Improving prediction accuracy and selection of open-pollinated seed-lots in Eucalyptus dunnii Maiden using a multivariate mixed model approach

KEY MESSAGE: Multivariate mixed models can be used to combine complex data into a single-step analysis to improve prediction accuracy of open-pollinated seed lots for all attributes and candidates, and identify elite seed lots. CONTEXT: Data available for genetic selection may be complex and unbalanced; however, utilisation of all available information for prediction of genetic value may improve prediction accuracy to better identify elite candidates for selection. AIMS: This study aimed to develop, implement and evaluate a single-step multivariate mixed model for complex and unbalanced data and use the results to identify elite candidates. METHODS: Multivariate mixed models were developed and applied to a case study of seed-orchard open-pollinated Eucalyptus dunnii families grown in progeny trials in Australia and Uruguay to identify elite seed lots for biofuel utilisation. This approach combined all available data across trials and ages and included models of spatial variation to predict OP seed lot values for growth and wood quality attributes. Predictions were used to estimate response to selection and correlations between breeding values of parents predicted from their own performance and seed lot values predicted from the progeny trials. RESULTS: Prediction accuracy was highest for a single-step multivariate model. Prediction of seed lot values using this model indicated that selection of the best 12.5 % resulted in a gain of 30 % in cellulose content, and breeding value of parents predicted from own performance was only weakly correlated with seed lot performance in progeny trials. CONCLUSION: Single-step multivariate approaches provide the most accurate prediction of genetic value for all attributes, for all candidates, and hence leads to greater selection response. In eucalypts, gain from selection using seed lot values predicted from progeny trials will be greater than from selection using breeding values of parents predicted from their own performance.