Integrating Genomics, Breeding, and Climate Resilience for Sustainable Improvement of Mungbean [Vigna radiata (L.)Wilczek]: Advances and Future Directions

Mungbean is a short-duration grain legume valued for its high protein content, biological nitrogen fixation capacity, and adaptability to low-input farming systems. Despite its importance for food and nutritional security, productivity remains below its genetic potential due to a narrow genetic base, susceptibility to multiple biotic and abiotic stresses, and complex trait architecture influenced by genotype-by-environment interactions. These constraints are further intensified by increasing climate variability, which exacerbates yield instability across diverse agro-ecological regions. Recent advances in genomics have transformed mungbean breeding by enabling the identification of genes and genomic regions associated with agronomic and stress-resilient traits. However, the translation of these discoveries into routine breeding applications remains limited. This review provides a critical synthesis of current progress in mungbean improvement, with emphasis on evaluating the strengths, limitations, and practical applicability of key genomic tools, including quantitative trait locus (QTL) mapping, genome-wide association studies (GWAS), marker-assisted selection (MAS), and genomic selection (GS). While QTL mapping and GWAS are effective for detecting trait-associated loci, their predictive efficiency is often constrained by population structure, environmental interactions, and limited effect sizes. In contrast, GS offers greater potential for improving complex polygenic traits and enhancing genetic gain across environments, although it requires large training populations and high-quality phenotypic datasets. The concept of climate resilience is broadened to include multi-stress interactions, environmental variability, and genotype stability across environments. Integration of genomic tools with physiological screening, high-throughput phenotyping, and multi-environment trials is essential for developing stable cultivars. The review proposes a climate-smart, data-driven breeding framework that integrates conventional and modern approaches to accelerate the development of high-yielding, nutritionally enhanced, and climate-resilient mungbean varieties for sustainable agriculture.