The type was evaluated in 5 424 young animals of twelve beef breeds. Ten type traits were evaluated: height at sacrum (HS), body length (BL), live weight (LW), front chest width (CW), chest depth (CD), pelvis (P), shoulder muscling (SM), back muscling (BM), rump muscling (RM) and production type (PT). The traits represent two groups: (1) traits scoring body measurements and body capacity (HS, BL, LW, CW, CD, P) and (2) traits scoring muscling (SM, BM, RM, PT). These fixed effects were included in the model: breed, sex, HYS, mother’s age, linear regression on age at evaluation and average gain from birth to evaluation. Fixed effects in the model explained 40% to 60% of variability. The highest values of heritability coefficient were estimated for HS (h² = 0.51) and LW (h² = 0.50). BL had the lowest values of heritability coefficient (h² = 0.25). The values h² = 0.25–0.32 were calculated for the traits scoring body capacity (CW, CD, P). The range of values for muscling traits was h² = 0.26–0.35. The coefficient of heritability for PT was h² = 0.34. All traits scoring muscling and PT showed high genetic correlations (r_g > 0.95). The traits scoring body capacity (P, CW, CD) were highly genetically correlated with muscling r_g > 0.83. Breeding values were determined by a multi-trait animal model. Standard deviations of breeding values were higher in HS (s_BV = 0.71) and LW (s_BV = 0.84), which was connected with a different method of evaluation of these traits. They were in the range of 0.19 (BL) to 0.30 (RM) in the other traits.  

Complex selection indexes are proposed for bulls of Holstein ca�le in the Czech Republic. Partial breeding values for milk performance, fertility and body conformation are combined in the index. Conformation is expressed either by linear classification or by general characteristics and body measurements. Variants according to the traits included in breeding objective and in performance recording were tested -breeding objective comprised milk, meat, milkability, health, reproduction, longevity; production traits and linear classification of body conformation or production traits and general characteristics of body conformation and/or production traits, and general characteristics of body conformation and body measurements were used as source of information (in performance recording). For practical use is recommended the variant of reduced selection index where milkability, health and meat performance were le� out from breeding objective, and production traits and linear classification of body conformation were employed as a source of information. The index guarantees 69.30% reliability of breeding value estimate and the ratio of selection effects for milk, meat and secondary traits 78 : 8 : 14. The respective importance of milk performance in total (breeding values for milk and components) of reproduction and linear body classification as sources of information in the index are 79.61%, 15.52% and 4.86%.

The objective of this study was to estimate genetic parameters for the results of classifying of carcass traits by the SEUROP method in beef cattle in the Czech Republic using linear and linear-threshold models. Genetic parameters were calculated and evaluated in a set of 4276 animals of eleven beef breeds and crosses with dairy and dual-purpose breeds (Aberdeen Angus – 1376, Hereford – 994, Simmental – 651, Charolais – 524, Piemontese – 185, Galloway – 162, Blonde d’Aquitaine – 147, Limousine – 106, Highland – 53, Gasconne – 44, Belgian Blue – 34) in 2005–2008. Aberdeen Angus, Hereford, Charolais and beef Simmental were the most numerous breeds. Fixed effect of a classifier, fixed regression on age at slaughter by means of Legendre polynomial of the second degree separately for the each breed and sex and fixed regression on heterosis coefficient were included in a model equation. Genetic parameters were estimated by a multi-trait animal model using a linear model and a linear-threshold model in which carcass weight (CW) was considered as the linear trait and carcass conformation (CC) and carcass fatness (CF) grading as threshold traits. The heritability coefficient for CW differed only moderately according to the method of the genetic parameter estimation (0.295 in linear model and 0.306 in linear-threshold model). The heritability coefficient for CC was 0.187 in linear model and 0.237 in linear-threshold model. The heritability coefficient for CF grading was 0.089 in linear model and 0.146 in linear-threshold model. Genetic correlation between CW and CC was high (0.823 in linear model and 0.959 in linear-threshold model), the correlation between CW and CF was intermediate (0.332 and 0.328, respectively) and it was low between CF and CC (0.071 and 0.053). If CW was included in the model equation as fixed regression using Legendre polynomial, lower heritability coefficients for CC (0.077 and 0.078) and CF (0.086 and 0.123) were calculated and the correlation between CC and CF was negative (–0.430 and –0.429).

Three selection indexes were constructed for bulls of beef cattle: IM for terminal crossing (in dairy herds), IZ for the selection of foundation sires for beef herd and IS for the selection of bulls for beef herd. Each index was constructed in five variants that differed in the number of used traits from the most important ones to all traits with known breeding values. The sources of information were breeding values routinely calculated in performance testing -10 breeding values for direct and maternal effects for easy calving and growth, breeding value for daily gain of bulls at performance-testing stations and 10 breeding values for the type traits of young animals. The reliability of partial breeding values that enter into the indexes ranged from 11% to 36%. Reliability influences subsequent accuracy of index selection for total genotype that is in the range of 30% to 46%. The discounting of economic values (0% or 10%) did not influence the selection indexes significantly. Index selection was expressed almost exclusively in genetic gain of direct effects while maternal effects were of only small importance in the breeding objective. Direct effects for daily gain until weaning and after weaning are of the highest importance in the breeding objective, accounting for 90% to 96% of the total selection effect. The most important information sources in selection indexes are direct effect of weaning weight (importance of approximately 74% to 95%) and maternal effect of weaning weight (importance of approximately 5% to 7%). The inclusion of daily gain of bulls at performance-testing stations with the importance of about 16% in the index decreased the importance of weaning weight. Selection can be aimed at these main traits -calving ease (direct and maternal effect) and weight at 210 days (direct and maternal effect) only because the importance of the other traits in the index is very low.

Sub-indexes were constructed for bulls of the Holstein breed according to groups of production traits -production index for milk (IPH), sub-indexes for reproduction (IRH), longevity (IDH), health resistance (IOH) and fitness (IFH). Every index for selection for a group of traits applies all available information -breeding values for traits of milk performance, fertility and linear type trait classification. The sub-indexes were compared with the total index (SIH). The accuracy of total selection index (SIH) was 69.30%, the accuracies of the other indexes were as follows: IPH 83.32%, IRH 56.04%, IDH 9.80%, IOH 15.57% and IFH 9.86%. All of the indexes were standardised to have the mean 100 and standard deviation 12. Simplified index ZSIH was developed as a sum of IPH, IRH and IDH; its correlation with the total index is r = 0.992. If IPH is used, the values of secondary functional traits decrease. The use of IDH, IOH and IFH results in a decrease in milk performance. Selection differences, i.e. changes in breeding values at various intensities of selection according to some indexes and breeding values for kg of milk and kg of milk proteins, were tested on the basis of breeding values in a sample of the domestic population of bulls. The production index was compared with production indexes used in other countries. Correlations between production indexes were higher than r = 0.790.