# Varietal selection for perennial crops where data relate to multiple harvests from a series of field trials

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## Abstract

Varietal selection for yield from a series of multi-environment trials can be regarded as a multi-trait selection problem in which the yields in different environments are synonymous with traits. As such an analysis of the data combined across environments should be conducted in order to form an index for selection. Analytical methods that include appropriate models for both the genetic variance structure (that is, the variances and covariances of genotype effects from different environments) and the residual variance structure (which typically comprises spatial covariance models for each trial) have been published previously. In the case of perennial crops, yields are often obtained from multiple harvests which implies that the data comprise short sequences of repeated measurements. Varietal performance in individual harvests is important for selection so that a combined analysis across both trials and harvests is required. The repeated measures nature of the data provides additional modelling challenges. In this paper we propose an approach for the analysis of multi-environment, multi-harvest data that accommodates the major sources of variation and correlation (including temporal). The approach is illustrated using two examples from sugarcane breeding programmes. The proposed models were found to provide a superior fit to the data and thence more accurate selection decisions than the common practice of conducting separate analyses of individual trials and harvests.

## Keywords

Multi-environment trials Varietal selection Perennial crops Mixed model## Notes

### Acknowledgements

We would like to thank BSES Limited staff Mark Hetherington, Dion Appo, Ross McIntyre, Michael Porta and Phil Lethbridge for technical assistance and the contribution of numerous field staff in the Mackay and Northern regions in collecting the data. We acknowledge the financial contribution of SRDC. We thank the referees for comments that have improved the manuscript. Finally we thank Prof. Robin Thompson who always inspires us to “try harder”.

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