Estimation of genetic parameters for milking temperament in Holstein-Gyr cows
Notice bibliographique
Résumé
Introduction Dairy cattle with poor temperament can cause several inconveniences during milking, leading to labor difficulties, increasing the risk of accidents with animals and workers, and compromising milk yield and quality. This study aimed to estimate variance components and genetic parameters for milking temperament and its genetic correlations with milk yield in crossbred Holstein-Gyr cattle. Methods Data were collected at three commercial farms, resulting in 5,904 records from 1,212 primiparous and multiparous lactating cows. Milking temperament (MT), measured as the milking temperament of each cow, was assessed during pre-milking udder preparation (RP) and when fitting the milking cluster (RF) by ascribing scores from 1 (cow stands quietly) to 8 (the cow is very agitated, with vigorous movements and frequent kicking). The number of steps and kicks were also recorded during pre-milking udder preparation (SRPand KRP, respectively) and when fitting the milking cluster (SRFand KRF, respectively). Milk yield (MY) was obtained from each farm database. In two of them, MY was recorded during the monthly milk control (that could or could not coincide with the date when the milking temperament assessments were carried out) and in the remaining farm, MY was recorded on the same day that the milking temperament assessments were made. Genetic parameters were estimated using the THRGibbs1f90 program applying a threshold model, which included 89 contemporary groups as fixed effects, animal age at the assessment day and the number of days in milking as covariates, and direct additive genetic and residual effects as random effects. Results and discussions The heritability estimates were MT= 0.14 ± 0.03 (for both, MRPand MRF), MY= 0.11 ± 0.08, SRP= 0.05 ± 0.03, KRP= 0.14 ± 0.05, SRF= 0.10 ± 0.05, and KRF= 0.32 ± 0.16. The repeatability estimates were 0.38 ± 0.05, 0.42 ± 0.02, and 0.84 ± 0.006 for MTRP, MTRF, and MY, respectively; and 0.38 ± 0.02, 0.30 ± 0.07, 0.52 ± 0.02, and 0.46 ± 0.15 for SRP, KRP, SRF, and KRF, respectively. The estimates of most genetic correlation coefficients between MTRP-MTRFwere all strong and positive (MTRR-MTRF= 0.63 ± 0.10, MTRP-SRP= 0.65 ± 0.12, MTRP-KRP= 0.56 ± 0.16, MTRF-SRF= 0.77 ± 0.06, and MTRF-KRF= 0.56 ± 0.34) except for MY (MTRP-MY= 0.26 ± 0.26 and MTRF-MY= 0.21 ± 0.23). Despite the low magnitude of MT heritability, it can be included as a selection trait in the breeding program of Holsteins-Gyr cattle, although its genetic progress will be seen only in the long term. Due to the low accuracy of the genetic correlation estimates between MT and MY and the high range of the 95% posterior density interval, it cannot be affirmed by this study that the selection of a milking temperament trait will infer on milk yield. More data is therefore needed per cow and more cows need to be observed and measured to increase the reliability of the estimation of these correlations to be able to accurately interpret the results.
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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 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| 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) | 0,001 | 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 ».