climate and climate change scenarios were higher than 0,933 in all cases (Table 1), which is why the data obtained are considered to have a high degree of reliability in assessing the environmental suitability for B. curtipendula(24,25). These results agree partially with those of a research conducted in grasslands of United States(26), where the analysis for B. curtipendula with 1,251 sampling data reflects a value of the AUC of 0.946; the higher value of the AUC of this study may be due to the use of a greater number of geographic references. On the other hand, in the 10 replicas of the model used in this investigation it was found that the lowest value of AUC was of 0.915 to a high of 0.976, the standard deviation was <0.013 in all cases; therefore, the results are believed to be reliable. Because both GCMs yielded similar data in the prediction of areas with potential suitability, subsequent investigations may use either one of them. Table 1: Average values of the area under the ROC curve, obtained in 10 replications, and average standard deviation, in the analysis of potential distribution of Bouteloua curtipendula in Mexico

GCM Reference climate GFDL-CM3 RCP4.5 2050 GFDL-CM3 RCP4.5 2070 GFDL-CM3 RCP8.5 2050 GFDL-CM3 RCP8.5 2070 HadGEM-ES RCP4.5 2050 HadGEM-ES RCP4.5 2070 HadGEM-ES RCP8.5 2050 HadGEM-ES RCP8.5 2070

Average AUC 0.934 0.933 0.955 0.956 0.935 0.957 0.937 0.956 0.934

Higher AUC 0.955 0.953 0.966 0.969 0.953 0.975 0.960 0.976 0.951

Lower AUC 0.920 0.915 0.928 0.932 0.920 0.932 0.923 0.928 0.920

Standard deviation 0.011 0.011 0.013 0.011 0.011 0.012 0.012 0.013 0.010

GCM= General circulation models; AUC= Area under the curve.

Changes in the mean annual temperature and accumulated precipitation In the analysis of the average annual temperature, the two GCMs used in this research predict average increases of 2.8 and 3.4 °C for the 2050 period and of 3.4 and 5.0 °C for the 2070 period with the RCP4.5 and RCP8.5, respectively. With regard to the annual cumulative 53


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precipitation, the two models predict a decrease of 3.12 and 6.5 % in the 2050 climate, and of 7 and 14.4 % in the 2070 climate. It is important to note that changes in the temperature and the precipitation will be different in each geographical area, with a general trend toward a more accentuated change in arid and semi-arid areas than in temperate and tropical zones of Mexico.

Areas with environmental suitability for B. curtipendula Figure 1 shows that the species is found naturally in an extension of 548,719 km2 (Table 2), located in central and northern Mexico; from the southeastern part of the state of Chihuahua to the northern part of Michoacรกn and Guerrero, in the areas of native grassland located in the Mexican plateau and the Transversal Volcanic Axis. The low presence of the species and the scarcity of surfaces with environmental fitness in the low areas and coastal plains in the coasts of the Pacific Ocean, Gulf of Mexico and Caribbean Sea are notorious. Figure 1: Current suitability area with environmental B. curtipendula in Mexico

1 Left side: a map of the MaxEnt model; the red color represents greater probability of occurrence, and the blue color, areas where occurrence is less likely. Right side: Map based on the previous one, with the surface of limited environmental fitness in the 10th decile of the value of probability; the dots indicate the sites of collection of the species.

The results obtained with the algorithm MaxEnt are influenced by environmental data used(27,28); Figure 1 (right hand side) shows the homogeneous area with the greatest aptitude for the environmental baseline climate, while Figure 2 depicts the areas with environmental suitability for future periods and the analyzed RCP.

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Table 2: Surface area with current environmental suitability for Bouteloua curtipendula in Mexico and variation with the general circulation models (GCMs) Surface with environmental suitability % In relation to the 2 Climate/MCG (km ) reference climatology Reference GFDL-CM3 RCP4.5 2050 GFDL-CM3 RCP4.5 2070 GFDL-CM3 RCP8.5 2050 GFDL-CM3 RCP8.5 2070 HadGEM-ES RCP4.5 2050 HadGEM-ES RCP4.5 2070 HadGEM-ES RCP8.5 2050 HadGEM-ES RCP8.5 2070

548,719 509,152 505,516 521,876 557,293 506032 520457 528419 552,799

100.0 92.8 92.1 95.1 101.6 92.2 94.8 96.3 100.7

The GCMs converge in the prediction of a slight decrease of the surface area with environmental suitability for B. curtipendula for the two future climates (except in the RCP8.5 in 2070); the decline in the registered surface area is primarily located in the eastern part of the state of Chihuahua, north of Durango, in the northeast of Coahuila and in small areas distributed in the center and the south of the Mexican Republic (Figure 2). In the 2070 period, an average increase of 1.6 % of the surface area is predicted to occur in RCP8.5 (Table 2), located primarily in the northeast of Chihuahua and north-central Coahuila, in addition to small areas scattered in other areas adjacent to the area with current environmental suitability. This phenomenon can be influenced by the type of metabolism of the species and in this case the physiology of B. curtipendula is of type C4(26), which is more efficient in the use of water and high temperatures(3,29).

Ecological descriptors of the geographical distribution of B. curtipendula The thermal oscillation and rainfall were the ecological descriptors that contributed most to the potential distribution of the grass B. curtipendula in Mexico (Table 3) in all environmental scenarios. Other studies have also shown the influence of precipitation and temperature in this species(3,13). When analyzing the thermal oscillation separately from the other variables (Figure 3), a sharp increase in the probability of occurrence of B. curtipendula with 14 to 20 degrees of difference between maximum and minimum temperatures (the thermal oscillation) was observed.

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Figure 2: Areas with environmental suitability for B. curtipendula and changes estimated for the periods 2041-2060 and 2061-2080, with RCP4.5 RCP8.5, and in relation to the baseline climate

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Table 3: Relative contribution (%) of the environmental variables that have the greatest influence on the environmental suitability for Bouteloua curtipendula in Mexico

Environmental variable ATO Nov-Apr PP May-Oct PRE Dec-Feb TO Dec-Feb IH May-Oct MT

Reference climate 29.9 14.6 11.1 8.2 7.2 2.6

RCP 4.5 2050 1 21.5 9.2 17.1 10.3 9.8 3.7

2050 2 22.8 8.4 15.6 12.8 8.0 1.6

2070 1 20.8 8.4 18.8 11.8 11.1 2.2

RCP 8.5 2070 2 23.8 8.3 14.6 12.6 9.8 1.8

2050 1 27.8 7.9 19.3 11.2 13.7 2.5

2050 2 31.6 7.6 23.8 13.9 8.6 1.7

2070 1 32.1 7.2 20.1 11.5 13.9 2.3

2070 2 30.5 6.7 25.5 14.3 11.1 1.5

ATO= annual thermal oscillation; PRE= precipitation; PP = photoperiod; TO= thermal oscillation; IH= index of humidity; MT= minimum temperature.

Figure 3: Environmental variables that influence the probability of presence of B. curtipendula

1 The left-hand side: changes in the prediction of each variable in the sample average. Right side: changes in the prediction of environmental variables separately. The shaded area represents the standard deviation. (ATO, annual thermal oscillation; p5-10, May-Oct precipitation; TO12-2, Dec-Feb thermal oscillation; IH122, December-February humidity rate).

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The annual precipitation in was one of the main ecological descriptors in the distribution of B. curtipendula, not directly but indirectly, through the rate of annual and seasonal humidity, which are the variables that contribute to explain the presence of the species. Based on Figure 3 it can be inferred that B. curtipendula is more likely to occur in areas with indices of annual humidity of 0.2 and 0.5, above which point this likelihood decreases quickly as the index approaches a value of 1.0; depending on these levels of humidity, B. curtipendula is distributed naturally in arid and semi-arid zones(30,31) and is less frequent in sub-humid and humid areas of Mexico. The contribution of the moisture content in the months of December to February indicates that B. curtipendula requires moisture in order to stay in its own ecological niche, as may be corroborated by the fact that the precipitation in the period between December and February contributed to the 5.4 % in the species' distribution, to even a greater extent than the annual cumulative precipitation which reached a value of 3.0 %. In this regard, in a research on B. curtipendula conducted in the Chihuahuan desert, precipitation was the key factor accounting for the net primary productivity, mainly when the rain is distributed in small but frequent events during the summer(32). A similar situation has been presented with respect to other species, as reported in a study carried out in Western Africa to model the occurrence of 302 species of grasses, according to which precipitation is the variable that most often affects the distribution of grass species in grasslands(11). Other studies also report that the precipitation is the determining factor in the distribution of grasses at the local and regional scales(33); they also mention that for 30 species native to the Great Plains of the United States the environmental descriptor that contributed the most to the probability of occurrence of B. curtipendula was the mean annual temperature(26), which may be explained by taking into account the latitude that causes very low temperatures with respect to the areas of distribution of this species in Mexico. When the contribution of annual rainfall is analyzed separately, the greatest probability of occurrence of this grass is on the sites with precipitations of 450 to 750 mm¸ smaller or larger precipitations reduce the likelihood of the occurrence of this species. With regard to the photoperiod, the contribution was 9.0 and 14.6 %, in the months from May to October and November to April, respectively (Table 3), with higher probabilities of presence in ranges from 12:30 to 13:00 h in the months of May to October, and from 1100 to 1130 h in the months of November to April (Figure 3); it should be noted that B. curtipendula has a wide genetic variation that allows it to adapt to a variety of settings(33,34), which explains its present in sites as extreme as Canada and Argentina(29,35).

Conclusions and implications The two models agree in the prediction that for the periods 2050 and 2070, there will be an increase of 2.8 to 5.0°C in the annual average temperature, and a decrease of 3.1 to 14.4 % 58


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in accumulated precipitation. The effect of these changes on the surface area with environmental suitability for B. curtipendula will have slight negative long-term consequences, because the species will have a lower presence in the period 2050 for both RCPs, and in the 2070 climate, only for the RCP4.5. However, it will recover in the 2070 period in the RCP8.5 and a mean increase of 0.9 % of the surface area with environmental suitability has been predicted in relation to the reference climate. The decline in the area with environmental suitability is located primarily in the states of Chihuahua, Durango and Coahuila and, to a lesser extent, in small areas of central and southern Mexico. The expected increase in area with environmental suitability will occur primarily in the northeast of Chihuahua and in north-central Coahuila. The environmental variables that contributed the most to explain the presence of B. curtipendula in Mexico were: annual thermal oscillation, precipitation in the period from May to October, thermal oscillation in the period from December to February, and humidity from December to February.

Acknowledgments Thanks to Dr. Carlos Morales Nieto for access to the database of the collections he carried out; and to the authorities of the Los Altos University Center of the University of Guadalajara, for the facilities provided for this research.

Literature cited: 1. SIAP-SAGARPA. Servicio de Información Agroalimentaria y Pesquera, Secretaría de Agricultura, Ganadería, Desarrollo Rural, Pesca y Alimentación, México. http://www.siap.gob.mx/. Consultado Feb 12, 2015. 2. Reeves MC, Moreno AL, Bagne KE, Running SW. Estimating climate change effects on net primary production of rangelands in the United States. Climate Change 2014;126:429-442. doi: 10.1007/s10584-014-1235-8. 3. Polley HW, Derner JD, Jackson RB, Wilsey BJ, Fay PA. Impacts of climate change drivers on C4 grassland productivity: scaling driver effects through the plant community. J Experim Botany 2014;65:3415-3424. doi:10.1093/jxb/eru009. 4. Durán PN, Ruíz CJA, González EDR, Ramírez OG. Impact of climate change on grasses cultivation potential of three altitudinal strata-agricultural lands of Mexico. African J Agric Res 2014;9:1396-1406.

59


Rev Mex Cienc Pecu 2020;11(Supl 2):49-62

5. Ruíz CJA, Medina GG, Ramírez DJL, Flores LHE, Ramírez OG, Manríquez OJD, et al. Cambio climático y sus implicaciones en cinco zonas productivas de maíz en México. Rev Mex Cienc Agr 2011; Publ Esp 2:309-323. 6. Zarazúa-Villaseñor P, Ruíz-Corral JA, González-Eguiarte DR, Flores-López HE, RonParra J. Impacto del cambio climático sobre la agroclimatología del maíz en Ciénega de Chapala, Jalisco. Rev Mex de Cienc Agr 2011; Publ Esp 2:351-363. 7. Corrales LR, Morales NCR, Melgoza CA, Sierra TJS, Ortega GJA, Méndez ZG. Caracterización de variedades de pasto banderita [Bouteloua curtipendula (Michx.) Torr.] recomendadas para rehabilitación de pastizales. Rev Mex Cienc Pecu 2016;7(2):201-211. 8. Beltrán LS, García DCA, Loredo OC, Núñez QT, González ELA, García DCA, Hernández AJA, et al. Navajita Cecilia y Banderilla Diana: Pastos nativos sobresalientes para el Altiplano de San Luis Potosí. INIFAP-CIRNE-Campo Experimental San Luis, Folleto Técnico # 33. 2007. 9. NRCS. “El Reno” sideoatas grama Bouteloua curtipendula (Michc.) Torr. USDA-Natural Resources Conservation Service: Manhattan KS, USA. 2011. 10. Morales NC, Quero CA, Le BO, Hernández GA, Pérez PJ, González MS. Caracterización de la diversidad del pasto nativo Bouteloua curtipendula Michx. Torr. mediante marcadores de AFLP. Agrociencia 2006;40:711-720. 11. Bocksberger G, Schnitzler J, Chatelain C, Daget P, Janssen T, Schmidt M, Thiombiano A, Zizka G. Climate and the distribution of grasses in West Africa. J Veget Sci 2016;27:306-317. 12. Pérez RIM, Roumet C, Cruz P, Blanchaird A, Autran P, Garnier E. Evidence for “a plant comunity economics spectrum” driven by nutrient and water limitations in a Mediterranean rangeland on Southern France. J Ecol 2012;100:1315-1327. 13. Báez S, Collins SL, Pockman WT, Johnson JE, Small EE. Effects of experimental rainfall manipulations on Chihuahuan desert grassland and shrubland comunnities. Oecol 2013;172:1117-1127. DOI 10.1007/s00442-2552-0. 14. Adler PB, Leiker J, Levine JM. Direct and indirect effects of climate change on a prairie plant community. PloS ONE 2009;4(9):e6887. doi:10.1371/journal.pone.0006887. 15. Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A. Very high resolution interpolated climate surfaces for global land areas. Internat J Climat 2005;25:1965-1978.

60


Rev Mex Cienc Pecu 2020;11(Supl 2):49-62

16. Collins WJ, Bellouin N, Doutriaux-Boucher M, Gedney N, Halloran P, Hinton T, et al. Development and evaluation of an Earth-System model – HadGEM2. Geosci Model Dev 2011;4:1051-1075. 17. Donner LJ, Wyman BL, Hemler RS, Horowitz LW, Ming Y, Zhao M, et al. The dynamical core, physical parameterizations, and basic simulations characteristics of the atmpospheric component AM3 of the GFDL global coupled model CM3. J Climate 2011;24:3484-3519. 18. Van Vuuren BDP, Edmonds J, Kainuma M, Riahi K, Thomson A, Hibbard K, et al. The representative concentration pathways: an overview. Climatic Change 2011;109:5-31. DOI 10.1007/s10584-011-0148-z. 19. UNEP. The Emissions Gap Report. United Nations Environment Programme (UNEP), Nairobi. 2016. 20. GBIF (Global Biodiversity Information Facility). GBIF.org (11th February 2017). GBIF Ocurrence Download http://doi.org/10.15468/dl.gfgh2t. 21. Eastman JR. Idrisi Selva Manual Version 17. Worcester, Mass., USA: Clark Labs, Clark University; 2012. 22. Norris D. Model thresholds are more important than presence location type: Understanding the distribution of lowland tapir (Tapirus terrestris) in a continuous Atlantic forest of southeast Brazil. Tropical Conserv Sci 2014;7(3):529-547. 23. INEGI (Instituto Nacional de Estadística Geografía e Informática). Guía para interpretación cartográfica: Uso de suelo-vegetación Serie III. México. DF. 2009. 24. Merow C, Smith MJ, Silander JA. A practical guide to MaxEnt for modeling species distributions: what it does and why inputs and settings matter. Ecography 2013; 36:1058-1069. 25. Phillips SJ, Dudik M, Schapire RE. Maximum entropy modeling of species geographic distributions. Ecolog Model 2006;190:231-259. 26. Martinson EJ, Eddy ZB, Commerford JL. Biogeographic distributions of selected North American grassland plant species. Physical Geo 2011;32(6):583-602. Doi: 10.2747/0272-3646.32.6.583. 27. Elith J, Phillips SJ, Hastie T, Dudik M, Chee YE, Yates CJ. A. statistical explanation of MaxEnt for ecologists. Diversity Distrib 2011;17:43-57. 28. Phillips SJ, Dudik M. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 2008;31:161-175. 61


Rev Mex Cienc Pecu 2020;11(Supl 2):49-62

29. Schellenberg MP, Biligetu B, McLeod GJ, Wang Z. Phenotypic variation of side-oats grama grass [Bouteloua curtipendula (Michx.) Torr.] collections from the Canadian prairie. Can J Plant Sci 2012;92:1043-1048. 30. UNEP. World Atlas of Desertification. 2nd ed. United Nations Environment Programme: Oxford University Press, England. 1997. 31. Robertson TR, Bell CW, Zak J, Tissue DT. Precipitation timing and magnitude differentially effect aboveground annual net primary productivity in three perennial species in a Chihuahuan dessert grassland. New Phytol 2009;181:230-242. 32. Edwards TJ, Smith SA. Phylogenetic analysis reveals the shady history of C4 grasses. PNAS 2010;107:2532-2537. 33. Morales NCR, Avendaño AC, Melgoza CA, Gil VKC, Quero CA, Jurado GP, et al. Caracterización morfológica y molecular de poblaciones de pasto banderita (Bouteloua curtipendula) en Chihuahua, México. Rev Mex Cienc Pecu 2016;7(4):455-469. 34. Chadwick AC. Bouteloua curtipendula. In: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, Fire Sciences Laboratory (Producer) 2003. http://www.fs.fed.us/database/feis/. Accesed Apr 5, 2017. 35. Siqueiros DME, Ainouche M, Columbus JT, Ainouche A. Phylogeny of the Bouteloua curtipendula complex (Poaceae: Chloridoideae) based on nuclear ribosomal and plastid DNA sequences from diploid taxa. Sistem Bot 2013;38(2):379-389.

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https://doi.org/10.22319/rmcp.v11s2.4694 Article

Effects of rainfall pattern changes due to global warming on soil water erosion in grasslands and other vegetation types in the state of Zacatecas, Mexico

Francisco Guadalupe Echavarría-Cháirez a* Guillermo Medina-García a José Ariel Ruiz-Corral b

a

Instituto Nacional de Investigaciones Forestales Agrícolas y Pecuarias (INIFAP). Campo Experimental Zacatecas, Km 24.5 Carretera Zacatecas-Fresnillo, Calera, Zac., México. b

Universidad de Guadalajara. Centro Universitario de Ciencias Biológicas y Agropecuarias. Camino Ramón Padilla Sánchez No. 2100 Nextipac, 44600, Zapopan, Jalisco, México.

*Corresponding author: echavarria.francisco@inifap.gob.mx.

Abstract: Temperature and precipitation anomalies were used to assess it for the 2021-2080 period, based on an assembly of 11 global circulation models, in order to generate future temperature and precipitation maps based on the reference climatology of the 1961-2010 period. In the state of Zacatecas, the grassland area has current average erosion values of approximately 16.3 t/ha, estimated using the RUSLE model. In other types of vegetation or land use as shrubs, forest or agricultural areas, values are even higher (50.74, 65.99, 161.39, t/ha/yr, respectively). The application of the ensemble model in RCP 4.5 and RCP 8.5 indicates that there will be an increase in temperature and a decrease in rainfall. In a scenario with less effect of global warming (SPC 4.5), a gradual reduction of grassland water erosion is expected, compared to 2010 of 2.3 % in 2030 (15.67 t/ha/yr) to 5.8 8% (15.18 t/ha /yr) in 2070. In the same period up to 2070, the rate of decline of water erosion is 14.1 kg/ha/yr. RCP 8.5, which was designed for a condition of continuous emission of greenhouse gases, exhibits even greater reduction values and for 2070 it establishes a decrease of 13.0 %, with an erosion reduction rate of 31.5 kg/ha/yr. Other types of vegetation and land use showed the same tendency to decrease, although at higher rates. The

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scenario of water erosion reduction seems favorable; however, an increase in soil loss values due to wind erosion in the arid regions of northern Zacatecas is not ruled out. The assessment of levels of water erosion contributes to the ordering of land use to reduce levels of degradation and desertification. Key words: Water erosion, RUSLE, Climate change, RCP.

Received: 12/11/2017 Accepted: 22/08/2018

Introduction Soil water erosion is only one of the possible types of erosion and one of the forms of soil degradation that exist. In particular, water erosion is a continuous process that affects the soils of the entire planet. Changes in the amount, intensity and distribution of rainfall accelerate or reduce the continuous effect of erosion. These changes are currently occurring as a result of the increase in greenhouse gases (GHG), which influence global warming(1) and climate change. Soil losses from water erosion are of greater importance, especially in places where the surface layer is shallow, as in the state of Zacatecas. Grassland soils are very shallow, while agricultural soils are deeper. The effect of soil losses will vary according to the land use, but in all cases it will lead to the gradual reduction of productivity and degradation. There is great uncertainty about the effects that climate change will cause in productive activities, as these activities and, consequently food security, depend on rainfall. However, indicators such as water erosion may not be of general interest, especially those indicators that are not routinely measured and whose effects, due to permanent occurrence, are not readily noticeable. Current and future knowledge of possible land losses and degradation in general are important for land use planning and management(2). The glossary of the American Society of Soil Science(3) defines degradation as: "the process through which a compound is transformed into simpler components". Desertification was defined in 1992 by the United Nations Convention to Combat Desertification as: "the degradation of drylands, semi-arid and dry sub-humid lands, resulting from various factors, such as climatic variations and human activities"(4). CONABIO(5) mentions that in 2011, the surface area of the country's natural ecosystems has been significantly reduced since the middle of the last century to become agricultural land, urban areas and infrastructure works, and that 28.7% of the territory had lost its natural ecosystems, while the remaining 71.3% maintained them with 64


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different degrees of conservation. Therefore, land use planning is essential to preserve stable levels of degradation and desertification through the maintenance of the agricultural frontier, and to minimize changes in land use. In this sense, it is important to evaluate the effect of climate change on the levels of water erosion in the areas of grassland and other types of vegetation, in order to contribute to the planning of protection actions, ecological management and reduction of soil degradation.

Material and methods The state of Zacatecas is located between the coordinates 25° 07´32´´ and 21°01´48´´ N, and between 100° 44´ 09´´ and 104° 24´08´´ W, and has a territory of 7´447,970.8 ha(6). The prevailing vegetation type in the state is xerophytic scrubland, which covers 3´173,280 ha, followed by the surface area of agricultural land, with 1´746,987 ha; the third is the grassland, with a surface area of 1´454,234 ha, and the fourth is forest, which occupies 1´125,285 ha(7); the rest is made up by tropical forest and different vegetation types of smaller size. The prevailing soil types in the state are 14 units, of which xerosol (38.8 %), litosol (14.3 %), pheozem (14.0 %), regosol (12.2 %) and castañozem (9.6 %)(8) are the most important.

Water erosion

The current water erosion was estimated using the Revised Universal Soil Loss Equation (RUSLE)(9): E=R*K*L*S*C*P

(1)

Where: E= soil loss per surface area unit (t/ha/yr); R= rain erosivity factor (MJ mm ha-1 h-yr-1); K= soil erodibility factor (t ha-1 h-1/MJ mm ha-1); L= slope length factor (non-dimensional); S= slope degree factor (non-dimensional); C= vegetation factor (non-dimensional); P= mechanical practice factor of erosion control (non-dimensional).

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The IDRISI Selva software(10) was used to measure it; this software includes the RUSLE command, which is a routine that improves the calculation of the L and S factors and including other additional routines that eliminates depressions, and well as others that allow a better generalization of the digital elevation model. The isoerosivity map, which divides the country into 14 regions, was used for estimating the R factor; the state of Zacatecas is located in regions 3, 4 and 7, whose equations are the following: Y = 3.67516x - 0.001720 X2 (2) Y = 2.89594x + 0.002983 X2 (3) Y = 0.03338x + 0.006661 X2 (4) Where Y is the R factor in terms of MJ mm/ha h, and X is the annual precipitation in mm. The values of the K parameter were estimated according to a table of erodibility values developed by Figueroa (11). The K factor is a function of the soil texture and class. These values were assigned to soil classes and textures within ejido lands, which were digitized to facilitate their management within geographic information systems (GIS), through the IDRISI Selva software(10). The output is the soil loss for each vegetation type. The effect of climate change was quantified using the information system of INIFAP for the Mexican Republic (12), which consists of the climate data base of the 1961-2010 period and the climate scenarios predicted for the years 2021 to 2080 in the Representative Concentration Routes (RCP) 4.5 and 8.5 of greenhouse gases (GHG) for, among others, an ensemble model formed from 11 general circulation models (GCM) ―reduced in scale and calibrated(13)― selected for Mexico (BCC ‐ CSM1‐ 1, CCSM4, GISS ‐ E2 ‐ R, HadGEM2 ‐ AO, HadGEM2 ‐ ES, IPSLCM5A ‐ LR, MIROC ‐ ESM ‐ CHEM, MIROC ‐ ESM, MIROC5, MRI ‐ CGCM3, NorESM1 ‐ M). The values of maximum temperature, minimum temperature and monthly precipitation were used for the years 2021 to 2080. The annual data for the climatic scenarios 2021-2040, 2041-2060 and 2061-2080, hereinafter referred to as climatologies or 2030, 2050 and 2070 years, respectively, were estimated based on the monthly data. The information of INEGI (14) on land use made it possible to divide the effects of rainfall and water erosion by predominant vegetation type, including grasslands as well as areas covered with other types of vegetation such as scrubs and forests, and agricultural land. With the information of water erosion estimated for the present and for the years 2030, 2050 and 2070, regression models(15) were obtained for the main vegetation types of the state, mentioned above, calculating an annual rate of change for each condition of vegetation and comparing it with that of the grassland area, in order to determine where the greatest impacts due to climate change will potentially occur.

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Results and discussion Unlike other change indicators, soil loss from water erosion will have a tendency to diminish as the global warming effect triggers a reduction of rainfall in the state of Zacatecas. Table 1 shows the areas susceptible to water erosion and the range of soil loss associated with each of them, both in the reference climatology and in the studied climatologies and climate change scenarios. The scenarios used to project the rainfall, on which the estimation of water erosion values was based, are those referred to as representative routes of greenhouse gas concentration (RCP) 4.5 and 8.5, for the 2030, 2050 and 2070 climatologies. The RCPs are characterized by their approximate calculation of the total radiative forcing expected for the year 2100 in relation to that of the year 1750 (IPCC, 2013), i.e. 4.5 W m-2 in the RCP 4.5 scenario (intermediate GHG emissions), and 8.5 Wm-2 in the RCP 8.5 scenario (high GHG emissions). Table 1: Land loss surface areas (ha) associated with grasslands, estimated for RCP 4.5 with the RUSLE model for the state of Zacatecas from 2010 to 2070 Erosion Years (t/ha) Difference (%) 2010 2030 2050 2070 0 - 10 888,417 894,767 906,833 911,278 2.57 10 - 100 533,431 529,621 519,460 514,380 -3.57 100 - 500 30,482 27,942 26,036 26,671 -12.50

Table 2 shows the surface projections associated with the range of soil loss under CPR 8.5, which is the condition in which no decrease in greenhouse effect is expected. Figure 1 shows the changes in water erosion in the grassland areas of the state of Zacatecas with RCP 4.5 and 8.5. Table 2: Land loss areas (ha) associated with pastures, estimated for CPR 8.5 with the RUSLE model for the state of Zacatecas from 2010 to 2070 Erosion Years (t/ha) Difference (%) 2010 2030 2050 2070 0 - 10 888,417 910,643 930,329 939,220 5.72 10 - 100 533,431 515,015 502,949 494,059 -7.38 100 - 500 30,482 26,671 19,051 19,051 -37.50

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Figure 1: Current water erosion (left) and water erosion projected for 2070 (upper right) with RCP 4.5 and RCP 8.5 (lower right) in the grasslands of the state of Zacatecas

In both Tables (1 and 2) the figures show a similar trend: the category that represents the largest area in both tables is the one associated with the erosion range of 0 to 10 t/ha, which amounts to little more than 60 % of the total grassland area in the state of Zacatecas. In both RCPs, this category tends to increase: in RCP 4.5 it is increased by 22,861 ha (2.57 %), while in RCP 8.5, the increase is 50,803 ha, which represents approximately twice (5.72 %) the area estimated with RCP 4.5 for the year 2070. A decrease in precipitation of 4.0 and 7.8 % (Tables 3 and 4) was projected for RCP 4.5 and RCP 8.5, respectively, up to the year 2070, which appears to be contradictory: as precipitation decreases, erosion increases. However, the categories with higher water erosion values, which range between 10 and 500 t/ha, will exhibit stability or even a decrease. Although the values of the categories 10100 and 100-500, correspond to the lowest values in surface area (40 %), it was in these categories where the highest values of water erosion occurred, so that the stability and decrease in them leads to a decrease in the total mean values for the grassland area. When the average erosion values for the state of Zacatecas were used in a regression analysis, negative and, in both cases, significant exchange rates (P<0.05) were found, with small reduction values per year in RCP 4.5 (14.1 kg/ha/yr) and RCP 8.5 (31.5 kg/ha/yr (Figure 2).

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Table 3: Mean precipitation projected with RCP 4.5 and percentage reduction in the 2010 -2070 period Land use Years Reduction (%) 2010 2030 2050 2070 Grassland 500 492 483 480 4.04 Scrubland 385 377 370 367 4.53 Agriculture 469 462 454 450 4.03 Forest 602 591 580 580 3.76

Table 4: Mean precipitation projected with RCP 8.5 and percentage reduction in the 2010 - 2070 period Land use Years Reduction (%) 2010 2030 2050 2070 Grassland 500 482 464 461 7.87 Scrubland 385 369 353 351 8.81 Agriculture 469 452 436 432 7.88 Forest 602 580 557 551 8.55

Figure 2: Soil annual average loss in the grasslands of the state of Zacatecas with RCP 4.5 (left) and RCP 8.5 (right)

20000 Rangeland losses (kg/ha/yr)

Rangeland losses (kg/ha/yr)

20000 15000 10000 5000 0

15000 10000 5000 0

2010

2030

2050

2070

2010

Years

2030

2050 Years

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Comparison with other vegetation types

The effect of the decrease in precipitation on the reduction of water erosion of the soil in the grasslands of Zacatecas is different from that of other types of vegetation in the state; assessment of the impact on the reduction of rainfall in the scrubland, forest and agricultural areas shows that the estimated values for the scrubland area are the lowest (Table 3), although the pattern is similar to that projected for the grasslands. Table 3 lists the estimated precipitation values for each type of vegetation and the percentage reduction. The reduction values are lower for RCP 4.5 than for RCP 8.5, because the former is associated with a lower effect of greenhouse gases (GHG). The reduction in precipitation values affects the average erosion values of the different types of vegetation studied. Tables 5 and 6 show the reduction in mean values of water erosion estimated using RCP 4.5 and RCP 8.5. Table 5 shows a decrease in percentage of 5 to 9 % of current values (2010); this is most prominent in the grasslands, where the values range from 16.13 to 15.18 t/ha/yr, amounting to a 5.89 % reduction in 60 yr and showing the lowest values among all vegetation types. However, this projection may be altered by changes in land use. Grazing is carried out in this area, which is thereby subject to risks of deterioration, especially when overgrazing occurs(16); this has been recognized as one of the main causes of increased runoff and water erosion, since, by reducing the vegetation cover, infiltration is reduced and runoff increases(17). The decrease in the vegetation cover can also be associated with the reduction of precipitation. Long periods of drought have been recognized as causing and contributing to the increase in erosion. The risk that the change of land use entails for the grasslands has been shown in the reports of land use change rates as they were converted to irrigation agriculture areas during the 1970s, in what was formerly the grassland area within the â&#x20AC;&#x153;Chupaderosâ&#x20AC;? aquifer in the state of Zacatecas, where the said rates were up to 1,913 ha/yr(18) and 678 ha/yr in the Aguanaval aquifer(18). A change in land use can induce changes in water erosion of a grassland area to erosion values typical of the agricultural area, whose average values range from 133 to 161 t/ha/yr. Table 5: Average values of water erosion (t/ha/yr) by vegetation type with rainfall projected by means of RCP 4.5 Vegetation Years Difference types (%) 2010 2030 2050 2070 Grassland 16.13 15.67 15.29 15.18 -5.89 Scrubland 50.74 49.76 48.62 48.11 -5.18 Forest 65.99 64.47 61.24 61.46 -6.86 Agriculture 161.39 157.32 146.35 145.56 -9.81

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Table 6: Average values of water erosion (t / ha / year) by vegetation type with rainfall projected by means of RCP 8.5 Vegetation Years Difference types (%) 2010 2030 2050 2070 Grassland 16.13 15.19 14.28 14.03 -13.02 Scrubland 50.74 47.94 45.68 45.01 -11.29 Forest 65.99 61.12 64.47 54.51 -17.40 Agriculture 161.39 151.2 142.32 133.18 -17.48 Likewise, it can be observed that average values of water erosion will decrease in the scrubland area and that they range from 5.18 to 11.29 %, which is related to the decrease in rainfall forecast for the coming years and whose exchange rates are significant (P<0.05). As in grasslands, overgrazing and changing land use are the greatest risks that this decrease in water erosion may not be as predicted. Another risk for this area is wind erosion, since it is in this type of vegetation that the lowest rainfall in the entire state will occur, which may favor the increase in wind erosion associated with drought(19,20). A situation similar to the previous two will be observed in the forest area, where reductions in water erosion from 6.86 to 17.4 % are expected, without the exchange rates being significant (P>0.05). In this vegetation type, the felling of trees or soil change, as well as the effect of drought and diseases on trees, which have been found recently, can be seen as the greatest risks(21). Finally, mention is made of the case of the agricultural lands, which, since they are almost always located in the lowest part of the basin, are subject to runoff of greater magnitude and greater erosion. They are also the sites with the greatest depth of soil, which renders them the areas with the largest amount of soil losses. However, given the prospect of decreasing precipitation, it is estimated that water erosion values will decrease from 9.81 to 17.48 %, this area being the most benefited in terms of decreasing water erosion, with lower exchange rates (P>0.05). The appropriate management of the soil resource, the management of slopes, tillage techniques for soil conservation and maintenance of plant cover are technological components that contribute to improving soil care. However, traditional soil management practices, furrows in the direction of the slope and tillage during periods of intense winds, can increase the values of water and wind erosion. The latter has been reported(11) as an erosion risk equivalent to that caused by runoff. The establishment of windbreak barriers and changes in tillage and moisture conservation techniques should be a priority to maintain agricultural productivity and not increase water erosion values, as predicted by the models used here. Additionally, attention should be paid to the reduction of authorizations for land use change, since, as mentioned above, the change in land use can lead to soil loss levels equivalent to those occurring in the agricultural area.

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On the other hand, although it seems that the most notorious effects of land losses in Zacatecas, occur in the agricultural areas, land losses in other vegetation types are as much or more harmful than in the agricultural areas, because soil depth in grassland, forest or scrubland areas, are much smaller than in agricultural lands. Therefore, a decrease of one centimeter of surface layer of soil causes more dramatic effects in those other areas than in agricultural lands. As mentioned at the beginning of this section in regard to soil losses due to water erosion, unlike other indicators, water erosion will tend to decrease as the global warming effect due to accumulation of gases in the atmosphere reduces precipitations in the state of Zacatecas. Results show that it is important to have information that may allow planning in the medium and long term, as well as expanding and including studies on related issues, such as wind erosion and other types of degradation associated with global warming, and other studies that may contribute to complete the understanding of the changes that will occur in the future and determine preventive actions and actions to care of the soil resource.

Conclusions and implications The effect of the decrease in rainfall estimated for the state of Zacatecas will reduce the soil losses in the grassland areas and among the main vegetation types in the state. The average values of water erosion are lower in the grassland areas, followed by the areas of scrubland, forest, while the highest values are found in the agricultural lands, although the impact on these last two categories is lower. Land use change towards agricultural activities can lead to potential losses of land equivalent to the average values. The effects of the decrease in precipitation on soil losses due to wind erosion and other types of soil degradation should be assessed.

Literature cited: 1.

IPCC. Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, RK Pachauri and LA Meyer editors]. IPCC, Geneva, Switzerland. 2014.

2.

Echavarría CFG, Medina GG, Rumayor RAF, Serna PA, Salinas, GH, Bustamante WJG. Diagnóstico de los recursos naturales para la planeación de la intervención tecnológica y el ordenamiento ecológico. INIFAP. CIRNOC. Libro Técnico Nº10. 2009.

3.

Soil Science Society of America. Glossary of soil terms. SSSA. Madison, WI. 53711. 1997.

4.

UNCED. Earth summit agenda 21: Program of action for sustainable development. New York. United Nations Department of Public Information. 1992.

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5.

Comisión Nacional para el Conocimiento y Uso de la Biodiversidad. Quinto Informe Nacional de México ante el Convenio sobre la Diversidad Biológica (cdb). Conabio. México. 2014.

6.

Secretaría de Medio Ambiente y Recursos Naturales. Inventario Estatal Forestal y de Suelos. Colección de inventarios forestales y de suelos. 2013–2014. México, DF. 2014.

7.

INEGI (Instituto Nacional de Estadística y Geografía). Anuario estadístico y geográfico de Zacatecas. Aguascalientes, Ags. 2014.

8.

INEGI. Conjunto de datos vectoriales. Edafología. Continuo Nacional. Escala 1:1´000,000. 1998.

9.

Renard KG, Foster GR, Weesies GA, McCool DK, Yoder DC. Predicting soil erosion by water: A guide to conservation planning with the Revised Universal Soil Loss Equation (RUSLE), Agricultural Handbook, 703. U.S. Government Printing Office, Washington, DC. 1997.

10. Eastman JR. Idrisi Selva manual. Versión 17. Clark labs Clark University. Worcester, Mass., USA. 2012. 11. Figueroa SB, Amante AO, Cortés HGT, Pimentel JL, Osuna ESC, Rodríguez JMO, Morales FJF. Manual de predicción de pérdidas de suelo por erosión. SARH Colegio de postgraduados. México, DF. 1991. 12. Ruiz-Corral JA, Medina-García G, Rodríguez-Moreno VM, Sánchez-González JJ, Villavicencio GR, Durán Puga N, Grageda GJ, et al. Regionalización del cambio climático en México Rev Mex Cienc Agríc Pub. Esp. Núm. 2013;13:2451-2464. 13. Walton D. Meyerson J. Neelin JD. Accessing, downloading, and viewing CMIP5 data. Earth System Grid Federation. 25 p. IPCC (Intergovernmental Panel on Climate Change). 2013. The physical science basis. Working group, I contribution to the fifth assessment report of the intergovernmental panel on climate change. Summary for policymakers. In: Stocker TF, et al editors. Switzerland. 2013. 14. INEGI. Conjuntos de datos de la Serie V de Uso del Suelo y Vegetación, escala 1:250 000. 2013. 15. SAS Institute Inc. SAS 9.3. Cary, NC: SAS Institute Inc. 2011. 16. Echavarría CFG, Serna PA, Bañuelos VR. Influencia del sistema de pastoreo con pequeños rumiantes en un agostadero del semiárido Zacatecano: II Cambios en el suelo. Téc Pecu Méx 2007;45(2):177-194. 17. Serna PA, Echavarría CFG. Caracterización hidrológica de un pastizal comunal excluido al pastoreo en Zacatecas, México. I. Pérdidas de suelo. Téc Pecu Méx 2002;40(1):37-53. 73


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18. Echavarría CFG. Recurso suelo, clasificación, uso, degradación y disponibilidad. En: Mojarro DF, et al., editores. Agua subterránea en Zacatecas. Universidad Autónoma de Zacatecas. México, DF. 2013:97-133. 19. Eltaif NI, Gharaibeh MA. Aplicación de un modelo matemático para predecir y reducción de la erosión eólica en tierras áridas no protegidas. Revista Chapingo Serie Ciencias Forestales y del Ambiente, Volumen XVII, Edición Especial: 2011;195-206. 20. Aimar SB, Méndez MJ, Buschiazzo DE. Predicción de la erosión eólica potencial con el modelo EWEQ en dos suelos loesicos: efectos de las condiciones climáticas. CI. SUELO 2011;29(2):253-264. 21. Moore B, Allard G. Los impactos del cambio climático en la sanidad forestal. Organización de las Naciones Unidas para la Agricultura y la Alimentación. Departamento Forestal. FAO. 2009.

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https://doi.org/10.22319/rmcp.v11s2.4704 Article

Estimation of the transport factor of the phosphorus index in climatology and climate change scenarios in Jalisco, Mexico

Hugo Ernesto Flores López a Álvaro Agustín Chávez Durán a José Ariel Ruíz Corral a Celia De La Mora Orozco a* Uriel Figueroa Viramontes b Agustín Hernández Anaya c

a

Universidad de Guadalajara. Centro Universitario de Ciencias Biológicas y Agropecuarias. Camino Ramón Padilla Sánchez No. 2100 Nextipac, 44600, Zapopan, Jalisco, México. b

INIFAP. Campo Experimental La Laguna. Coahuila. México.

c

Universidad de Guadalajara. Centro Universitario de los Altos. México.

*Corresponding author: delamora.celia@inifap.gob.mx

Abstract: The phosphorus index (PI) is a planning tool for identifying agricultural or livestock fields with the potential to contribute phosphorus to water bodies and distinguish those nutrient management practices that favor this process. The transport factor of the PI (PITF) implicitly includes non-controllable elements of the environment, such as rainfall, which contributes to agriculture uncertainty, and it is favored by the current climate change process. In Mexico, few studies have considered the PITF; therefore, the objective of this work was to apply the calculation methodology for the PITF and identify those areas that are vulnerable to the loss of phosphorus from land to water bodies in two climate change scenarios and three climates of Jalisco. The PI model of Gburek was applied in two representative routes of concentration

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of greenhouse gases (CPR): 4.5 and 8.5, with climatologies for 2030, 2050 and 2070, and for 2010 as baseline. The PITF was calculated using ARCGIS and the IDRISI GIS. The results showed levels of vulnerability to the loss of phosphorus ranging from very low to high at the baseline, while in RCP4.5 the PITF was rated very low to medium, and in the RCP8.5, very low to high. An element that stood out in the PITF was the high vulnerability of the plots located near a drainage network or water body. Key words: Phosphorus loss, Environmental risk, Water quality.

Received: 20/11/2017 Accepted: 22/08/2018

Introduction The loss of phosphorus (P) from diffuse agricultural and livestock sources pollution is the main cause of eutrophication of freshwater in the agricultural regions in the developed countries(1,2) and in developing countries like Mexico(3,4). In some regions of Mexico, with high concentration of livestock, such as the Highlands of Jalisco(4), or with a high intensity of land use, such as the central region of Jalisco(5), the effects are visible in the superficial water bodies, due to the excessive growth of algae and aquatic weeds(6-10). This problem has been addressed by using the Phosphorus Index (PI)(11). In the United States of America, it is used as a common tool for strategic planning of the use of nutrients(12). The PI allows to identify the potential for P contribution from agricultural fields or cattle ranchers to the water bodies and distinguish the management practices that reduce the losses of P and which contribute to preserve the quality of the soil and water(13). The PI has been evaluated and calibrated for the Highlands de Jalisco(9). The PI address is characterized by two types of factors: 1) The transport factors of P which are soil erosion, the superficial runoff and the distance between the plots and a superficial drainage network or a surface water body (connectivity), and 2) the source of P, constituted by the phosphorus content in the soil, the frequency and method of application of chemical fertilizers, and the organic sources of P(11). The Phosphorus Index Transport Factors (PITF) take into account the transfer of dissolved and adsorbed P in runoff by the sediments that travel from the plot to the surface water bodies or to the superficial drainage network. The PITF implicitly include non-controllable elements of the environment, such as rain, which provides agriculture with a high uncertainty for production, but also with the mechanisms for the phosphorous loss. Each factor is classified into five levels of vulnerability according to 76


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its intensity, a rate that is subsequently multiplied by a weighting value. The PITF are the result of the multiplication of each weighted factor in order to obtain vulnerability levels of P loss with values ranging from 0 to 1. Finally, the value of the PITF is multiplied by the source of the P factor in order to obtain the PI(14,15). From the point of view of climate change, several effects on ecosystems associated with climatological and hydrological processes with extreme events related to floods, large water runoff, periods of drought or drought and forest fires with direct implications for the PITF are expected(16,17,18). However, the effects of rain events of rain that cause soil erosion and cause severe land degradation and environmental deterioration are particular important(1,19,20). The Universal Soil Loss Equation (USLE) is used to estimate the water erosion in the PI(9). The rainfall erosivity factor (R) of the USLE determines the current potential strength of the soil erosion from rain(19,21) as an effect expected in the future due to the climate change(22). For the above reasons, the changing precipitation patterns and the superficial runoff from the climate modification have caused a high degree of uncertainty for agriculture and stockbreeding in Jalisco, particularly because of the possibility of increases in the diffuse phosphorus pollution that these primary activities generate. Hence the need to evaluate the PITF under different climatologies and climate change scenarios in Jalisco. The aim of the present work was to apply the Phosphorous Index Transport Factor methodology and calculation and identify those areas that are vulnerable to the loss of phosphorus from the land to the water bodies in two climate change scenarios and three climatologies of Jalisco, Mexico.

Material and methods This study was developed for the state of Jalisco, Mexico. This has a surface area of 1'487.832 ha, of which 3.26 % are forests, 64.82% are utilized for grazing by livestock, 21.84% have an agricultural use, and 10.08% have some other use. Of the agricultural area, 292,903 ha are sown with irrigation crops, and 1'343.167 ha, with rain-fed crops(23). The larger proportion of the surface with livestock grazing is an indicator of the importance of this activity in Jalisco, which is greatly supported by agriculture, also a primary activity that devotes to corn crops 72.1 % of the planted surface area. The average annual precipitation in Jalisco in the period from 1961 to 2010 was 897 mm, with a maximum of 1,934 mm and a minimum of 461 mm. In this regard, 82.9 % of the rain is concentrated in the months from June to September, with the highest amount in July. The Phosphorus Index Transport Factor (PITF) components determined by Gburek et al.(11) were utilized. The estimated value of the PITF ranges between 0 and 1, with very low levels 77


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of vulnerability when the PITF is less than 0.15, 0.15 to 0.3; medium, when 0.3 to 0.5; high, when 0.5 to 0.8, and very high when above 0.8(24).

