Optimal allocation of trials to sub-regions in crop variety testing with multiple years and correlated genotype effects

TL;DR

This paper develops a novel method for optimally allocating trial resources across sub-regions in crop variety testing, leveraging pedigree-based genotype information and statistical modeling to improve prediction accuracy.

Contribution

It introduces a combined theoretical and numerical approach to determine optimal trial allocations considering pedigree data and multiple years in large-scale breeding programs.

Findings

The method effectively handles hundreds of genotypes in large breeding programs.

Optimal allocations improve prediction accuracy across sub-regions.

The approach integrates pedigree information into the variance-covariance modeling.

Abstract

Plant breeding and variety trials are usually conducted in multiple environments sampled from a defined target population of environments in order to characterize the performance of breeding lines or varieties. When the population is large and heterogeneous, it may be sub-divided into sub-regions or zones according to administrative and agro-ecological criteria. Analysis then focuses on prediction of performance in the individual sub-regions. Modelling the genotype effect in each sub-region as random, information can be borrowed across sub-regions using best linear unbiased prediction based on a suitable variance-covariance matrix for the genotype-zone effects. Here, we consider the important case where kinship of pedigree information is available for the genotypes under test. This information can be integrated into the variance-covariance matrix for genotype-zone effects. The objective we pursue here is to determine the optimal allocation of a fixed budget of trials to sub-regions. This design problem is solved using a combination of theory and explicit equations on one hand and numerical optimization on the other hand. Our proposed novel approach allows obtaining the optimal allocation when the number of genotypes is in the hundreds, a common setting in large plant breeding programs as well as in variety testing for economically important crops.