Evaluating multiple stability methods to screen bread wheat genotypes (F7 generation) under drought-stressed...

Wheat, as a staple food crop, faces productivity challenges under diverse environmental conditions, particularly in semi-arid regions. Enhancing genotype performance and stability across multiple environments is essential for sustainable wheat production and food security. This study aimed to evaluate the grain yield performance and stability of 165 F₇ wheat genotypes, along with four check cultivars, across four agro-ecologically distinct environments: Karaj, Zarghan, Kermanshah, and Nishapur, with the latter representing drought-prone conditions. The genetic materials used were derived from both local germplasm and International Maize and Wheat Improvement Center; "Centro Internacional de Mejoramiento de Maíz y Trigo" (CIMMYT) sources, ensuring a wide genetic base and reflecting the benefits of international collaboration in crop improvement. A combination of univariate and multivariate stability analyses, including combined-analysis of variance (ANOVA), the additive main effects and multiplicative interaction (AMMI) and genotype and genotype × environment interaction (GGE) model heatmap-clustering, and correlation plots, was conducted to explore the genotype-by-environment interactions (GEI). An R-based script was developed and introduced to facilitate reproducible and efficient computation of these stability models (Supplementary Materials). The ANOVA revealed significant genotype-by-environment interaction (GEI) effects, highlighting the necessity for robust statistical approaches. AMMI analysis showed that the first two interaction principal components (IPCA1 and IPCA2) accounted for over 83% of the interaction variance, effectively capturing differential genotype responses. Genotypes G48, G46, and G122 were consistently high-yielding and stable across all environments. Local genotypes demonstrated broad adaptability, while CIMMYT-derived lines exhibited superior performance under drought conditions, especially in Nishapur. This study demonstrates the utility of integrating classical and modern statistical tools for selecting high-performing and stable wheat genotypes, providing valuable insights for breeders targeting multi-environment adaptation and drought tolerance. The identified genotypes offer promising candidates for additional breeding programs aimed at improving yield stability, which can be considered in future studies focusing on validating the genotypes.