Health recording in Canadian Holsteins: Data and genetic parameters
Notice bibliographique
Résumé
The objective of this study was to investigate if health data recorded by Canadian dairy producers can be used for genetic selection. Eight diseases are recorded by producers on a voluntary basis: mastitis, displaced abomasum, ketosis, milk fever, retained placenta, metritis, cystic ovaries, and lameness. Between 40 to 60% of all herds had to be excluded by editing procedures for each trait, assuming unreliable health recording. All analyses were carried out for first-lactation Holstein cows. The majority of disease cases occurred in the first month of lactation. Mean disease frequencies were 12.6, 3.7, 4.5, 4.6, 10.8, 8.2, and 9.2% for mastitis, displaced abomasum, ketosis, retained placenta, metritis, cystic ovaries, and lameness, respectively. Milk fever was very rare in first-lactation cows with a frequency of only 0.20%, and was, therefore, not considered in the analyses. Univariate and bivariate linear animal models were fitted. Heritabilities for mastitis, displaced abomasum, ketosis, retained placenta, metritis, cystic ovaries, and lameness were 0.02, 0.06, 0.03, 0.03, 0.02, 0.03, and 0.01, respectively. Genetic correlations between diseases were mostly positive. The strongest genetic correlations were found between displaced abomasum and ketosis (0.64) and between retained placenta and metritis (0.62). The remaining genetic correlations ranged from -0.22 (between metritis and lameness) to 0.49 (between mastitis and lameness). In agreement with the genetic correlations, the largest phenotypic correlations were found between displaced abomasum and ketosis (0.27) and retained placenta and metritis (0.14). All other phenotypic correlations were low and close to zero (0.00 to 0.06). Pearson correlations between breeding values for health traits and other routinely evaluated traits were computed, which revealed noticeable favorable relationships to direct herd life and fertility. In addition, a moderate favorable association was found between mastitis and somatic cell score. Mastitis is the most promising trait to be included in routine genetic evaluation, because it is the most recorded disease and has a high frequency and positive genetic correlations to all other health traits. Although, about 40% of all Canadian dairy producers participate in the health-recording system, a large proportion of the data are lost after data validation. Thus, dairy producers should be encouraged to keep accurate and complete health data.
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 | 0,001 | 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 | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 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 ».