A comparison of machine learning techniques for predicting insemination outcome in Irish dairy cows

Abstract

Reproductive performance has an important effect on economic efficiency in dairy farms with short yearly periods of breeding. The individual factors affecting the outcome of an artificial insemination have been extensively researched in many univariate models. In this study, these factors are analysed in combination to create a comprehensive multivariate model of conception in Irish dairy cows. Logistic regression, Naive Bayes, Decision Tree learning and Random Forests are trained using 2,723 artificial insemination records from Irish research farms. An additional 4,205 breeding events from commercial dairy farms are used to evaluate and compare the performance of each data mining technique. The models are assessed in terms of both discrimination and calibration ability. The logistic regression model was found to be the most useful model for predicting insemination outcome. This model is proposed as being appropriate for use in decision support and in general simulation of Irish dairy cows.