June 25–28, 2017 Pittsburgh, PA
Journal of Dairy Science ® Volume 100, Supplement 2
A. Arazi1 and D. Rak
Applied Research Department, Afimilk, Kibbutz Afikim, Israel
Calving is a crucial event in a productive cows’ life cycle and has a significant influence on herd profitability and cow’s welfare. Calving detection is a key factor to ensure successful calving with minimal harm to the calf and the cow. It is used to decide if intervention is needed when to move a cow to a maternity pen and to obtain a proper colostrum administration soon afterward. An automatic monitoring system to detect the onset of parturition could contribute to reduce calves morbidity and mortality and ensure better performance in the consequent lactation.
The objective of this study was to test a real-time, automatic cow monitoring system for detecting calving in dairy cows based on rest and activity behaviors.
The study was conducted on four Israeli dairy herds, between 10/08/2015 and 22/10/2015. Herds ranging from 356 to 1,012 Israeli Holstein milking cows. Cows were fitted with two tags (AfiTag II, Afimilk, Israel) on front and rear legs, when moved to the close-up pen. Calving times were recorded by the herds’ teams. Calving alerts generated by the system (AfiAct II, Afimilk, Israel) were compared with the actual calving time.
In total 231 and 187 successful calving detection alerts were recorded for cows fitted with tags on rear and front leg, respectively (not all the cows were fitted with tags on the front leg). Detection timing prior to calving were similar for front and rear legs. The distribution was about 35.5%, 28%, 26.5%, 8% and 2% for the last 1 hour, 1-2 hours, 2-4 hours, 4-8 hours and more than 8 hours before calving, respectively.
In all four herds, 50% and more of the alerts were provided in the 2 hours preceding calving for both legs (range 50%- 79%) and more than 80% of the alerts were in the last 4 hours before calving (range 81.9%-94.8%). The average time from detection to calving was about 2 hours for both front and rear legs (range 01:18-02:38 hours).
These results suggest that a real-time automatic monitoring system based on cows’ rest and activity behavior can be a useful tool for detecting calving events in dairy cows. The use of such a system can help improve calving management and human interventions.
Special thanks to the staff of RC Center “PLINOR” for the high level of organization of the event, all-round assistance and support.
We will continue our training programs in 2019. Follow the announcements on our website and on the website of RC PLINOR and do not miss the start of applications for the next seminar.
See you again!