The data sets consisted of field performance data from 54 848 purebred and 16 175 crossbred animals (Czech data set, CZ) and 16 610 purebred and 9 228 crossbred animals (Slovak data set, SK). Animals from the following breeds were included: Duroc, Hampshire, Piétrain, sire line of Large White (CZ) or Yorkshire (SK), Czech Meat pig (CZ) or Slovak Meat pig (SK), Belgian Landrace (SK). Two-trait animal models were calculated for average daily gain from birth to the end of the field test (ADG) and lean meat content (LM, only in CZ) or backfat thickness (BF, only in SK). The models included additive breed and breed heterotic effects. Piétrain was the breed with the highest LM and the lowest BF. The additive genetic breed effect was about 1·5% LM (CZ) in comparison with Large White or −0·4 mm BF (SK) in comparison to Yorkshire. The sire line of Large White (CZ) or the Yorkshire breed (SK) clearly exceeded all the remaining sire breeds in ADG (on average by 30 to 50 g/day). There was a clear tendency to negative heterosis in LM in all crossbred combinations (CZ). In BF, heterotic effects between −0·2 mm and +0·3 mm were estimated, mostly not being significant (SK). The estimates of the heterotic effects for ADG were positive throughout. Higher values up to 40 g/day (7%) were observed in the Czech data set. Though heterotic breed effects are of some importance especially for ADG, their inclusion in the equations for breeding value estimation will have only a minor impact on the predicted breeding values.

Functional longevity of dairy cows has been routinely evaluated in France since 1997 using a survival analysis model. Recently, we proposed a genetic trend validation test that could be used before including national data in an international evaluation of bulls on longevity of their daughters. Its application to the French Holstein data revealed a large overestimation of the genetic trend. It was found that the bias is the result of a change in the baseline hazard rate over time. A new proportional hazards model is proposed which accounts for this change. In the new model, the baseline is described as a stratified, piecewise Weibull hazard function within lactation, i.e. a function of the number of days since the most recent calving. Stratification is within year and parity. Different Weibull hazard functions are used over four periods: 0 to 270 days, 271 to 380 days, 381 days to day when dried, dry period until the next calving. The non-genetic effects included in the model were slightly different from the previous one. In particular the interaction effects between the within herd-year class of production and lactation number × stage of lactation on the one hand and year-season were accounted for. The estimated genetic variance was smaller than with the old model. The new genetic trend is almost flat. An illustration of the efficiency of selection on the estimated breeding values for longevity is presented.

The advent of genetic evaluations for fertility traits in the UK offers valuable information to farmers that can be used to control fertility problems and safeguard against involuntary culling. In addition to estimated genetic merit, proof reliabilities are required to make correct use of this genetic information. Exact reliabilities, based on the inverse of the coefficient matrix, cannot be estimated for large data sets because of computational restrictions. A method to calculate approximate reliabilities was implemented based on a six-trait sire model. Traits considered were interval between first and second calving, interval between first calving and first service, non-return rate 56 days post first service, number of inseminations per conception, daily milk yield at test nearest day 110 and body condition score. Sire reliabilities were calculated in four steps. Firstly, the number of effective daughters was calculated for each bull, separately for each trait, based on total number of daughters and daughter distribution across herd-year-seasons. Secondly, multiple-trait reliabilities were calculated, based on bull daughter contribution, applying selection index theory on independent daughter groups. Thirdly, (great-) grand-daughter contribution was added to the reliability of each bull, using daughter-based reliability of sons and maternal grandsons. An adjustment was made to account for the probability of bull and son or grandson having daughters in the same herd-year-season. Without the adjustment, reliabilities were inflated by proportionately 0·15 to 0·25. Finally, parent (sire and maternal grandsire) contribution was added to the reliability of each bull. The procedure was first tested on a data subset of 28 061 cow records from 285 bulls. Approximate reliabilities were compared with exact estimates based on the inverse of the coefficient matrix. Mean absolute differences ranged from 0·014 to 0·020 for the six traits and correlation between exact and approximate estimates neared unity. In a full-scale application, sire reliability for the fertility traits increased by proportionately 0·47 to 0·79 over single-trait estimates and the number of bulls with a reliability of 0·60 or more increased by 42 to 115%.

