Comparison of genetic values for reproductive performance in beef cattle
Donoghue, Katherine Alison
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The first objective of this study was to compare methods for handling censored fertility records for the trait days to calving (DC). Censored records 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 distributions. The PM of residual variances were significantly higher for DCPEN than DCSIM, especially at higher levels of censoring. Similar trends in variance components were observed in a study using field data for the trait. Little difference was observed between the two methods for correlations between true breeding values and posterior means of animal effects for sires, indicating that no major re-ranking of sires would be expected. The results from both studies suggest that either censored data handling technique could be successfully used in a genetic evaluation for DC. The second objective of this study was to develop an evaluation for fertility that uses information from both natural service (NS) and artificial insemination (AI) matings. Firstly, mating records were used to examine the relationship between probability of calving to first insemination (CFI) in both types of data. Some differences were observed between PM for herd-year variance, which may be a reflection of the higher incidence of extreme category problem in the AI data. A high genetic correlation was observed between the two traits (0.821). This result, along with the observed lack of heterogeneity for additive variance, implies that an analysis of CFI with a common additive variance for AI and NS data could be undertaken. In a separate study, the relationship between DC and two measures of fertility in AI data; CFI and calving success (CS); was examined. High genetic correlations were observed between DC-CFI (-0.681) and DC-CS (-0.751), indicating a strong, negative relationship between DC and both measure of fertility in AI data. The magnitude of the correlations between DC and CS/CFI suggest that it may be possible to use a multi-trait approach to the evaluation of artificial insemination and natural service data, and report one genetic value that could be used for selection purposes.