Abstract
ABSTRACT The objective of this study was to estimate genetic parameters for body weight of beef cattle in performance tests. Different random regression models with quadratic B-splines and heterogeneous residual variance were fitted to estimate covariance functions for body weights of Nellore and crossbred Charolais × Nellore bulls. The criteria −2 residual log-likelihood (−2RLL), Akaike Information Criterion (AIC), and consistent AIC (CAIC) were used to choose the most appropriate model. For Nellore bulls, residual variance was modeled with six classes [...]
Highlights
Using genetically superior young bulls is important for the increase in efficiency of beef cattle production
The values of −2RLL, Akaike Information Criterion (AIC), and consistent AIC (CAIC) were higher in the model with homogeneous residual variance for both genetic groups (Table 1)
The AIC showed the lowest values for models with nine age classes for Nellore, and ten and thirteen age classes of residual variances for MA group (Table 1)
Summary
Using genetically superior young bulls is important for the increase in efficiency of beef cattle production. Body weight is an important selection criterion for beef cattle breeding programs (Campos et al, 2014), which is measured several times over the life of the animal. Despite the advantages observed in random regression analyses, its use with Legendre polynomials can lead to problems when the variances increase at extremes of age intervals (Meyer, 2005b). Overcoming these issues involves increasing the order of Legendre functions or using more robust (e.g., spline) functions that allow low-degree polynomials to fit over short segments of the growth trajectory
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