Potential accuracy of genetic evaluation for calving difficulty with incomplete data on calving difficulty and/or birth weight using a bivariate threshold-linear animal model.

The purpose of this study was to evaluate the potential loss of accuracy in direct and maternal predicted breeding values (PBV) for calving difficulty (CD) with different levels of missing records of CD and/or birth weight (BW), using a bivariate threshold-linear animal model. Data obtained from the American Gelbvieh Association included 84,420 first-parity records with both CD and BW available. The final pedigree file included 178,858 animals. The model included fixed calf-sex-dam-age, random herd-year-season, and animal direct and maternal effects. Different levels of missing observations for CD and BW were obtained by randomly deleting 0, 25, 50, 75, and 100% of records for both traits in various combinations. Correlation estimates between PBV for CD obtained with complete and incomplete data were used to measure the changes in PBV for different levels of missing records. Reported correlations are means of three replicates. The results suggest that the information on direct and maternal PBV provided by CD records is more reliable than the information provided by BW records. The difference was especially large when a high proportion of CD records were missing. Correlations above 0.96 and 0.95 for direct and maternal PBV, respectively, when missing 25% or 0% of the CD or BW records suggest that small changes would be predicted with a low proportion incomplete data. For genetic prediction of popular sires (with > 100 pogeny), a higher proportion of missing records could be tolerated. The results suggest that the bivariate threshold-linear animal model is useful for routine genetic evaluation of CD with incomplete field data.