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Variance components and breeding values for protein yield were estimated with REML without and with correction for heterogeneity of variances. Three different sire models were applied, which all accounted for genotype×environment (G×E) interaction. The first model included a sire×herd-year-season subclass (HYS) interaction. The second model divided all records in four different types of management groups, based on estimated HYS subclass effect. The third model, the reaction norm model, performed a random linear regression on the estimated HYS effect. For comparison, a standard model that did not take G×E interaction into account was also applied. Data consisted of 102,899 305-d first-lactation protein records of Holstein Friesians of 1000 of the largest Dutch dairy herds. All animals calved in 1997, 1998, or 1999. Estimated breeding values (EBV) for 2150 bulls with at least five daughters were calculated. The interaction model detected an interaction variance of 2.5% of the phenotypic variance. The EBV showed a correlation of 1.00 with those of the standard model without interaction. The model with the division in groups showed correlations between groups ranging from 0.73 to 0.86. The EBV showed correlations from 0.84 to 0.91 with the EBV of the standard model. The reaction norm model calculated EBV that had a correlation of 1.00 with the EBV of the standard model. The reaction norm model was not able to detect significant variance of the slope for the protein data corrected for heterogeneity of variances.

The aim of the paper was to estimate variance components for somatic cell scores for Italian Holsteins using data from three different areas of the country. A total of 2,202,804 first-parity test-day records, collected from 1990 to 1997 in three areas of Italy (Mantova, Milano, and Parmigiano cheese area), were available for study. The areas differ in herd size, feeding systems and especially in milk use. A minimum standard of quality is also required by some specific methods of cheese production, as for example from the Parmigiano Reggiano cheese chain. These reasons, taken together, affect the attention given to the quality of milk production in herds, and, therefore, to the sanitation levels.

The objectives of this study were threefold: 1) estimation of additive and dominance genetic variances for fertility traits for Austrian Simmental and Brown Swiss dairy cattle; 2) use of method ℜ and the preconditioned conjugate gradient compared to solving for method ℜ by second-order Jacobi iteration; and 3) study of the impact of inclusion of parental subclass effects on solutions for other random effects. Dominance variances were modeled for the inseminated cow and ranged from 0.32 to 1.36% of total variance. These values were similar to values for additive effects, which were approximately 1% of total variance. Convergence was clearly improved with preconditioned conjugate gradient and number of extrapolations reduced. Variance for permanent environment under a model without dominance could be split into a new estimate of permanent environmental variance and parental subclass variance. Solutions for parental subclass dominance effects were approximately proportional to permanent environment effects, but highly dependent on the number of animals contributing dominance relationships, especially full-sibs and three-quarter-sibs. For animals with a lot of dominance information (full-sibs, three-quarter-sibs, cousins), permanent environment and parental subclass dominance effects were nearly independent. Changes in additive effects were negligible, probably because both variances for parental subclass dominance effects and additive genetic effects were very small compared with residual variance.

Monthly BW to 600 d of age along with BW at first calving of 743 heifers were analyzed to estimate the genetic relationship between BW at first calving and other growth variables. The Von Bertalanffy function and a two-phase logistic function were used to fit growth patterns of individual heifers. The two-phase logistic function fitted the data more adequately than did the Von Bertalanffy function. Calves from first parity dams were born at a lower degree of maturity than calves from third parity dams. Calves with a high proportion of Holstein genes had a higher BW both at first calving and at maturity. Heritabilities of the variables of the Von Bertalanffy and the two-phase logistic function ranged from 0.02 to 0.31 and 0.15 to 0.26, respectively. Estimated mature BW had a highly negative correlation with rate of maturation (–0.56 to –0.82); BW at first calving had a high genetic correlation (0.74 to 0.90) with estimated mature BW and a negative correlation with maturation rate (–0.18 to –0.40). Results of this study indicated that BW at first calving can be used as an indirect trait when mature BW is included in the breeding goal.

Genetic and phenotypic parameters of body weight (BW), hip height, heart girth, and linear conformation traits were estimated from field data for 7344 lactating Holstein heifers from 560 herds. Mean BW was around 540kg at calving, decreased to a minimum of 514kg by wk 6, and increased to 590kg by wk 45 of lactation. The statistical model for BW included month of recording, lactation stage, pregnancy stage, proportion of Holstein genes, parity of the dam, calving age, herd, and animal. Variation between herds accounted for 33% of the phenotypic variation of BW. Estimated heritability was 0.33 for BW and ranged from 0.32 to 0.54 for heart girth, hip height, and conformation traits. Genetic correlations of BW with heart girth, hip height, body depth, rump width, and muscularity ranged from 0.48 to 0.77. The accuracy of a selection index was 0.90 for direct selection and 0.83 for indirect selection using information on those conformation traits of 50 daughters. Genetic variation of BW is considerable, and genetic evaluation of BW can be based on data for conformation traits from a field recording system with only a limited loss of accuracy.