Curve Fitting with Nonlinear Regression and Grey Prediction Model of Broiler Growth in Chickens

Abstract

In this study, it was aimed to model broiler growth curves of chickens with nonlinear regression analysis and grey prediction model. For this, the growth of 118 broilers was analyzed by using their weekly individual live weights from hatch to 49 day-old. In the analysis, nonlinear functions and Rolling-Grey Model (1,1) prediction method were used. The time-dependent growths of mixed sexes broilers were analyzed in the aspects of testing the parallelism of female and male growth samples, determining the best fitted growth model and designating the biological meaningful parameters (inflection point age, weight and growth rate) of growth functions. Analyses showed that the growth profiles of female and male chicks found not to be parallel using profile analysis, and the male chicks had a higher body weight than the females (P < 0.01) starting from 14-21st days until the end of experiment. For this reason, the prediction models were created separately and compared by MAPE (%) and accuracy rate (rho) criteria in order to find out the most consistent growth model for female and male broiler chicks. The results indicate that Rolling-Grey Model (1,1) is more consistent than Von Bertalanffy, Gompertz and Logistic and can be used as an alternative to nonlinear regression models in growth analysis.