PSV-2 Difference in Sire Pta Evaluated By Taiwan Dhi and the Original Countries
Notice bibliographique
Résumé
Abstract Taiwan is located in a region with a tropical/subtropical oceanic climate characterized by high temperatures and humidity, which significantly affects the milk production, health, and reproduction of dairy cows. Most dairy herds in Taiwan have utilized semen of superior foreign sires from countries in temperate climate, mainly from USA and Canada. However, the performance of daughters in Taiwan of those foreign sires may differ from those in temperate countries. Therefore, the objective of this study was to evaluate the performance of foreign sire semen in Taiwan's dairy herds, and to compare the PTAs of those sires in both Taiwan and their original countries. Data and pedigree from the Dairy Herd Improvement program (DHI) were collected between 2009 and 2021. In total, 108,348 lactation records from 54,269 cows and 76,024 animals in the pedigree were obtained from 120 herds. Traits to evaluate include milk yield (MY), milk fat percentage (MF%), milk fat yield (MFY), milk protein percentage (MP%), and milk protein yield (MPY) standardized to 305d-2X yield. The herd-year-season and lactation were considered as fixed effects in the model with additive genetic effect and permanent environmental effect as random effects. In addition, an extra fixed effect, physical situation of lactation (PSL), was included to account for abnormal physiology or subclinical mastitis in those dairy cows. The ASReml program was used to estimate the variance and covariance components as well as heritability and correlation with multiple-trait animal models. Not only the accuracy of predicted transmitting ability (PTATW) ≥ 75% of those superior foreign sires using DHI data and their PTAs obtained from the original countries (PTAInt) were compared, but also the rank correlation between PTATW and PTAInt was also calculated. The results indicated that only when the accuracy of PTATW increased to 90%, a moderate correlation was observed. Moreover, PTATW and PTAInt of all the sires with an accuracy of PTATW ≥ 75% were tracked to the lactation performance of their daughters in Taiwan using regression analysis. The results showed that selecting sires with an increase of 1 kg in PTATW and in PTAInt would increase the average production of their daughters in Taiwan's dairy herds by 1.620 kg and 0.213 kg in milk yield, 1.328 kg and 1.201 kg in fat yield, and 1.493 kg and 0.550 kg in protein yield, respectively. Therefore, selecting offspring through PTATW of sires is much more efficient, and increasing the number of daughters for each sire and their DHI records would improve the accuracy of PTATW prediction and ranking, making it an important indicator for selecting progeny in Taiwan dairy herds.
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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 | 0,001 | 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) | 0,002 | 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 ».