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Context Australian sheep breeding values (ASBVs) for the categorical trait of lambing ease are routinely estimated by Sheep Genetics via a threshold model. This has been pedigree-only, and has not utilised genomic information. Aim The present study aimed to update the genetic evaluation model and parameters for lambing ease for terminal sire sheep (dominated by White Suffolk and Poll Dorset breeds). The model includes correlations with birthweight and gestation length. Cross-validation was used to determine the value of the improved models and the inclusion of genomic information. Methods New data-preparation pipelines were developed to accommodate improved data-filtering methods and contemporary group construction. Genetic parameters, including correlations among traits, were estimated using continuous and threshold models, with various combinations of effects in mixed animal models. Cross-validation of breeding values was performed against progeny performance, by using forward prediction. Key results The increased volume of data, improved data preparation steps and enhanced evaluation software now allow a more complex model to be fitted, including maternal, sire by flock-year and genetic group effects, which were significant for all traits, along with the inclusion of multiple sire groups in the pedigree. However, the inclusion of the direct-maternal covariance and sire by flock-year terms resulted in unrealistically inflated estimates of some components, and thus the final covariance matrices required some adjustments. Cross-validation of breeding values was performed against progeny performance using forward prediction. For all traits, the phenotype accuracies and estimated breeding value correlations were higher from the new model without genomics than were those from the current routine evaluation. The benefit from including genomic information based on cross-validation is minimal currently but is expected to improve as the size of the reference population grows. Further work is required to define acceptable data-quality thresholds for the construction of datasets for routine breeding value estimation. Conclusions The new model and parameters resulted in ASBVs with an improved predictive ability, with increased accuracy and reduced bias compared with the current analysis. Furthermore, a small increase in accuracy was observed for all traits from utilising genomic information in the model. Implications The new genetic evaluation procedures and models will be used to update those being applied in the routine Sheep Genetics evaluation system and also support further index development for the terminal sire breeds in Australia.

Context Net reproduction rate defined as number of lambs weaned for joined ewes reflects conception, litter size and the ability of ewes to rear lambs to weaning age. These three attributes can be treated as separate traits for genetic evaluation, allowing breeders to focus selection for each appropriately. Aims The aim of the present paper was to present characteristics of the data, models and genetic parameters estimated from Merino data, along with results demonstrating the predictive capacity of breeding values, for conception, litter size, rearing ability, maternal behaviour and body condition score. Methods Industry data from MERINOSELECT were used to derive phenotypes for conception, litter size and rearing ability traits separately for yearling and adult ewes, along with maternal behaviour and body condition score. Indirect traits provided correlated information, including scrotal circumference, weight, scanned fat and eye muscle depths at up to three ages (post-weaning, yearling and hogget), along with pre-joining weight and condition score for ewes. Co-variances were estimated among the 19 traits included in the analysis. Breeding values were estimated using single step genomic methodology using consensus estimates of genetic parameters, with the predictive capacity of breeding values being estimated using forward-validation. Key results Heritabilities ranged from 0.04 to 0.06 for conception, litter size and rearing ability, and were moderate for maternal behaviour (0.13) and condition score (0.20). Component traits were genetically controlled by some common genes, but with genetic correlations less than 0.5, each component can be selected for independently. Weight, fat depth and muscle depth measures were moderately to highly correlated with body condition score. These body development traits generally had low, favourable genetic correlations with ewe reproductive traits. Conclusions These procedures provide better use of data recorded by breeders subject to both data limitations and opportunities, and provide separate breeding values for conception, litter size, rearing ability, maternal behaviour and body condition score. Implications New breeding values with good predictive ability are now available for the component traits of reproduction. Direct recording data of the traits is required to achieve accuracy of breeding values. Australian Merino breeders are now able to select directly for components of reproduction and specific maternal characteristics.