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

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 roboticmilking 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.

Smart and Climate-Smart Agricultural Trends as Core Aspects of Smart Village Functions

“…Similarly, temperature around a sick cattle’s gluteal region differs significantly from those of other parts when studied using thermal infrared scanning [ 57 ]. As reported by Steensels et al [ 58 , 59 ], temperature management in poultry, as well as early mastitis identification are some other areas where thermal scanning was reported to be useful.…”

Prospects for predictive modeling of transition cow diseases

“…The independent variables can be either categorical or continuous (Ma, 2018). Steensels et al (2016) developed a CART model to detect postcalving lameness and metritis, and reported acceptable sensitivity and specificity at 69 and 87%, respectively. CARTs have also been used to predict difficult calvings (Zaborski et al, 2016;Fenlon et al, 2017).…”

Élevage de précision et bien-être en élevage : la révolution numérique de l’agriculture permettra-t-elle de prendre en compte les besoins des animaux et des éleveurs ?

“…1. L'élevage de précision pour mieux prédire la santé et le bien-être des animaux grâce aux données de comportement Plusieurs systèmes d'EdP (tableau 1) sont proposés afin de détecter précocement les troubles de santé, ceux-ci ayant des répercussions économiques importantes (Steensels et al, 2016 ;Steensels et al, 2017).…”