Estimation of the Factors of Transport of the Phosphorus Index (PITF) The conceptual model shown in Figure 1 summarizes the PITF process of evaluation for determining the levels of vulnerability to the phosphorus loss. This figure describes the process of estimating water erosion with the USLE constituted by the factors rainfall erosivity of the soil (R), soil erodibility (K), length and steepness of the slope (LS), soil cover (C), and soil management practices (P); the annual runoff is evaluated with the Curve Number, and the phosphorus contributing distance between a plot and the drainage network or surface water body. The value of vulnerability with the levels of PITF varies from 0.072 to 1. Figure 1: Conceptual model with the Phosphorus Index Transport Factors

P Transport factors These include water erosion, surface runoff, and the return period or the distance to the surface water bodies or the surface drainage network. Each of these components is described below.

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Water erosion. Water erosion was estimated with the USLE, which was designed to calculate sheet erosion and erosion in furrows of plots(25); it consists in a multifactorial mathematical model that integrates six processes involved in erosion, as indicated by the expression(26): E= RKLSCP where: E is the annual loss of soil in (t)·(ha·yr)-1, R is the soil erosivity factor from the rain in (MJ·mm)·(ha·h)-1, K is the soil erodibility factor (in t·ha-1)·(ha·h)·(MJ·mm)-1, L is the length of the slope (dimensionless), S is the factor of the degree of the slope (dimensionless), C is the factor of crop management (dimensionless), P is the factor of mechanical practices for erosion control (dimensionless). The factor R. Maps were generated with the average annual rainfall of the area of study for the 1961-2010, 2021-2040, 2041-2060 and 2061-2080 climate scenarios(27). The R factor was estimated for each climate change scenario with the equations presented by Figueroa et al(28), corresponding to regions IV, VII and X of the Mexican Republic, where the state of Jalisco is located. The following models were applied: (Region IV) Y = 2.8959X + 0.002983X 2 with R2=0.92, (Region VII) Y=-0.0334X+0.006661X2 with R2=0.98 and (Region X) Y = 6.8938X + 0.000442X 2 with R2=0.95, where Y is the value of the annual EI30 mm in MJ·mm·(ha·h)-1, and X is the average annual precipitation in mm. For Tepatitlán de Morelos, Jalisco, located in region VII, Flores(29) estimated the rainfall erosivity of the soil for 2002 and 2003 with an annual precipitation of 1,074.2 and 1264.75 mm, respectively. This author used the equation of Figueroa et al(28) for this region VII and the model of Wischmeier and Smith(26) to estimate the soil erosivity. The model of the region VII has a tendency to increase soil erodibility when the rainfall augments; according to the precipitation data available for the years 2002 and 2003, the soil erosivity was 9,400 and 10,183 (MJ·mm)·(ha·h)-1, respectively. With an average annual rainfall in the period of 1983 to 2017 of 890.2 mm, the soil erosivity estimated with the equation for region VII was 5,255 (MJ·mm)·(ha·h)-1. This value is lower than those obtained for 2002 and 2003 because in these years the rainfall was above the average of the locality. The K factor values were used as indicated by Figueroa et al(28) for each unit of the soil charts of INEGI(30) in the state of Jalisco, with the FAO soil classification. The length of the land slope (L). The following function was used for calculating the length 

m

of the slope (L): L = (22.13) , where  is the length of the slope in m; m is an exponent 79


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β

(

sen θ

)

incorporating the amendment proposed by Foster et al(31): m = (1+ β), β = 3.0 (sen0.0896 , θ)0.8 + 0.56 where  is the angle of the slope in degrees. The length of the slope for each pixel is adjusted 90

with the following relationship:  = cos θ (32). The average value of each pixel was 90 m. The slope factor (S) was calculated using the following equations: S = 10.8 senθ + 0.03, if S < 9%, S = 16.8 senθ − 0.50, if S  9, where  is the angle of the slope in degrees(32). The crop cover and crop management factor (C). The use of the soil was derived from the vector maps of series IV of INEGI. As for the land with agricultural use, it was considered to be planted with corn, and therefore the factor applied for this use was C= 0,433; grasslands were assigned a value of C= 0.16. Flores et al(33) report other values of C for land use. The mechanical practices factor (P). The values of the P-factor for erosion control in agricultural land recommended by Williams et al(34) were used. These are a ratio of the slope percentage to the maximum length of the contour ploughed furrows, and they were applied only to soils used for rain-fed agriculture. On land with other uses (livestock and forestry), the P value was equal to 1, because it was assumed that no mechanical practices are developed in them. Surface runoff. The effect of surface runoff on the transport of phosphorus is evaluated based on the Curve Number (CN). The CN was calculated using the following procedure: 1)

The parameter of moisture retention (s) was estimated using the average runoff

volume and the amount of rain, with the following expression(35): s = 5 (MAP + 2Q − √4Q2 + 5MAPQ), where Q is the mean annual runoff sheet flow in mm, MAP is the mean annual precipitation rainfall (mm), and s is a parameter of soil moisture retention (mm). The average runoff volume was estimated by means of the following expression(36): Q = c MAP, where Q is the mean annual runoff sheet flow in mm, c is the coefficient of surface runoff, and MAP is the mean annual precipitation in mm(1). The value of c was determined according to the information about the use of the soil, the slope and soil texture in the study area, with the values indicated by Flores-López et al(37). The MAP served as the basis for the calculation of R in the climatological scenarios studied in Jalisco. The use of the soil was obtained from the INEGI vector maps of series IV; the texture, from soil maps of series III by INEGI, and the slope, from the digital elevation model of INEGI. 2)

The CN was determined based on the parameter s, using the following equation(38): 25,400

CN = s + 254. The estimated CN is combined with the value of the slope in order to determine the class of runoff. 80


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Distance between a plot and the drainage network or surface water body. The distance from the site of origin to the point of connection with the drainage network or surface water body was determined using ARCGIS, with the commands flow direction and flow accumulation, applied to the digital elevation model of the INEGI for Jalisco.

Climate change scenarios These were estimated using the median of 11 general circulation models (GCMs) of monthly precipitation generated by Ruiz-Corral et al(27), belonging to the CMIP5 (Intercomparison of Coupled Models Phase 5): BCC-CSM1-1, Ccsm4, GISS-E2-R, HadGEM2-AO, HadGEM2IS, Ipsl-CM5A-LR, MIROC-ESM-CHEM, MIROC-ESM, MIROC5, MRI-CGCM3, NorESM1-M. The reduced and calibrated results for the rainfall of two representative routes of concentration of greenhouse gases (RCP) were utilized: RCP8.5 and RCP4.5, applied to three climatologies in the study area â&#x20AC;&#x201D;2030, 2050 and 2070â&#x20AC;&#x201D;, and at the rainfall baseline, the climatology for 1961 to 2010, identified in the analysis as 2010 and generated in previous study by Ruiz-Corral et al(27).

Analysis of the information The PITF was estimated based on the annual rainfall of the climatologies for 2010, 2030, 2050 and 2070, according to the methodology described in raster images with a resolution of 3" for the routes of concentration of greenhouse gases (RCP) 4.5 and 8.5 in the state of Jalisco. The same land use was considered for the future scenarios. The changes in the FTPI were obtained with the subtraction on images of the 2010-2030, 2010-2050 and 2010-2070 periods, a calculation performed with IDRISI Selva. The rate of change in the PITF was evaluated with the linear regression slope between the surface of PITF strata in the years of evaluation for RCP 4.5 and 8.5.

Results and discussion The Phosphorus Index Transport Factor (PITF) on the RCP4.5 scenario Figures 2a, b, c and d show the PITF in Jalisco with RCP 4.5 in the climatologies for 2010, 2030, 2050 and 2070, respectively. The PITF for baseline climatology ranged from 0.072 to 0.54; in the climatology for 2030 and 2050, it was 0.072 to 0.491, and in the climatology for 2070, it changed from 0 to 0.486. Table 1 shows the area occupied by the levels of vulnerability to loss of P.

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Figure 2: Phosphorus Index Transport Factor for the RCP 4.5 scenario in the climatologies for a) 2010, b) 2030, c) 2050, and d) 2070, in the lands of Jalisco

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Table 1: Surface area occupied by each stratum with the Phosphorus Index Transport Factor (FTIP) in climate change scenario RCP4.5 with three climatologies Level of vulnerability Description PITF value Very low < 0.15 Low 0.15 to.30 Medium 0.30 to.50 High 0.50 to.80

PITF per year of climatology (thousand ha) 2010 2030 2050 2070 4,682.6 4,676.9 4,675.2 4,674.1 889.3 906.5 913.7 919.8 2,188.3 2,177.0 2,171.4 2,166.3 0.109 0 0 0

At the baseline, the level of vulnerability to the phosphorus loss in the land is rated very low to high risk, while in the climatologies for 2030, 2050 and 2070, the level of risk is rated very low to medium, and the high level disappears. The very low level of vulnerability due to the PITF (< 0.15) occupies the largest area, followed by the average (0.30 to 0.50) and low (0.15 to 0.30) levels. The tendency of each layer of the PITF of the RCP4.5 scenario in the climatologies studied with the occupied surface area is depicted with the slopes of linear regression models shown in Table 5. These slopes show that surfaces at the very low and medium vulnerability levels have a greater tendency to decrease per year, while at the low level, the tendency is to increase. Given that the risk of transport of phosphorus is associated with the mobility generated by water, producing particle detachment due to the splashing of rain water and its contained kinetic energy, the flow of subsurface and surface water(39). This process is identified in the medium and high risk values of the RCP 4.5 scenario and the studied climatologies, associated mainly to plots with close proximity to the drainage networks or bodies of water. This result is consistent with other studies at a watershed scale(40,41,42). For this reason, the value of the vulnerability due to the current PITF with regard to the RCP 4.5 scenario in the assessed climatologies does not reflect major changes, as the precipitations estimated for future climatologies in Jalisco are not expected to increase significantly, and in some areas they are even expected to diminish, causing a decrease in the risk of PITF at very low and medium levels, adding this surface to the very low level of risk that tends to increase. This trend is similar to that estimated in the PITF for Lake Poyang in China(43) in the climate change scenarios RCP2.6, 4.5, and 8.5, even when including changes in the intensity of extreme events and their frequency. Table 2 shows the comparison between the PITF baseline and that estimated for the climatologies for 2030, 2050 and 2070 in the RCP4.5 climate scenario. The PITF negative changes indicate a higher value of the index in the future scenario; on the other hand, when the change is positive, the index decreases, and the surface area of the future climatology diminishes. Within this context, the positive change in the PITF implies a reduction in the 83


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risk due to diffuse phosphorus pollution. The 2010-2030 period exhibits the largest surface with a negative change in the PITF; however, in the 2010-2050 and 2010-2070 periods, the situation is reversed, with higher PITF in 2010 than in 2050 and 2070. This implies a greater risk due to diffuse phosphorus pollution in the 2010-2030 period and a lower risk in the climatologies for 2010-2050 and 2010-2070 in Jalisco. Table 2: Surface area with expected positive and negative changes in the Phosphorus Index Transport Factor (PITF) in the climatologies for 2030, 2050 and 2070, in relation to 2010, under the RCP4.5 scenario Level of change in the PITF Negative change (< 0) Positive change (â&#x2030;Ľ 0)

Surface area (thousand ha) 2010 to 2030 2010 to 2050 2010 to 2070 7,492.4 11.5 18.5 267.9 7,748.7 7,741.8

The precipitation of the climatologies for 2030 to 2070 in the RCP4.5 climate change scenario expressed no significant increases in the annual rainfall utilized by the PI model. The most important change is expected in rainfall patterns with events of greater intensity(16), but the PITF model uses only the annual rainfall in the climatology of the baseline and future climatologies. With extreme events in the future precipitations, the effects will possibly be reflected in a greater hydric erosion and a larger amount of surface runoff; however, the current knowledge does not allow to identify these characteristics in climate prediction models(16,18).

Transportation Factor of the Phosphorus Index (PITF) in the RCP8.5 scenario Figures 3 a, b, c and d show the distribution of the PITF for the lands of Jalisco in the climatologies for 2030, 2050 and 2070 in the RCP 8.5 scenarios. Based on these maps, strata were identified by level of vulnerability of the PITF shown in Table 3. The PITF for the baseline climatology and the climatologies for 2030, 2050 and 2070 ranged between 0.03 and 0.54, with generation of the strata with the PITF shown in Table 3. The strata with the greatest surface were very low (PITF < 0.15) and medium (PITF 0.30 - 0.50), with a tendency to reduce the transport factor of the future climatologies, while at the low and high levels, despite having a low surface area, they tended to increase it in the future climatologies.

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Figure 3: Phosphorus Index Transport Factor for the RCP 8.5 scenario in the climatologies for a) 2010, b) 2030, c) 2050 and d) 2070

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Table 3: Surface area by strata of Transport Factor of the Phosphorus Index (PITF) in the climatology of reference and three future climatologies in the RCP8.5 scenario Level of vulnerability Description PITF value Very low < 0.15 Low 0.15 to.30 Medium 0.30 to.50 High 0.50 to.80

PITF per year of climatology (thousand ha) 2010 2030 2050 2070 4,682.6 4,675.1 4,673.9 4,672.0 889.3 919.6 931.3 944.2 2,188.3 2,165.3 2,154.5 2,143.3 0.109 0.269 0.486 0.825

The comparison between the values of the PITF in the climatologies for 2010-2030, 20102050 and 2010-2070 is shown in Table 4, which summarizes the change in the surface areas associated with the various levels of the PITF in this scenario and in the studied climatologies. This comparison led to changes in the surface of PITF from less than zero to over 0.10, all of them considered to be very low vulnerability levels. In the level with a PITF below 0, the surface area was larger in 2030 with respect to 2010 by more than 54 thousand ha; however, in the 2010-2050 and 2010-2070 periods this level disappears. Table 4: Estimated surface area due to the level change in the Phosphorus Index Transport Factor (PITF) in Jalisco, from 2010 to 2030, 2050 and 2070 under the RCP8.5 scenario

Level of change in PITF <0 0 - 0.05 0.05 - 0.10 > 0.10

Change of climatology in the RCP 8.5 scenario (Thousand ha) 2010 to 2030 2010 to 2050 2010 to 2070 54.6 0 0 7,703.5 5,242.2 5,257.6 0.283 448.8 440.7 1.8 2,069.3 2,062.0

The PITF level of 0 to 0.05 exhibited the largest surface area in the 2010-2030 period, with a significant reduction on the surface for the 2010-2050 and 2010-2070 periods. In the levels of 0.05-0.10 and above 0.10 of the PITF, the surface area increases, particularly in the PITF level above 0.10 in the 2010-2050 and 2010-2070 periods. These changes are attributed to the expected increase in the rainfall, which leads to a greater phosphorus loss in agricultural lands, similarly to those reported for the RCP 8.5 of Lake Poyang in China(43). The exchange rates observed in the surfaces of each level of vulnerability of the PITF and study climatologies of study are shown in Table 5. Although the response observed in the PITF is very low, it is the product of the minimum changes in rainfall of the climatologies for 2030, 2050 and 2070 in the RCP 8.5 scenario; it is also a reflection of the small increase 86


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in annual rainfall used by the PI model. For this reason, it is possible that the PITF is being underestimated, as a change in rainfall patterns is expected with events of greater intensity(16,44) that the PITF model does not consider in its components of soil erosion and runoff. In this regard, on September 7, 2003, Flores(29) reported a rainfall event of 150.05 mm in 24 h with its maximum intensity in 30 min of 68.5 mm/h in Tepatitlán, Jalisco. A possible solution is to calculate the water erosion and surface runoff at a monthly or even daily scale, as indicated in the PITF(11), for use with future climate information(27). Table 5: Linear regression models between the surface areas occupied by each level of vulnerability of the PITF with the year of the climatology Level of vulnerability PITF Description value Very Low < 0.15 Low 0.15 to.30 Medium 0.30 to.50 High 0.50 to.80

RCP 4.5 scenario Model y = -0.136x + 4953.8 y = 0.495x - 101.6 y = -0.357x + 2904.8

RCP 8.5 scenario R² 0.86 0.94 0.96

Model y = -0.165x + 5012.0 y = 0.882x - 877.7 y = -0.729x + 3649.8 y = 0.012x - 23.7

R² 0.84 0.94 0.96 0.97

The models to estimate soil erosivity due to rainfall with a monthly scale are achieving good results in recent studies(45,46) and are generating new mathematical functions for monthly and daily time scales(47), which it is important for Mexico to develop, given the predicted expectations of climate change(16), and regarding which there is little progress to date. Although the current models for the calculation of the rainfall soil erosivity(28) show the tendency to increase the aggressiveness of the rains with the increase in annual precipitation, it is advisable to evaluate this index under broader conditions than those referred to in the present study. In addition, however, there is an urgent need to obtain future rainfall estimates at a daily scale, because these events may be underestimated when using a monthly or annual scale(48,49). Although the surfaces with the PITF were similar in the RCP4.5 and RCP8.5 scenarios in the studied climatologies, the risk of diffuse phosphorus pollution persists with a high level of risk in areas near surface water bodies and drainage networks, which should be addressed, in both the current and future scenarios, by designing good agricultural practices to restrain the diffuse phosphorus pollution in these areas.

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Conclusions and implications The results of this study demonstrate the feasibility of applying the PITF to the conditions of Jalisco with the baseline climatology for 2010 and climate change scenarios with proposed future climatologies. With results obtained, it was possible to identify tendencies in the route of the concentration of greenhouse gases under the RCP 4.5 and RCP 8.5 scenarios in Jalisco. For the RCP4.5 scenario, the negative change in the PITF implied an increase in the P index, which entails a higher risk due to diffuse phosphorus pollution; however, a positive change brings about a reduction of the risk of diffuse phosphorus pollution. In contrast to the RCP8.5 the largest surface area was identified with a very low and medium vulnerability, with a tendency to reduce the PITF, whereas in the strata with low and high levels of vulnerability, the tendency was to increase it. In general, the PITF in scenarios RCP 4.5 and RCP 8.5 of the assessed climatologies do not reflect major changes in the value of vulnerability due to PITF, as no significant increases are expected in the amounts of rainfall estimated for Jalisco in the future climatologies. Because the PITF model is calculated based on the annual precipitation, this time scale does not consider rainfall patterns with high intensity events or the heavier precipitations expected in the climate change scenarios; therefore, it is advisable to develop functions to estimate the rainfall soil erosivity and the runoff at a monthly or even daily scale when calculating the PITF, the surface runoff and the hydric erosion. In the studied RCP scenarios and climatologies, areas with proximity to water bodies and surface drainage network represent a greater vulnerability to the PITF.

Literature cited: 1.

Zhou B, Vogt RD, Xu C, Lu X, Xu H, Bishnu JP, Zhu L. Establishment and validation of an amended phosphorus index: Refined phosphorus loss assessment of an agriculture watershed in Northern China. Water Air Soil Pollut 2014;(225):2103.

2.

Sharpley AN, Daniel TC, Edwards DR. Phosphorus movement in the landscape. J Produ Agric 1993;6(4):492-500.

3.

Flores LHE, Ireta MJ, Pérez DJF, Ruíz CRC, Díaz MP. Identificación de buenas prácticas agrícolas para reducir la degradación del suelo e incrementar la calidad del agua. Jalisco, México. INIFAP. 2009.

4.

FAO. Food and Agriculture Organization. Reporte de la iniciativa de la ganadería, el medio ambiente y el desarrollo – Integración por zonas de la ganadería y de la agricultura especializadas (AWI) - Opciones para el manejo de efluentes de granjas porcícolas de la zona centro de México. 2003. http://www.fao.org/wairdocs/LEAD/X6372S/x6372s00.htm Consultado 28 Jun, 2007.

88


Rev Mex Cienc Pecu 2020;11(Supl 2):75-92

5.

SIAP. Servicio de Información Agroalimentaria y Pesquera. Estadísticas de producción. 2016. Tomado de: http://www.gob.mx/siap/ Consultado 12 Oct, 2016.

6.

De La Mora OC, Flores LHE, García VJ, Chávez DAA, Ruíz CJA. Caracterización taxonómica del plancton en la presa El Jihuite en Tepatitlán de Morelos, Jalisco. Jalisco, México: INIFAP. 2011.

7.

Flores LHE, Hernández JAL, Figueroa VU, Castañeda VAA. Calidad Microbiológica del agua por contaminación difusa de la aplicación de estiércoles en maíz y pasto. Tecnologías y Ciencias del Agua. TyCA-RETAC 2012; (III):127-141.

8.

Román MMR. Confort térmico y características del sistema de producción de bovinos de leche en la cuenca hidrográfica el Jihuite de los Altos de Jalisco [tesis licenciatura]. Tepatitlán de Morelos, Jalisco: Universidad de Guadalajara; 2009.

9.

Flores LHE, Figueroa VU, De La Mora OC, Núñez GG, Valdivia GL. Evaluación y calibración del índice de fósforo en los Altos de Jalisco, México. Rev Mex Cienc Agríc 2014;5(3):367-378.

