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

2026-04-30

AI Summary

This study addresses the challenge of optimally allocating limited resources in multi-environment genotype trials to accurately predict regional performance under large-scale heterogeneity. It proposes a novel approach that integrates pedigree-based relatedness into multi-location trial design by constructing a structured variance–covariance matrix for genotype-by-environment effects, enabling information sharing across regions through best linear unbiased prediction (BLUP). By combining analytical derivations with numerical optimization, the method determines the optimal allocation of sub-regional testing efforts under a fixed budget. The framework scales efficiently to hundreds of genotypes, significantly enhancing resource use efficiency while maintaining high prediction accuracy, and is well-suited for large-scale breeding programs and major crop variety testing systems.

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.

Problem

• optimal allocation
• crop variety testing
• sub-regions
• genotype effects
• trial design

Innovation

• optimal allocation
• genotype-by-environment interaction
• kinship information
• best linear unbiased prediction
• variance-covariance modeling