Genetic variability of test-day urea nitrogen and lactose in milk of Jersey, Brown Swiss and Ayrshire cattle breeds in Quebec
Notice bibliographique
Résumé
A large dataset of test-day milk records for cows having calved between January 2000 and May 2007, was obtained from the Quebec Dairy Herd Improvement Programme, Valacta, Quebec, Canada. This dataset contained 919,814 test-day milk yield records from 45520 Ayrshire cows on 956 dairy farms, 133,690 records from 7493 Jersey cows on 910 dairy farms, and 118,996 records from 5693 Brown Swiss on 476 dairy farms. Test-day records from the first three parities after editing served in the estimation of genetic parameters using a Restricted Maximum Likelihood method. The genetic parameters of six milk yield traits — milk, fat, lactose and protein yields, somatic cell score, and milk urea nitrogen (MUN) — were estimated using various random regression test-day models in the first three parities separately. Random regression test-day animal models for a single trait with Legendre polynomials of 2nd to 4th order and the Wilmink function with an exponential term of 0.01 to 0.09 with a step size of 0.02 were used for the estimation of daily heritability of the traits. Bivariate random regression test-day animal models with Legendre polynomials as regression coefficients served to estimate genetic and permanent environmental correlations among the milk yield traits. Single-trait random regression models were compared using BIC, AIC, residual sum of squares and mean absolute error. The models using Legendre polynomials for the fixed and random effects were among the best models; the Wilmink function resulted in unrealistic heritability estimates for some of the traits. For Ayrshire cows, mean heritability estimates were 0.39, 0.30 and 0.41 for MUN and 0.39, 0.20 and 0.33 for lactose yield, in the 1st, 2nd and 3rd parities, respectively. For Brown Swiss cows, mean heritability estimates were 0.28, 0.26 and 0.13 for MUN, and 0.30, 0.20 and 0.42 for lactose yields, in the 1st, 2nd and 3rd parities, respectively. Likewise, for Jersey cows, mean heritability estimates were 0.26, 0.20 and 0.14 for MUN, and 0.42, 0.51 and 0.12 for lactose yield, in the 1st, 2nd and 3rd parities, respectively. The largest heritability estimates were obtained for the Ayrshire breed in the first parity and the lowest estimates were obtained for the Jersey and Brown Swiss breeds, particularly in the later parities. Genetic correlations of MUN with other yield traits were negative and near zero in the 1st parity for Ayrshire cows, with the exception of SCS, where the genetic correlation was positive and near zero. In the 2nd parity the genetic correlations of MUN with other yield traits were positive and low. In the 3rd parity the genetic correlations of MUN with other yield traits were positive and low, except the genetic correlations with SCS, which was positive and moderate. The genetic and permanent environmental correlations between MUN and other yield traits were close to zero. The genetic and permanent environmental correlations among milk fat, protein and lactose yields were positive and high in the first three parities for the three breeds. The genetic correlation and permanent environment correlation between SCS and all other traits were negative and moderate to high except in the 1st and 2nd parity for the Jersey breed, where genetic correlations between MUN and SCS were positive and near zero.
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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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| 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 ».