ArticlePloS one2026
Multi-model assessment of yield stability in lentil across multi-environments.
Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Developing high-yielding and stable lentil genotypes is essential for sustaining productivity under increasingly variable climatic conditions. However, strong genotype × environment (G × E) interactions often complicate the identification of widely adapted genotypes. Although several stability models are available, each has distinct strengths and limitations, making it uncertain whether a single model or a combination of models provides the most reliable selection. Therefore, this study compared multiple stability models to identify high-yielding and stable lentil genotypes and to evaluate their effectiveness for genotype selection. A set of lentil genotypes was evaluated across four diverse environments using a randomized complete block design (RCBD). Adjusted mean data were analyzed using the Eberhart and Russell regression model, AMMI (Additive Main Effects and Multiplicative Interaction), BLUP (Best Linear Unbiased Prediction), and WAASBY (Weighted Average of Absolute Scores and Yield). Significant G × E interactions confirmed differential genotype responses across environments. The Eberhart and Russell model identified stable genotypes based on regression coefficients close to unity and minimal deviation from regression. AMMI and BLUP effectively partitioned G × E effects and distinguished genotypes with broad and specific adaptation. WAASBY integrated yield and stability into a single index and was particularly effective in identifying genotypes with superior performance and wide adaptability. Correlation analysis among stability indices and the Genotype Selection Index (GSI) further strengthened the comparison and integration of model outputs. Genotypes G4, G8, G9, and G3 consistently exhibited high yield and stable performance across environments, with G4 emerging as the most promising and widely adapted genotype. This study demonstrates that integrating regression, mixed model, and multivariate based stability analyses provides a more robust framework for selecting stable, high-yielding lentil genotypes than relying on a single method, thereby supporting breeding for diverse agro-ecological conditions in Pakistan.
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