Automated detection of health disorders in lactating dairy cattle on pasture: a preliminary study.

Since previous health monitoring systems have shown themselves to be unsuccessful in predicting health disorders in dairy cows managed on pasture, the aim of this study was to evaluate the performance of automated health monitoring integrated in an accelerometer-based oestrus detection system (ODS) for dairy cows on pasture. Mixed-breed lactating dairy cows (n=109) in a seasonal-calving herd managed at pasture were fitted with an ODS that provided automated health monitoring. The ODS performed multimetric analysis of behavioural patterns to generate health alerts. Data were collected during the artificial insemination period of 66 days. Clinical examinations and farmer's observations were used to evaluate the performance of automated health monitoring. During the insemination period, the farmer generated two health alerts, which were classified false positives (2/2; 100%). The ODS generated 31 automated health alerts. Of all automated health alerts, 3/31 (9.7%) were confirmed as true health disorders and 28/31 (90.3%) alerts were classified as false positives. The positive predictive value (PPV) of automated health monitoring was 9.7 (95% CI=2-25.8) %. The ODS was able to alert lactating dairy cows on pasture suffering from health disorders. True health disorders were alerted by the ODS before the farmer noticed them, which could provide early and successful treatment when using the system on-farm for automated health monitoring. The evaluated accuracy of automated health monitoring is opposed to a targeted use of the system for on-farm health monitoring. For further validation, testing on other farms and during the transition period would be of interest.

Automated oestrus detection using multimetric behaviour recognition in seasonal-calving dairy cattle on pasture.

AIM To evaluate the performance of a novel accelerometer-based oestrus detection system (ODS) for dairy cows on pasture, in comparison with measurement of concentrations of progesterone in milk, ultrasonographic examination of ovaries and farmer observations. METHODS Mixed-breed lactating dairy cows (n=109) in a commercial, seasonal-calving herd managed at pasture under typical farming conditions in Ireland, were fitted with oestrus detection collars 3 weeks prior to mating start date. The ODS performed multimetric analysis of eight different motion patterns to generate oestrus alerts. Data were collected during the artificial insemination period of 66 days, commencing on 16 April 2015. Transrectal ultrasonographic examinations of the reproductive tract and measurements of concentrations of progesterone in milk were used to confirm oestrus events. Visual observations by the farmer and the number of theoretically expected oestrus events were used to evaluate the number of false negative ODS alerts. The percentage of eligible cows that were detected in oestrus at least once (and were confirmed true positives) was calculated for the first 21, 42 and 63 days of the insemination period. RESULTS During the insemination period, the ODS generated 194 oestrus alerts and 140 (72.2%) were confirmed as true positives. Six confirmed oestrus events recognised by the farmer did not generate ODS alerts. The positive predictive value of the ODS was 72.2 (95% CI=65.3-78.4)%. To account for oestrus events not identified by the ODS or the farmer, four theoretical missed oestrus events were added to the false negatives. Estimated sensitivity of the automated ODS was 93.3 (95% CI=88.1-96.8)%. The proportion of eligible cows that were detected in oestrus during the first 21 days of the insemination period was 92/106 (86.8%), and during the first 42 and 63 days of the insemination period was 103/106 (97.2%) and 105/106 (99.1%), respectively. CONCLUSIONS and CLINICAL RELEVANCE The ODS under investigation was suitable for oestrus detection in dairy cows on pasture and showed a high sensitivity of oestrus detection. Multimetric analysis of behavioural data seems to be the superior approach to developing and improving ODS for dairy cows on pasture. Due to a high proportion of false positive alerts, its use as a stand-alone system for oestrus detection cannot be recommended. As it is the first time the system was investigated, testing on other farms would be necessary for further validation.