Assessment of feeding, ruminating and locomotion behaviors in dairy cows around calving – a retrospective clinical study to early detect spontaneous disease appearance

• Published in: PloS one Vol. 17; no. 3; p. e0264834
• Main Authors: Fadul, Mahmoud, D’Andrea, Luigi, Alsaaod, Maher, Borriello, Giuliano, Di Lori, Antonio, Stucki, Dimitri, Ciaramella, Paolo, Steiner, Adrian, Guccione, Jacopo
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 04.03.2022; Public Library of Science (PLoS)
• Subjects: Algorithms; Analysis; Animal behavior; Animals; Behavior; Biology and Life Sciences; Cattle; Computer and Information Sciences; Dairy cattle; Disease; Eating; Engineering and Technology; Farms; Feeding Behavior; Female; Intervals; Lactation; Locomotion; Management; Medicine and Health Sciences; Regression models; Retrospective Studies; Social Sciences; Veterinary medicine; Switzerland; Italy
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0264834

The study aims to verify the usefulness of new intervals-based algorithms for clinical interpretation of animal behavior in dairy cows around calving period. Thirteen activities associated with feeding-ruminating-locomotion-behaviors of 42 adult Holstein-Friesian cows were continuously monitored for the week (wk) -2, wk -1 and wk +1 relative to calving (overall 30’340 min/animal). Soon after, animals were retrospectively assigned to group-S (at least one spontaneous diseases; n = 24) and group-H (healthy; n = 18). The average activities performed by the groups, recorded by RumiWatch® halter and pedometer, were compared at the different weekly intervals. The average activities on the day of clinical diagnosis (dd0), as well as one (dd-1) and two days before (dd-2) were also assessed. Differences of dd0 vs. dd-1 (ΔD1), dd0 vs. wk -1 (ΔD2), and wk +1 vs. wk -1 (Δweeks) were calculated. Variables showing significant differences between the groups were used for a univariate logistic regression, a receiver operating characteristic analysis, and a multivariate logistic regression model. At wk +1 and dd0, eating- and ruminating-time , eating- and ruminate-chews and ruminating boluses were significantly lower in group-S as compared to group-H, while other activity time was higher. For ΔD2 and Δweeks, the differences of eating- and ruminating-time , as well as of eating- and ruminate-chews were significantly lower in group-S as compared to group-H. Concerning the locomotion behaviors, the lying time was significantly higher in group-S vs. group-H at wk +1 and dd-2. The number of strides was significantly lower in group-S compared to group-H at wk +1. The model including eating-chews , ruminate-chews and other activity time reached the highest accuracy in detecting sick cows in wk +1 (area under the curve: 81%; sensitivity: 73.7%; specificity: 82.4%). Some of the new algorithms for the clinical interpretation of cow behaviour as described in this study may contribute to monitoring animals’ health around calving.