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Enregistrement W3157575418 · doi:10.3168/jds.2020-19833

Herd health and production management visits on Canadian dairy cattle farms: Structure, goals, and topics discussed

2021· article· en· W3157575418 sur OpenAlexaffabout
Caroline Ritter, Linda Dorrestein, D.F. Kelton, Herman W. Barkema

Notice bibliographique

RevueJournal of Dairy Science · 2021
Typearticle
Langueen
DomaineAgricultural and Biological Sciences
ThématiqueVector-Borne Animal Diseases
Établissements canadiensUniversity of GuelphUniversity of CalgaryUniversity of Prince Edward Island
Organismes subventionnairesnon disponible
Mots-clésDescriptive statisticsAgricultural scienceProduction (economics)Duration (music)WelfareHealth management systemMedicineBusinessMathematicsStatisticsBiology

Résumé

récupéré en direct d'OpenAlex

Regular veterinary visits to improve herd health and production management (HHPM) are important management components on many dairy cattle farms. These visits provide opportunities for constructive conversations between veterinarians and farmers and for shifting management from a reactionary approach to proactively optimizing health and welfare. However, little is known about the structure of HHPM farm visits and to what extent veterinarians provide assistance beyond purely technical services. Therefore, our aims in this cross-sectional study were to describe HHPM farm visit structure, determine which dairy-specific topics were discussed, and assess whether the focus of the visits aligned with farmers' priorities. Veterinary practitioners (n = 14) were recruited to record audio and video of regularly scheduled HHPM farm visits (n = 70) using an action camera attached to their chest or head. A questionnaire was distributed to farmers containing closed- and open-ended questions to assess their goals and perceptions related to farm management and HHPM farm visits. Descriptive statistics and negative binomial and Poisson regression models were used to study dairy-specific topics initiated by the farmer or veterinarian during various activities. A mean of 51% of the visit duration was dedicated to transrectal pregnancy and fertility diagnostics, and a considerable amount of time (30%) was spent on visit preparation, transitions between tasks, and leaving. A total of 488 discussions were initiated by either the veterinarian (55%) or the farmer (45%). Mean length of discussions was 2 min, and only 17% of the HHPM visit duration was spent discussing dairy-specific topics. Veterinarians initiated 62% of their discussions about herd issues, whereas farmer-initiated discussions revolved around herd health in 39% of the discussions. Discussion topics most frequently raised by participants included fertility, udder health, calf health and management, and transition diseases. Consistently, farmers' answers to a rank question regarding their main HHPM farm visit goals indicated that their priorities were to have transrectal pregnancy and fertility diagnostics performed and to improve herd fertility and general herd health. Answers to an open-ended question revealed that additional aims of many farmers were to receive information, have questions answered, and identify and discuss problems. A farmer's belief that HHPM farm visits were "absolutely" tailored toward his or her goals was positively associated with number of discussions during the visit and their conviction that they "always" voiced their wishes and needs to the veterinarian. Opportunities to broaden the focus of HHPM farm visits and improve communication between farmers and veterinarians should be identified and veterinarians should be trained accordingly, which would increase veterinarians' ability to add value during HHPM farm visits.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,858
Score d'incertitude au seuil0,958

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,017
Tête enseignante GPT0,246
Écart entre enseignants0,228 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations12
Publié2021
Routes d'admission2
Résumé présentoui

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