A decision-tree model to detect post-calving diseases based on rumination, activity, milk yield, BW and voluntary visits to the milking robot

Animal

A. Antler¹  E. Maltz²  M. Steensels²  D. Berckmans²  I. Halachmi³  C. Bahr³ 

[1] Institute of Agricultural Engineering, Agricultural Research Organization (ARO), The Volcani Center, PO Box 6, IL-50250 Bet Dagan, Israel;Department of Biosystems, M3-BIORES: Measure, Model and Manage Bioresponses, KU Leuven, Kasteelpark Arenberg 30, Bus 2456, BE-3001 Heverlee, Belgium;Institute of Agricultural Engineering, Agricultural Research Organization (ARO), The Volcani Center, PO Box 6, IL-50250 Bet Dagan, Israel;

关键词: individual dairy cows;    health;    automatic milking system;    behaviour sensor;    precision livestock farming;   

DOI  :  

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown