Prediction of carcass weight using multiple regression, Bayesian networks and artificial neural networks in Nigerian indigenous chickens based on earlier expressed phenotypic traits
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Abstract
The prediction potential of carcass weight (CW) in chicken is an important undertaking that can help commercial enterprises make better management decisions. The goal of this study was to examine alternative modeling approaches for predicting CW in meat-type Nigerian indigenous chickens using 19 biometric variables, as well as to discover early expressed traits that may be employed in CW breeding selection. Using multiple linear regression (MLR) and stepwise regression (SWREG), artificial neural networks (ANNs), and Bayesian networks (BN), the biometric traits of 320 chickens were modeled to predict CW. The accuracy of the models was evaluated based on their values of root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), coefficient of determination (R2), and correlation coefficient (r) between the predicted and the observed values of CW. The results showed that the MLR model was the least capable of predicting CW (MAE = 0.608, RMSE = 2.020, and MAPE = 93.244), followed by SWREG (MAE = 0.426, RMSE = 0.855, and MAPE = 77.168) compared to the ANNs and BN models. The estimated values of MAE, RMSE, and MAPE for the ANN1 model were 0.091, 0.201, and 52.891 respectively while that of ANN3 were 0.081, 0.101, and 36.765 respectively. The estimated values of MAE, RMSE, and MAPE for the MMHC model were 0.095, 0.129, and 63.551 respectively while that of RSMAX2 were 0.099, 0.132, and 66.193 respectively. Although it is possible to achieve a higher-performing SWREG model, in this study the SWREG (R2 = 57.84%) cannot be considered an optimum model for predicting CW. Based on statistical parameters (i.e., R2, MAE, r, and MAPE), the result of the study showed that the BN models provided a more powerful tool than the regression models and ANNs for predicting CW. The findings of this study showed that day-old chick weight, hatched weight, live weight, and body weight at 8 weeks are good predictors of CW. This could be used for management decisions in the chicken industry in the determination of CW at an earlier age of chickens.
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