10. Flores LHE, Paredes MR, Ruvalcaba GJM, De La Mora OC, Pérez DJF, Ireta MJ. Metodología para la evaluación del valor agregado del programa de maíz de alto rendimiento (PROEMAR) 2010 en Jalisco y Guanajuato. Jalisco, México. INIFAP. 2011. 11. Gburek WJ, Sharpley AN, Heathwaite L, Folmar GJ. Phosphorus management at the watershed scale: a modification of the phosphorus index. J Environ Quality 2000; (29):130-144. 12. Sharpley AN, Weld JL, Beegle DB, Kleinman PJA, Gburek WJ, Moore Jr PA, Mullins G. Development of phosphorus indices for nutrient management planning strategies in the United States. J Soil Water Conserv 2003;(58):137-152. 13. Dechmi F, Isidoro D, Stambouli T. A phosphorus index for use in intensive irrigated areas. Soil Use Manage 2013;29 (Suppl 1):64–75. 14. Marjerison RD, Dahlke H, Easton ZM, Seifert S, Walter MT. A Phosphorus Index transport factor based on variable source area hydrology for New York State. J Soil Water Conserv 2011;(66):149-157. 15. Mallarino AP, Stewart BM, Baker JL, Downing JD, Sawyer JE. Phosphorus indexing for cropland: Overview and basic concepts of the Iowa phosphorus index. J Soil Water Conserv 2002;57(6):440-447. 16. IPCC. Intergovernmental Panel on Climate Change. Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of 89


Rev Mex Cienc Pecu 2020;11(Supl 2):75-92

the Intergovernmental Panel on Climate Change [Core Writing Team. Pachauri RK and Meyer LA, editors]. IPCC, Geneva, Switzerland. 2014. 17. Burlando P, Rosso R. Extreme storm rainfall and climatic change. Atmos Res 1991; (27):169-189. 18. Zhang GH, Nearing MA, Liu BY. Potential effects of climate change on rainfall erosivity in the yellow river basin of china. Trans ASAE 2005;(48):511−517. 19. Diodato N, Bellocchi G, Romano N, Chirico GB. How the aggressiveness of rainfalls in the Mediterranean lands is enhanced by climate change. Climatic Change 2011;(108):591–599. 20. Guhathakurta P, Sreejith OP, Menon PA. Impact of climate change on extreme rainfall events and flood risk in India. J Earth Syst Sci 2011;120(3):359–373. 21. Heckrath G, Bechmann M, Ekholm P, Ule`n B, Djodjic F, Andersen HE. Review of indexing tools for identifying high risk area of phosphorus loss in Nordic catchments. J Hydrol 2008;(349):68–87. 22. NASEM. National Academies of Sciences, Engineering, and Medicine. Attribution of Extreme Weather Events in the Context of Climate Change. Washington, DC: The National Academies Press; 2016. 23. SIAP. Servicio de Información Agroalimentaria y Pesquera. SIACON 2017. https://www.gob.mx/siap/acciones-y-programas/produccion-agricola-33119 Consultado 15 Dic, 2017. 24. Troitiño F, Trasar-Cepeda C, Leirós MC, Gil-Sotres F. Validation and modification of the phosphorus loss index as applied to a small catchment. Soil Use Manage 2013;29(Suppl 1):114–123. 25. Wischmeier WH. Use and misuse of the universal soil loss equation. J Soil Water Conserv 1976;31(1):5-9. 26. Wischmeier WH, Smith DD. Predicting rainfall erosion losses-a guide to conservation planning. Agriculture Handbook 537. USDA, Washington, DC; 1978. 27. Ruiz-Corral JR, Medina-García G, Rodríguez-Moreno VM, Sánchez-González JJ, Villavicencio-García R, Durán Puga N, et al. Regionalización del cambio climático en México. Rev Mex Cienci Agríc 2016; Pub Esp (13):2451-2464. 28. Figueroa SB, Amante OA, Cortés THG, Pimentel LJ, Osuna CES, Rodríguez OJM, Morales FFJ. Manual de predicción de pérdidas de suelo por erosión. San Luis Potosí, México: SARH-Colegio de Posgraduados; 1991. 90


Rev Mex Cienc Pecu 2020;11(Supl 2):75-92

29. Flores LHE. Rutas de transporte superficial de nitrógeno y fósforo en un área de drenaje de Jalisco, México [tesis doctorado]. Montecillo, Texcoco, Estado DE México: Colegio de Posgraduados; 2004. 30. Instituto Nacional de Estadística, Geografía e Informática (INEGI). Cartas Edafológicas, escala 1:1’000,000. México, DF; 1993. 31. Foster GR, Meyer LD, Onstad CA. A runoff erosivity factor and variable slope length exponents for soil loss estimates. Trans ASAE 1977;(20):683–687. 32. McCool DK, Brown LC, Foster GR, Mutchler CK, Meyer CK. Revised slope steepness factor for the Universal Soil Loss Equation. Trans ASAE 1987;10(5):1387–1396. 33. Flores LHE, Pérez DJF, Ireta MJ. Estimación de la erosión hídrica en agave tequilero en Jalisco. Jalisco, México. INIFAP; 2010. 34. Williams JR, Dyke PT, Fuchs WW, Benson VW, Rice OW, Taylor ED. EPICErosion/Productivity Impact Calculator: 2 User manual. Technical Bulletin 1768. USDA-ARS, Temple, Texas. 1990. 35. Jasso IR, Sánchez CI, Stone JJ, Melgoza CA, Simanton JR, Martínez RJG. Estimación de parámetros para la modelación del escurrimiento superficial y erosión hídrica. En: Sánchez CI, et al, editores. Uso de la lluvia artificial para parametrizar modelos de procesos hidrológicos. Gómez Palacio, Durango. INIFAP. 1999. 36. SARH-CP. Manual de Conservación del Suelo y del Agua. 2da ed. Texcoco, México: Colegio de Postgraduados; 1982. 37. Flores LHE, Martínez MM, Oropeza MJL, Mejía SE, Carrillo GR. Integración de la EUPS a un SIG para estimar la erosión hídrica del suelo en una cuenca hidrográfica de Tepatitlán, Jalisco, México. Terra 2003;(21):233-244. 38. NRCS. Natural Resources Conservation Service. Phosphorus Assessment Tool for Texas. USDA-NRCS. Texas, USA; 2012. 39. Schoumans OF, Chardon W. Risk assessment methodologies for predicting phosphorus losses. J Plant Nutr Soil Sc 2003;(166):403-408. 40. Reichmann O, Chen Y, Iggy LM. Spatial model assessment of P transport from soils to waterways in an Eastern Mediterranean watershed. Water 2013;(5):262-279. 41. Zhou H, Gao C. Assessing the risk of phosphorus loss and identifying critical source areas in the Chaohu Lake Watershed, China. Environ Manage 2011;(48):1033–1043.

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42. Ortega-Achury SL, Martinez-Rodriguez GA, Sotomayor-Ramirez DR, Ramirez-Avila JJ. Caribbean phosphorus index validation and management practices evaluation on fields under manure applications. An ASABE Meeting Presentation Paper. Reno, Nevada. 2013. 43. Jiang S, Zhang Q. Modelling phosphorus transport and its response to climate change at upper stream of Poyang Lake-the largest fresh water lake in China. Geophys Res Abst 2017;(19):EGU2017-2365-1. 44. Meelh GA, Arblaster JM, Tebaldi C. Understanding future patterns of increased precipitation intensity in climate model simulations. Geophys Res Lett 2005;(32):L18719. 45. Lee MH, Lin HH. Evaluation of annual rainfall erosivity index based on daily, monthly, and annual precipitation data of rainfall station network in southern Taiwan. INT J DISTRIB SENS N 2015; 11(6):1-15 http://journals.sagepub.com/doi/full/10.1155/2015/214708 Accessed 14 Dec, 2017. 46. Sadeghi SH, Tavangar S. Development of stational models for estimation of rainfall erosivity factor in different timescales. Nat Hazards 2015;77(1):429â&#x20AC;&#x201C;443 https://link.springer.com/article/10.1007/s11069-015-1608-y Accessed 14 Dec, 2017. 47. Bonilla CA, Vidal KL. 2011. Rainfall erosivity in Central Chile. J Hydrol 2011;(410):126â&#x20AC;&#x201C;133. 48. Sun Y, Solomon S, Dai A, Portmann RW. How often does it rain? J climate 2006; (19):916-934. 49. Sun Y, Solomon S, Dai A, Portmann RW. How often will it rain? J climate 2007; (20):4801-4818.

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https://doi.org/10.22319/rmcp.v11s2.4705 Article

Impact of climate change on the potential distribution of Tithonia diversifolia (Hemsl.) A. Gray in Mexico

Noé Durán Puga a José Lenin Loya Olguín b José Ariel Ruiz Corral a* Diego Raymundo González Eguiarte a Juan Diego García Paredes b Sergio Martínez González b Marcos Rafael Crespo González a

a

Universidad de Guadalajara. Centro Universitario de Ciencias Biológicas y Agropecuarias. Camino Ramón Padilla Sánchez No. 2100 Nextipac, 44600, Zapopan, Jalisco, México. b

Universidad Autónoma de Nayarit. Unidad Académica de Agricultura. Carretera TepicCompostela, km 9. Xalisco, Nayarit, México.

*Corresponding author: ariel.ruiz@academicos.udg.mx

Abstract: The aim of this study was to estimate the possible impact of future climate changes on the potential distribution of T. diversifolia in Mexico. Distribution niches were modelled with MaxEnt for the 1951-2000, 2041-2060 and 2061-2080 climatologies, considering 20 bioclimatic and two topographical variables. For future climates, a HadGEM2-ES general circulation model (GCM) was considered, with two representative concentration pathways of greenhouse gases (RCP4.5 and RCP8.5). This information was obtained from the Global Climate Data Website WorldClim and processed with the Idrisi Selva system as raster images with 2.5 arc min resolution. The environmental variables that contributed the most to explain

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the geographical distribution of T. diversifolia were the May-October mean accumulated precipitation (pa5-10) and the mean maximum temperature of the warmest month (MMAX). The 10th percentile training presence logistic threshold reported a predicted suitable area (for the reference climatology) accounting for 30.71 % of the extent of Mexico. Niche modeling under climate change scenarios reported expansion as well as retraction areas for environmental suitability; however, after computing them, suitable areas are expected to present a small increment with respect to the climate reference period, 1950-2000: 31.62 %, 31.83 %, 32.45 % and 32.45 % of Mexican territory in scenarios for 2041-2060 in RCP 8.5, 2041-2060 in RCP 4.5, 2061-2080 in RCP 4.5 and 2061-2080 in RCP 8.5, respectively. Thus, climate change would bring more benefits than constrains for T. diversifolia dispersion. Key words: Climate change, Tithonia diversifolia, Ecological descriptors, Niche distribution.

Received: 20/11/2017 Accepted: 04/07/2018

Introduction Food production is becoming a real challenge in a climate that is changing. Under this context, the diversification of animal feed is a key aspect to better adapt to climate change. Hence, the need to assess the impact of climate change on the presence and potential distribution of forage species. This is the specific case of T. diversifolia, a native species with current distribution in the lowlands of southeastern Mexico, Central and South America, and which is considered an important genetic resource and an exceptional plant as an alternative in animal feed(1,2 ). The future risk for many plant species due to climate change has not been well established yet. The global temperature has increased 0.85 ° C in the period from 1901 to 2011(3). Based on climate prediction models, it has been established that the saturation pressure of the vapor has been very sensitive to temperature changes; therefore, ruptures in the global water cycle are expected to manifest in the future(4). On the other hand, the availability of water will reportedly exhibit a marked annual reduction in the southeastern United States, the Caribbean and various parts of Mexico(5). In addition, agriculture expands and is intensified mainly in the tropics(6); it is estimated that approximately 80% of the new farmland will be based on forest substitution(7). Therefore, delaying or restraining the expansion of agriculture in the tropics will reduce carbon emissions and loss of biodiversity(8). 94


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Climate change can modify the diversity of climates, as well as the composition of ecosystems(9); these modifications would include alterations in the distribution, phenology and an increased risk of endangered species(10). Several components of climate change are considered to affect all levels of biodiversity, from organisms and populations to biotic areas(11). At basic levels, climate change can reduce genetic diversity in populations due to directional selection and dynamic migration, which may interfere with the resilience and functionality of ecosystems(12); therefore, it may cause the modification of the network of interactions at the community level(13). In addition, an effect of climate change may induce the invasion of potentially dangerous species to an ecosystem(14); affecting the physiology, phenology and behavior of species(15). In the face of the above, Mexico is interested in building an inventory related to the expected effects of climate change on desirable wild species. Thus, the aim of this study was to estimate the impact of climate change on the potential distribution of T. diversifolia. As reported by several authors, the use of Tithonia include forage production, soil erosion control, building material and bird shelter(16). As a counterpart, T. diversifolia is considered an allelopathic herb with water-soluble allelochemicals in parts of the plant and with such phytotoxic potency, that it could suppress the growth and accumulation of nutrients in associated crops(17).

Material and methods Database Data from 52 population sites of T. diversifolia were considered, with current distribution in the lowlands of southeastern Mexico(1,2). The databases were obtained from two sources: the National Forest and Soil Inventory (INFyS) of the National Forestry Commission of Mexico (CONAFOR) and the website http://www.tropicos.org/.

Potential distribution areas In this study, the MaxEnt model (maximum entropy)(18) was utilized to model the ecological niche and predict the most likely geographic distribution of T. diversifolia. MaxEnt has been widely used to estimate potential plant and animal distributions with high precision; certain terrestrial groups are excellent examples(19).

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In the MaxEnt model, the distribution of a species is represented by a probability function P on a set of sites X in the study area. A P model is constructed using a set of restrictions derived from empirical data on the presence of the species(20). Restrictions are expressed as simple functions of known environmental variables. The MaxEnt algorithm forces the average of each function of each variable to approach the actual average in those areas where the species is present. Of all the possible combinations of functions, the one that minimizes the entropy function, measured with the Shannon index, is selected. The general expression of the probability function for i environmental variables is(21): đ?&#x2018;&#x192;(đ?&#x2018;Ľ) = đ?&#x2018;&#x2019; đ?&#x153;&#x2020;â&#x2C6;&#x2122;đ?&#x2018;&#x201C;(đ?&#x2018;Ľ) /đ?&#x2018;?đ?&#x153;&#x2020; Where: P(x)= probability function; Îť= weighting coefficient vector; f= corresponding vector of environmental variable functions; The P (x) values thus obtained represent values of relative suitability for the presence of the species, thus constituting the basis for a potential distribution model. A high value of the distribution function in each pixel indicates that it has favorable conditions for the species(22). In this study, a model was generated using layers of environmental parameters and data of the occurrence of the species. 75 % of the occurrence records were used as training points, and 25 %, as validation points; in addition the AUC (area under the curve) index was used to evaluate the statistical model, since this index is one of the most commonly utilized to measure the quality of the models(20). The settings that can be determined in the MaxEnt model affect its accuracy, since the model can work with simple or complex environmental variables.

Databases and environmental parameters The data of monthly, seasonal and annual precipitation, maximum temperature, minimum temperature, average temperature and thermal oscillation of the 1950-2000 (reference climatology) and 2041-2060 and 2061-2080 (future climatology) periods were utilized for an environmental characterization of the sites where the species occurs. These climatic data were obtained from the WorldClim Global Climate Data website, and the images were processed in ASCII and raster formats, with a resolution of 2.5 min arc. For the 2041-2060 and 2061-2080 periods, the HadGEM2-ES GCM was considered with two representative concentration pathways of greenhouse gases: RCP 4.5 and RCP 8.5. This model has been used in the new climate change scenarios for Mexico; study presented in the Fifth Climate Change Assessment Report by the IPCC (Intergovernmental Panel on Climate Change) (23). 96


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Other variables that were included in the modeling of the distribution of T. diversifolia, were the altitude and the slope of the land, images that were obtained from the National Environmental Information System (SIAN) of the National Institute for Research on Forestry, Agriculture and Livestock (INIFAP)(24). The list of variables used for the space niche model were: slope of the land (%), altitude (m), mean annual maximum temperature (MAMT), May-October mean maximum temperature (MMT5-10), November -April mean maximum temperature (MAX11-4), maximum temperature of the warmest month (MMAX), mean annual temperature (MAT), mean temperature from May to October (MT5-10), mean temperature November-April (MT11-4), minimum temperature of the coldest month (MTCM), average temperature of the coldest month (ATCM), mean minimum annual temperature (MATmin), minimum average temperature from May to October (Tmin5-10), minimum average temperature November-April (Tmin11-4) , accumulated annual precipitation (Pann), accumulated precipitation May to October (P5-10) accumulated precipitation November-April (P11-4), accumulated precipitation of the driest month (Pmin), precipitation of the wettest month (Pmax), mean annual thermal oscillation (MATO), thermal oscillation from May to October (TO5-10), and thermal oscillation from November to April (TO11-4)

Areas with probability of environmental suitability The model developed from the MaxEnt prediction for the potential distribution of T. diversifolia was examined with the Idrisi Selva system(25), and a map was made with the threshold values of the pixels corresponding to the 10th percentile(26). For the calculation of the potential distribution area of the species, the areas occupied by water bodies and urban centers were not considered. These thematic layers were obtained by manipulating the use of the soil and the vegetation layer(27).

Ecological descriptors The ecological descriptors for T. diversifolia were determined on the basis of environmental intervals derived from the characterization of the parameters of sites of species presence. This was done using the IDRISI system and raster images of each environmental variable and the geographic coordinates of each site where the species is present.

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Results and discussion In the model of the potential distribution niche of T. diversifolia, the operational curve showed satisfactory results (Figure1). When considering the training data (75 %), the area under the curve (AUC) of T. diversifolia was 0.979; while when the test data is used (25 %), the validation of the model reported an AUC of 0.966, which indicates that the ability of the model to represent the presence of species was satisfactory(28). Figure 1. Operational curve for T. diversifolia

Figure 2 shows the current geographical distribution of T. diversifolia. Its presence is mainly in the Central-South region, the Gulf of Mexico, the Pacific coastal areas and the Yucatan Peninsula, which is the tropical part of the country (<23 ° 26 '14' 'North). Most of the populations of T. diversifolia are concentrated in the southeastern part of Mexico, which corresponds to the asseveration about their geographical origin (Mexico and Central and South America)(2), as well as in tropical and subtropical areas around the world, which has allowed it to grow under a wide range of environmental conditions and, therefore, develop a wide range of adaptation(29).

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Figure 2. Current distribution of T. diversifolia, and suitable, unsuitable, retraction and expansion areas for this species under four climate change scenarios in Mexico

The ecological descriptors of T. diversifolia and the environmental variables that contribute to the construction of the model can be observed in Table 1. According to these results, the distribution of T. diversifolia is mainly determined by variable P5-10 (78.2 %), i.e., the amount of precipitation during the May-October period, which is a key element for the occurrence of this species, as well as for other species, where precipitation in small but frequent events during the summer is key for its good development(30). The descriptor of T. diversifolia for this variable indicates a range of 297 to 3,404 mm (for the annual rainfall of 356 to 3,828 mm), which allows for a wide variation of the spatial precipitation. These results are consistent in the sense that this species grows properly under a wide range of precipitation. However, annual rainfall limits of 600 and 5,000 mm(30) have been established. Therefore, it can be concluded that T. diversifolia can grow even with less seasonal and annual rainfall than that cited by the current literature. The second most contributing ecological descriptor was MMAX (mean maximum temperature of the warmest month), and T. diversifolia also exhibited a broad thermal range, of 21.9 to 34.4 ° C. Given that T. diversifolia also has a wide range for the MTmin descriptor (mean minimum temperature of the coldest month, 1.8 to 19.8 ° C, Table 1), it can be concluded that this species is capable of exploring and colonizing thermally extreme environments. Other authors(2) had previously indicated that it adapts to different climates and altitudes. 99


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Table 1. Ecological descriptors of environmental variables that condition the geographic distribution of T. diversifolia to the largest extent Variable P5-10, mm MMAX, °C Slope, % Tmin, °C Pmax, mm

Minimum value

Maximum value

297 21.9 0 11 67

3404 34.4 14 26.15 713

Contribution (%) 78.2 5.9 3 2.8 2.1

The results of potential niches for T. diversifolia under the reference climatology (1951-2000) as well as future climatologies, are also shown in Figure 2. The maps in this figure show the presence of adequate, inadequate, retraction areas and expansion for Tithonia diversifies compared to the scenarios of the reference climatology and future climatologies. In all future scenarios, both retraction areas and expansion areas will appear, showing the potential dynamics of the areas with environmental suitability according to the future climatic changes. The training presence logistic threshold of the 10th percentile estimates an area with environmental suitability (for the reference climatology) that represents 30.71 % of the territorial extension of Mexico. After a balance between the areas of contraction and expansion of climate change climatologies, the optimal areas will increase to 31.62 %, 31.83 %, 32.45 % and 32.45 % of the Mexican territory, in the 2041-2060 RCP8.5, 2041 2060 RCP 4.5 and 2061-2080 RCP 4.5, 2061-2080 RCP 8.5 scenarios, respectively. These results show that future climate changes will be apparently beneficial to T. diversifolia. Considering that the most significant variables in the models of the distribution niches are the accumulated precipitation from May to October and the average maximum temperature of the warmest month, it may be inferred that the combinations of these parameters under climate change scenarios will have a positive effect on areas with environmental suitability for T. diversifolia. The effect of the impact of climate change on the distribution of species may result in ‘winner’ or ‘loser’ species(31). The final result will depend on their evolutionary adaptation. For example, expansions of suitable areas have been reported due to climate change for other species like Leucospermum hypophyllocarpodendron subsp. hypophyllocarpodendron(11). Most studies on the effects of climate change on species distribution have reported negative impacts, with the retraction areas being greater than the expansion areas(32). In the present study the opposite was obtained, i.e., larger areas of expansion than the retraction areas. As can be seen in Figure 2, these two types of areas have a geographical pattern; contraction areas are mainly located in the western region of the country and near the Pacific coast, while expansion areas are located mainly on the eastern side and near the coast of the Gulf of Mexico. This fact points to two regions of the country where future weather patterns differ 100


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in environmental conditions and, therefore, in their level of suitability for T. diversifolia. This particular situation has already become manifest, since the region corresponding to the retraction areas has been previously reported with changes in crop patterns as a result of regional climate changes(33,34). According to the maps exemplified in Figure 2, no current population of T. diversifolia appears to be affected by the retraction of environmental fitness. However, in the event that some populations of T. diversifolia are located in retraction areas in the coming years, it should be considered that these populations will have only two options to survive â&#x20AC;&#x201D;a) migrate to more favorable environments, or b) adapt to the new climate conditions(35)â&#x20AC;&#x201D;, which will depend mainly on the genetic diversity of the species(36,37). In the case of the present study, it can be hypothesized that environmental suitability will gain a little more surface area due to climate change, and apparently this may promote the future dispersion of T. diversifolia to new areas. However, it is important to consider the potential capabilities of this species to compete against others in the ecosystem or against invasive species that constitute a threat to the stability of the ecosystem(38,39,40). In addition, it must be considered that, in the future, T. diversifolia will also depend on its ability to adapt to climate change(41), which is a function of its ability to colonize new areas, or (when necessary) its ability to implement physiological modifications in order to adapt to the new environment(42). The ecological plasticity of T. diversifolia(1,30) is a key aspect for adapting to climate change and new climates(43). According to the results obtained, the expected climate change for both periods will have a more positive than negative effect on the environmental suitability of T. diversifolia (Figure 3), thus allowing its potential territorial expansion in the future. However, it is clear that this environmental advantage to be caused by future climate change will be favorable as well for other species that may compete with T. diversifolia. Under this type of scenario, the wide range for most of the ecological descriptors of T. diversifolia (Table 1) could be an advantageous feature, all the more if one considers the genetic diversity of this species in other parts of the world, which may increase the adaptation and colonization capacities of T. diversifolia(36,44).

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Figure 3. Dynamics of the surface area with environmental suitability for T. diversifolia in the current climate and four future climatologies

Although the current and future climate scenarios appear to be favorable for T. diversifolia, measures should be taken to preserve the current populations of the species in order to ensure its presence in its natural environment. On the other hand, since it has been found that T. diversifolia is an allelopathic weed with water-soluble allelochemicals in various parts of the plant(17), attention should be paid to supervise its possible territorial expansion due to possible effects on associated crops. Also, the fight against the presence of T. diversifolia as a weed in crops should also be monitored, as it may threaten the natural populations of this species.

Conclusions and implications At present, the areas with environmental suitability for T. diversifolia represent about one third of the Mexican territory, adapting to a broad range of temperature and humidity conditions. The presence and spatial distribution of this species is determined by the amount of accumulated rainfall during the May-October period, ranging from 297 to 3,404 mm. Future climate changes will cause both retraction and expansion of areas with environmental suitability in different parts of the country; an analysis between these two effects leads to the conclusion that in the present century these areas will increase slightly, between 0.91 and 1.74 %, according to the models utilized. Therefore, it may be considered that this species 102


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can be a good alternative for forage production for the future. On the other hand, given that T. diversifolia has been found to be an allelopathic herb, attention should be paid to the supervision of its possible territorial expansion, due to the possible effects on associated crops. The fight against the presence of T. diversifolia in crop fields should also be monitored in order to prevent it from constituting a threat to the natural populations of this species.

Acknowledgements To the National Forest and Soil Inventory (INFYS) of the National Forest Commission (CONAFOR), for providing valuable information on the presence sites of T. diversifolia in Mexico. Also, the authors are also grateful to the National Institute for Research on Forestry, Agriculture and Livestock (INIFAP) for having allowed the use of raster images of the altitude and slope of the land of the National Environmental Information System.

Literature cited: 1.

Pérez A, Montejo I, Iglesias JM, López O, Martín GJ, García DE, Milián I, Hernández A. Tithonia diversifolia (Hemsl.) A. Gray. Pastos y Forrajes 2009;32(1):1-15.

