Cross-talk between stability parameters and selection models: a new procedure for improving the identification of the superior genotypes in multi-environment trials

Objective
Evaluating new promising genotypes across multiple environments emphasizes the importance of grain yield stability and increasing production in sustainable agricultural systems. One way to achieve this is through multi-environment trials (METs) studying genotype-by-environment interaction (GEI) effects. GEI analysis has significantly advanced over the years, with various models and methods developed to better understand and utilize this phenomenon in plant breeding and finally identification of high-yielding and stable genotypes. This report aimed to integrate various stability parameters and selection models to achieve better decisions in selecting superior genotypes. Moreover, modified R-based scripts for selection models have been presented.
Results
According to the combined analysis of variance (ANOVA) and additive main effects and multiplicative interaction (AMMI) model, the main effects of environment (E), genotype (G), and their interaction (GEI) were significant for grain yield data. Our results showed that integrating stability parameters and selection models successfully identified superior genotypes. The selected genotypes by FAI-BLUP and MGIDI in addition to stability have higher performances than other genotypes, while the ranking method only selected genotypes with high stability. In conclusion, three genotypes G3, G4, and G6 were identified as high-yielding and stable genotypes for more evaluation in the warm regions of Iran.