An optimal genetic evaluation model for milk, fat, and protein yield, and somatic cell score traits across the first 3 parities in South Korean Holsteins was developed by comparing alternative random regression models.Variance component estimations and model comparisons were made among 5 different random regression test-day models incorporating herd-related factors, including Herd-Test-day (HTD), Herd-Year of calving (HY), Herd-Year-Season of calving (HYS) and Herd-Year-Month of calving (HYM).The models were compared using multiple indicators to measure the bias, the goodness of fit, and the accuracy of genetic evaluation.When HTD factor was considered as a fixed effect, the model demonstrated the best goodness of fit but showed a large decrease in the accuracy of genetic evaluation.By contrast, when HTD factor was included as a random effect together with the contemporary group factor, both high accuracy of genetic evaluation and superior goodness of fit were maintained.Furthermore, as the contemporary group definition was refined from HY to HYS and to HYM, additive genetic variance, heritability estimates, and the accuracy of genetic evaluation increased markedly, indicating that HYM provides a more appropriate definition of the contemporary group.Consequently, Model HYM+HTD(R) was determined to be the most appropriate genetic evaluation model for milk production and somatic cell score traits in South Korean Holsteins.
Cha et al. (Wed,) studied this question.