Bivariate analysis to improve genetic evaluations with incomplete databases in Charolais cattle
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Date
2021-03-17
Open Access Location
Journal Title
Journal ISSN
Volume Title
Publisher
Universidad de Cordoba
Rights
(c) 2021 The Author/s
CC BY-NC-SA 4.0
CC BY-NC-SA 4.0
Abstract
Objective: Estimate (co)variance components and genetic parameters of live weight traits and examine the effect of selection culling when using bivariate analysis in registered Charolais beef cattle. Materials and methods: The effect of incomplete data over accuracies was compared, expected progeny differences (EPD) and standard errors of prediction (SEP) were obtained and evaluated by comparing univariate and bivariate models for birth (BW), weaning (WW) and yearling (YW) weights. Results: Bivariate models for WW and YW, improved accuracies of EPDs and reduced the SEPs. Joint analysis for BW and WW increased in a 38% the accuracies and reduced SEP estimators for YW (p̌0.001). Accuracies of EPD for BW obtained from univariate models were improved when BW was included in bivariate models. Conclusions: The results support the use of bivariate genetic analysis in limited or incomplete live weight indicators databases that were registered after birth, such as weaning and yearling weight.
Description
Keywords
Incomplete records, live weights, culling selection (Sources: USDA, Tesauro ICYT de Biologia Animal)
Citation
Herrera-Ojeda J, Parra-Bracamonte GM, López-Villalobos N, Herrera-Camacho J, Orozco-Durán KE. (2021). Bivariate analysis for the improvement of genetic evaluations with incomplete records in Charolais cattle. Revista MVZ Cordoba. 26. 2. (pp. 01-08).