Comparação de métodos no ajustamento de curvas de lactação de bovinos por meio de Simulação

Abstract

RESUMO Curvas de lactação representam a produção de leite de uma fêmea leiteira em função do tempo. Uma vez que tais curvas variam aleatoriamente de animal para animal, devido a fatores tanto genéticos quanto ambientais, o modelo misto conhecido como regressão aleatória é apropriado para ajustar os dados de produção de um rebanho. A regressão aleatória foi avaliada neste estudo mediante simulação de dados. Produções de cinco animais em cinco idades foram simuladas em mil conjuntos de dados independentes, em três níveis de precisão, ou seja, a razão entre a variância de regressão e a total igual a 0,9 (variância alta), 0,5 (variância mé-dia) e 0,1 (variância baixa) e três graus médios de parentesco entre os animais. A regressão aleatória foi utilizada de duas maneiras, admitindo-se ou não que as variâncias genéticas e de meio fossem conhecidas, comparando-a com o método de quadrados mínimos ordinário. Pelos resultados pode-se concluir que, com baixa precisão, os modelos tiveram comportamento semelhante quanto ao erro quadrático médio e desvios absolutos médios. O nível de precisão foi o fator mais influente do que o grau de parentesco no desempenho relativo dos modelos. De maneira geral, a regressão aleatória com variâncias conhecidas propiciou predições mais precisas, seguida da regressão aleatória estimando-se tais variâncias. Concluiu-se que a regressão aleatória é potencialmente útil sob condições de alta variabilidade ambiental relativa, mesmo com variâncias desconhecidas. TERMOS PARA INDEXAÇÃO:Regressão aleatória, modelos mistos, curva de lactação. COMPARISON OF METHODS FOR FITTING LACTATION CURVES IN DAIRY CATTLE BY SIMULATION ABSTRACTLactation curves represent milk production of a dairy dam as a function of time. Since such curves vary randomly among individuals, due to environment and genetic factors, the mixed model known as random regression is suitable for fitting such curves, and it was evaluated in this study through data simulation. Milk production of five animals in five ages was simulated in one thousand random and independent data sets, considering three precision levels, variation due to the model in relation to the residual variation of 0,9 (high variance), 0,5 (medium variance) and 0,1 (low variance) and three average degrees of relationship. Random regression was used in two ways, admitting or not that the variances of the model (genetic and of environment) were known, and compared to the ordinary least squares method. Results showed that, under low variance, the models had similar fits, with regard mean square error and mean absolute deviation. The level of precision was more influential than the degree of relationship on relative performance of models. In general, random regression with known variances yielded the most precise predictions, followed by random regression estimating such variances. Results suggest that random regression is potentially useful under high environmental heterogeneity, even if variances are unknown. INDEX TERMS:Random regression, mixed models, lactation curve. INT...

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