Abstract #W15

# W15
Evaluation of technologies to predict the onset of calving in Holstein dairy cows.
Véronique Ouellet*1, Elsa Vasseur2, Wolfgang Heuwieser3, Onno Burfeind3, Xavier Maldague4, Édith Charbonneau1, 1Département des Sciences Animales, Université Laval, Québec, QC, Canada, 2Organic Dairy Research Center, University of Guelph, Alfred, ON, Canada, 3Clinic for Animal Reproduction, Faculty of Veterinary Medicine, Freie Universität Berlin, Berlin, Germany, 4Département de Génie électrique et de Génie Informatique, Université Laval, Québec, QC, Canada.

Dystocias are common in dairy cows and are known to have labor, production, reproduction, welfare and economic implications. A technology that would accurately predict the onset of calving would help minimize the effects of dystocia by allowing producer intervention in a timely matter. The aim of this study was to assess the performances to predict calving using a decrease in vaginal temperature, rumination time, and lying time or an increase in number of lying bouts measured by 3 technologies. The combination of technologies allowing simultaneous measure of the variables was also tested. Forty 2 multiparous Holstein cows housed in tie-stall were fitted with a temperature logger, a rumination sensor and an accelerometer 7 ± 2 d before their expected calving date. Data collected during the last 120 h before calving were summarized per day and in 6 h periods. Differences between days and between periods were analyzed using proc GLIMMIX of SAS. Test performances (sensitivity, specificity, predictive values) were conducted to predict calving within the next 24, 12 or 6 h. Vaginal temperature were 0.3°C lower (P < 0.05) whereas rumination and lying time were respectively 41 and 52 min lower (P < 0.05) on calving day compared with the 4 d precalving. Cows had 2 more lying bouts on calving day (P < 0.05). While comparing tested variables, a decrease of vaginal temperature achieved the best performance to predict calving within the next 24, 12, and 6 h. Between those periods, the best performance was achieved for a prediction within the next 24 h with a sensitivity, specificity, positive and negative predictive values respectively of 74, 74, 51, and 89%. Combining the technologies enhanced the performance to predict calving with best results obtained by the combination of the 3 technologies for a prediction within the next 24 h (sensitivity: 77%, specificity: 77%, positive and negative predictive values: 56 and 90%). These results suggest that technologies are better at identifying events during which the cow did not calve than calving events. Therefore, a device that would be able to measure the 4 variables may not be able to accurately predict calving time but would provide insightful information for calving monitoring.

Key Words: technologies, calving, dystocia