Multi-trait selection indices identify superior mungbean (Vigna radiata L.) genotypes under semi-arid rainfed conditions
ABSTRACT Identifying mungbean (Vigna radiata L.) genotypes that combine high yield, multi-trait superiority, and stability across environments remains a major challenge for breeding under semi-arid rainfed conditions. This study evaluated 110 diverse mungbean genotypes across three growing seasons (2023–2025) at a semi-arid location in Rajasthan, India, using an integrated analytical framework comprising REML/BLUP, the Multiple Trait Stability Index (MTSI), Multi-Trait Genotype–Ideotype Distance Index (MGIDI), FAI-BLUP, Smith–Hazel (SH) index, and Genotype × Yield × Trait (GYT) biplot analysis. REML/BLUP revealed substantial genetic variability among nine agronomic traits, with moderate to high heritability and high selection accuracy, indicating strong potential for genetic improvement. All multi-trait indices identified superior genotypes with balanced performance across yield, yield components, phenology, and plant architecture. G11 was the only genotype consistently selected by all four multi-trait indices, while G51, G56, and G70 were identified by three or more indices and were further supported by GYT biplot analysis. Among the evaluated methods, MGIDI achieved the greatest improvement in seed yield, whereas FAI-BLUP showed superior gains for branching capacity and seeds per pod. Overall, the multi-trait indices outperformed direct single-trait selection by achieving more balanced genetic gains across the evaluated traits. These findings identify G11, G51, G56, and G70 as promising candidates for multi-location evaluation and demonstrate that integrating complementary multi-trait selection indices with GYT biplot analysis provides a robust and reproducible framework for accelerating mungbean improvement in semi-arid rainfed environments.