An analysis has been carried out of over 88000 calving survey records for bulls of eight beef breeds tested in commercial dairy herds in England and Wales. The data were collected from 1981 to 1993. The traits analysed were the degree of difficulty at calving, assessed from no difficulty to serious difficulty, calf mortality to 48 h, gestation length, and subjective assessments of calf size and conformation.

Major sire breeds in the survey were the Hereford (30070 records, 93 sires), Charolais (25816 records, 105 sires) and the Limousin (15420 records, 55 sires). Other sire breeds were Belgian Blue (5391 records, 24 sires), Simmental (3864 records, 19 sires), Aberdeen Angus (2886 records, 12 sires), Piedmontese (2423 records, 7 sires) and the Blonde d'Aquitaine (2271 records, 8 sires).

Information was routinely recorded on cow age (heifer or mature), sex of the calf, and the year and month of the calving, while cow size and the regional location of the herd were also recorded in later years.

Heifers had more difficult calvings, higher calf losses and shorter gestations than mature cows (all P < 0·05) Seriously difficult calvings and calf mortality were higher for male calves, which were also larger and had better conformation than heifer calves (P < 0·05).

Sire breed effects were significant for all traits, although sire breed ranking for calving ease was different for cow and heifer calvings and some interactions involving sire breed were also significant. For cow calvings, the easiest calving breeds were the Hereford (1·1%) and the Aberdeen Angus (1·4%), while the Charolais (4·3%), Blonde d'Aquitaine (3·7%) and Simmental (3·1%) had most difficult calvings.

Gestation length differed between sire breeds, from Aberdeen Angus (281·0 days) and Hereford (282·7 days) up to the Limousin (288·1 days) and the Blonde d'Aquitaine (289·0 days). For calf size and conformation, the Simmental, Charolais and Blonde d'Aquitaine were the best.

In general, summer months were associated with lower incidences of difficult calvings, lower losses and shorter gestations.

Large cows had calves which were larger and of better conformation than small or medium cows, but large cows also had longer gestations, more difficult calvings and poorer calf survival (all P < 0·05).

Statistical models are discussed that should be used for the genetic evaluation of calving survey traits, when beef bulls are to be used in dairy herds in the United Kingdom.

Genetic parameters were estimated for traits recorded in a calving survey for beef sires used in commercial dairy herds in England and Wales. The five traits included in the survey were calving difficulty score, calf mortality to 48 h after birth, gestation length, and subjectively assessed calf size and conformation. The data file examined included over 88 000 records, on 323 sires of eight beef sire breeds, namely Aberdeen Angus, Belgian Blue, Blonde d'Aquitaine, Charolais, Hereford, Limousin, Piedmontese and Simmental.

Estimates of heritability and genetic and phenotypic correlations were obtained for all traits by REML procedures using a sire model. The effects of cow age, calf sex, year and season of the calving and sire breed were included in the model, together with interactions.

Heritability estimates were 0·09 (s.e. 0·01) for calving difficulty score, 0·32 (s.e. 0·03) for gestation length, 0·02 (s.e. 0·003) for mortality, 0·09 (s.e. 0·01) for calf size and 0·06 (s.e. 0·01) for calf conformation.

