A Comparison of Neural Network and Nonlinear Regression Predictions of Sheep Growth

This study evaluated the potential of Artificial Neural Networks (ANN) as an alternative to the traditional statistical regression techniques for the purpose of predicting Baluchi sheep growth. Weekly body weight data of 70 Baluchi lambs were recorded from birth to approximately 150th days of age. About 6 nonlinear regressions forms of von Bertalanffy, Gompertz, Logistic (with 3 and 4 parameters) Brody and Richards were employed as counterparts to ANN. Goodness of fit and accuracy of the models were determined by coefficient of determination (R-2), Mean Absolute Deviation (MAD), Mean Square Error (MSE), Mean Absolute Percentage Error (MAPE) and the bias. These forecasting error measurements are based on the difference between the estimated and observed values. ANN generated a slightly better descriptive sheep growth curve than the best one which generated from nonlinear models and made the most accurate prediction. It is concluded that ANN represents a valuable tool for predicting of lamb body weight.