Weekly milk prediction on dairy goats using neural networks

Abstract

Artificial neural networks (NN) have been widely used for both prediction and classification tasks in many fields of knowledge; however, few studies are available on dairy science. In this work, we use NN models to predict next week’s goat milk based on the current and previous milk production. A total of 35 Murciano-Granadina dairy goats were selected from a commercial farm according to number of lactation, litter size and body weight. Input variables taken into account were diet, milk production, stage of lactation and days between partum and first control. From the 35 goats, 22 goats were used to build the neural model and 13 goats were used to validate the model. It is important to emphasize that these 13 goats were not used to build the model in order to demonstrate the generalization capability of the network. Afterwards, the neural models that provided better prediction results were analysed in order to determine the relative importance of the input variables of the model. We found that the most important inputs are present and previous milk production, followed by days between parturition, and first milk control, and type of diet. Besides, we benchmark NN to other widely used prediction models, such as auto-regressive system modelling or naïve prediction. The results obtained with the neural models are better than with the rest of models. The best neural model in terms of accuracy provided a root mean square error equal to 0.57 kg/day and a low bias mean error equal to − 0.05 kg/day. Dairy goat farmers could make management decisions during current lactation from one week to the next (present time), based on present and/or previous milk production and dairy goat factors, without waiting until the end of lactation.