An Investigation into Dairy Cow Welfare in Canada: A Cow and a Human Perspective
Notice bibliographique
Résumé
This thesis is an investigation into better understanding dairy cow welfare in Canada. A national cross-section study was undertaken to assess and provide producers feedback on dairy cattle housing, management and care in Canada. This included the assessment of hock and knee injuries and their risk factors for cows housed in tiestall systems. On average, 56% of cows within a herd were discovered to have hock injuries, and 43% of cows within a herd were discovered to have knee injuries. Factors associated with greater odds of hock and knee injuries included factors such as stall dimensions, stall surface, BCS, DIM and lying time. One year following this project, phone interviews were undertaken with these producers. Simultaneously, a separate survey was undertaken with dairy experts to assess the difficulty of making changes to improve cow welfare on farm. It was discovered that a majority (72%) of producers implemented some sort of change related to improving animal welfare following the intervention, however 17% of producers implemented changes that were not related to the weaknesses identified on their farm. The most common barriers identified to implementing changes to improve dairy cow welfare were lack of time and lack of fund. Dairy experts identified stall design changes as the most difficult to improve dairy cow welfare. Lastly, a Delphi survey was completed by dairy experts in Canada to better understand how the dairy industry defines and measures dairy cow welfare and seek consensus on a set of gold standard animal-based targets for realistically optimal dairy cow welfare. The study resulted in consensus within dairy stakeholders on how to define and measure dairy cow welfare. Most (72%) responded that they include a combination of natural living, health, affective state and production factors in their definition of dairy cow welfare, and all stated they would use animal-based measures, often in combination with other measures to assess dairy cow welfare. Lameness was the most frequently mentioned animal-based measure to assess dairy cow welfare. The survey participants were able to come to a consensus on 16 of 21 animal-based targets to describe a herd with realistically optimal welfare.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,004 |
| Études des sciences et des technologies | 0,020 | 0,006 |
| Communication savante | 0,006 | 0,002 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 source (Gemma direct ou Codex distillé), 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 ».