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

Authors :
J. B. Herrera-Ojeda
R. Ramírez-Valverde
R. Núñez-Domínguez
N. Lopez-Villalobos
J. F. Vázquez-Armijo
K. E. Orozco-Durán
G. M. Parra-Bracamonte
Source :
The Journal of Agricultural Science. 160:397-403
Publication Year :
2022
Publisher :
Cambridge University Press (CUP), 2022.

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.

Details

ISSN :
14695146 and 00218596
Volume :
160
Database :
OpenAIRE
Journal :
The Journal of Agricultural Science
Accession number :
edsair.doi...........8a4a7ea2509aa139f2051980290ed50e
Full Text :
https://doi.org/10.1017/s0021859622000454