Estimation of body weight from morphological measurements in Boer goats with the application of artificial neural networks and some regression models

Abstract

In this study, examination of the characteristics of body measurements affecting the body weight of Boer goats and the estimation of the body weight were investigated. To examine their body morphological features, 400 live animals were taken into consideration. The morphological measurements taken from the goats in the study were body weight (BW), body length (BL), heart girth (HG), withers height (WH), rump height (RH), rump length (RL), ear length (EL) and head with (HW) respectively. These animals were between 1-6 years old; 112 of them were male and 288 of them were female. Multiple regression, ridge regression and artificial neural networks (ANN) methods were applied to estimate the body weight. In the prediction of body weight as a dependent variable, the ANNs predictive model produced high predictive performance. Mean square error (MSE), mean absolute error (MAD) and mean absolute percent error (MAPE) statistics were used to determine model performance. Using the Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN) learning algorithm, the body features that had the greatest impact on body weight were determined. Comparison of the predictive performance of the put forward model against both multiple regression and state of the ridge regression methods showed that the artificial neural networks outperformed both competing models by achieving the least values for MAD, MSE and MAPE in both training and testing data sets. The results of artificial neural networks were promising and accurate in the prediction of the body weight of goats.