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A practical way to detect approaching calving of the dairy cow by a behaviour sensor
Year:
2007
Authors :
Antler, Aharon
;
.
Maltz, Ephraim
;
.
Volume :
Co-Authors:
Maltz, E., ARO, Volcani Center, Institute of Agricultural Engineering, P.O. Box 6, Bet Dagan 50250, Israel
Antler, A., ARO, Volcani Center, Institute of Agricultural Engineering, P.O. Box 6, Bet Dagan 50250, Israel
Facilitators :
From page:
141
To page:
146
(
Total pages:
6
)
Abstract:
A basic algorithm, based on once-daily observations of activity and lying behaviour of 15 cows that calved, was applied to data obtained from 12 dry cows before calving. These cows were kept in a dry-cow barn equipped to enable automatic downloading of behavioural data collected by a behaviour sensor fitted to each cow. On average the cows' steady behaviour changed significantly within 24 h prior to calving, with increases in daily steps and restlessness, and decreased lying time. The prediction of individual calvings was improved by incorporation of qualitative limits into the algorithm: 10 of the 12 calvings could be detected a day in advance, and 9 false positive alarms were reduced to 4 by incorporation of a restlessness variable, i.e., less than 5% of all the cow-days measured.
Note:
Related Files :
Algorithms
Behaviour
Calving
Dairies
Dairy
Dairy cow
False positive
Lying
Lying time
sensors
Show More
Related Content
More details
DOI :
Article number:
Affiliations:
Database:
Scopus
Publication Type:
Conference paper
;
.
Language:
English
Editors' remarks:
ID:
19299
Last updated date:
02/03/2022 17:27
Creation date:
16/04/2018 23:27
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Scientific Publication
A practical way to detect approaching calving of the dairy cow by a behaviour sensor
Maltz, E., ARO, Volcani Center, Institute of Agricultural Engineering, P.O. Box 6, Bet Dagan 50250, Israel
Antler, A., ARO, Volcani Center, Institute of Agricultural Engineering, P.O. Box 6, Bet Dagan 50250, Israel
A practical way to detect approaching calving of the dairy cow by a behaviour sensor
A basic algorithm, based on once-daily observations of activity and lying behaviour of 15 cows that calved, was applied to data obtained from 12 dry cows before calving. These cows were kept in a dry-cow barn equipped to enable automatic downloading of behavioural data collected by a behaviour sensor fitted to each cow. On average the cows' steady behaviour changed significantly within 24 h prior to calving, with increases in daily steps and restlessness, and decreased lying time. The prediction of individual calvings was improved by incorporation of qualitative limits into the algorithm: 10 of the 12 calvings could be detected a day in advance, and 9 false positive alarms were reduced to 4 by incorporation of a restlessness variable, i.e., less than 5% of all the cow-days measured.
Scientific Publication
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