Prediction of dry matter intake in growing Black Bengal goats using artificial neural networks

Dry matter intake (DMI) determination is essential for effective management of meat goats, especially in optimizing feed utilization and production efficiency. Unfortunately, farmers often face challenges in accurately predicting DMI which leads to wastage of feed and an increase in the cost of production. This investigation aimed to predict DMI in Black Bengal goats by using body weight (BW), body condition score (BCS), average daily gain (ADG), and metabolic body weight (MBW) by applying an artificial neural network (ANN) model. A total of 144 observations were collected from 18 goats over a 4-month period for each input (BW, ADG, MBW and BCS) and output (DMI) variable. These input variables were taken fortnightly and correlated with DMI. The presence of a significant positive correlation between DMI with BW (r = 0.968, p < 0.01), BCS (r = 0.687, p < 0.01), ADG (r = 0.608, p < 0.01), and MBW (r = 0.971, p < 0.01) indicated potential for ANN model development. ANN model with 10 hidden layer neurons trained using GDX and LOGSIG transfer function emergeds as the high-performing model for predicting DMI in Black Bengal goats, achieving the highest R² (0.9693) and the lowest MSE (0.0013) among the configurations considered. Comparison between the three models revealed that the DMI was estimated more accurately by the ANN model than by linear and second-order non-linear models. ANN may therefore be used to predict DMI with high accuracy and reliability in place of other regression methods.