The Hungarian Simmental cattle is a dual-purpose breed, having good milk and meat production characteristics. Simmental cows have some other important traits, for example, a longer productive life. The longevity or productive life is the time period between first calving and culling. The conformation contributes to productive life, reproduc...tion, and milk production.
The aims of this study were to analyze the longevity of Hungarian Simmental dual-purpose cows, to evaluate the effects of the size of the herd, age at first calving, main type traits (frame, musculature, feet and legs, mammary system), and combination of main type traits (frame and musculature, feet and legs and mammary system).
Animal, age at first calving, herd*calving, musculature, the mammary system as well as the combination of mammary system and feet and legs were significant effects on longevity. The highest risk ratio was observed for cows first calved after 31 months. The risk of culling increased with increasing scores of musculature and decreasing scores of the mammary system. The highest risk ratio was estimated in category 11 (lower scores of mammary system with lower scores of feet and legs). In this case, the risk ratio was 36% higher than the reference group.
The aim of the current research was to estimate variance components and genetic parameters of weaning weight in Hungarian Simmental cattle. Weaning weight records were obtained from the Association of Hungarian Simmental Breeders. The dataset comprised of 44,278 animals born from 1975 to 2020. The data was analyzed using the restricted maxi...mum likelihood methodology of the Wombat software. We fitted a total of six models to the weaning weight data of Hungarian Simmental cattle. Models ranged from a simple model with animals as the only random effect to a model that had maternal environmental effects as additional random effects as well as direct maternal genetic covariance. Fixed effects in the model comprised of herd, birth year, calving order and sex. Likelihood ratio test was used to determine the best fit model for the data. Results indicated that allowing for direct-maternal genetic covariance increases the direct and maternal effect dramatically. The best fit model had direct and maternal genetic effects as the only random effect with non-zero direct-maternal genetic correlation. Direct heritability, maternal heritability and direct maternal correlation of the best fit model was 0.57, 0.16 and -0.78 respectively. The result indicates that problem of (co-)sampling variation occurs when attempting to partition additive genetic variance into direct and maternal components.