Evaluation of alternative nonlinear mixed effects models for estimating pig growth parameters

Abstract

Animal growth models are used to identify alternative strategies to improve the efficiency of livestock production and to estimate daily nutrient requirements for the animal of different age and sex group. In this study, the efficiency of nonlinear mixed effects models were explored and a comparison was made between the predictive power of fixed effects models and the mixed effects models. Three hundred body weight (BW) data including male and female pigs were used for model fitting. One pig specific random effect was included in each of the models. The random function was a random deviation of mature BW of the individual from average mature BW of its genotype. Logistic, Gompertz and Von-Bertalanffy fixed and mixed models were explored for these data. It was found that Logistic mixed effects model performed better than the other nonlinear mixed effects models based on mean square error (MSE) and root mean square error (RMSE)