Comparison of three nonlinear and spline regression models for describing chicken growth curves

This study compared three non-linear growth models (Richards, Gompertz, and logistic) and the spline linear regression model using BW measurements from an unselected, randombred chicken population. Based on the goodness of fit criteria, the nonlinear models (NLM) fitted the data better than the spline regression model. The four-parameter Richards model was expected to have the best overall fit; however, because the shape parameter predicted was close to 1.0, which corresponded with the Gompertz curve, there were no differences between the Richards and Gompertz models for the data used. The growth parameters predicted with the logistic model were different from those predicted by the Richards and Gompertz models. It was concluded that growth parameters predicted with different models with fixed inflection points are not directly comparable. The spline linear regression model is a compound function consisting of a series of linear models. It can be used to compartmentalize growth into segments and also substitute as an alternative to asymptotic models when the data are truncated before the asymptote is attained. The relationship between the growth trajectory (in) and carcass composition measurements were also investigated. Carcass yields among the different growth trajectories (in) were similar within sexes. However, the proportions of the major breast muscle and abdominal fat differed according to the m a bird followed.