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Black and White dual-purpose cattle (DSN) are kept in diverse production systems, but the same set of genetic parameters is used for official national genetic evaluations, neglecting the herd or production system characteristics. The aim of the present study was to infer genetic (co)variance components within and across defined herd descriptor groups or clusters, considering only herds keeping the local and endangered DSN breed. The study considered 3659 DSN and 2324 Holstein Friesian (HF) cows from parities one to three. The 46 herds always kept DSN cows, but in most cases, herds were ‘mixed’ herds (Mixed), including both genetic lines HF and DSN. In order to study environmental sensitivity, we had a focus on the naturally occurring negative energy balance in the early lactation period. In consequence, traits were records from the 1st official test-day after calving for milk yield (Milk-kg), somatic cell score (SCS) and fat-to-protein ratio (FPR). Genetic parameters were estimated in bivariate runs (separate runs for the three genetic lines Mixed, HF and DSN), defining the same trait from different herd groups or clusters as different traits. Additive-genetic variances and heritabilities were larger in herd groups that indicated superior herd management, implying that cow records from these herds allow a better genetic differentiation. Superior herd management included larger herds, low calving age, high herd production levels and low intra-herd somatic cell count. Herd descriptor group differences in additive-genetic variances for Milk-kg were stronger in HF than in DSN, indicating environmental sensitivity for DSN. Similar variance components and heritabilities across groups, clusters and genetic lines were found for data stratification according to geographical descriptors altitude and latitude. Considering 72 bivariate herd group runs, 29 genetic correlations were very close to 1 (mostly for Milk-kg). Somatic cell score was the trait showing the smallest genetic correlations, especially in the DSN analyses, and when stratifying herds according to genetic line compositions (rg=0.11), or according to the percentage of natural service sires (rg=0.08). For estimations based on the results of a cluster analysis considering several herd descriptors simultaneously, indications for genotype × environment interactions could be found for SCS, but genetic correlations were larger than 0.80 for Milk-kg and FPR. In conclusion, we suggest multiple-trait animal model applications in genetic evaluations, in order to select the best sires for specific herd environments or herd clusters.

Black and White dual-purpose cattle (DSN) are kept in diverse production systems, but the same set of genetic parameters is used for official national genetic evaluations, neglecting the herd or production system characteristics. The aim of the present study was to infer genetic (co)variance components within and across defined herd descriptor groups or clusters, considering only herds keeping the local and endangered DSN breed. The study considered 3659 DSN and 2324 Holstein Friesian (HF) cows from parities one to three. The 46 herds always kept DSN cows, but in most cases, herds were 'mixed' herds (Mixed), including both genetic lines HF and DSN. In order to study environmental sensitivity, we had a focus on the naturally occurring negative energy balance in the early lactation period. In consequence, traits were records from the 1st official test-day after calving for milk yield (Milk-kg), somatic cell score (SCS) and fat-to-protein ratio (FPR). Genetic parameters were estimated in bivariate runs (separate runs for the three genetic lines Mixed, HF and DSN), defining the same trait from different herd groups or clusters as different traits. Additive-genetic variances and heritabilities were larger in herd groups that indicated superior herd management, implying that cow records from these herds allow a better genetic differentiation. Superior herd management included larger herds, low calving age, high herd production levels and low intra-herd somatic cell count. Herd descriptor group differences in additive-genetic variances for Milk-kg were stronger in HF than in DSN, indicating environmental sensitivity for DSN. Similar variance components and heritabilities across groups, clusters and genetic lines were found for data stratification according to geographical descriptors altitude and latitude. Considering 72 bivariate herd group runs, 29 genetic correlations were very close to 1 (mostly for Milk-kg). Somatic cell score was the trait showing the smallest genetic correlations, especially in the DSN analyses, and when stratifying herds according to genetic line compositions (r g =0.11), or according to the percentage of natural service sires (r g =0.08). For estimations based on the results of a cluster analysis considering several herd descriptors simultaneously, indications for genotype × environment interactions could be found for SCS, but genetic correlations were larger than 0.80 for Milk-kg and FPR. In conclusion, we suggest multiple-trait animal model applications in genetic evaluations, in order to select the best sires for specific herd environments or herd clusters. [ABSTRACT FROM AUTHOR]