Plant breeding programs use data obtained from multi-environment selection experiments to produce improved varieties with the ultimate aim of maintaining high levels of genetic gain. Selection accuracy can be improved with the use of advanced statistical analytical methods that use informative and parsimonious variance models for the set of genotype by environment interaction effects, include information on genetic relatedness and appropriately accommodate non-genetic sources of variation within the framework of a single step estimation and prediction algorithm. Maximal gains from using these advanced techniques are more likely to be achieved if the designs used match the aims of the selection experiment and make full use of the available resources. In this paper we present an approach for constructing designs for selection experiments which are optimal or near optimal against a robust and sensible linear mixed model. This model reflects the models used for analysis. The approach is flexible and introduces an additional step to accommodate efficient resource allocation of replication status to genotypes, which is undertaken prior to the allocation of plots to genotypes. A motivating example is used to illustrate the approach, two illustrative examples are presented one each for single and multiple environment selection experiments and several in-silico simulation studies are used to demonstrate the advantages of these approaches.

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