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פותח על ידי קלירמאש פתרונות בע"מ -
A decision-tree model to detect post-calving diseases based on rumination, activity, milk yield, BW and voluntary visits to the milking robot
Year:
2016
Source of publication :
Animal
Authors :
אנטלר, אהרון
;
.
הלחמי, אילן
;
.
מלץ, אפרים
;
.
סטנסלס, מכטלד
;
.
Volume :
10
Co-Authors:
Steensels, M., Department of Biosystems, M3-BIORES: Measure, Model and Manage Bioresponses, KU Leuven, Kasteelpark Arenberg 30, Bus 2456, Heverlee, Belgium, Institute of Agricultural Engineering, Agricultural Research Organization (ARO), Volcani Center, PO Box 6, Bet Dagan, Israel
Antler, A., Institute of Agricultural Engineering, Agricultural Research Organization (ARO), Volcani Center, PO Box 6, Bet Dagan, Israel
Bahr, C., Department of Biosystems, M3-BIORES: Measure, Model and Manage Bioresponses, KU Leuven, Kasteelpark Arenberg 30, Bus 2456, Heverlee, Belgium
Berckmans, D., Department of Biosystems, M3-BIORES: Measure, Model and Manage Bioresponses, KU Leuven, Kasteelpark Arenberg 30, Bus 2456, Heverlee, Belgium
Maltz, E., Institute of Agricultural Engineering, Agricultural Research Organization (ARO), Volcani Center, PO Box 6, Bet Dagan, Israel
Halachmi, I., Institute of Agricultural Engineering, Agricultural Research Organization (ARO), Volcani Center, PO Box 6, Bet Dagan, Israel
Facilitators :
From page:
1493
To page:
1500
(
Total pages:
8
)
Abstract:
Early detection of post-calving health problems is critical for dairy operations. Separating sick cows from the herd is important, especially in robotic-milking dairy farms, where searching for a sick cow can disturb the other cows' routine. The objectives of this study were to develop and apply a behaviour- and performance-based health-detection model to post-calving cows in a robotic-milking dairy farm, with the aim of detecting sick cows based on available commercial sensors. The study was conducted in an Israeli robotic-milking dairy farm with 250 Israeli-Holstein cows. All cows were equipped with rumination- and neck-activity sensors. Milk yield, visits to the milking robot and BW were recorded in the milking robot. A decision-tree model was developed on a calibration data set (historical data of the 10 months before the study) and was validated on the new data set. The decision model generated a probability of being sick for each cow. The model was applied once a week just before the veterinarian performed the weekly routine post-calving health check. The veterinarian's diagnosis served as a binary reference for the model (healthy-sick). The overall accuracy of the model was 78%, with a specificity of 87% and a sensitivity of 69%, suggesting its practical value. © The Animal Consortium 2016.
Note:
Related Files :
Automatic milking system
Behaviour sensor
Health
Individual dairy cows
Precision livestock farming
עוד תגיות
תוכן קשור
More details
DOI :
10.1017/S1751731116000744
Article number:
0
Affiliations:
Database:
סקופוס
Publication Type:
מאמר
;
.
Language:
אנגלית
Editors' remarks:
ID:
30076
Last updated date:
02/03/2022 17:27
Creation date:
17/04/2018 00:51
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Scientific Publication
A decision-tree model to detect post-calving diseases based on rumination, activity, milk yield, BW and voluntary visits to the milking robot
10
Steensels, M., Department of Biosystems, M3-BIORES: Measure, Model and Manage Bioresponses, KU Leuven, Kasteelpark Arenberg 30, Bus 2456, Heverlee, Belgium, Institute of Agricultural Engineering, Agricultural Research Organization (ARO), Volcani Center, PO Box 6, Bet Dagan, Israel
Antler, A., Institute of Agricultural Engineering, Agricultural Research Organization (ARO), Volcani Center, PO Box 6, Bet Dagan, Israel
Bahr, C., Department of Biosystems, M3-BIORES: Measure, Model and Manage Bioresponses, KU Leuven, Kasteelpark Arenberg 30, Bus 2456, Heverlee, Belgium
Berckmans, D., Department of Biosystems, M3-BIORES: Measure, Model and Manage Bioresponses, KU Leuven, Kasteelpark Arenberg 30, Bus 2456, Heverlee, Belgium
Maltz, E., Institute of Agricultural Engineering, Agricultural Research Organization (ARO), Volcani Center, PO Box 6, Bet Dagan, Israel
Halachmi, I., Institute of Agricultural Engineering, Agricultural Research Organization (ARO), Volcani Center, PO Box 6, Bet Dagan, Israel
A decision-tree model to detect post-calving diseases based on rumination, activity, milk yield, BW and voluntary visits to the milking robot
Early detection of post-calving health problems is critical for dairy operations. Separating sick cows from the herd is important, especially in robotic-milking dairy farms, where searching for a sick cow can disturb the other cows' routine. The objectives of this study were to develop and apply a behaviour- and performance-based health-detection model to post-calving cows in a robotic-milking dairy farm, with the aim of detecting sick cows based on available commercial sensors. The study was conducted in an Israeli robotic-milking dairy farm with 250 Israeli-Holstein cows. All cows were equipped with rumination- and neck-activity sensors. Milk yield, visits to the milking robot and BW were recorded in the milking robot. A decision-tree model was developed on a calibration data set (historical data of the 10 months before the study) and was validated on the new data set. The decision model generated a probability of being sick for each cow. The model was applied once a week just before the veterinarian performed the weekly routine post-calving health check. The veterinarian's diagnosis served as a binary reference for the model (healthy-sick). The overall accuracy of the model was 78%, with a specificity of 87% and a sensitivity of 69%, suggesting its practical value. © The Animal Consortium 2016.
Scientific Publication
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