Prediction of Cow Performance with a Connectionist Model

Since the main reason for disposal of dairy cows is low milk yield, implementation of an optimum selection program requires the prediction of cow performance with regard to production. The prediction of fat and protein content in milk are also rapidly becoming important factors for decisions related to breeding and herd policy. While, on average, traditional lactation models furnish good results, some improvement is possible when predicting the yield for an individual cow early in lactation. Artificial neural networks (ANNs), known to perform well in pattern recognition, may constitute an effective alternative to the traditional models. The objective of this research was to investigate how ANNs might be used to predict total milk, fat, and protein production for individual cows. Results indicated that ANNs generally performed at least as well overall as the model currently used by Canadian milk recording agencies, especially in the first third of lactation. This has important implications for early identification of superior animals. Predictions from both methods were relatively similar for the later stages of lactation. The addition of nontraditional data inputs such as average milk herd production and weight of cow improved the quality of prediction. Three different techniques were used to analyze the sensitivity of the ANN to different inputs, and their relative abilities are discussed. Results illustrate the potential effectiveness of ANNs in predicting milk yield and its composition and appear to justify further pursuit of this research.