2.

Loya OJL, Martínez GS, Prado ROF, Valdés GYS, Gómez, DAA, Escalera V F, Macedo BR, Durán PN. El Sistema Silvopastoril. Sistema Superior Editorial. Nayarit, México; 2014:102.

3.

Intergovernmental Panel on Climate Change. Climate Change. The Physical Science Basis. Working group I. Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Summary for Policymakers (Thomas FS, Dahe Q, Gian-Kasper P, Melinda MBT, Simon KA, Judith B, Alexander N, Yu X, Vincent B, Pauline MM. Cambridge University Press; 2013:29.

4.

Milly PCD, Dunne KA, Vecchia AV. Global pattern of trends in stream low and water availability in a changing climate. Nature 2005;438(17):347-350.

5.

William GJ. Causes of observed changes in extremes and projections of future changes. In: Thomas R. Karl, et al editors. Weather and climate extremes in changing climate regions of focus: North America, Hawaii, Caribbean and Pacific Islands, USA. A Report by the US Climate Change Science Program. Department of Commerce, NOAA’s National Climatic Data Center, Washington, D.C., USA; 2008:81-116.

6.

DeFries R, Rosenzweig C. Toward a whole-landscape approach for sustainable land use in the tropics. Proc Natl Acad Sci 2010;107:19627-19632.

103


Rev Mex Cienc Pecu 2020;11(Supl 2):93-106

7.

Gibbs HK, Ruesch AS, Achard F, Clayton MK, Holmgren P, Ramankuttly N, Foley JA. Tropical forests were the primary sources of new agricultural land in the 1980s and 1990s. Proc Natl Acad Sci 2010;107(38):16732-16737.

8.

Foley JA, Asner GP, Heil C M, Coe MT, DeFries R, Gibbs HK, Howard EA, Olson S, Patz J, Ramankutty N, Snyder P. Amazonia revealed: forest degradation and loss of ecosystem goods and services in the Amazon Basin. Front Ecol Environ 2007;5(1):2532.

9.

Schneider RR, Hamann A, Farr D, Wang, X, Boutin S. Potential effects of climate change on ecosystem distribution in Alberta. Can J For Res 2009;39:1001-1010.

10. Vale MM, Alves MAS, Lorini ML. Mudanças climáticas: desafios e oportunidades para a conservação da biodiversidade brasileira. Oecol Bras 2009;13:518–535. 11. Parmesan C. Ecological and evolutionary responses to recent climate change. Annu Rev Ecol Syst 2006;37:637-669. 12. Schneider RR, Hamann A, Farr D, Wang X, Boutin S. Potential effects of climate change on ecosystem distribution in Alberta. Can J For Res 2009;39:1001-1010. 13. Bellard C, Bertelsmeier C, Leadley P, Thuiller W, Courchamp F. Impacts of climate change on the future of biodiversity. Ecology Letters 2012;15:365-377. 14. Kirilenko AP, Belotelov NV, Bogatyrev BG. Global model of vegetation migration: incorporation of climatic variability. Ecological Modelling 2000;132:125-133. 15. Morueta HN, Fløjgaard C, Svenning JC. Climate change risks and conservation implications for a threatened small-range mammal species. PLoS ONE 2010;5(4):10360-101371. 16. Fasuyi AO, Dairo FAS, Ibitayo FJ. Ensiling wild sunflower (Tithonia diversifolia) leaves with sugar cane molasses. 2010. Livest Res Rural Develop 2015;42(22) from http://www.lrrd.org/lrrd22/3/fasu22042.htm. 17. Otusanya OO, Ilori OJ, Adelusi A.A. Allelopathic effects of Tithonia diversifolia (Hemsl) A. Gray on germination and growth of Amaranthus cruentus. Res J Environ Sci 2010;1(6):285-293. 18. Phillips SJ, Dudík M, Elith J, Graham CH, Lehmann A, Leathwick J, Ferrier S. Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecological Applications 2009;19:181–197.

104


Rev Mex Cienc Pecu 2020;11(Supl 2):93-106

19. Sobek S, Kluza S, Cuddington DAK, Lyons DB. Potential distribution of emerald ash borer: What can we learns from ecological niche models using Maxent and GARP? Forest Ecol Management 2012;281(1):23-31. 20. Moreno R, Zamora R, Molina JR, Vásquez A, Herrera M. Predictive modeling of microhabitats for endemic birds in South Chilean temperate forest using Maximum entropy (Maxent). Ecol Informat 2011;6:364-370. 21. Phillips SJ, Dudík M. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 2008;31:161–175. 22. Morales SN. Modelos de distribución de especies: Software Maxent y sus aplicaciones en conservación. Rev Conserv Ambi 2012;2(1):1-5. 23. Magaña RVO. Guía para generar y aplicar escenarios probabilísticos regionales de cambio climático en la toma de decisiones. Centro de Ciencias de la Atmósfera. Universidad Nacional Autónoma de México. México; 2010. 24. Díaz PG, Guajardo PRA, Medina GG, Sánchez CI, Soria RJ, Vázquez AMP, et al. Potencial productivo de especies agrícolas de importancia socioeconómica en México. 1a. ed. INIFAP. Xalapa, Ver., México; 2012. 25. Eastman JR. Idrisi Selva Manual, Version 17. Clark Labs, Clark University. Worcester, Mass, USA; 2012. 26. Escalante T, Rodríguez TG, Linaje M, Illoldi RP, González LR. Identification of areas of endemism from species distribution models: threshold selection and nearctic mammals. Rev Especializada Cienc Químico-Biol 2013;16(1):5-17. 27. Instituto Nacional de Estadística Geografía e Informática (INEGI). Guía para interpretación cartográfica: Uso de suelo-vegetación Serie III. DF., México. 2009. 28. Parolo G, Rossi G, Ferrarini A. Toward improved species niche modelling: Arnica montana in the Alps as a case study. J Appl Ecol 2008;45(5):1410-1418. 29. Chapin III FS, Bloom AJ, Field CB, Waring RH. Plant responses to multiple environmental factors. BioScience 1987;37(1):49-57. 30. Murgueitio E, Rosales M, Gómez ME. Agroforestería para la producción animal sostenible. Centro para la Investigación en Sistemas Sostenibles de Producción Agropecuaria. Cali, Colombia; 2001. 31. Hoffman AA, Sgró CM. Climate change and evolutionary adaptation. Nature 2011;470:479-485.

105


Rev Mex Cienc Pecu 2020;11(Supl 2):93-106

32. Sork V, Davis F, Westfall R, Flints A, Ikegami M, Wang H, Grivet DD. Gene movements and genetic association with regional gradients in California valley oak (Quercus lobata Née) in the face of climate change. Mol Ecol 2010;19:3806-3823. 33. Ramírez LMR, Ruiz CJA, Medina GG, Jacobo CJL, Parra QRA, Ávila MMR, Pilar AJ. Perspectivas del sistema de producción de manzano en Chihuahua, ante el cambio climático. Rev Mex Cienc Agr 2011;(Pub Esp 2):265-279. 34. Santillán ELE, Blanco MF, Magallanes QR, García HJL, Cerano PJ, Delgadillo RO, Valdez CRD. Tendencias de temperatura extremas en Zacatecas, México. Rev Mex Cienc Agr 2011;( Pub Esp 2):207-219. 35. Jump AS, Peñuelas J. Running to stand still: Adaptation and the response of plants to rapid climate change. Ecology Letters 2005;8:1010-1020. 36. Burke MB, Lobell DB, Guarino L. Shifts in African crop climates by 2050, and the implications for crop improvement and genetic resources conservation. Global Environmental Change 2009;19(3):317-325. 37. Mercer KL, Perales HR. Evolutionary response of landraces to climate change in centers of crop diversity. Evol Appl 2010;3(5-6):480-493. 38. Hellmann JJ, Byers JE, Bierwagen BG, Dukes JS. Five potential consequences of climate change for invasive species. Conservation Biol 2008;22(3):534-543. 39. Howard G, Ziller SR. Alien alert: Plants for biofuel may be invasive. Bioenergy Business 2008:14-16. 40. Mainka SA, Howard GW. Climate change and invasive species: Double jeopardy. Integrative Zoology 2010;5:102-111. 41. Alsos IG, Alm T, Normand S, Brochmann C. Past and future range shifts and loss of diversity in dwarf willow (Salix herbacea L.) inferred from genetics, fossils and modelling. Global Ecol Biogeogr 2009;18:223–239. 42. Chown SL, Hoffman AA, Kristensen TN, Angilletta Jr, Stenseth MJ, Pertoldi NCh, C. Adapting to climate change: a perspective from evolutionary physiology. Climate Res 2010;43:3-15. 43. Peters K, Breitsameter L, Gerowitt B. Impact of climate change on weeds in agriculture: a review Agron Sustain Dev 2014;34:707-721. 44. Ruiz CJA, Hernández CJM, Sánchez GJJ, Ortega CA, Ramírez OG, Guerreo HMJ, et al. Ecología, adaptación y distribución actual y potencial de las razas mexicanas de maíz. Libro Técnico Núm. 5. INIFAP-CIRPAC-Campo Exp Centro Altos de Jalisco. 2013. 106


https://doi.org/10.22319/rmcp.v11s2.4697 Review

Mitigation and adaptation to climate change through the implementation of integrated models for the management and use of livestock residues. Review

Alberto Jorge Galindo-Barboza a* Gerardo Domínguez-Araujo a Ramón Ignacio Arteaga-Garibay b Gerardo Salazar-Gutiérrez a

a

Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias (INIFAP). Campo Experimental Centro Altos de Jalisco. México. b INIFAP.

Centro Nacional de Recursos Genéticos. Tepatitlán, Jalisco, México.

*Corresponding author: galindo.alberto@inifap.gob.mx

Abstract: The need to optimize the management and the use of the excreta is due to the fact that animal species do not take advantage of 100 % of the nutrients consumed of the food, the excrement being a potential source of these. The amount and quality of excretion depends on factors such as food, animal species, production status, health status and type of facilities. Integrated models for waste management should consider the revaluation of these as raw material, in order to develop technologies that enable the recovery of nutrients. Pig waste silage, compost, vermicompost, and anaerobic digestion systems are part of these schemes. On the other hand, the importance of bioremediation lies in the use of the metabolic potential of microorganisms to transform organic pollutants, and can be used to clean contaminated spaces or water. The technological adoption strategy is designed and started by establishing the characteristics of the material to be treated, its conditioning and the conditions of operation of the process, to select the criteria and methods for its scaling in any production system.

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Key words: Raw material, Manure, Exploitation, Environment, Integrated Systems, Bioremediation.

Received: 14/11/2017 Accepted: 06/07/2018

Introduction The human population has grown twice since 1960, demanding food and services; this growth is reflected in increases in animal population inventories. In terms of food, the production of meat, milk and eggs increases in proportion to the inventories of animal production; it is estimated that by the year 2020 approximately 200 billion liters of milk and 100 million kilograms of meat must be generated in order to satisfy the demand(1). Like food production, the generation of organic waste is increasing, the agricultural sector being a major contributor to air pollution. In 2013, at the global level, it was estimated that 14.5 % of the total greenhouse gases induced by the human being (7.1 gigatonnes of CO2-equivalent for 2005, and 10 Gt for the year 2010) are represented by the livestock supply chain. Of these, 41 % correspond to the production of beef, 20 % to milk, 9 % to the production of pork, 8 % to the production of chicken meat and egg, 6 % to the production of milk and meat of small ruminants, and the rest, to that of other species of birds and ruminants(2). In Mexico, according to the national inventory of greenhouse gas emissions, an emission of 748.25 megatonnes (Mt) of CO2-equivalent were estimated in 2010, 12.3 % of which (Mt 92.18) corresponds to the emissions from agriculture, contributing from enteric fermentation and manure management with the emission of 3.74 Mt CO2-equivalent(2,3). Thus, organic livestock waste represents a growing and constant source of pollutants. The polluting potential of such waste (manure or excreta) lies in the presence of undigested nutrients, since no species takes advantage of the total nutrients consumed in the diet(4,5). Therefore, excreta can be considered a potential source of nutrients, which can be exploited through various processes. The production and quality of excreta is linked to factors such as: species, zootechnical end, production stage, quality of diets, digestibility, among others. Similarly, the infrastructure of

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the unit of production, the management and the equipment available for the collection, are factors that are linked to the physical and chemical characteristics of the waste(2,3). The characterization of the organic livestock waste is the key for planning its management, use and disposal and, thus, to mitigate its emissions and its polluting effect. Therefore, the objective of this work is to present the processes that can be adopted under an integrated model for the development of livestock waste while preserving and recycling nutrients derived from animal production systems.

Integrated models for the management and exploitation of waste The integrated models for the management and utilization of waste consists in the integration of technologies that lead to this purpose. These models must be adaptable to different livestock production systems (family, medium-scale, large scale, intensive, extensive, and mixed) and interact with agriculture. Their main objective is the diversification of production and income, establishing environmentally-friendly processes to achieve sustainability. The most important and most promising challenge of these models is the articulation with the local and national production and commercial chains. In this sense, the efforts that have been made to address different problems are numerous, but isolated, and the solution is a holistic approach to the waste management needs in the agricultural sector. For a long time, work has been done to identify, quantify, and treat the organic residues of livestock holdings, conceptualized as waste, with the aim of establishing strategies and policies to mitigate the impact that they have on the environment(6-12). However, it is of vital importance to revalue the residues as raw materials in order to implement sustainable processes and consolidate integrated schemes for mitigating the negative impact on the environment and generating stability and profitability(2,13,14), and, therefore, it is necessary to determine their availability, composition, physical and chemical characteristics(15,16) and safety(17) â&#x20AC;&#x201D;all of which are indispensable for determining the level of achievement in the various processes which they can be subjected. Currently, importance has been given to technologies that prioritize the recovery of nutrients contained in livestock waste (mainly nitrogen and phosphorus), especially in swine manure, with alternatives such as the generation of protein biomass for use in animal nutrition (18). However, despite being a viable alternative to mitigate the environmental impact generated by waste, it is limited and does not consider aspects of food safety and toxicology. On the other hand, there are viable alternatives to achieve the objective of mitigating the environmental impact and utilize the waste; however, the lack of training to design and operate them, coupled with inadequate management, suggests or leads one to believe that 109


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they are useless. An example of this are the anaerobic digestion systems (biodigesters); when these fail to treat adequate volumes, have not established a load regime or lack adequate levels of water retention for the characteristics of the residues, they generate effluents that may not be suitable for exploitation in agriculture, due to their high concentration of nutrients(19). Therefore, it is essential to generate, validate and adapt technologies according not only to the needs of a wide range of schemes of agricultural production, but also to the characteristics of the raw material intended to be processed and to the production goal to be achieved, taking into account that, in this sense, savings in the environmental cost of production are implicit. In order to achieve this, it is necessary to generate integrated models that can include one or more processes (technologies for the management of sewage treatment) contributing to a common purpose. At the National Institute for Research on Forestry, Agriculture and Livestock (Instituto Nacional de Investigaciones Forestales, AgrĂ­colas y Pecuarias, INIFAP) various processes for packaging, handling, use, and revaluation of organic waste in the livestock sector have been designed and studied; these include: silage or fermentation of swine manure for animal feed(20-22), compost and vermicompost for the production of organic fertilizers(23), anaerobic digestion systems for the generation of renewable energy, and wastewater treatment (24). These technologies have the capacity to generate byproducts with added value and condition the raw material to be subjected to another process that may also yield a byproduct, or prepare for a bioremediation scheme (Figure 1). Figure 1: Model of integration of processes for the management and exploitation of livestock waste

The agricultural production systems are the providers of resources (organic matter) for processes (technological alternatives) that generate marketable products where a supplier can become a customer.

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Conditioning of the waste and generation of byproducts through processes silage or fermentation of swine manure For a long time, fresh or dried swine manure has been used for animal feed due, to a certain extent, to the lack of information about the risks and disadvantages that it entails, generating health problems and probably exacerbating diseases. And although there are studies that determine the feasibility of its use, they consider only productive aspects(25-29), but not conclusive aspects in relation to animal health, carcass quality, quality and organoleptic properties of milk, among others of importance to animal welfare and food safety; on the other hand, inadequate processing generates an environmental problem.Currently, the use of dried swine manure continues to rise with inclusions of up to 70 % in the diets, with losses of crude protein of up to 12 % of the total content of fresh excreta(30,31), which represents a limitation for the use of the nutrients contained in it. Depending on the disadvantages, risks and opportunities that the use of swine manure for animal feed entails, a process for conditioning, called swine manure or silage fermentation of swine manure has been developed and perfected, which consists in subjecting the excreta (from pigs in stages of weaning-completion) to a anaerobic fermentation process(20); the swine manure silage is the final result and can be used for feeding ruminants(22,31-34), pigs(3538) and even, in view of the characteristics of this ingredient, other species such as fish, birds and rabbits. The main objective of this process, is to reduce the pH to levels below 5, in order to eliminate microorganisms indicating fecal contamination(39), the process by which, organisms, viruses and parasites are also eliminated(17,40), provided that the process is carried out properly. The same principle of anaerobic fermentation is used for the treatment of human food residues and their use as food for pigs(41), as it provides advantages over their chemical, physical and microbiological characteristics, preventing spoilage. In this sense, the silage of swine manure has also been used with the objective of conferring immunity to pigs and reducing the microbe count in hog farms(42); this does not involve a risk, unlike the self-immunization strategies utilized in the presence of outbreaks of epidemic swine diarrhea(43) or other diseases in Mexico. It is important to emphasize that the main benefit of swine manure silage lies in the reduction of production costs; recent work, suggest a reduction in the cost of production of up to 7 %, with the inclusion of 30 % at the stages of growth-development-completion of the hogs(21) and of up to 60 % in the feed for ruminants. It is essential to consider that swine manure silage is a highly available ingredient for the formulation of diets; therefore, it is indispensable to know its chemical composition (which tends to vary according to the quality 111


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of the raw materials used) in order to create formulations based on the nutritional needs of animals that are to be fed. It is worth mentioning that, as specific research on the possible uses and applications of swine manure silage is carried out, an integral development will be achieved for the benefit of the various livestock production systems, the producers and the environment.

Compost and vermicompost

Unlike swine manure silage, which uses hog manure only at the wean-to-finish stage, composting is a versatile process which allows conditioning a large variety of agricultural waste products. Although composting is not a new practice, the adequacy of the technique and the acceleration of the process renders it innovative. Although, in general, composting is considered a simple process, the practice suggests that it requires complex physical, chemical and microbiological conditions(23), and the lack of care or considerations has an impact on the quality of the final product (stabilized compost). Compost possesses an important content of organic matter and nutrients that can be utilized in a variety of ways in agriculture and in the preservation of the soil(44,45). In order for the composting process to be carried out efficiently and for the compost to be rich in nutrients, it is important to consider the quality and composition of the raw materials. In this regard, the excreta of sows in reproduction and ruminants in general provide ideal characteristics for being mixed with an extensive range of hard to compost agricultural crops with a high carbon/nitrogen ratio, for their processing and utilization(13). Another way to use and to give added value to compost is to utilize it as input for the generation of vermicompost through vermiculture(46). Vermiculture is considered a biotechnology, where the worm serves as a working tool for the transformation of residues in organic products such as the vermicompost; this contains active substances that act as plant growth regulators, has a high content of humic acids, and increases the capacity of moisture retention, facilitating aeration and soil drainage(23). In addition, vermicompost boasts a high content of potassium and phosphorus(46.47). Vermicompost also increases the microbial activity in the soil considerably, and there is evidence that plant growth regulators such as auxins, cytokinins, humic acids and micro-organisms promote plant growth regardless of supplementation with nutrients(48,49). Vermiculture yields not only vermicompost but also other subproducts with a high economic value, such as earthworm leachate and earthworm biomass(44,46).

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Currently, importance is given to aspects of ecotoxicology and environmental safety(50), analyzing the risk associated with the use of compost in the generation of antimicrobial resistance(51), degradation of antibiotics(52,53), bioavailability of heavy metals(50), emission of gases(54), and persistence of pathogens(55), among others; however it is important to consider basic aspects related to the raw material, including the feeding of the animals, handling, health and biosafety, which are a guarantee of innocuous, high-quality food and waste.

Anaerobic digestion systems

The systems of anaerobic digestion are a viable alternative for the pre-treatment of agricultural waste(56). Its main function is to degrade organic matter and transform it into methane; effluents have also been used as fertilizers for crop lands(57,58). The above will depend on the efficiency of the reactor (biodigester). There are different types of biodigesters: among those considered to be high-load are the anaerobic sequential batch reactor (ASBR), and the upflow anaerobic sludge blanket (UASB). This type of biodigesters offers the advantage of reducing the loads of solids of the wastewater in a relatively short time; however, the required investment is high(59). In Mexico, the most commonly used biodigesters for the treatment of effluents from livestock production units are the covered lagoon digesters(60), several versions of which have been developed to facilitate their management and useful life, through the implementation of systems of sludge extraction and agitation(61). The common, widespread management of this type of biodigesters consists in channeling 100 % of the solid waste generated in the production unit by means of high volumes of water, in the form of "haulage". This type of practice occurs even in areas where there is a marked shortage of water, which is inconsistent with the aim of mitigating the environmental impact(62). As a result, there is a need for large digesters that require a large space and a high investment. In some cases, this type of biodigesters have been proven to have efficiencies of 78 % to 90 % in the removal of the chemical oxygen demand(63) and in the total reduction of helminth eggs(60). Another very popular type of biodigesters in Mexico is the tubular polyethylene (Taiwan) type; these biodigesters have been efficient in backyard systems for the generation and selfsupply of the generated biogas; however, this type of biodigesters under livestock production schemes tend to be surpassed by the production of waste, so that their use fails to bring a benefit. In addition, recent studies have shown that this type of biodigesters under continuous flow in pig farms are unable to eliminate certain pathogens such as: L. intracellularis, S. 113


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aureus, E. coli, Salmonella spp, mesophiles, Clostridium sulphite reducers, total coliforms and coccidia(64,65), and therefore the use of their effluents as biol or fertilizer entails a health risk. On the other hand, various sectors (including livestock), have used the biodigesters as generators of renewable energies, and some research institutions have bet on the development and industrialization of this technology. The use of various raw materials(66,67), the conservation of raw materials for use in the production of biogas(68), and the development of systems of purification, compression and use in spark ignition engines(69), are some of the topics of research. However, in the livestock sector the main purpose of the biodigesters is the production of electrical energy to self-supply their productive processes; in this sense, pig farming is the most promising for this purpose, given the characteristics of the waste and its peculiarities in the production system. This provides the industry with an opportunity to be competitive â&#x20AC;&#x201D; in economic, social and environmental termsâ&#x20AC;&#x201D; in the generation of electric power(70,71). In recent years, the adoption of biodigestion systems has become popular among small and medium-sized producers; the main reasons for this are: the novel idea to generate biogas or energy, pressure from the authorities to establish a process for the treatment of waste, the introduction into the market of low-priced designs, and financing facilities. However, before implementing a biodigester (regardless of the scale of the production unit), it is necessary to know the quantity and characteristics of the waste generated in order to develop a strategy of technology integration and to direct the waste to each one of the processes as appropriate. If the biodigester carries out one or more of the processes under consideration, it is useful to determine its level of achievement and its purpose, i.e., the pretreatment of wastewater, the generation of (heat or electric energy), or both. This makes it possible to establish its design-operation and to measure and maximize its performance.

