Prevalence of lameness and associated risk factors on dairy farms in the Maritime Provinces of Canada
Notice bibliographique
Résumé
Lameness in dairy cattle is a major issue for the industry due to the effects on the welfare of the animal, the economic impact, and consumer perception. The aim of this study was to determine the prevalence of lameness and explore potential risk factors in the Maritime Provinces of Canada. Cows were scored for lameness and potential risk factors and were assessed in 46 freestall herds and 33 tiestall herds in Nova Scotia, New Brunswick, and Prince Edward Island. In freestall herds, lameness was assessed using the most common method, locomotion scoring. A cow with a gait score of ≥3 out of 5 was considered to be lame. In tiestall herds, lameness was assessed using an alternative method known as stall lameness scoring. This assessment consisted of observation of the cow for 4 behavioral changes: standing on the edge of the stall, shifting weight, resting a limb, and uneven weight bearing when moved side to side. A cow displaying 2 or more of these behaviors was considered to be lame. At the time of the assessment, other animal-, environmental-, and management-based measurements were collected. These measurements were used in multivariable logistic regression analysis to determine risk factors that were associated with lameness for both freestalls and tiestalls independently. The prevalence of lameness was 21% for freestall-housed cattle and 15% for tiestall-housed cattle. Of the 1,488 tiestall-housed cows that were assessed, 68% showed no behavioral changes, whereas 15, 15, 2, and <1% showed 1, 2, 3, or 4 changes, respectively. In freestalls, higher odds of lameness were seen when cows spent ≥3 h/d in the holding area for milking compared with those that spent <3 h/d. In tiestall herds, higher odds of lameness were seen when bedding material was wet compared with when it was dry. For both lactating cow facility types, housing the dry cows and heifers on a deep bedded pack compared with tiestalls or freestalls was associated with a decreased odds of lameness. There were also many cow-level variables associated with lameness, including parity, daily milk production, stage of production, body condition, and width at the tuber coxae (hook bones). If producers become aware of the risk factors associated with lameness, they can make informed decisions on where to implement changes to help reduce the level of lameness in their herd.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,001 | 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 ».