Speaker
Description
New crop varieties are extensively tested in multi-environment trials in order to obtain a solid basis for recommendations to farmers. When the target population of environments is large, a division into sub-regions is often advantageous. If the same set of genotypes is tested in each of the sub-regions, a linear mixed model (LMM) may be fitted with random genotype-within-sub-region effects. The first analytical results to optimizing allocation of trials to sub-regions have been obtained in Prus and Piepho (2021) and Prus and Piepho (2024). Prus and Piepho (2021) considered one-year experiments. In Prus and Piepho (2024) multi-year experiments were investigated, for which the same conditions we originally assumed for all years. However, in praxis the number of genotypes or even the total number of locations may change from year to year. In this work the general LMM is considered, where the number of genotypes and the total number of locations for different years are not the same. The latter numbers turn out to have influence on the optimal allocations of trials. The obtained analytical results are illustrated by real data examples.
Prus, M. and Piepho, H.-P. (2021). Optimizing the allocation of trials to sub-regions in multi-environment crop variety testing. Journal of Agricultural, Biological and Environmental Statistics, 26, 267–288.
Prus, M. and Piepho, H.-P. (2024). Optimizing the allocation of trials to sub-regions in crop variety testing with multiple years and locations. Journal of Agricultural, Biological and Environmental Statistics.
85717610248