Bioremediation

Bioremediation is a branch of the biotechnology that uses the metabolic potential of microorganisms to transform organic contaminants in compounds with minimal or no side effects, and, therefore, it can be used to clean polluted spaces or water, with very ample perspectives(72,73). However, it is important to mention certain considerations in relation to bioremediation: compared to the chemical methods that are based on the transfer of contamination between 114


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the three physical states in which it occurs (gaseous, liquid, and solid), bioremediation transfers little pollution from one medium to another because this technology is not very intrusive and generally does not require significant structural or mechanical components; moreover, it is economically profitable, because it is a natural process which is accepted in a context that goes beyond the technical implications(74). Bioremediation has some disadvantages and limitations. For example, incomplete biodegradation can generate unacceptable metabolic intermediaries with a polluting power that is similar or even superior to that of the original product. On the other hand, some compounds, are resistant to or inhibit bioremediation. The time required for proper treatment can be difficult to predict; in addition, following up and controlling the speed and the extent of the process are laborious tasks. The efficiency of this technique depends on several factors such as: a) The properties of the polluting agent or agents (biodegradability). b) The presence of microbial communities with the ability to metabolize the enzymatic or compounds. Microorganisms can be indigenous (intrinsic bioremediation or attenuation)(75), added to the system in order to improve the degradation (bioaugmentation) or by providing optimal conditions that stimulate microbial activity (biostimulation) by supplying oxygen, nutrients or modifications of pH, among others. c) Availability of the pollutant. It is a more important critical factor than the presence of microbial communities itself. In order for the degradation of a contaminant to occur, the microbial cells must interact directly with the pollutant, preferably in an aqueous medium(76).

Bioremediation for decontamination in livestock waste

The selection of processes and the design of the strategy for the bioremediation of water and soils contaminated with organic compounds such as livestock waste begin by clearly establishing the characteristics of the material to be bioremedied (effluent from livestock production units or contaminated soils), the microorganisms to be used, the types of reactor (e.g. anaerobic digesters or lagoon systems), the pretreatment of the contaminating material (mainly excreta, which can be pretreat or conditioned with the alternatives mentioned above), and the conditions of operation of the process (given by the production system and the adopted integrated model). It is necessary to consider also the laboratory evaluations in order to explore operation alternatives and quantify the degradation speeds in terms of critical operation parameters such as pH, oxygen and oxidation-reduction potential, with the purpose of determining the effectiveness and efficiency of the bioremediation process. At a small scale, the physicochemical phenomena must be observed, and specific conditions for improving the process must be determined. These aspects provide an important basis for 115


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criteria and methods of scaling the processes (from pilot to semi-commercial to commercial), as well as the requirements for their implementation and control(77,78).

Characteristics of distribution of pollutants

Before selecting any alternative bioremediation process, the site or material to be cleaned must be very well characterized, a technical and economic pre-feasibility study must be performed, and the physical, chemical and microbiological hazards must be clearly established, in order to accurately determine the details of cleaning speed, as well as the factors that influence it, and subsequently proceed to obtain data on the kinetics and balance in physical, chemical and biological processes reaction mechanisms that are important for the design of the process. Also, the type of contaminant, its concentration, the extent of the problem must be known, as well as the bioavailability of the substance, especially in leaching processes(79).

Determination of the microorganisms to be utilized

Degradation tests with different microorganisms are essential to determine which must be used; this requires information mainly about the medium (water, soil) on which the process will take place, the organic matter content, and the particle size distribution profile(77). Microbiological analyses include parameters such as the biochemical oxygen demand, the determination of viable count, in vitro degradation studies prior to the escalation of the process(73); and, from the biochemical point of view, the metabolic pathways involved during the biodegradation of the contaminants and potential beneficial or harmful effects toward the same process of degradation(80). It is important to consider the conditions of temperature, oxygen, nutrient supply and availability of the contaminant, as they can limit the speeds of degradation, mainly at the beginning of the processes where even the limiting factors are not well defined. The experience says that the best microorganisms for a process of bioremediation are, precisely, at the site to be bioremedied, that is to say, it is preferable to use a native microorganism(81). However, it is important to determine the efficiency and speed of biodegradation because the cell concentration or biomass of native microorganisms is generally low, or because there are no microorganisms capable of biodegrading the contaminating material(76), allowing for the use of a collection microorganism(82,83).

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Bioremediation of wastewater and agricultural soils, with microbiological, biochemical and engineering support, is one of the most promising strategies for decontaminating these resources and is currently an additional alternative for the integrated systems of management and utilization of waste and the business incubation through proper use of positive results that are generated from research projects, with technological application at reduced costs, and with tangible benefits for the population and the environment.

Conclusion The integration of technologies for the management and use of organic livestock waste and bioremediation of soil and water is feasible. This type of model must be articulated with local, national and international markets and with environmental policies in order to meet the demand for food in both quantity and quality, with the premise to exploit and conserve the natural resources. Its adoption provides an opportunity to obtain economic, environmental, social and technological benefits.

Literature cited: 1.

Martinez J, Dabert P, Barrington S, Burton C. Livestock waste treatment systems for environmental quality, food safety, and sustainability. Bioresour Technol 2009;100(22):5527–36.

2.

Gerber PJ, Steinfeld H, Henderson B, Mottet A, Opio C, Dijkman J, et al. Enfrentando el cambio climático a través de la ganadería. Una evaluación global de las emisiones y oportunidades de mitigación. Roma: Organización de las naciones unidas para la alimentación y la agricultura (FAO). 2013.

3.

SEMARNAT. Secretaria de Medio Ambiente y Recursos Naturales. Inventario nacional de emisión de gases de efecto invernadero 1990-2010. Primera ed. México. 2013.

4.

Brockmann D, Hanhoun M, Négri O, Hélias A. Environmental assessment of nutrient recycling from biological pig slurry treatment - Impact of fertilizer substitution and field emissions. Bioresour Technol 2014;(163):270–279.

5.

Quiroga-Garza HM, Cueto-Wong JA, Figueroa-Viramontes U. Efecto del estiércol y fertilizante sobre la recuperación de 15N y conductividad eléctrica. Terra Latinoam 2011;29(2):201–209.

6.

Méndez-Novelo R, Castillo-Borges T, Vázquez-Borges E, Briceño-Pérez O, CoronadoPedaza V, Pat-Canuel R, et al. Estimación del potencial contaminante de las granjas 117


Rev Mex Cienc Pecu 2020;11(Supl 2):107-125

porcinas y avícolas del estado de Yucatán. Ingeniería. 2009;13(2):13–21. 7.

Pérez R. Porcicultura y contaminación del agua en La Piedad, Michoacán, México. Rev Int Contam Ambient 2001;17(1):5–13.

8.

García A, León R, Míreles S, Castro JP, García AA, Roa JJ, et al. Contaminación ambiental en explotaciones porcinas mexicanas e incumplimiento de la norma ambiental. Rev Comput Prod Porc 2010;17(3):243–246.

9.

Jimenez Y, Negrin A, Valdés LA, Vidal V, Costa Y, Castro M, et al. Diagnostico de sistemas de tratamientos en el sector porcino no especializado de la provincia Ciego de Avila. Rev Comput Prod Porc. 2014;21(2):79–88.

10. Philippe FX, Nicks B. Review on greenhouse gas emissions from pig houses: Production of carbon dioxide, methane and nitrous oxide by animals and manure. Agric Ecosyst Environ 2015;199:10–25. doi.org/10.1016/j.agee.2014.08.015. 11. Bonilla-Cardenas JA, Lemus-Flores C. Emisión de metano entérico por rumiantes y su contribución al calentamiento global y al cambio climático. Revisión. Rev Mex Ciencias Pecu 2012;3(2):215–246. 12. Garzón-Zuñiga MA, Buelna G. Caracterización de aguas residuales porcinas y su tratamiento por diferentes procesos en México. Rev Int Contam Ambient 2014;30(1):65–79. 13. Dominguez-Araujo G, Galindo-Barboza AJ, Salazar-Gutiérrez G, Barrera-Camacho G, Sánchez-Garcia FJ. Las excretas porcinas como materia prima para procesos de reciclaje utilizados en actividades agropecuarias. Prometeo E. Tepatitlán de Morelos, Jalisco, México: Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias INIFAP; 2014. 14. Gómez-Rosales S, Angeles M de L, Espinosa-García JA, González-Orozco TA. Caracterización de sistemas de producción animal, manejo de excretas y opciones para su aprovechamiento integral. Querétaro, Querétaro: Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias INIFAP; 2008. 15. Anthony WB. Animal waste value-nutrient recovery and utilization. J Anim Sci 1971;32(4):799–802. 16. Bhattacharya AN, Taylor JC. Recycling animal waste as a feedstuff : A Review. J Anim Sci 1975;41(5):1438–1457. 17. Fontenot JP, Webb KEJ. Health aspects of recycling animal wastes by feeding. J Anim Sci 1975;40(6):1267–1276. 118


Rev Mex Cienc Pecu 2020;11(Supl 2):107-125

18. Mohedano RA, Velho VF, Costa RHR, Hofmann SM, Belli Filho P. Nutrient recovery from swine waste and protein biomass production using duckweed ponds (Landoltia punctata): Southern Brazil. Water Sci Technol 2012;65(11):2042–2048. 19. Jimenez-Peña Y, Valdés LA, Vidal-Olivera V, Castro-Carrillo M, Molineda A. Evaluación de efluentes anaerobios en el sector porcino no especializado de la provincia Ciego Ávila. Rev Comput Prod Porc 2014;21(3):140-145. 20. Castellanos-Aceves A, Salazar-Gutiérrez G, Hernández-Morales P, Domínguez-Araujo G, Barrera-Camacho G. Uso de ensilado de cerdaza en la alimentacion animal. Prometeo Editores. Tepatitlán de Morelos, Jalisco, México: Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias INIFAP. 2010. 21. Galindo-Barboza AJ, Domínguez-Araujo G, Salazar-Gutiérrez G, Sánchez-Garcia FJ, Avalos-Castro MA. Uso de ensilado de cerdaza en la alimentacion animal. En: Hernández Virgen R, Pérez Domínguez JF, editores. Memoria tecnica Vamos al campo 2012. Prometeo E. Tepatitlán de Morelos, Jalisco, México: Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias INIFAP; 2012:57–62. 22. Avalos-Castro MA, Dominguez-Araujo G, Galindo-Barboza AJ, Ruvalcaba-Gomez JM, Arias-Chávez LE, Salazar-Gutiérrez G. Efecto de la adición de ensilado de cerdaza en la dieta de vacas en lactación sobre paramétros productivos y las caracteristicas fisicoquímicas de la leche. Congreso Nacional, Mitigación del daño ambiental en el sector agropecuario de México. 2013:127–136. 23. Xelhuantzi-Carmona J, Salazar-Gutiérrez G, Dominguez-Araujo G, Arias-Chávez LE, Chávez-Durán ÁA, Galindo-Barboza AJ. Manual para la elaboración de abonos organicos apartir de técnicas como la composta y lombricomposta. Graficos Lara editores. Tepatitlán de Morelos, Jalisco, México: Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias INIFAP; 2012. 52 p. 24. Dominguez-Araujo G, Salazar-Gutiérrez G, Galindo-Barboza AJ, Xelhuantzi-Carmona J, Castañeda-Castillo M, Sánchez-Garcia FJ, et al. Implementación de biodigestores para pequeños y medianos productores porcícolas. Graficos Lara editores. Tepatitlán de Morelos, Jalisco, México: Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias INIFAP; 2012. 28 p. 25. Padilla-Goyo EC, Castellanos-Ruelas AF, Cantón-Castillo JG, Moguel-Ordoñez YB. Impacto del uso de niveles elevados de excretas animales en la alimentación de ovinos. Livest Res Rural Dev 2000;12(1):14–22. 26. Ortega-Zuñiga I. Digestibilidad in vivo en ovinos de dietas con dos niveles de cerdaza y melaza. [tesis licenciatura]. Zapopan, Jalisco. Universidad de Guadalajara; 1996. 119


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27. Newton GL, Utley PR, Ritter RJ, McCorminck WC. Performance of beef cattle fed wastelage and digestibility of wastelage and dried waste diets. J Anim Sci 1977;(44):447–451. 28. Mejía W, Quintero A, Rodríguez E, Calatayud D. Efecto de la administración de cerdaza sobre el rendimiento productivo de cerdos en etapa de engorde. Arch Latinoam Prod Anim 1997;5(1):300–301. 29. Heredia-Cruz MR. Utilización de cerdaza en dietas de levante para terneros pos destete [tesis licenciatura]. Zamorano, Honduras: Escuela Agrícola Panamericana; 2012. 30. Orellana-González CA, Dimas-Fontanals JA. Evaluación y formulación de un concentrado a partir de cedaza como fuente de proteina para engorde de conejos y pollos broiler [tesis licenciatura]. Antiguo Cuscatlán, El Salvador. Universidad Dr. Jose Matias Delgado; 2009. 31. Castrillón-Quintana O, Jiménez-Pérez RA, Bedoya-Mejía O. Porquinaza en la alimentación animal. Rev Lasallista Investig 2004;1(1):1–5. 32. Berger JCA, Fontenot JP, Kornegay ET, Webb KEJ. Feeding swine waste. II. Nitrogen utilization , digestibility and palatability of ensiled swine waste and Orchardgrass hay or corn grain fed to sheep. J Anim Sci 1981;(52):1404–1420. 33. Dominguez-Araujo G, Galindo-Barboza AJ, Salazar-Gutiérrez G, Arias-Chávez LE, Quiñones-Islas N. Efecto en el comportamiento productivo de bovinos en finalización al utilizar dietas conteniendo ensilado de cerdaza. Rev Mitigación del Daño Ambient Agroaliment y For México. 2014;1(1):23–33. 34. Martínez-Barrera VM, Serna-Roman MG. Utilización de cerdaza ensilada en la alimentación de ovinos de engorda [tesis licenciatura]. Zapopan, Jalisco, México. Universidad de Guadalajara; 1999. 35. Guillemin Rubio JJ. Utilizacion de la cerdaza fermentada en la etapa de destete [tesis licenciatura]. Zapopan, Jalisco, México. Universidad de Guadalajara; 1995. 36. Galindo-Barboza AJ, Dominguez-Araujo G, Salazar-Gutiérrez G, Avalos-Castro MA, Sánchez-Garcia FJ. Efecto de la adición de ensilado de cerdaza en dietas de cerdos en cebo, una alternativa para la reutilización de solidos en granjas porcícolas. Congreso Nacional, Mitigación del daño ambiental en el sector agropecuario de México. Guadalajara, Jalisco, México; 2013:61–71. 37. Zaldivar-Reynoso MA, Corona-Santos G. Restricción del consumo de alimento en cerdos en atapa de finalización, utilizando estiercol fermentado de cerdo [tesis licenciatura]. Zapopan, Jalisco, México. Universidad de Guadalajara; 1995. 120


Rev Mex Cienc Pecu 2020;11(Supl 2):107-125

38. Hernandez-Mata A. Evaluacion de solidos recuperados fermentados con alimento de lechon de predestete y efectos en su aceptacion y crecimiento [tesis licenciatura]. Guadalajara, Jalisco, México.Universidad de Guadalajara; 1994. 39. Galindo-Barboza AJ, Dominguez-Araujo G, Salazar-Gutiérrez G, Arteaga-Garibay RI, Martínez-Peña MD, Ruvalcaba-Gomez JM. Disminución de las UFC como indicadores de contaminación fecal en el ensilado de cerdaza, considerando el pH como factor determinante. Rev Mitigación del Daño Ambient Agroaliment y For México 2014;1(1):34–43. 40. Caballero-Hernández AI, Castrejón-Pineda F, Martínez-Gamba R, Angeles-Campos S, Pérez-Rojas M, Buntinx SE. Survival and viability of Ascaris suum and Oesophagostomum dentatum in ensiled swine faeces. Bioresour Technol 2004;94(2):137–142. 41. Yang SY, Ji KS, Baik YH, Kwak WS, McCaskey TA. Lactic acid fermentation of food waste for swine feed. Bioresour Technol 2006;97(15):1858–1864. 42. Galindo-Barboza AJ, Dominguez-Araujo G, Salazar-Gutiérrez G, Arteaga-Garibay RI, Martinez-Peña MD, Sanchez-Garcia FJ. Ensilado de cerdaza, una oportunidad para el manejo de la bioseguridad y el microbismo en granjas porcícolas. Prometeo Editores. Tepatitlán de Morelos, Jalisco, México: Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias INIFAP; 2013. 43. Jung K, Saif LJ. Porcine epidemic diarrhea virus infection: Etiology, epidemiology, pathogenesis and immunoprophylaxis. Vet J 2015;204(2):134–143. 44. FAO. Manual de compostaje del agricultor, Experiencias en América Latina. Oficina Regional de la FAO para América Latina y el Caribe. 2013. http://www.fao.org/3/ai3388s.pdf 45. Villar I, Alves D, Garrido J, Mato S. Evolution of microbial dynamics during the maturation phase of the composting of different types of waste. Waste Manag 2016;(54):83–92. 46. Gómez-Rosales S, Espinosa-García JA, González-Orozco TA, Salazar-Gutiérrez G. Alternativas para el reciclaje de excretas animales: Producción de humus de lombriz. Impresos G. editores. Ajuchitlán, Colon, Queretaro: Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias INIFAP; 2007. 47. Delgado-Arrollo MM, Porcel-Cots MÁ, Miralles de Imperal-Hornedo R, BeltránRodríguez EM, Beringola-Beringola L, Martín-Sánchez JV. Efecto de la vermicultura en la descomposición de residuos orgánicos. Rev Int Contam Ambient 2004;20(2):83– 86. 121


Rev Mex Cienc Pecu 2020;11(Supl 2):107-125

48. Chaoui HI, Zibilske LM, Ohno T. Effects of earthworm casts and compost on soil microbial activity and plant nutrient availability. Soil Biol Biochem 2003;35(2):295– 302. 49. Masciandaro G, Ceccanti B, Garcia C. “In situ” vermicomposting of biological sludges and impacts on soil quality. Soil Biol Biochem 2000;32(7):1015–1024. 50. Wang Q, Wang Z, Awasthi MK, Jiang Y, Li R, Ren X, et al. Evaluation of medical stone amendment for the reduction of nitrogen loss and bioavailability of heavy metals during pig manure composting. Bioresour Technol 2016;(220):297–304. 51. Kang Y, Hao Y, Shen M, Zhao Q, Li Q, Hu J. Impacts of supplementing chemical fertilizers with organic fertilizers manufactured using pig manure as a substrate on the spread of tetracycline resistance genes in soil. Ecotoxicol Environ Saf 2016;(130):279– 288. 52. Selvam A, Wong JWC. Degradation of antibiotics in livestock manure during composting. In: Wong JW-C, Tyagi RD, Pandey A. Current developments in biotechnology and bioengineering. Elsevier; 2017:267–292. 53. Xie W-Y, Yang X-P, Li Q, Wu L-H, Shen Q-R, Zhao F-J. Changes in antibiotic concentrations and antibiotic resistome during commercial composting of animal manures. Environ Pollut 2016;(219):182–190. 54. Zang B, Li S, Jr. FM, Li G, Luo Y, Zhang D, et al. Effects of mix ratio, moisture content and aeration rate on sulfur odor emissions during pig manure composting. Waste Manag 2016;(56):498–505. 55. Hénault-Ethier L, Martin VJJ, Gélinas Y. Persistence of Escherichia coli in batch and continuous vermicomposting systems. Waste Manag 2016;(56):88–99. 56. Trejo LW, Vázquez GLB, Uicab AJ, Castillo CJ, Caamal MA, Belmar CR, Santos RRH. Eficacia de remoción de materia orgánica de aguas residuales porcinas con biodigestores en el estado de Yucatán, México. Trop Subtrop Agroecosystems 2014; (17): 321–323. 57. Acevedo P. Biodigestor de Doble Proposito - Producción e Investigación - para residuos de granja porcicola. Revista Ion 2006;19(1):1–6. 58. Cepero L, Savran V, Blanco D, Díaz PMR, Suarez J, Palacios A. Producción de biogás y bioabonos a partir de efluentes de biodigestores. Pastos y Forrajes 2012;35(2):219– 226. 59. Pérez-Pérez T, Pereda-Reyes I, Oliva-Merencio D, Zaiat M. Anaerobic digestion technologies for the treatment of pig wastes. Cuba J Agric Sci 2016;50(3):343–354. 122


Rev Mex Cienc Pecu 2020;11(Supl 2):107-125

60. Blanco D, Suárez J, Jiménez J, González F, Álvarez MN, Cabeza E, et al. Eficiencia del tratamiento de residuales porcinos en digestores de laguna tapada. Pastos y Forrajes 2015;38(4):441–447. 61. Campos CB. Metodología para determinar los parámetros de diseño y construcción de biodigestores para el sector cooperativo y campesino. Rev Cienc Téc Agropec 2011;20(2):37–41. 62. Pérez PT, Pereda RI, Oliva MD, Zaiat M. Anaerobic digestion technologies for the treatment of pig wastes. Cuban J Agr Sci 2016;50(3):343–354. 63. Orrico J, Marco AP, Orrico A, De Lucas JJ. Influência da relação volumoso: concentrado e do tempo de retenção hidráulica sob a biodigestão anaeróbia de dejetos de bovinos. Engenharia Agrícola 2010;(30):386–394. 64. Betancur HO, Betancourt EA, Estrada AJ, Henao UF. Persistence of pathogens in liquid pig manure processed in manure tanks and biodigesters. Rev MVZ Córdoba 2016;21(1):5237–49. 65. Cañon-Franco WA, Henao-Agudelo RA, Pérez-Bedoya JL. Recovery of gastrointestinal swine parasites in anaerobic biodigester systems. Rev Bras Parasitol Veterinária 2012;21(3):249–53. 66. Angelidaki I, Alves M, Bolzonella D, Borzacconi L, Campos JL, Guwy AJ, et al. Defining the biomethane potential (BMP) of solid organic wastes and energy crops: a proposed protocol for batch assays. Water Sci Technol 2009;59(5):927-934. 67. Mustafa SS, Mustafa SS, Mutlag AH. Kirkuk municipal waste to electrical energy. Int J Electr Power Energy Syst. 2013;44(1):506–513. http://dx.doi.org/10.1016/j.ijepes.2012.07.053 68. Teixeira Franco R, Buffière P, Bayard R. Optimizing storage of a catch crop before biogas production: Impact of ensiling and wilting under unsuitable weather conditions. Biomass and Bioenergy 2017;(100):84–91. 69. Porpatham E, Ramesh A, Nagalingam B. Investigation on the effect of concentration of methane in biogas when used as a fuel for a spark ignition engine. Fuel 2008;87(8– 9):1651–1659. 70. Venegas Venegas JA, Espejel García A, Pérez Fernández A, Castellanos Suárez JA, Sedano Castro G. Potencial de energía eléctrica y factibilidad financiera para biodigestor-motogenerador en granjas porcinas de Puebla. Rev Mex Cienc Agr 2017;8(3):735–740.

