Prediction of 305-day milk yield in Brown Swiss cattle using artificial neural networks

Abstract

Artificial neural networks (ANNs) have been shown to be a powerful tool for system modelling in a wide range of applications. In this paper, we focus on the capability of ANNs to predict 305-d milk yield in early lactation of Brown Swiss cattle, based on a few test-day records, and some environmental factors such as age, number of lactation and season of calving. The ANNs that were developed were compared with multiple linear regressions (MLR). The various ANNs were modelled and the best performing number of hidden layers, neurons and training algorithms retained. The best ANN model had input, hidden and output layers of tansig transfer function. The layers had 4, 8, and 1 neurons, respectively. It was determined that the mean predicted values calculated by the ANNs were closer to the real mean values without showing any statistical difference. On the other hand, the predicted mean values calculated by MLR and the real mean values were significantly different from each other. The best prediction in ANN method was seen in 1st, 2nd, 3rd, and 4th test-day records when these were recorded to the system as X1‒X8 in the ANN system. In this study, the prediction of 305-d milk yield by ANN gave better results that those of MLR, suggesting that ANN can be used as an alternative prediction tool.

Keywords: Prediction, milk yield, ANN, back propagation, test day records