Forward cross-validation analyses were used to quantify the changes in BREEDPLAN EBVs from single-step genetic evaluations compared to traditional pedigree-based evaluations for Angus, Brahman, Hereford, Santa Gertrudis and Wagyu breeds. EBVs were generated from full multi-trait evaluations for each breed and compared to EBVs from an evaluation where all the phenotypic records were removed from the last four year drops of animals (termed Validation). Results for the sub-set of validation animals that were SNP genotyped showed the population-based accuracy of single-step EBVs were higher than pedigree-based accuracies for all breeds and traits. However, the magnitudes of the accuracy increases differed across breeds and traits, and generally reflected differences in the size of the training populations for each trait. The largest increase in accuracy, averaged across all traits in a breed, was observed for Angus (24%) and the smallest for Santa Gertrudis (5%). Across breeds, the largest increases in accuracy occurred for the growth trait EBVs compared to smaller increases for abattoir carcase, female reproduction and NFI EBVs. This study has shown the benefits of single-step genomic evaluations, and the opportunity to increase rates of genetic progress, through the increased accuracy generated. The study also highlighted breeds and traits which could benefit from additional recording to increase accuracies from single-step.

Net feed intake (NFI) is the residual portion of daily feed intake (DFI) not explained by growth or maintenance requirements. The NFI phenotype (NFIp) is based on a 70-day test period where DFI and fortnightly weights (to calculate average daily gain (ADG) and maintenance as metabolic mid-weight (MMWT)) are measured. Recording NFIp is costly, and shortening the test length would be advantageous. However, research has shown that ADG cannot be accurately measured from a shortened test. Genetic NFI EBVs (NFIg) were calculated using DFI EBV adjusted for ADG and MMWT EBV and were shown to have a Pearson correlation of 0.99 with the NFIp EBV from 3,088 Angus steers. The regression slope between NFIg and NFIp EBVs was 1.14. Alternative NFIg models where growth and maintenance requirements were obtained from BREEDPLAN live weight traits instead of live weights recorded in the test period, demonstrated high Pearson correlations (r=0.87 to 0.93) and regression slopes between 0.63 and 0.97 with NFIp EBVs. Results suggest that genetic NFI EBVs can be obtained, with growth and maintenance requirements being determined from BREEDPLAN live weight traits. This provides the opportunity to determine if the length of the test to measure DFI can be shortened, reducing the cost of recording NFI per animal.

BREEDPLAN net feed intake (NFI) EBV is derived from a phenotypic regression based on a 70-day feed intake test. Genetic NFI (NFIg) EBVs have been proposed as an alternative EBV and this recent development may also allow for a shortened feed intake test period. This study used feed intake records of 3,088 Angus steers from the full 70-day test and compared them to daily feed intake (DFI) from shortened test periods. Results showed DFI from shortened test periods had similar means but increased phenotypic variation. Phenotypic correlation with DFI from the full test period decreased as the test period decreased in weekly intervals and ranged between 0.75 and 0.99. NFIg EBVs were predicted using DFI from different length tests. The mean of all NFIg EBVs was close to zero, but the EBV standard deviation increased as the test period decreased. Pearson correlations between NFIg EBVs from a full test period and reduced test periods ranged between 0.73 and 0.99, the regression slope of NFIg from reduced test periods on NFIg from the full test period ranged between 0.73 and 0.95, and the bias ranged between 0.00 and 0.02. These results indicate that as the test period decreases, the spread of EBVs increases, resulting in extreme animals having overestimated NFIg EBVs. A shortened DFI test period could be used to estimate NFIg EBVs.