123


Rev Mex Cienc Pecu 2020;11(Supl 2):107-125

71. Venegas Venegas JA, Perales Salvador A, del Valle Sánchez M. Energía renovable una opción de competitividad en granjas porcinas en México. Rev Mex Cienc Agr 2015;(1):503–509. 72. Glazer AN, Nikaido H. Microbial biotechnology: Fundamentals of applied microbiology. New York, USA: WH Freeman and Company; 1995. 73. Atlas RM, Bartha R. Ecología microbiana & microbiología ambiental. Pearson Educación, Madrid. 2001. 74. Frutos FJG, Escolano O, García S, Mar Babín M, Fernández MD. Bioventing remediation and ecotoxicity evaluation of phenanthrene-contaminated soil. J Hazard Mater 2010;(183):806–813. 75. Rosenberg E, Ron EZ. Bioremediation of petroleum contamination. In: Crawford RL &Crawford DL editors. Bioremediation. Principles and applications biotechnology research series 6. University Press, Cambridge. 1996:100-124. 76. Azubuike CC, Chikere CB, Okpokwasili GC. Bioremediation techniques-classification based on site of application: principles, advantages, limitations and prospects. World J Microbiol Biotechnol 2016;32(11):180. 77. Verma JP, Jaiswal DK. Book review: advances in biodegradation and bioremediation of industrial waste. Front Microbiol 2016;(6):1–2. 78. Firmino PIM, Farias RS, Barros AN, Buarque PMC, Rodrıguez E, Lopes AC, et al. Understanding the anaerobic BTEX removal in continuous-flow bioreactors for ex situ bioremediation purposes. Chem Eng J 2015;(281):272–280. 79. Smith E, Thavamani P, Ramadass K, Naidu R, Srivastava P, Megharaj M. Remediation trials for hydrocarbon-contaminated soils in arid environments: evaluation of bioslurry and biopiling techniques. Int Biodeterior Biodegradation 2015;(101):56–65. 80. Khan FI, Husain T, Hejazi R. An overview and analysis of site remediation technologies. J Environ Manag 2004;(71):95–122. 81. Philp JC, Atlas RM. Bioremediation of contaminated soils and aquifers. In: Atlas RM, Philp JC, editors Bioremediation: applied microbial solutions for real-world environmental cleanup. American Society for Microbiology (ASM) Press, Washington, 2005:139–236. 82. Mohan SV, Sirisha K, Rao RS, Sarma PN. Bioslurry phase remediation of chlorpyrifos contaminated soil: process evaluation and optimization by Taguchi design of experimental (DOE) methodology. Ecotoxicol Environ Saf 2007;(68):252–262. 124


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83. Zangi-Kotler M, Ben-Dov E, Tiehm A, Kushmaro A. Microbial community structure and dynamics in a membrane bioreactor supplemented with the flame retardant dibromoneopentyl glycol. Environ Sci Pollut Res Int 2015;(22):17615â&#x20AC;&#x201C;17624.

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https://doi.org/10.22319/rmcp.v11s2.4742 Review

Causes and consequences of climate change in livestock production and animal health. Review

Berenice Sánchez Mendoza a Susana Flores Villalva a Elba Rodríguez Hernández a Ana María Anaya Escalera a Elsa Angélica Contreras Contreras a*

a

Instituto Nacional de Investigaciones Forestales Agrícolas y Pecuarias-CENID-FyMA. Querétaro, Querétaro, México.

*Corresponding author: elsangeli@hotmail.com

Abstract: The accumulation of greenhouse gases in the Earth's atmosphere is causing an unprecedented climate change with serious implications, such as extreme weather events and changes in the function and composition of ecosystems. Due to its importance it is relevant to analyze the impact of climate change on livestock systems. An area that requires special attention is precisely animal health, the emergence and re-emergence of vector-borne diseases in numerous regions of the planet are a clear example of the association between climate change and its effects on the human/animal health interface. The effects on health animal can obey multiple social and environmental factors causing the so-called "diseases of production", which influence the appearance of emerging diseases. However, each region and each livestock system has its own vulnerabilities. These aspects must be taken into account for the design of local and regional risk maps, as well as for the efficient design, implementation and socialization of risk management processes for diseases. Key words: Climate change, Animal diseases, Livestock production, Adaptation measures.

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Received: 07/01/2018 Accepted: 04/06/2018

Introduction The climate of planet Earth varies according to the epochs and the areas where the observed climate changes generally extend through long periods. However, in recent decades, these changes seem to have accelerated according to certain indicators, such as the increase in temperature, the reduction in the area of Arctic ice and of the continental glaciers, the rising of the mean global level of the ocean, and bio-indicators such as the displacement of the populations of terrestrial and marine animals; as well as the displacement of the stages of agricultural activities. Therefore, climate change goes far beyond global warming and its consequences. Climate change causes more profound implications, such as extreme weather, alteration of the water cycle, ocean acidification, and changes in the role and composition of ecosystems. This whole set of drastic changes causes the formation of destructive natural phenomena such as hurricanes, cyclones or tsunamis. It is predicted that these weather patterns will result in the spread or increase in prevalence of different animal and human diseases, as well as in the extinction of animal and plant species(1,2). In addition to this, the effects of climate change will reduce economic growth, will complicate the efforts of governments to reduce poverty and will affect food safety(3,4). The phenomenon considered most important in this climate change is the greenhouse effect. It is originated by the energy coming from the sun, formed by waves of frequencies that pass through the atmosphere with ease, after which the energy transmitted outwards from the earth, being formed by waves of lower frequencies, is absorbed by greenhouse gases (GHGs), thus producing the greenhouse effect(5). Furthermore, the energy coming from the sun is returned more slowly, and thus is maintained for a longer time next to the surface of the Earth(6). The main GHG emissions associated with the phenomenon of global warming, are carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCS), perfluorocarbons (PFCS) and sulfur hexafluoride (SF6)(6,7).

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Greenhouse gases Since the industrial revolution in the 18th century, and up until the present, the atmospheric composition of CO2, CH4 and N2or has exceeded the values that were given during the previous 10 000 years. The increase in their concentration has led to the absorption and reemission of infrared radiation into the atmosphere and the surface of the earth, having generated an increase of the temperature by about 0.6°C during the 20th century. This trend has been attributed to the accumulation of CO2 and other greenhouse gases in the atmosphere derived from human activity(8). CO2, for example, participates in the carbon cycle in nature; 1.012 t pass through the natural carbon cycle, in the process of photosynthesis. In addition, it has a collection of the radiation of up to 49 % and has an atmospheric lifetime of between 50 and 200 years(9). Thanks to international agreements such as the Montreal and Kyoto protocols, as well as the recent summits in Copenhagen and Cancun, as well as to the existence of governmental and non-governmental organizations around the world, many countries are taking actions aimed at the mitigation of GHG emissions. In this way, the first action was to determine the GHG inventory that each country emits considering their various socio-economic activities(7). The International Protocol The Kyoto Protocol sets limits for the different greenhouse gases and establishes the commitment for developed countries to assess and quantify the concentrations of these gases, and, in particular, to develop techniques for reducing them. The Intergovernmental Panel on Climate Change(10) (IPCC) has established, through various working groups, models for calculating emissions, suggesting different emission factors according to the level of knowledge and data from each geographical area and agricultural and livestock production. Although these are merely estimates, this constitutes the only consensual model at the global level, because it allows making approximations of the emissions that can be used for comparative purposes between productive systems. In the particular case of Mexico, the inventory of GHG emissions before the United Nations Framework Convention on Climate Change (UNFCCC) is being carried out since 1997(11). In accordance with this inventory, as shown in Figure 1, the energy sector is the biggest emitter of GHG emissions (20 %), whereas in the case of agriculture and livestock production, it contributes only 6.4 % of the anthropogenic emissions of CH4, CO2 and N2O into the atmosphere. Reports of the Food and Agriculture Organization of the United Nations (FAO) classify the intensive production of cattle as the main contributor to environmental pollution(6), enteric fermentation being one of the major sources of CH4. This process has a polluting potential of 23 to 30 times higher than CO2; for example, in the year 2014 it reached a maximum atmospheric concentration of 1.833 ppm, equivalent to 254% of its pre-industrial level(12). 128


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Figure 1: National inventory of greenhouse gas and compound emissions in Mexico

Source: Adapted from IPCC, 2017(10).

Due to the rapid increase in atmospheric concentrations of CH4 in recent years, as well as to the effects on the climate and on atmospheric chemistry, its emissions should be controlled and reduced(11,13). As a result, ruminants are in first place of importance within the stockbreeding, since, in Mexico livestock contributes 84 % of the total CH4 issued by the livestock sector, of which 89 % is generated by the stabled beef and dual-purpose bovine cattle, 10 % by the milk-producing cattle, and 1 % by the rest of the farm animals(14). On the other hand, it is estimated that by the year 2050 the world food production will have to increase by 60 % in order to meet the increasing demand. This production will have to use current agricultural lands under the premise of producing more while using fewer natural resources; hence, the need to create eco-efficient environments for adaptation to climate change(15).

Impact of climate change on livestock production The livestock sector is facing a paradox. On the one hand, it is blamed for the generation of GHG emissions, according to data from the FAO(15), since at the global level the production of beef and milk is responsible for the majority of the emissions, as it contributes with 41 and 29%, respectively, of the emissions of the sector. Pork and poultry eggs contribute 9 and 8% 129


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of the emissions of the sector. The production and processing of feed and enteric fermentation due to ruminant animals are the two main sources of emissions, responsible for 45 and the 39 % of the emissions of the sector. The storage and processing of manure contribute 10 %. The remaining part is attributed to the processing and transport of livestock products (16). On the other hand, food production provides 40 % of the value of the world agricultural production and supports the livelihoods and food safety of almost 1.300 million people in the world(17). In many developing countries, livestock is a multifunctional activity; beyond its direct role in the generation of food and income, livestock is a valuable asset, serving as a stock of wealth and a warranty for credits, and constituting an essential safety net in times of crisis(18). Due to its importance, it is relevant to analyze the impact of climate change on livestock production systems. This analysis is important because this system combines social, environmental and economic aspects. The effects of climate change will have a direct impact on the social organization of production units, on food safety and on human and animal health(19). From a social perspective, taking into account the local specificities, the effects of climate change on agricultural production will depend, among other factors, on the type of system, which can be either intensive or extensive(18). Intensive systems get 90 % of the cattle feed from external systems; they engage in the production of a single species, driving high densities per surface area unit, and use balanced foods based on cereals; therefore, in these systems, land is not such an important factor as, for example, technology; their production is intended primarily for sale and does not use family labor(20). On the contrary, extensive production systems are more closely tied to the natural conditions of the medium and use family labor, and their production is intended mainly for home consumption. The production units are small and run by families, and their economic logic is not to pursue the maximum profit, but rather to seek family welfare (21). Therefore, these differences in production systems cause opposite impacts(14). In both production systems, the difference is due to several factors, including the unequal distribution of resources and conditions for the development and deployment of capabilities for decision-making, i.e., to how vulnerable a system is with regard to climate change (22). In order to deal with the effects of climate change, adaptive measures are implemented that have to do with environmental, social and ecological adjustments. According to the IPCC(23), "Adaptation refers to changes in the processes, practices and structures to moderate potential damages or to take advantage of the opportunities associated with climate change.â&#x20AC;?

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Adaptation involves taking actions aimed at preserving the resilience and increasing the adaptive capacity of agro-ecosystems and the social actors in the agricultural sector(18). In this sense, working on climate change adaptation strategies with a family producer is not the same as working with a producer who exports meat. It is estimated that small producers will be most affected, given their low access to technologies, inputs and monetary resources to adopt adaptive measures(14,24,25). For example, the impact of climate change on the extensive systems translates into reduced availability of food, a consequence of the decline in agricultural production and the inadequacy of conditions for a livestock production that requires large amounts of pasture land to maintain the cattle, which, in sum, results in a diet that is poor in nutrients for the most vulnerable populations. The conditions become all the more severe because the dependence of producers on the natural cycles of production, and even the geographic location of the lands where they dwell places them in a situation of vulnerability(19). Within this context, an aspect that requires special attention is related to animal health. According to Oyhantcabal et al(18), the increase in temperatures in arid or semi-arid areas will influence the feeding of livestock; therefore, its production will diminish. This will result in a situation of physiological stress; in close relationship, problems of access to and need for water will appear, an inconvenient to be shared with humans. Thus, the absence of food and water can trigger diseases in the animals that affect their productivity. The emergence and reemergence of vector-borne diseases in many regions of the planet is a clear example of association between climate change and effects on the interface of human/animal health (12,26). In response to the intensified frequency of extreme events, the number of climate-related deaths and diseases may increase, since their effects on animal health may be due to multiple environmental factors that cause the so-called "production diseases"(18,27). Taking as reference the model of convergence to classify the factors that influence the emergence and reemergence of diseases, among a number of social and economic factors, the climate factor stands out(28). According to Oyhantcabal et al(18), the relationships can be simplified or can be broken down further, taking into consideration that the social and ecological factors interact with each other, instead of each one acting on its own. Certain scientists, like Black and Nunn(29), refer to the system as complex, calling him socioecological system or eco-social approach of health. Studies investigating trends in emerging infectious diseases have confirmed that these are almost always caused by socio-economic, environmental and ecological factors, so that new approaches are required to supplement traditional methods(30,31). It is important to specify that the purpose of the models is to help understand the relationships between the factors and to improve the capacity for adaptation and anticipation for the future(29).

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Thus, the socio-economic and environmental factors influence the occurrence of emerging diseases, which represent a threat to global health(27,32,33). However, what is worrying is that the distribution of resources for surveillance measures is not risk-based but is related to the increased capacity and availability of resources in each country. However, each region and each production system has its own vulnerabilities; these aspects must be taken into account in designing maps of local and regional risks, as well as designing, implementing and efficiently socialize risk management processes in the face of diseases(34).

Impact of climate change on animal health There is a vast literature on the contribution of agricultural activities to the generation of GHG emissions and, hence, to climate change; however, the effects of climate change on animal diseases have received very little attention(31,33, 35-37), despite their direct relationship to poverty and their impact on public health. Animal diseases have always appeared and evolved, changing for various reasons; however, the rapid changes in habitat distributions can alter the behavior. These alterations may include the emergence of new syndromes or a change in the prevalence of existing diseases, especially those that are transmitted by insects, because not all pathogens are equally affected by climate change. For some species it can mean an increase in area of influence, while for other can mean a decrease(32,38,39). Climate change can affect infectious diseases through own factors of the pathogen, the vector, the guest, epidemiology and other indirect factors(3,40). Microorganisms have the ability to mutate in order to adapt to environmental changes. For example, RNA (ribonucleic acid) viruses have high rates of mutation due to their rapid replication and lack of DNA correction (proof-reading) mechanisms(33). Another example that illustrates this rapid adaptation to climate change was observed with the virus that causes the Venezuelan equine encephalitis in Mexico; a single amino acid substitution in a membrane glycoprotein allowed its adaptation to another vector, the Ochlerotatus (Aedes) taeniorhynchus mosquito(41). This vector became more abundant in the regions of the Pacific coast of Mexico after the deforestation of 80 years destroyed the habitat of the Culex taeniopus mosquito, identified as one of the main vectors of the virus at that time(41-43). Thus, climate change affects not only the geographical distribution and abundance of vectors, but also the interaction between the pathogen and the vector, through its transmission to new vectors. Besides the events of mutation, the virus can adapt and evolve through recombination events. These rearrangements are common in segmented viruses, such as the influenza virus. In addition to this, climate change may reduce the available habitats, forcing the species to live in smaller areas. This favors the exchange of pathogens between animal species of various types, a

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phenomenon that favored the spread of the highly pathogenic avian influenza virus (H5N1)(44). Many animal diseases of importance are associated with insects and arthropods such as mosquitoes, flies and ticks, which serve as vectors. Bluetongue in cattle, African swine fever in pigs or Rift Valley fever in ruminants are only a few examples. Some diseases are not zoonotic, but their impact on the livestock industry can be devastating due to the loss of trade opportunities and to the costs of monitoring(45). These diseases can reach new territories through the spread of the vector to new geographical areas. This is considered to have occurred in the case of the bluetongue virus in United Kingdom in the year 2006(27). The geographical distribution of the vectors is highly dependent on environmental variables such as temperature, humidity and wind. For example, the extrinsic incubation period, defined as the period between which a vector that is fuelled by a host is able to transmit the infection to another susceptible host, extends to low temperatures(40). It has also been observed that the bluetongue virus is transmitted more efficiently by C. imicola at temperatures of 28 to 30 °C, being less efficient at temperatures close to 10 °C. In this way the warm temperatures favor the transmission of certain diseases(42). In the same way, the feed rate of arthropods augments at higher temperatures, which increases the exposure of livestock to pathogens, favoring their dissemination(21). The abundance of mosquitoes and midges is increased during periods of heavy rainfall that favor the formation of puddles or bodies of water that are ideal for oviposition. In Africa, for example, there have been outbreaks of Rift Valley fever in the warm phase of El Niùo(27). In particular, climate change may open territory that was previously uninhabitable for arthropod vectors, as well as increase the rate of reproducibility and stings (mosquitoes)/bites (ticks) and shorten the incubation period of pathogens(3,33). Many arthropods that feed on blood, such as ticks, spend most of their life cycle in the environment. Their development, survival and population dynamics depend on factors such as the availability of a host, the vegetation and the climate, among others(46-48). It is clear that climate change alters, directly or indirectly, the distribution and incidence of a broad range of diseases. However, the complexity of the host-pathogen relationships and their interaction with the environment makes it difficult to accurately predict the occurrence or modification of these diseases(30). An example illustrated by Gallana et al.(49) demonstrates the complexity of the process: Arctic warming has allowed the white-tailed deer (Odocoileus virginianus) and moose (Cervus canadensis) to expand their territories to the north, so that they now coexist with the musk ox and the caribou. The white-tailed deer and the moose harbor parasites that are new to the ox and the caribou, and therefore they do not have a natural resistance to the new parasites, which renders them more susceptible. Now the musk ox and the caribou are being infected with new parasites and, in addition, they are dealing with a higher parasite load, due to the increase in temperature that favors the life cycle, threatening their survival(49,50).

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Tick-borne diseases and climate change Ticks are the most important disease vectors after mosquitoes. They are hematophagous ectoparasites which feed on the blood of both animals and humans. This condition, gives them the ability to transmit a wide variety of pathogens such as viruses, bacteria and protozoa (flavivirus, erlichiosis, anaplasmosis, babesiosis, ricketsiosis, among others). Unfortunately, in Mexico there is no routine diagnosis of tick-borne diseases in animals or people; however, according to the Official Mexican Norm NOM-017-SSA2-2012(51), it is mandatory to notify the occurrence of spotted fever caused by Rickettsia rickettsii (R. rickettsii) in humans. Likewise, it is estimated that there are clinical cases of patients infected with Anaplasma and Ehrlichia chaffeensis, Ehrilichia canis(52). Despite the risk, it has not yet been possible to control tick infestations and, therefore, the diseases they transmit; for this reason, those areas where this vector is distributed still entail a risk to animal and human health(53). Recent evidence indicates that climate change has a direct or indirect effect in tick-borne diseases; the increase in temperature impacts their distribution and frequency. In addition to the effects of deforestation, land use change, among other factors, also have an impact on the hosts, the vectors and the pathogens(54,55). Some studies in Europe and the United States of America documented changes in the distribution of ticks associated with climate change. In Sweden, the expansion of the tick Ixodes ricinus has been reported in much of the territory(56), but mainly in the north, where the distribution of the tick doubled in 26.8 % of the territory in a period of 18 yr. Another study performed in Russia reported an increase in the abundance of I. ricinus in the eastern region of that country(57). The ticks of the genus Ixodes are the primary vectors of Lyme disease in North America, and their distribution depends largely on climate changes(58,59). The abiotic environment is crucial to their survival because much of their life cycle takes place in the vegetation; therefore, the climate is a determining factor in the distribution and establishment of tick populations(59,60). Lyme disease is the main emerging zoonosis transmitted by ticks in the United States of America and Europe. In Mexico the first reports were associated to infection close to parks in the City of Mexico, La Marquesa and Nevado de Toluca; later, cases were reported in the states of Nuevo Leรณn and Tamaulipas(61). To date, the distribution of ticks infected with Borrelia burgdorferi is very broad, covering regions from the Yucatan Peninsula and all the way to the north of the country. For this reason, it is considered that climate change will be of great importance in the distribution of this tick in future years(61).

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Temperature affects the activity of nymphs and adults of tick(62); for example, the species Ixodes ricinus can survive in temperatures of 14.4 to 18.9°C for a period of exposure of 24 h. Considering the high degree of tolerance to low temperatures, it is believed that climate change could increase the niche of this and other ticks in Europe or in nearby areas(26). Other species can withstand low temperatures, since they are well adapted to survival in sub-zero temperatures, as is the case of Dermacentor reticulatus, vector of canine babesiosis(62). According to some reports, the adaptation of ticks to climate change will not be the same in all regions, as it will depend largely on the species concerned. Through the model of ecological niche for I. ricinus in Europe, an expansion of habitat of the 3.8% was predicted to occur throughout that continent. The expansion of the habitat would encompass Scandinavia among other regions, while there would be a reduction of habitats in the Alps, Italy and a part of Poland(63). Climate change is also expected to affect the reproductive capacity of Ixodes scapularis in Canada and the United States of America(1). The effect of climate change on tropical areas could adversely affect some species, affecting the optimum habitat and forcing them to colonize new places; in this way, it is estimated that the gradual increase in temperature will force the tropical bont tick, Ambylomma variegatum, to colonize new areas where there is prolonged drought in Zimbabwe(64). There are studies that correlate the presence of Mediterranean fever with an increase mediated by global warming in the number of tick bites in dogs(65). Also, in the north of Russia climate change has been the catalyst for the expansion of the habitat of Ixodes persulcatus and for the incidence of tick-borne encephalitis(66). In contrast, there are studies which indicate that, in spite of climate change, the distribution of some ticks will not be affected in a major way. In a predictive model using a maximum entropy approximation, by geographical correlation data and climatic variables, it was determined that the habitat for the distribution of I. scapularis infected with Borrelia burgdorferi between Texas and Mexico should remain relatively stable over the next 33 yr(30). The inconsistency between these studies will give rise to the controversy over whether climate change will impact or not the vectors and the diseases they transmit(31). Other studies, for example, mention that effects associated to climate change are undoubtedly involved in the increase of various diseases. The meta-analysis of more than 200 effects on 61 species of parasites suggests that a decrease in biodiversity may increase the human and animal diseases, as well as decrease agricultural and forestry production(67). Thus, although the relationship between the rate of development of ticks and the temperature is not yet clear(68), the influence of climate change not only in the redistribution of disease vectors but in the life of any organism that inhabits the earth is unquestionable(69). As a consequence of the adaptation of these vectors to new climates, the risk of infectious diseases transmitted by these vectors may be potentiated. A comprehensive understanding of the climatic effects requires multidisciplinary study that allows the analysis of the ecosystem of the pathogens and their vectors, in order to identify whether they have the potential to 135


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affect human and animal populations under a scenario of climate change. For this reason, mappings are being conducted at the National Laboratory of Genomics in Health (LANGESA) in Hidalgo, Mexico, for the purpose of determining the distribution and frequencies of the vectors and reservoirs in the country. This information will allow to determine the impact of climate change on the tick-borne diseases in Mexico.

Impact of climate change on other diseases As mentioned, various animal diseases are affected by climate change, either directly or indirectly and vector-borne diseases are the most studied. However, diseases associated with flooding or standing water such as leptospirosis, anthrax, cryptosporidiosis, fascioliasis, among others, also require special attention(35). Table 1 lists are some of them.

Table 1: Main animal diseases affected by climate change Classification

Disease

Causal agent

Bluetongue

Vector-borne diseases

Vector

Bluetongue virus Culicoides (Orbivirus) midge African horse Ahsv (Orbivirus) Culicoides sickness midge Occasional transmission by mosquitoes (Culex, Anopheles, Aedes spp.) and ticks (Hyl, Rhipicephalus) has also been reported. Rift Valley fever Rift Valley fever Mosquitoes virus (Aedes spp.) (Phlebovirus) West Nile virus West Nile Virus Mosquitoes infection (Flavivirus) (Culex spp.)

