Hostname: page-component-78c5997874-j824f Total loading time: 0 Render date: 2024-11-10T14:10:44.875Z Has data issue: false hasContentIssue false

Use of an aridity index to classify season with an application in genetic evaluation of Braunvieh cattle

Published online by Cambridge University Press:  08 August 2022

J. B. Herrera-Ojeda
Affiliation:
Departamento de Ciencias Básicas, Instituto Tecnológico del Valle de Morelia, Instituto Tecnológico Nacional, Morelia, Michoacán, México
R. Ramírez-Valverde
Affiliation:
Departamento de Zootecnia, Universidad Autónoma Chapingo, Texcoco, México
R. Núñez-Domínguez
Affiliation:
Departamento de Zootecnia, Universidad Autónoma Chapingo, Texcoco, México
N. Lopez-Villalobos
Affiliation:
Centro Universitario Temascaltepec, Universidad Autónoma del Estado de México, Temascaltepec, México School of Agriculture and Environment, Massey University, Palmerston North, New Zealand
J. F. Vázquez-Armijo
Affiliation:
Centro Universitario Temascaltepec, Universidad Autónoma del Estado de México, Temascaltepec, México
K. E. Orozco-Durán
Affiliation:
Facultad de Agrobiología, Universidad Michoacana de San Nicolás de Hidalgo, Uruapan, Michoacán, México
G. M. Parra-Bracamonte*
Affiliation:
Centro de Biotecnología Genómica, Instituto Politécnico Nacional, Reynosa, Tamaulipas, México
*
Author for correspondence: G. M. Parra-Bracamonte, E-mail: gparra@ipn.mx

Abstract

One of the most important aspects of genetic evaluation (GE) is the definition of contemporary groups (CG), commonly defined as animals of the same sex born in the same herd, year and season. The objective of this study was to use an aridity index (AI) to classify season and evaluate the implications on the GE of Braunvieh cattle. A data set with 32 777 and 22 448 birth weight (BW) and weaning weight adjusted to 240 days (WW) records, respectively, was used to compare two methods of classification of climatic seasons to be used in the definition of CG for GE models. The first method considered rain season criterion (RC), and the second method is a proposed classification using an AI. Both methods were compared using two approaches. The first approach examined differences in mixed models using the RC and AI season to select the best model for BW and WW, evaluated by different goodness of fit measures. The second approach considered fitting a GE model including the season classifications into the CG structure. Lower probability values for season effect and better goodness of fit measures were obtained when the season was classified according to the AI. Results showed that although differences are small, the AI allows a better model fitting for live-weight traits than RC and revealed a re-ranking effect on expected progeny differences data. Further analysis with other traits would demonstrate the extended utility of AI indicators to be considered for fitting models under a climatic change environment.

