Comparative Evaluation of Traditional Selection Indices and the Multi-trait Genotype-ideotype Distance Index (MGIDI) for Selection of EMS-Induced Mungbean (Vigna radiata L. Wilczek) Mutants

Abstract

Aims: Induced mutagenesis is an effective strategy to broaden the genetic base of mungbean (Vigna radiata (L.) Wilczek), especially where conventional breeding is limited by low genetic variability and hybridisation barriers. This study aimed to identify superior EMS-induced mutant lines of the mungbean variety DGGV 2 using both conventional and multivariate selection approaches. Study Design: Fifty-five M₄ lines were evaluated in an alpha-lattice design. Place and Duration of Study: The experiment was conducted in the experimental plots of the Main Agricultural Research Station, Dharwad. Methodology: Fifty-five M₄ lines were evaluated in an alpha-lattice design, along with susceptible and resistant checks, for eleven agronomic traits and responses to major foliar diseases under natural epiphytotic conditions. Results: The analysis of variance showed significant genetic variability for most traits. High heritability coupled with high genetic advance for yield per plant, clusters per plant, pods per plant, and hundred-seed weight indicated the predominance of additive gene action and suggested that selection would be effective. Clusters per plant and pods per plant showed significant correlations with seed yield and were major contributors to yield. Principal component and factor analyses explained 60.5% of the total phenotypic variation, with traits grouped into three biologically meaningful factors related to plant architecture, seed and disease attributes, and yield. Three different types of Smith–Hazel selection indices consistently identified DGGM 122 and DGGM 170 as superior genotypes, although rankings varied with economic weighting and multicollinearity, illustrating limitations of conventional selection indices. In contrast, the Multi-trait Genotype–Ideotype Distance Index (MGIDI) effectively integrated multiple correlated traits and identified DGGM 122, DGGM 13, DGGM 119, DGGM 116, and DGGM 3 as superior ideotype-like genotypes with balanced performance for yield, yield components, and disease resistance. Strengths–weaknesses analysis indicated the trait-specific advantages and limitations of selected mutants. Conclusion: These findings demonstrate that MGIDI provides a robust and unbiased framework for simultaneous multi-trait selection and the identification of promising EMS-induced mungbean mutants for future breeding programmes.