136

Zoonosis No No

Yes

Yes


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Venezuelan equine Venezuelan encephalitis equine encephalitis virus (Alphavirus) Chagas Disease Trypanosoma cruzi Leishmaniasis Several authors

Babesiosis Dirofilariosis

Lyme Disease

Anthrax Leptospirosis Diseases associated with flooding or stagnant water. Cryptosporidiosis

Fasciolasis

Mosquitoes Yes (Aedes spp., Culex spp.)

Bed bugs of the subfamily Triatominae Protozoa of the Sandfly of the genus Leishmania genus Lutzomyia Protozoa of the Ticks of the genus Babesia genus Ixodes Nematode Mosquitoes Dirofilaria (Aedes, immitis. Anopheles, Culex) Bacterium Ticks of the Borrelia genus Ixodes burgdorferi Bacteria, Bacillus Does not apply anthracis Bacterium Does not apply Leptospira interrogans Coccidia, Does not apply Crystosporidium spp. Fluke, Fasciola Snails of the hepatica. genus Lymnaea

Yes

Yes Yes Yes

Yes

Yes Yes

Yes

Yes

Source: Adapted from several authors(27,29-36).

The list of diseases in table 1 aims to summarize those diseases that deserve special attention because of their impact on the public and livestock health. The increase in temperature, humidity and rainfall may increase the prevalence of vector-borne diseases. However, there are other diseases that can generate outbreaks associated with the increase of humidity by excessive rains or floods(48). The temperature, relative humidity and soil moisture favor the germination of the spores of anthrax; while the heavy rains can activate them. Anthrax outbreaks have been associated with the alternation of heavy rains, drought and high temperatures(39,70). Leptospirosis and cryptosporidiosis are diseases with epidemic potential after heavy rains(71). Finally, the prevalence of diseases of global distribution like haemoncosis and fasciolasis may be increased; the larvae of Haemonchus contortus can survive for months on earth under appropriate conditions of temperature and humidity. Likewise, the formation of puddles or water bodies and the increase of rainfall favor the 137


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survival of the snail that transmits Fasciola hepatica. These diseases cause significant economic losses, due to the decrease in the production parameters of livestock. In addition to this, it is expected that the increase in the prevalence of these diseases will favor the development of resistance to antiparasitic drugs that will render them difficult to control.

Conclusions Human influence on global warming is clear; recent climate changes require rethinking of the manner in which the stockbreeding sector is acting and implement more sustainable systems that will maintain the resilience of the cattle system; this will improve the supply of products and services derived from this industry, decreasing the impact on the environment and, consequently, on the emergence and reemergence of animal and human diseases. This implies a major challenge for developing countries that still have pending, among other things, the reduction of poverty in which an important part of its population lives. Therefore, it is clear that interventions aimed to promote and facilitate adaptation to climate change must not be divorced from social, cultural and health interventions.

Literature cited: 1.

Ogden NH, Lindsay LR. Effects of climate and climate change on vectors and vectorborne diseases: Ticks are different. Trends Parasitol 2016;32(8):646-656. https://doi.org/10.1016/j.pt.2016.04.015

2.

Tercera Comunicación Nacional de Cambio Climático ante la Convención Marco de las Naciones Unidas sobre Cambio Climático. IDEAM. Instituto de Hidrología, Meteorología y Estudios Ambientales. México 2017. http://documentacion.ideam.gov.co/cgibin/koha/opacdetail.pl?biblionumber=38147.

3.

Patz JA, Epstein PR, Burke TA, Balbus JM. Global climate change and emerging infectious diseases. JAMA, 1996;275(3):217-223.

4.

Field CB, Barros VR, Dokken DJ, Mach KJ, Mastrandrea MD, Bilir TE, et al. White LL. (eds.). IPCC: Climate Change 2014: Impacts, adaptation, and vulnerability. Part A: Global and sectoral aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press; 2014.

138


Rev Mex Cienc Pecu 2020;11(Supl 2):126-145

5.

Rosenzweig C, Hillel D. Climate change and the global harvest: potential impacts of the greenhouse effect on agriculture. Oxford University Press; 1998:324.

6.

Seinfeld JH, Pandis SN. Atmospheric chemistry and physics from air pollution to climate change. New York. John Wiley and Sons, Incorporated; 1998.

7.

Protocolo de Kyoto de la Convención Marco de Las Naciones Unidas sobre el cambio climático. Naciones Unidas. 1998.

8.

Hansen JE, Sato M. Trends of measured climate forcing agents. Proc Nat Acad Sci. United States of America 2001;98(26);14778-14783.

9.

Watson RT, Rodhe H, Oeschger H, Siegenthaler U. Greenhouse gases and aerosols. In: Climate change: the IPCC Scientific Assessment. Houghton JT, et al, editors. Cambridge: Cambridge University Press; 1990.

10. IPCC (Intergovernmental Panel on Climate Change) 2016. https://www.ipcc.ch/. Consultado 10 Feb, 2017. 11. Convención Marco de las Naciones Unidas sobre el Cambio Climático y su Protocolo de Kioto. (CMNUCC) https://www.gob.mx/semarnat/acciones-yprogramas/convencion-marco-de-las-naciones-unidas-sobre-el-cambio-climatico-y-suprotocolo-de-kioto-cmnucc?idiom=es Consultado 7 Feb, 2017. 12. FAO. Food and Agriculture Organization of the United Nations. La ganadería a examen. Estado mundial de la agricultura y la alimentación. Roma. 2009. http://www.fao.org/docrep/012/i0680s/i0680s.pdf. 13. OMM (Organización Meteorológica Mundial). 2015. Boletín sobre los gases de efecto invernadero. https:// www.wmo.int/media/es/content/las-concentraciones-de-gases-deefecto-invernadero-vuelven-batir-un-r%C3%A9cord. Consultado 4 Feb, 2017. 14. FAO. Food and Agriculture Organization of the United Nations. La larga sombra del ganado: problemas ambientales y opciones. Roma. 2009. http://www.fao.org/docrep/011/a0701s/a0701s00.htm. 15. FAO. Food and Agriculture Organization of the United Nations. Tackling Climate Change Through Livestock. A global assessment of emissions and mitigation opportunities Roma. 2013. http://www.fao.org/docrep/018/i3437e/i3437e00.htm. 16. Grain. Campo y crisis climática. Soberanía Alimentaria. Biodiversidad y Culturas. Barcelona. 2010.

139


Rev Mex Cienc Pecu 2020;11(Supl 2):126-145

17. Barbier EB. Agricultural expansion, resource booms and growth in Latin America: Implications for long-run economic development. World Develop 2004;32(1):137-157. 18. Oyhantçabal W, Vitale E, Lagarmilla P. El cambio climático y su relación con las enfermedades animales y la producción animal. En: Compendio de los temas técnicos presentados ante la Asamblea mundial de los delegados o a las Comisiones regionales de la OIE–2009, Paris: Organización Mundial de Sanidad Animal (OIE); 2010:169-177. 19. Lorente-Saiz A. Ganadería y cambio climático: una influencia recíproca. GeoGraphos. Revista Digital para Estudiantes de Geografía y Ciencias Sociales 2010;1(3):1-22. http://web.ua.es/revista-geographos-giecryal. 20. Bravo-Ortega C, Lederman D. Agriculture and national welfare around the world: Causality and international heterogeneity since 1960. World Bank Policy Research Working Paper. 2005. 21. Ardila A, Wilson V. El sector pecuario frente al cambio climático: una realidad incómoda. Rev Cienc Anim 2012;(5):107-120. 22. Oswald Ú. Cambio climático, conflictos sobre recursos y vulnerabilidad social. En Delgado GC, Gay C (coordinadores). México frente al cambio climático. Retos y oportunidades, UNAM, México. 2010:51-83. 23. IPCC. Intergovernmental Panel on Climate Change Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Geneva, Switzerland. 2014. http://www.ipcc.ch/report/ar5/wg3/ 24. Fischer G, Shah M, Tubiello FN, Van-Velhuizen H. Socio-economic and climate change impacts on agriculture: an integrated assessment, 1990-2080. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 2005;360(1463):2067-2083. https://doi.org/10.1098/rstb.2005.1744. 25. Mendelsohn R. The impact of climate change on agriculture in developing countries. J Nat Resour Policy Res 2008;1(1):5-19. https://doi.org/10.1080/19390450802495882 26. Porretta D, Mastrantonio V, Amendolia S, Gaiarsa S, Epis S, Genchi C, et al. Effects of global changes on the climatic niche of the tick Ixodes ricinus inferred by species distribution modelling. Parasites & Vectors 2013;(6):271. https://doi.org/10.1186/17563305-6-271. 27. Lubroth J. Climate change and animal health. En: FAO: Building resilience for adaptation to climate change in the agriculture sector. Roma. 2012:63-70. 140


Rev Mex Cienc Pecu 2020;11(Supl 2):126-145

28. King L. What is one health and why is it relevant to food safety? Workshop Improving Food Safety Through One Health, Forum on Microbial Threats; Washington, DC: Institute of Medicine; 2011. 29. Black P, Nunn M. Repercusiones de los cambios climáticos y medioambientales en las enfermedades animales emergentes y reemergentes y en la producción animal. Conf. OIE 2009:27-39. 30. Feria-Arroyo TP, Castro-Arellano I, Gordillo-Pérez G, Cavazos AL, Vargas-Sandoval M, Grover A, et al. Implications of climate change on the distribution of the tick vector Ixodes scapularis and risk for Lyme disease in the Texas-Mexico transboundary region. Parasites & Vectors 2014:7-199. https://doi.org/10.1186/1756-3305-7-199 31. Gilbert L. Altitudinal patterns of tick and host abundance: a potential role for climate change in regulating tick-borne diseases? Oecologia 2010:162 (1):217-225. https://doi.org/10.1007/s00442-009-1430-x 32. Shuman EK. Global climate change and infectious diseases. New England J Medicine 2010;362(12):1061-1063. https://doi.org/10.1056/NEJMp0912931 33. Shope R. Global climate change and infectious diseases. Environmental Health Perspectives 1991;(96):171-174. 34. McBean G. Climate change: Global risks, challenges and decisions. Eos Trans. AGU, 2012;93(18):182. 35. Colwell DD, Dantas-Torres F, Otranto D. Vector-borne parasitic zoonoses: emerging scenarios and new perspectives. Vet Parasitol 2011;182(1):14-21. https://doi.org/10.1016/j.vetpar.2011.07.012. 36. Mills JN, Gage KL, Khan AS. Potential influence of climate change on vector-borne and zoonotic diseases: a review and proposed research plan. Environment Health Perspect 2010;118(11):1507-1514. https://doi.org/10.1289/ehp.0901389 37. Cumming GS. Comparing climate and vegetation as limiting factors for species ranges of African ticks. Ecology 2002;83(1):255-268. https://doi.org/10.2307/2680136. 38. Wu X, Lu Y, Zhou S, Chen L, Xu B. Impact of climate change on human infectious diseases: Empirical evidence and human adaptation. Environment Int 2015;86:14-23. 39. Rossati A. Global warming and its health impact. Int J Occupat Environment Med 2017;8(1-963):7-20.

141


Rev Mex Cienc Pecu 2020;11(Supl 2):126-145

40. Baylis M, Githeko AK. The effects of climate change on infectious diseases of animals. Report for the Foresight Project on Detection of Infectious Diseases, Department of Trade and Industry. UK: UK Government. 2006. 41. Brault AC, Powers AM, Ortiz D, Estrada-Franco JG, Navarro-Lopez R, Weaver SC. Venezuelan equine encephalitis emergence: enhanced vector infection from a single amino acid substitution in the envelope glycoprotein. Proc Nat Acad Sci. United States of America 2004;101(31),11344-11349. https://doi.org/10.1073/pnas.0402905101. 42. Gale P, Drew T, Phipps LP, David G, Wooldridge M. The effect of climate change on the occurrence and prevalence of livestock diseases in Great Britain: a review. J Appl Microbiol 2009;106(5):1409-1423. https://doi.org/10.1111/j.1365-2672.2008.04036.x 43. Lindahl JF, Grace D. The consequences of human actions on risks for infectious diseases: a review. Infection Ecology & Epidemiology 2015:5. https://doi.org/10.3402/iee.v5.30048 44. Forrest HL, Webster RG. Perspectives on influenza evolution and the role of research. Anim Health Res Rev 2010;11(1):3-18. https://doi.org/10.1017/S1466252310000071 45. Thornton PK, Steeg J. Van de Notenbaert A, Herrero M. The impacts of climate change on livestock and livestock systems in developing countries: A review of what we know and what we need to know. Agric Syst 2009;01(3):113-127. http://dx.doi.org/10.1016/j.agsy.2009.05.002 46. Dantas-Torres F, Figueredo LA, Otranto D. Seasonal variation in the effect of climate on the biology of Rhipicephalus sanguineus in southern Europe Parasitology 2011;138(4): 527-536. https://doi.org/10.1017/S0031182010001502 47. Estrada-PeĂąa A, Gray JS, Kahl O, Lane RS, Nijhof AM. Research on the ecology of ticks and tick-borne pathogens--methodological principles and caveats. Frontiers Cellular Infection Microbiol 2013;3:29. https://doi.org/10.3389/fcimb.2013.00029 48. Jore S, Vanwambeke SO, Viljugrein H, Isaksen K, Kristoffersen AB, Woldehiwet Z, et al. Climate and environmental change drives Ixodes ricinus geographical expansion at the northern range margin. Parasites & Vectors 2014;7:11. https://doi.org/10.1186/1756-3305-7-11 49. Gallana M, Ryser-Degiorgis MP, Wahli T, Segner H. Climate change and infectious diseases of wildlife: Altered interactions between pathogens, vectors and hosts. Current Zoology 2013;59(3):427-437. https://doi.org/10.1093/czoolo/59.3.427

142


Rev Mex Cienc Pecu 2020;11(Supl 2):126-145

50. Black PF, Butler CD. One Health in a world with climate change. Revue Scientifique et Technique (International Office of Epizootics) 2014;33(2):465-473. 51. Norma Oficial Mexicana NOM-017-SSA2-2012, para la vigilancia epidemiológica. 52. Sosa-Gutiérrez CG, Solórzano-Santos F, Walker DH, Torres J, Serrano CA, GordilloPérez G. Fatal monocytic ehrlichiosis in woman, México, 2013. Emerg Infect Diseases 2016;22(5):871-874. https://doi.org/10.3201/eid2205.151217 53. De la Fuente J, Kocan KM. Strategies for development of vaccines for control of ixodid tick species. Parasite Immunol 2006;28(7):275-283. https://doi.org/10.1111/j.13653024.2006.00828.x 54. Parham PE, Waldock J, Christophides GK, Hemming D, Agusto F, Evans KJ, et al. Climate, environmental and socio-economic change: weighing up the balance in vectorborne disease transmission. Philosoph Transact Royal Soc of London. Series B, Biological Sci 2015;370:(1665). https://doi.org/10.1098/rstb.2013.0551 55. Medlock JM, Leach SA. Effect of climate change on vector-borne disease risk in the UK. Lancet Infectious Diseases 2015;15(6):721-730. https://doi.org/10.1016/S14733099(15)70091-5. 56. Jaenson TG, Hjertqvist M, Bergström T, Lundkvist Å. Why is tick-borne encephalitis increasing? A review of the key factors causing the increasing incidence of human TBE in Sweden(a). Parasites & Vectors 2012;5:184. https://doi.org/10.1186/1756-3305-5184 57. Korotkov Y, Kozlova T, Kozlovskaya L. Observations on changes in abundance of questing Ixodes ricinus, castor bean tick, over a 35-year period in the eastern part of its range (Russia, Tula region). Med Vet Entomol 2015;29(2):129-136. https://doi.org/10.1111/mve.12101 58. Dennis DT, Nekomoto TS, Victor JC, Paul WS, Piesman J. Reported distribution of Ixodes scapularis and Ixodes pacificus (Acari: Ixodidae) in the United States. J Medic Entomol 1998;35(5):629-638. 59. Bertrand MR, Wilson ML. Microclimate-dependent survival of unfed adult Ixodes scapularis (Acari:Ixodidae) in nature: life cycle and study design implications. J Medic Entomol 1996;33(4):619-627. 60. Fish D. Population ecology of Ixodes damini. In: Ecology and environmental management of Lyme disease. New Brunswick, NJ: Rutgers University Press; 1993:2542. 143


Rev Mex Cienc Pecu 2020;11(Supl 2):126-145

61. Gordillo-Pérez G, Vargas M, Solórzano-Santos F, Rivera A, Polaco OJ, Alvarado L, et al. Demonstration of Borrelia burgdorferi sensu stricto infection in ticks from the northeast of Mexico. Clinical Microbiol Infection: European Soc Clinical Microbiol Infect Diseases 2009;15(5):496-498 https://doi.org/10.1111/j.1469-0691.2009.02776.x 62. Medlock JM, Hansford KM, Bormane A, Derdakova M, Estrada-Peña A, George JC, et. al. Driving forces for changes in geographical distribution of Ixodes ricinus ticks in Europe. Parasites & Vectors 2013;6(1):1. https://doi.org/10.1186/1756-3305-6-1 63. Boeckmann M, Joyner, TA. Old health risks in new places? An ecological niche model for I. ricinus tick distribution in Europe under a changing climate. Health & Place 2001;30:70-77. https://doi.org/10.1016/j.healthplace.2014.08.004. 64. Estrada-Peña A, Horak IG, Petney T. Climate changes and suitability for the ticks Amblyomma hebraeum and Amblyomma variegatum (Ixodidae) in Zimbabwe (19741999). Vet Parasitol 2008;151(2-4):256-267. https://doi.org/10.1016/j.vetpar.2007.11.014 65. Parola P, Socolovschi C, Jeanjean L, Bitam I, Fournier PE, Sotto A, et al. Warmer weather linked to tick attack and emergence of severe rickettsioses. PLoS Negl Trop Dis. 2008;2(11):e338. 66. Tokarevich NK, Tronin AA, Blinova OV, Buzinov RV, Boltenkov VP, Yurasova ED, Nurse J. The impact of climate change on the expansion of Ixodes persulcatus habitat and the incidence of tick-borne encephalitis in the north of European Russia. Glob Health Action. 2011;4:8448. doi: 10.3402/gha.v4i0.8448. 67. Civitello DJ, Cohen J, Fatima H, Halstead NT, Liriano J, McMahon TA, et al. Biodiversity inhibits parasites: Broad evidence for the dilution effect. Proc Natl Acad Sci USA. 2015;112(28):8667-71. Doi: 10.1073/pnas.1506279112. 68. Estrada-Peña A, Ayllon N, De la Fuente J. Impact of climate trends on tick-borne pathogen transmission. Frontiers Physiol 2012;3:64. https://doi.org/10.3389/fphys.2012.00064 69. Shevenell AE, Ingalls AE, Domack EW, Kelly C. Holocene Southern ocean surface temperature variability west of the Antarctic Peninsula. Nature 2011; 470(7333):250254. https://doi.org/10.1038/nature09751 70. Parker R. Anthrax and livestock. Guide B-120. En Cooperative Extension Service, College of Agriculture and Home Economics. Las Cruces, Nuevo Mexico. University of New Mexico. 2002.

144


Rev Mex Cienc Pecu 2020;11(Supl 2):126-145

71. McMichael AJ. Extreme weather events and infectious disease outbreaks. Virulence 2015;6(6):543-547. https://doi.org/10.4161/21505594.2014.975022

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Rev. Mex. Cienc. Pecu. Vol. 11 Suplemento 2, pp. 1-145, MARZO-2020

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CONTENIDO CONTENTS

Efecto de la temperatura del agua sobre la constante de velocidad de reacción de los contaminantes en un humedal construido para el tratamiento de aguas residuales porcícolas Water temperature effect on the reaction rate constant of pollutants in a constructed wetland for the treatment of swine wastewater Celia De La Mora-Orozco, Rubén Alfonso Saucedo-Terán, Irma Julieta González-Acuña, Sergio Gómez-Rosales, Hugo Ernesto Flores-López…………………………………….........................................1

Estimación de la producción de metano entérico en ranchos de producción familiar de leche bovina en el sur del estado de Querétaro, México Estimation of enteric methane production in family-run dairy farms in the south of the State of Querétaro, Mexico

Sergio Gómez Rosales, María de Lourdes Ángeles. José Luis Romano Muñoz, José Ariel Ruíz Corral………………………………………………………………………….…….……......….…18

Efecto del calentamiento global sobre la producción de alfalfa en México Global warming effect on alfalfa production in Mexico Guillermo Medina-García, Francisco Guadalupe Echavarría-Cháirez, José Ariel Ruiz-Corral, Víctor Manuel Rodríguez-Moreno, Jesús Soria-Ruiz, Celia De la Mora-Orozco...............................................34

Áreas con aptitud ambiental para [Bouteloua curtipendula (Michx.) Torr.] en México por efecto del cambio climático Environmental suitability areas for [Bouteloua curtipendula (Michx.) Torr.] in Mexico due to climate change effect José Ángel Mar�nez Sifuentes, Noé Durán Puga, José Ariel Ruiz Corral, Diego Raymundo González Eguiarte, Salvador Mena Munguía........................................................................................49

Efecto en la erosión hídrica del suelo en pastizales y otros tipos de vegetación por cambios en el patrón de lluvias por el calentamiento global en Zacatecas, México Effects of rainfall pattern changes due to global warming on soil water erosion in grasslands and other vegetation types in the state of Zacatecas, Mexico Francisco Guadalupe Echavarría-Cháirez, Guillermo Medina-García, José Ariel Ruiz-Corral.............................................................................................................................................63

Estimación del factor de transporte del índice de fósforo con climatologías y escenarios de cambio climático en tierras de Jalisco, México Estimation of the transport factor of the phosphorus index in climatology and climate change scenarios in Jalisco, Mexico Hugo Ernesto Flores López, Álvaro Agus�n Chávez Durán, José Ariel Ruíz Corral, Celia De La Mora Orozco, Uriel Figueroa Viramontes, Agus�n Hernández Anaya........................................................75

Impacto del cambio climático en la distribución potencial de Tithonia diversifolia (Hemsl.) A. Gray en México Impact of climate change on the potential distribution of Tithonia diversifolia (Hemsl.) A. Gray in Mexico Noé Durán Puga, José Lenin Loya Olguín, José Ariel Ruiz Corral, Diego Raymundo González Eguiarte, Juan Diego García Paredes, Sergio Mar�nez González, Marcos Rafael Crespo González......................93

REVISIONES DE LITERATURA Mitigación y adaptación al cambio climático mediante la implementación de modelos integrados para el manejo y aprovechamiento de los residuos pecuarios. Revisión Mitigation and adaptation to climate change through the implementation of integrated models for the management and use of livestock residues. Review Alberto Jorge Galindo-Barboza, Gerardo Domínguez-Araujo, Ramón Ignacio Arteaga-Garibay, Gerardo Salazar-Gu�érrez......................................................................................................107

Causas y consecuencias del cambio climático en la producción pecuaria y salud animal. Revisión Causes and consequences of climate change in livestock production and animal health. Review Berenice Sánchez Mendoza, Susana Flores Villalva, Elba Rodríguez Hernández, Ana María Anaya Escalera, Elsa Angélica Contreras Contreras.............................................................................126

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