The purpose of this study was to compare methods for handling censored days to calving records in beef cattle data, and verify results of an earlier simulation study. Data were records from naturalservice matings of 33,176 first-calf females in Australian Angus herds.Three methods for handling censored records were evaluated. Censored records (records on noncalving females) were assigned penalty values on a within-contemporary group basis under the first method (DCPEN). Under the second method (DCSIM), censored records were drawn from their respective predictive truncated normal distributions, whereas censored records were deleted under the third method (DCMISS). Data were analyzed using a mixed linear model that included the fixed effects of contemporary group and sex of calf, linear and quadratic covariates for age at mating, and random effects of animal andresidual error. A Bayesian approach via Gibbs sampling was used to estimate variance components and predict breeding values.Posterior means (PM) (SD) of additive genetic variance for DCPEN, DCSIM, and DCMISS were 22.6d² (4.2d²), 26.1d²(3.6d²), and 13.5d²(2.9d²),respectively. The PM (SD) of residual variance forDCPEN, DCSIM, and DCMISS were 431.4d²(5.0d²),371.4d² (4.5d²), and 262.2d²(3.4d²), respectively. ThePM (SD) of heritability for DCPEN, DCSIM, andDCMISS were 0.05 (0.01), 0.07 (0.01), and 0.05 (0.01),respectively. Simulating trait records for noncalvingfemales resulted in similar heritability to the penaltymethod but lower residual variance. Pearson correlationsbetween posterior means of animal effects for sireswith more than 20 daughters with records were 0.99between DCPEN and DCSIM, 0.77 between DCPENand DCMISS, and 0.81 between DCSIM and DCMISS.Of the 424 sires ranked in the top 10% and bottom 10%of sires in DCPEN, 91% and 89%, respectively, werealso ranked in the top 10% and bottom 10% in DCSIM.Little difference was observed between DCPEN andDCSIM for correlations between posterior means of animaleffects for sires, indicating that no major rerankingof sires would be expected. This finding suggests littledifference between these two censored data handlingtechniques for use in genetic evaluation of days to calving.

Mating and calving records for 47,533 first-calf heifers in Australian Angus herds were used to examine the relationship between days to calving (DC) and two measures of fertility in AI data: 1) calving to first insemination (CFI) and 2) calving success (CS). Calving to first insemination and calving success were defined as binary traits. A threshold-linear Bayesian model was employed for both analyses: 1) DC and CFI and 2) DC and CS. Posterior means (SD) of additive covariance and corresponding genetic correlation between the DC and CFI were −0.62 d (0.19 d) and −0.66 (0.12), respectively. The corresponding point estimates between the DC and CS were −0.70 d (0.14 d) and −0.73 (0.06), respectively. These genetic correlations indicate a strong, negative relationship between DC and both measures of fertility in AI data. Selecting for animals with shorter DC intervals genetically will lead to correlated increases in both CS and CFI. Posterior means (SD) for additive and residual variance and heritability for DC for the DC-CFI analysis were 23.5 d² (4.1 d²), 363.2 d2 (4.8 d²), and 0.06 (0.01), respectively. The corresponding parameter estimates for the DC-CS analysis were very similar. Posterior means (SD) for additive, herd-year and service sire variance and heritability for CFI were 0.04 (0.01), 0.06 (0.06), 0.14 (0.16), and 0.03 (0.01), respectively. Posterior means (SD) for additive, herd-year, and service sire variance and heritability for CS were 0.04 (0.01), 0.07 (0.07), 0.14 (0.16), and 0.03 (0.01), respectively. The similarity of the parameter estimates for CFI and CS suggest that either trait could be used as a measure of fertility in AI data. However, the definition of CFI allows the identification of animals that not only record a calving event, but calve to their first insemination, and the value of this trait would be even greater in a more complete dataset than that used in this study. The magnitude of the correlations between DC and CS-CFI suggest that it may be possible to use a multi-trait approach in the evaluation of AI and natural service data, and to report one genetic value that could be used for selection purposes.

