Multi-traits and stability-based selection of high-yielding mungbean (Vigna radiata L.) genotypes in Benin

Mungbean [ Vigna radiata (L.) R. Wilczek var. radiata ] is an important warm season grain legume for sustainable food systems diversification in Africa owing to its nutrient-dense seeds, short life cycle, resilience to marginal conditions and its suitability for integration into crop rotation systems. This study evaluated the performance of 288 mungbean genotypes for yield and yield-related traits under contrasting environments. The experiment was conducted in 2024 using a 12 × 24 alpha lattice design with three replications across three agro-ecological zones in Benin. A total of 13 quantitative traits were analyzed using linear mixed models to estimate genetic parameters, and the Additive Main effects and Multiplicative Interaction (AMMI), Weighted Average Absolute Scores from BLUP (WAASB), and Multi-Trait Genotype Ideotype Distance Index (MGIDI) approaches for selecting superior genotypes. Significant genetic variability and genotype × environment interactions (G × E) were detected for all traits. Adjusted mean grain yield showed wide differences in adaptability among genotypes. Grain yield and related traits exhibited low broad-sense heritability ( H 2 ), while heritability on an entry-mean basis ( H 2 gm ) was moderate for these traits. Days to 50% flowering and hundred-seed weight exhibited high potential for genetic gain through selection. AMMI 1 and WAASBY biplots identified mungbean lines G83, G109, G44 and G134 combining high yield and stability. Based on MGIDI factor analysis, 20 multi-trait superior genotypes with favorable selection differentials for grain yield and yield-related traits were selected. Environmental variability among the three test sites significantly influenced yield performance, highlighting the importance of site-specific recommendations. Overall, the combined use of WAASBY, MGIDI, and AMMI analyses provides a robust framework for the selection of climate-resilient, high-yielding, and stable mungbean genotypes adapted to diverse production systems. The selected genotypes are promising candidates for advanced yield trials and participatory evaluation for boosting mungbean production in Benin.