Designing high performing and stable genotypes through complementary crossing schemes

Breeding programmes aim to release new genotypes with improved performance for key traits such as yield, quality, and disease resistance. These traits are often subject to genotype by environment interaction (GEI), reflecting the differential response of genotypes to changes in their environment.As a consequence, genotypes vary in their stability across the target population of environments (TPE).We propose a novel crossing approach, termed dCross, which utilises this variation for generating high performing and stable genotypes by pairing parents based on their complementary interaction patterns.We also outline the role of stability within plant breeding, noting that crossing for stability generally makes sense for developing products but is problematic for improving a breeding population.Stochastic simulations were used to compare dCross with traditional selection criteria for generating high performing and stable products, given varying numbers of environments and levels of GEI.The results show that dCross was superior in maximising the mean performance across the TPE for all targeted levels of stability.When targeting genotypes in the top 1% of stability, dCross achieved up to 16.7% higher mean performance than selecting on the mean alone and up to 9.9% higher than using an index which penalises (in)stability.We conclude that rather than creating populations with high stability, breeding programmes should aim to create populations that are enriched with favourable alleles covering the TPE space.Stability can then be generated by designing crosses that strategically assemble these alleles into stable products for commercial release.