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 of age, and direct additive genetic and permanent environment effects were modeled with quadratic B-splines with two and one intervals, respectively. For crossbred bulls, quadratic B-splines with one interval fitted direct additive genetic and permanent environment effects and nine classes of age were needed to fit residual variance. Pooling classes of age with up to 40% in difference of residual variances does not compromise the fit of the model. Heritability for body weight in performance tests are moderate (>0.25, for crossbred bulls) to high (>0.5, for Nellore bulls) and genetic correlation between weights over the test are also high (>0.65). Then, selection of young bulls in performance test is an efficient tool to increase body weight in beef cattle.

Resumo: O objetivo deste trabalho foi determinar os parâmetros genéticos para o peso de bovinos Nelore, do nascimento até 1.000 dias de idade, por meio de modelos de regressão aleatória. Utilizaram-se 115.096 registros de peso de 19.417 animais. Os parâmetros genéticos foram obtidos por modelos de regressão aleatória via inferência bayesiana. A trajetória média de crescimento foi ajustada com um polinômio de Legendre quártico. O efeito genético aditivo direto foi ajustado com um polinômio quadrático de Legendre. Os efeitos de ambiente permanente direto e materno foram ajustados com polinômios de Legendre quíntico e quadrático, respectivamente. A variância residual foi modelada com três classes de idades. Os valores genéticos dos pesos, do nascimento até 1.000 dias de idade, foram utilizados para a análise da tendência genética, por meio de superfícies de resposta. As herdabilidades variaram entre 0,16 e 0,47. Os efeitos de ambiente permanente direto e materno foram responsáveis por 5 a 77% e 0,2 a 11% da variância fenotípica, respectivamente. As correlações genéticas dos pesos em diferentes idades foram altas e superiores a 0,40. Os valores genéticos foram crescentes ao longo dos anos, e a tendência genética foi máxima para peso aos 500 dias.

Abstract: The objective of this work was to determine the genetic parameters for body weight of Nellore cattle, from birth to 1,000 days of age, by random regression models. Data from 115,096 body weight records of 19,417 animals were used. The genetic parameters were obtained by random regression models and Bayesian inference. The average growth trajectory was fitted by a fourth-order Legendre polynomial. The additive direct effect was fitted by a quadratic Legendre polynomial. Direct and maternal permanent environmental effects were fitted by the fifth- and second-order Legendre polynomials, respectively. Residual variance was modeled by three classes of age. The breeding values of weights obtained from birth to 1,000 days of age were used for the analysis of genetic trend by response surfaces. Heritability ranged from 0.16 to 0.47. The direct and maternal permanent environmental effects explained 5 to 77% and 0.2 to 11% of the phenotypic variance, respectively. The genetic correlations of body weights at different ages were high and above 0.40. Breeding values increased throughout the years, and genetic trend was maximum for body weight at 500 days.

The objective of this study was to estimate (co)variance components for weight at 550 days, average daily gain and an index with both traits, and to compare alternatives for evaluation and ranking of Nellore young bulls in pasture performance tests. The heritability estimates were 0.73, 0.31 and 0.44 for weight at 550 days, average daily gain and index, respectively. Animals were ranked according to their predicted breeding values or the phenotypic deviations in relation to the mean of the test. Although the correlations between breeding values and phenotypic deviations were high, there were differences in the number of animals selected in common when the selection criteria were the predicted breeding values or the phenotypic deviations. Mixed models are more appropriate than the least squares method and should be utilized in the evaluation of young bulls in performance tests.