Automation of oestrus detection in dairy cows: a review

Abstract

The economic importance of traits like longevity, health and reproduction has increased compared to milk yield in dairy cows. Effective oestrus detection is important for improved reproduction. Commonly, oestrus detection is performed by visual observation, but this is particularly difficult on large dairy farms because of short observation periods during feeding and milking. As a result of technical progress in monitoring cows using computers, automatic oestrus detection has become possible. In many studies different traits have been analysed for utilisation in automatic oestrus detection. The best results were found for detection using pedometers. Results of oestrus detection varied depending on the used threshold value, the number of cows, housing and treatment of cows and the utilised method of time series analysis. The detection rate of most investigations is sufficiently high at 80–90%. Error rates between 17 and 55% and specificities between 96 and 98% indicate a large number of false positive oestrus warnings. The main problem of automatic oestrus detection is to reduce the false positive alerts. In recent years several authors have combined different traits with the objective of improving detection rates. Best multivariate analyses results were found for combinations with activity. Further research should be performed using data from a commercial dairy farm. A comparison of different time series methods and multivariate analysis of traits would be useful.