The AMMI approach (Additive Main Effects and Multiplicative Interaction) is one of the most informative tools for studying genotype performance in multi-environment trials because it integrates two complementary statistical principles within a single framework. The additive component, represented by analysis of variance (ANOVA), allows the researcher to partition the total variation into genotype and environment main effects, whereas the multiplicative component, represented by principal component analysis (PCA), reveals the internal structure of genotype × environment interaction (GEI).

The objective of the present research was to determine how strongly genotype (G), environment (E), and their interaction (G×E) contributed to grain yield variation in winter wheat and, on this basis, to identify genotypes capable of combining high productivity with stable performance under contrasting growing conditions. The experimental design included a net plot area of 10 m², three replications, and three growing seasons spanning 2022 to 2024. The evaluation of genotype × environment interaction was carried out using the AMMI model, in which the additive main effects were examined through ANOVA and the interaction structure was decomposed using PCA. The testing network comprised 20 varieties assessed across 17 environments.