Heritability estimates for the scored categorical and binomial (mortality) traits were also obtained using threshold model analysis. As expected, these estimates were higher than the REML estimates; 0·16 (s.e. 0·01) for calving difficulty score, 0·08 (s.e. 0·01) for mortality, 0·14 (s.e. 0·01) for calf size and 0·09 (s.e. 0·01) for calf conformation. Calving difficulty score was strongly correlated genetically with calf size (-0·84), mortality (0·74) and calf conformation (-0·72), and moderately correlated with gestation length (0·26). Calf size and conformation were very highly correlated genetically (0·86), and both were also strongly correlated with mortality (-0·63 and -0·52 respectively). In all cases, the genetic correlations were stronger than the phenotypic correlations.

Sire genetic merit was predicted for all traits and the distribution of these predictions is described. Sire predictions of genetic merit for calving difficulty score on the underlying scale were backtransformed to predict the expected incidence of serious difficulties in future calvings and these predictions are illustrated for the major sire breeds.

Customized sire selection indices were developed for Australian dairy bulls in order to promote more objective use of estimated breeding values in commercial breeding programmes. It was assumed that the breeding goal for commercial dairy farmers is the profitability of a bull's progeny. Seven characteristics of the progeny were identified as having a major impact on profitability and were included in the breeding objective: milk, fat and protein yield, survival, body weight, milking speed and temperament. Traits in the selection indices used to predict profit were milk, fat and protein yield, survival, milking speed, temperament, size, overall type and fore teat placement. Size was included because of its correlation with body weight, and overall type and front teat placement because of their correlation with survival. To avoid double counting the benefits of milk production traits, temperament and milking speed, the survival trait in the objective was defined as survival independent of voluntary culling for these traits. Customization of the breeding objective was achieved by adjusting the economic weights for traits in the objective to take account of important characteristics of farmers' herds, the milk payment system under which they operate and make allowance for their own value judgements. An assessment of the impact of customization suggested that, even though there is a wide range in the economic weights that are applicable in different areas of Australia, there would be little loss of efficiency in using a single national index. However, customization is still believed to be desirable given that it is likely that a substantial proportion offarmers will be reluctant to use a national index, especially in those states which have quotas and focus on the liquid milk market. The algorithms described in the paper have been incorporated into a user-friendly microcomputer program called $electabull which is now commercially available to farmers.

Stochastic simulation was used to compare selection response and rate of inbreeding in four nucleus breeding systems and a sire referencing scheme for sheep: an open nucleus system (ONS), an open nucleus system with sequential selection of the nucleus (ONS-S), a sire referencing scheme (SRS) and a dispersed open nucleus system (DONS). Selection was based on best linear unbiased prediction of breeding values for a single trait measurable on all individuals prior to selection. Selection in a population of 1200 ewes equally divided into 10 flocks was simulated over a 15-year period. The mean rate of genetic gain was proportionately about 0-15 higher in ONS-S and DONS compared with ONS and SRS. The rate of inbreeding in SRS was considerably lower and in ONS-S, considerably higher, than in the other systems. The level of prolificacy in the population did not influence the relative ranking of the breeding systems but may have implications for their optimal structure.

An animal model and an approximative method for calculating repeatabilities of estimated breeding values are used in Finnish dairy cow evaluation. Changes in estimated breeding values over time as daughters accumulate were studied. Special emphasis was given to the accuracy and potential bias in the pedigree indices of young sires. The data set used was the same as in the national evaluation and the traits investigated were protein yield and somatic cell count. The average repeatability in evaluation of bulls without daughters was 0·37. The empirical repeatability defined as a squared correlation between the pedigree index and the final sire proof was only 0·15. The reduction in the repeatability was attributed to the selection on pedigree index. The upward bias observed in pedigree indices was 5 kg (approx. 0·3 of genetic standard deviation). The bias was caused by the overestimation of bull dams' breeding value. Also the proofs of bull sires increased after the second crop of daughters. The correlation between the evaluations of the same sire calculated from two separate equal size daughter groups was 0·91 when the bull had 10 to 50 daughters and 0·87 with over 100 daughters. This illustrates how the relative weight of the pedigree decreases while more progeny information is accumulated in the evaluation.