Prevalence of and risk factors for hock and knee injuries on dairy cows in tiestall housing in Canada
Notice bibliographique
Résumé
Leg injuries on dairy cows are a common and highly visible welfare concern on commercial dairy farms. With greater attention being placed on food animal welfare and limited research being conducted on tiestall farms, this study aimed to identify prevalence and risk factors for hock and knee injuries on dairy cows housed in tiestall barns in Ontario (n=40) and Quebec (n=60). A sample of 40 cows was purposively selected per farm and several animal- and farm-based measures were taken. Both hocks and both knees on each cow were scored as injured (presence of lesions or swelling) or not injured (no alterations or hair loss), and the highest score of each of the 2 knees and the 2 hocks was considered the cow's hock or knee score. Possible animal- and farm-based risk factors were incorporated into 2 separate multivariable logistic models for hock injuries and knee injuries respectively at the cow level. Mean (±SD) percentage of cow with hock injuries per farm was found to be 56±18% and mean percentage of knee injuries per farm was found to be 43±23%. Animal-based factors found to be associated with a greater odds of hock injuries at the cow level were increased days in milk (DIM), lower body condition score (BCS), lameness, higher parity, higher cow width, median lying bout duration, and median number of lying bouts. Environmental factors found to be associated with hock injuries at the cow level were province, stall width, tie rail position, stall base, chain length, and age of stall base. Animal-based factors found to be associated with knee injuries at the cow level were DIM, BCS, and median lying time. Environmental factors found to be associated with knee injuries at the cow level were stall width, chain length, province, stall base, and bed length. Quadratic and interaction terms were also identified between these variables in both the hock and knee models. This study demonstrates that hock and knee injuries are still a common problem on tiestall dairy farms in Canada. Several animal- and housing-based factors contribute to their presence. Further research to confirm causal relationships between these factors would help identify the cause of knee and hock injuries and determine how to best reduce the incidence of injuries in cows on commercial tiestall dairy farms in Canada.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».