Mating and calving records for 51,084first-parity heifers in Australian Angus herds wereused to examine the relationship between probabilityof calving to first insemination (CFI) in artificial inseminationand natural service (NS) mating data. Calvingto first insemination was defined as a binary trait forboth sources of data. Two Bayesian models were employed:1) a bivariate threshold model with CFI in AIdata regarded as a trait separate from CFI in NS dataand 2) a univariate threshold model with CFI regardedas the same trait for both sources of data. Posteriormeans (SD) of additive variance in the bivariate analysiswere similar: 0.049 (0.013) and 0.075 (0.021) forCFI in AI and NS data, respectively, indicating lack ofheterogeneity for this parameter. A similar trend wasobserved for heritability in the bivariate analysis, withposterior means (SD) of 0.025 (0.007) and 0.048 (0.012)for AI and NS data, respectively. The posterior means(SD) of the additive covariance and corresponding geneticcorrelation between the traits were 0.048 (0.006)and 0.821 (0.138), respectively. Differences were observedbetween posterior means for herd-year variance:0.843 vs. 0.280 for AI and NS data, respectively, whichmay reflect the higher incidence of 100% conceptionrates within a herd-year class (extreme category problem)in AI data. Parameter estimates under the univariatemodel were close to the weighted average of thecorresponding parameters under the bivariate model.Posterior means (SD) for additive, herd-year, and servicesire variance and heritability under the univariatemodel were 0.063 (0.007), 0.56 (0.029), 0.131 (0.013),and 0.036 (0.007), respectively. These results indicatethat, genetically, cows with a higher probability of CFIwhen mated using AI also have a high probability ofCFI when mated via NS. The high correlation betweenthe two traits, along with the lack of heterogeneity forthe additive variance, implies that a common additivevariance could be used for AI and NS data. A single-traitanalysis of CFI with heterogeneous variances forherd-year and service sire could be implemented. Thelow estimates of heritability indicate that response toselection for probability of calving to first inseminationwould be expected to be low.

Evidence of heterogeneity of parameters and genotype by country interactions was investigated for birth weight (BWT),weaning weight (WWT) and postweaning gain (PWG) between Australian (AUS), Canadian (CAN), New Zealand (NZ) andUSA populations of Charolais cattle. An animal model was fit to data sets for each individual country to compare the within countryparameter estimates for homogeneity. The direct heritability estimates of BWT in AUS (0.34) and NZ (0.31) were lessthan CAN (0.55) and USA (0.47). Maternal BWT heritabilities (0.13–0.18), direct WWT heritabilities (0.22–0.27), andmaternal WWT heritabilities (0.12–0.18) were similar across all four countries. Direct PWG heritability for AUS (0.14) wassmaller than the same estimate in the other three countries (0.24–0.31). The phenotypic variances for all three traits were similaracross AUS, CAN and USA; however, NZ was higher for BWT and WWT and lower for PWG. A multiple trait animal modelthat considered each trait as a different trait in each country was also fit to the data for pairs of countries. Direct (maternal)estimated genetic correlations for BWT for AUS–CAN, AUS–USA, USA–CAN, NZ–CAN and NZ–USA were 0.88 (0.86),0.85 (0.82), 0.88 (0.82), 0.85 (0.83), and 0.84 (0.80), respectively. Direct (maternal) estimated genetic correlations for WWT forAUS–CAN, AUS–USA, USA–CAN, NZ–CAN and NZ–USA were 0.96 (0.91), 0.95 (0.90), 0.95 (0.91), 0.95 (0.92), and0.95 (0.92), respectively. Direct estimated genetic correlations for PWG for AUS–CAN, AUS–USA, USA–CAN, NZ–CANand NZ–USA were 0.89, 0.91, 0.94, 0.90, and 0.91, respectively. The magnitude of the across-country genetic correlationsindicates that genotype by country interactions were biologically unimportant. However, strong evidence exists forheterogeneity of parameters across the countries for some traits and effects. Therefore, combining these countries into one singleanalysis to produce a common set of genetic values will depend on the development of methods to adjust for heterogeneousparameters for models containing both direct and maternal effects, and for circumstances where constant variance ratios orheritabilities are not present across populations.