Barriers to recording calf health data on dairy farms in Ontario
Notice bibliographique
Résumé
Establishing accurate illness and treatment rates in dairy calves is crucial, yet calf health records are often incomplete. Thus, the objective of this study was to investigate barriers for dairy farmers for recording calf illnesses and treatments on dairy farms in Ontario, Canada. An online survey was completed by a convenience sample of 88 Ontario dairy farms in 2022, with 34 questions regarding farm demographics, current practices surrounding record keeping and analysis, and factors that would improve recording compliance. Multivariable models were built to assess associations between explanatory variables and the following outcomes: likelihood of making management or treatment protocol changes based on records analysis, factors that would increase the use of electronic recording methods, and whether all calf illnesses and treatments are recorded. Pearson's chi-squared tests were also used to investigate associations between explanatory variables and whether the respondent agreed or disagreed with a proposed reason for why a calf illness or treatment would not be recorded on their farm. Producers had 3.45 times greater odds of recording all antimicrobial treatments if they used a computer software system compared with those that did not. With respect to anti-inflammatory treatments, producers had 3.11 times greater odds of recording these treatments if records were located in the calf barn than elsewhere. Nonfamily employees had 6.08 times greater odds of recording all supportive therapy treatments than farm owners. When calf health records were kept in the calf barn, respondents were less likely to report that illnesses were not recorded due to time constraints (5% vs. 36% if records were elsewhere) or because calf health records were not analyzed (10% vs. 34% if records were elsewhere). On farms that recorded calf treatments in a paper booklet, respondents were more likely to report that treatments were not recorded because calf health records were not analyzed (44% for paper records vs. 21% for other systems). The most commonly indicated factors that would increase recording of illness were recording with a mobile app (27% of respondents) and for the recording system to be easy to use (31% of respondents). Overall, these data indicate that recording may be improved by keeping calf health records in close proximity to the calves and using a recording method that allows for data analysis. An easy-to-use mobile app may also improve recording if it could be used in the calf barn, provide data analytics, and allow for time-efficient data entry.
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 | 0,004 | 0,019 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».