Identification of discriminating behavioural and movement variables in lameness scores of dairy cows at pasture from accelerometer and GPS sensors using a Partial Least Squares Discriminant Analysis
| Autor: | Sébastien Couvreur, Aurélien Madouasse, A. Relun, L. Riaboff, C.-E. Petiot, M. Feuilloy |
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| Přispěvatelé: | Biologie, Epidémiologie et analyse de risque en Santé Animale (BIOEPAR), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), École supérieure d'électronique de l'ouest [Angers] (ESEO), Unité de Recherche sur les Systèmes d'Elevage (URSE), Ecole supérieure d'Agricultures d'Angers (ESA), This study was funded by Terrena Innovation (Ancenis, France), R´egion Pays-de-la-Loire and BIOEPAR (INRAE, Oniris, Nantes, France). |
| Rok vydání: | 2020 |
| Předmět: |
040301 veterinary sciences
GPS [SDV]Life Sciences [q-bio] Lameness Animal 030231 tropical medicine Cattle Diseases Accelerometer PLS-DA Pasture Dairy cow lameness 0403 veterinary science 03 medical and health sciences 0302 clinical medicine Animal science Food Animals Bout duration Grazing Partial least squares regression Accelerometry Animals Lactation Least-Squares Analysis Mathematics 2. Zero hunger geography geography.geographical_feature_category Behavior Animal business.industry Discriminant Analysis 04 agricultural and veterinary sciences Linear discriminant analysis Dairying Lameness Behaviour and movement Global Positioning System Geographic Information Systems Animal Science and Zoology Cattle Female business |
| Zdroj: | Preventive Veterinary Medicine Preventive Veterinary Medicine, Elsevier, 2021, 193, pp.105383. ⟨10.1016/j.prevetmed.2021.105383⟩ |
| ISSN: | 1873-1716 0167-5877 |
| DOI: | 10.1016/j.prevetmed.2021.105383⟩ |
| Popis: | International audience; The behaviour and movement of lame dairy cows at pasture have been studied little, yet they could be relevant to improve the automatic detection of lameness in cows in pasture-based systems. Our aim in this study is to identify behavioural and movement variables of dairy cows at pasture that could discriminate lameness scores. Individual cow behaviours were predicted from accelerometer data and movements measured using GPS data. Sixty-eight dairy cows from three pasture-based commercial farms were equipped with a 3-D accelerometer and a GPS sensor fixed on a neck collar for 1–5 weeks, depending on the farm, in spring and summer 2018. A lameness score was assigned to each cow by a trained observer twice a week. Behaviours were predicted every 10 s based on accelerometer data, and then combined with the GPS position. Segmentation on behavioural time series was used to delineate each behavioural bout within each outdoor period. Thirty-seven behavioural and movement variables were then calculated from the behavioural bouts for each cow. A partial least square discriminant analysis was performed to identify the variables that best discriminate lameness scores. Time spent grazing, grazing bout duration, duration before lying down in the pasture, time spent resting, number of resting bouts, distance travelled during grazing, and dispersion were the most discriminant variables in the PLS-DA (VIP > 1). Severely lame cows spent 4.5 times less time grazing and almost twice as much time resting as their sound congeners, especially in the lying position. Exploratory behaviour was also reduced for both moderately and severely lame cows, resulting in 1.2 and 1.7 times less distance travelled respectively, especially during grazing. These variables could be used as additional variables to improve the performance of existing lameness detection devices in pasture-based systems. |
| Databáze: | OpenAIRE |
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