Dairy producers' attitudes toward reproductive management and performance on Canadian dairy farms
Notice bibliographique
Résumé
The objectives of this study were to explore Canadian dairy producers' attitudes toward reproductive performance and challenges they perceive to be related to reproduction and reproductive management practices. A survey in both English and French was developed, validated, and administered to Canadian dairy farmers between March and May 2014 to collect general farm, reproduction management, and reproductive performance data, as well as opinions and perceptions about different facets of reproduction. Associations between management practices and the perceived importance of reproduction were tested using a logistic regression model. Thematic network analysis was used to identify themes from the open-ended survey questions about challenges concerning reproduction. Finally, questions that were answered on a Likert scale were graphically represented using diverging stacked bar charts. A total of 832 questionnaires were completed online and by mail, which represents approximately 7% of all dairy farms in Canada. Respondents that ranked reproduction in lactating dairy cows as 1 of the 3 most important challenges faced on their farm (66%) were more likely to house their lactating cows in a tiestall and to have a lower herd annual 21-d pregnancy rate. Estrus detection and conception risk were 2 major themes raised and discussed by the respondents. Other concepts, including housing and milk production, were also perceived to affect estrus detection and conception risk. Whereas analysis of open-ended survey questions does not allow for quantification of the importance of different themes in the sample as a whole, it does show that respondents are aware of the multifactorial complexity of reproductive challenges on dairy farms. Improving performance was the main factor influencing decisions concerning reproduction for 80% of the respondents, and they adopted tools and technologies such as synchronization programs and automated activity monitoring systems to improve herd reproductive performance. More research is required to describe how this performance is defined and perceived by the respondents, and how it relates to the actual variability of performance (i.e., pregnancy rate) among farms.
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 | 0,002 | 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 | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 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 ».