Type
Animal Research Paper
Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Akaike, H (1973) Information theory as an extension of the maximum likelihood principle. Proceedings of the 2nd International Symposium on Information Theory. Akademiai Kiado, Budapest Hungary, pp. 267–281.Google Scholar
Cherlet, M, Hutchinson, C, Reynolds, J, Hill, J, Sommer, S and von Maltitz, G (2018) World Atlas of Desertification. Luxembourg: Publication Office of the European Union.Google Scholar
Greve, P, Roderick, ML, Ukkola, AM and Wada, Y (2019) The aridity index under global warming. Environmental Research Letters 14, 124006.CrossRefGoogle Scholar
Herrera-Ojeda, JB, Parra-Bracamonte, GM, Herrera-Camacho, J, López-Villalobos, N, Magaña-Monforte, JG, Martínez-González, JC and Vázquez-Armijo, JF (2018 a) Información climática asociada a estaciones productivas para el ajuste de modelos estadísticos de sistemas bovinos bajo condiciones extensivas. Archivos de Zootecnia 67, 2128.CrossRefGoogle Scholar
Herrera-Ojeda, JB, Parra-Bracamonte, GM, López-Villalobos, N, Vázquez-Armijo, JF, Orozco-Durán, KE, Magaña-Monforte, JG and Jahuey-Martínez, FJ (2018 b) Épocas de nacimiento basadas en un índice climático para el ajuste de modelos estadísticos para peso vivo de ganado bovino en México. Revista Mexicana de Ciencias Pecuarias 9, 646666.CrossRefGoogle Scholar
Lobit, P, Gómez, TA, Bautista, F and Lhome, JP (2018) Retrieving air humidity, global solar radiation, and reference evapotranspiration from daily temperatures: development and validation of new methods for Mexico Part III: reference evapotranspiration. Theoretical and Applied Climatology 133, 787.CrossRefGoogle Scholar
Magaña, JG and Segura, JC (1997) Heritability and factors affecting growth traits and age at first calving of Zebu beef heifers in south-eastern Mexico. Tropical Animal Health and Production 29, 185192.CrossRefGoogle ScholarPubMed
Middleton, NJ and Thomas, DSG (1992) World Atlas of Desertification: United Nations Environmental Programme. London, UK: Edward Arnold Publ Ltd.Google Scholar
Núñez, DR, Ramírez, VR, García, MJG, Larios, SN and Hidalgo, MJA (2021) Grupos Contemporáneos. In Resumen de la evaluación genética para sementales Suizo Europeo Asociación Mexicana de Criadores de Ganado Suizo Europeo. Chapingo, México: Universidad Autónoma Chapingo, pp. 811.Google Scholar
Parra, BGM, Ortiz, FEL, Herrera, OJB and Vega, EA (2020) ProClima v10 Manual de Usuario. Tamaulipas: CBG. Available at https://wwwipnmx/assets/files/cbg/docs/descargas/ProClimaUserMpdf (Accessed 16 June 2020).Google Scholar
Pereira, AR (2005) Simplificado o balanço hídrico de Thornthwaite-Mather Bragantia. Revista de Ciencias Agronómicas 64, 311313.Google Scholar
Ramírez-Valverde, R, Núñez-Domínguez, R, Ruíz-Flores, A, García-Muñiz, JG and Magaña-Valencia, F (2008) Comparison of contemporary group definitions for genetic evaluations of Braunvieh cattle. Técnica Pecuaria en México 46, 359370.Google Scholar
R Core Team (2018) R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. Available at https://www.R-project.org/ (Accessed 12 May 2019).Google Scholar
Rivera, M, Ramírez, J, Moncada, H and Trujillo, LE (2013) Propuesta metodológica para la determinación de las épocas climáticas con aplicación a los estudios de producción y reproducción bovina en el trópico. Revista Colombiana de Ciencias Pecuarias 15, 302314.Google Scholar
Robertson, A (1959) The sampling variance of the genetic correlation coefficient. Biometrics 15, 469485.CrossRefGoogle Scholar
Robertson, A, Stewart, A and Ashton, ED (1956) The progeny assessment of dairy sires for milk: the use of contemporary comparisons. Proceedings of the British Society of Animal Production, Vol. 1956, pp. 4350.Google Scholar
SAS (2017) SAS/STAT User´s Guide (Release 6.4). Cary, NC, USA: SAS Inst. Inc.Google Scholar
Schwarz, G (1978) Estimating the dimension of a model. Annals of Statistics 6, 461464.CrossRefGoogle Scholar
Spinoni, J, Vogt, J, Naumann, G, Carrao, H and Barbosa, P (2015) Towards identifying areas at climatological risk of desertification using the Köppen–Geiger classification and FAO aridity index international. Journal of Climatology 35, 22102222.CrossRefGoogle Scholar
Van Vleck, LD and Boldman, KG (1993) Sequential transformation for multiple traits for estimation of (co) variance components with a derivative-free algorithm for restricted maximum likelihood. Journal of Animal Science 71, 836844.CrossRefGoogle ScholarPubMed
Weigel, KA, VanRaden, PM, Norman, HD and Grosu, H (2017) A 100-year review: methods and impact of genetic selection in dairy cattle – from daughter–dam comparisons to deep learning algorithms. Journal of Dairy Science 100, 1023410250.CrossRefGoogle ScholarPubMed