Factors influencing how Canadian dairy producers respond to a downer cow scenario
Notice bibliographique
Résumé
Understanding how downer cattle are managed allows for the evaluation of strengths and weaknesses in these practices, which is an important step toward improving the care these animals receive. The objective of this cross-sectional study was to analyze factors associated with the care and management of downer cattle by Canadian dairy producers. Data were obtained from the 2015 National Dairy Study, and analysis was limited to the 371 respondents completing the downer cow scenario. The scenario described a downer cow that the producer wanted to keep in their herd but must be moved, and was followed by questions addressing the cow's care and management. Using multivariable logistic regression models, associations between respondent demographics and farm characteristics, and the presence of downer cow protocols, we assessed decisions regarding euthanasia and use of behavioral prognostic indicators. Written downer cow protocols were reported by 18.2% of respondents, 67% indicated that they had a nonwritten protocol, and 14.8% reported that they did not have a protocol (either written or nonwritten). Respondents from western provinces were more likely to have a written protocol than those from Ontario. Nineteen percent of the respondents with a written or unwritten protocol reported veterinary involvement in developing their downer cow protocol, which occurred more commonly on farms with more frequent herd health visits and a good producer-veterinarian relationship. An area to move a downer cow to was present on 88% of farms, with respondents who were farm staff being less likely to report having knowledge of a designated area than respondents who were the farm owner. In addition, approximately half (45%) of respondents reported moving downer cattle with hip lifters as their most common method. Behavioral prognostic indicators chosen by respondents were associated with the respondent's geographic region, age, farm size, and education. Most notably, older respondents were more likely to use appetite, and less likely to use attitude, as a prognostic indicator compared with younger respondents. Using perceived pain as a prognostic indicator was more common among respondents from western and Atlantic provinces compared with respondents from Ontario, and more common among respondents with a college or university education. These results highlighted herd and farmer demographics that were associated with how Canadian dairy producers managed downer cattle in 2015 and could be used as a benchmark for evaluating how these management practices compare with those currently implemented.
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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 | 0,002 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».