Oestrus detection in dairy cows based on serial measurements using univariate and multivariate analysis

Abstract. As visual oestrus detection is difficult to perform in large herds, different technical devices were developed to facilitate oestrus detection. In this investigation the significance of the traits activity, milk yield, milk flow rate and electrical conductivity due to oestrus was analysed. The traits were recorded automatically during each milking on a commercial dairy farm. Oestrus detection was performed for 862 cows on basis of time series consisting of 15 days before oestrus, the day of oestrus and 15 days after oestrus. The day of oestrus was determined by the insemination which caused a calving after 265 to 295 days.

The univariate analyses of traits were performed by the time series methods day-to-day comparison, moving average, exponential smoothing and Box-Jenkins three parameter smoothing. For multivariate analyses a fuzzy logic model was developed and modified for the different combinations of traits. The efficiency of the detection models and traits was determined by the parameters sensitivity, specificity and error rate.

A moving average was the best suited time series method for oestrus detection by activity data. Sensitivity ranged between 94.2 and 71% and error rate was between 53.2 and 21.5% for threshold values between 40 and 120%. The traits milk yield, milk flow rate and electrical conductivity were not suitable for univariate oestrus detection. Depending on the considered traits multivariate analyses resulted in sensitivities between 87.0 and 87.9%. The error rate varied between 28.2 and 31.0%. Further analyses should include previous information such as time since last oestrus.