Comparison of five mathematical models that describe growth in tropically adapted dual-purpose breeds of chicken

Abstract

Mathematical models provide valuable information for livestock improvement programmes. In this study, we evaluated the ability of five mathematical models (3P and 4P Gompertz, 3P and 4P logistic and neural network) to predict the growth of six tropically adapted dual purpose (TADP) chicken breeds (Fulani, FUNAAB Alpha, Kuroiler, Noiler, Sasso and Shika-Brown) under on-station and on-farm in Nigeria. Data for body weight were collected every 14 days from 1939 birds reared on-station, and every 28 days from 58,639 birds reared on-farm. Parameters used to evaluate the growth models were the adjusted coefficient of determination (AdjR2), Akaike’s information criterion (AIC), Bayesian information criterion (BIC) and root mean square error (RMSE). The AdjR2 for Gompertz 3P was higher than or equal to the AdjR2 for logistics 3P, Gompertz 4P and logistics 4P but was equal to or lower than the AdjR2 for the neural network (NN) for all TADP chickens raised on-station. Based on the goodness-of-fit criteria, Gompertz 3P had the best predictive values (AdjR2 = 0.989–0.998) for TADP chickens raised on-station, while logistic 3P was the best-fit model for TADP chickens raised on-farm. In conclusion, non-linear models and NN models yielded a good fit with the age-weight data of TADP chickens on-station and on-farm.

Keywords

• body weight
• mathematical models
• dual-purpose chickens
• neural network
• performance