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SummaryThe traits activity, milk yield, milk flow rate and electrical conductivity were analysed in preparation for automatic oestrus detection. Collection of data was performed on a commercial dairy farm and milking took place in a rotary milking parlour. Between February and December 1998 1,090,031 observations from 2,422 Holstein Friesian cows were accumulated. Around 30% of cows were milked thrice daily. For each trait and each cow a daily value was calculated. The fixed effects test day, parity, calving season, milking frequency, week of lactation and the random effect cow were considered in statistical analyses. With increasing number of parity, activity decreased and milk yield, milk flow rate and electrical conductivity increased. The milking frequency had significant influence on all analysed traits and for the effect calving season no consistent trend was found. All traits showed characteristic patterns during lactation. Between test days high variations were found for the trait activity. The remaining traits showed a steady level except for small fluctuations. Repeatability was 27.4% for activity and between 70 and 78.7% for the milk parameters. The repeatabilities verified the collected field data having a satisfying structure for application in automatic oestrus detection. The repeatability of the trait activity indicated high differences between and within cows. The right skewed distribution indicated the activity as a promising trait for further analyses. Introduction Income in milk production is directly influenced by reproductive performance. For example, increased calving intervals lead to a reduction of milk yield per cow and year, an increased number of replaced heifers, a reduced genetic impact on traits with economic importance and an increased amount of voluntary and involuntary culling (BRITT, 1985). MACK (1996) calculated costs between 0.59 and 1.17€ per day for prolonged calving interval. A decrease in conception rate of 1% results in costs of 1.52€ per cow and year (BOICHARD, 1990). An effective possibility to improve fertility performance is to intensify oestrus detection. Efficient oestrus detection by visual observation is time-consuming and requires diligent attention. As pointed out by ERADUS et al. (1992) visual oestrus detection is especially difficult to realise in large herds. Despite twice daily observation periods, 32% of oestrus are undetectable (WILLIAMS et al., 1981). Oestrus detection can be simplified and improved by application of technical equipment. Automatically measurable traits promise successful oestrus detection results. Several traits for univariate and multivariate oestrus detection are presented in a literature review of FIRK et al. (2001). In this investigation, the traits activity, milk yield, milk flow rate and electrical conductivity are analysed concerning the influence of the fixed effects test day, parity, calving season, milking frequency and week of lactation. Additionally, the random effect of the cow was considered. In preparation for further analy...

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.