Adaptability Studies of Introduced Mungbean (Vigna radiata L. Wilczek) Genotypes in Nigeria

Abstract

Mungbean (Vigna radiata L. Wilczek) is a nutrient-rich legume with significant potential for improving food security, soil fertility, and agricultural sustainability in Nigeria. However, its adaptability and genetic variability within the country's diverse agro-ecological zones remain largely unexplored. This study assessed the phenotypic diversity, genetic variation, and environmental adaptability of 120 introduced mungbean genotypes across four distinct environments in Nigeria: Awka (southeastern Nigeria) and Uyo (south-south Nigeria) during both dry and rainy seasons (Awka (dry and rainy seasons) and Uyo (dry and rainy seasons)). The research employed a multi-faceted approach, integrating phenotypic characterization, statistical modelling, and molecular analysis using Diversity Arrays Technology Sequencing (DArTseq). Seventeen morphological traits were evaluated to assess phenotypic variation, with Principal Component Analysis (PCA) explaining over 70% of total variance. Genotypic characterization identified 5,037 high-quality Single Nucleotide Polymorphisms (SNPs) distributed across 11 chromosomes, with chromosome 1 having the highest SNP density (689 SNPs, 13.68%). Genome-wide association studies (GWAS) were conducted using five genetic models within the multi-random mixed linear model (mrMLM) approach: pLARmEB (Polygenic-background-control-based least angle regression plus empirical Bayes), FASTmrMLM (Fast multi-locus random-SNP-effect Mixed Linear Model), FASTmrEMMA (Efficient Mixed Model Association), ISIS EM-BLASSO (Iterative Sure Independence Screening Extended Bayesian LASSO), pKWmEB (Polygenic-background-control-based Kruskal-Wallis empirical Bayes). These analyses revealed significant Marker-Trait Associations (MTAs) for yield and protein content, with a total revealed a total of 16 significant marker-trait associations (MTAs) for yield and 10 MTAs for protein content across the four environments detected. The markers associated with yield were distributed on chromosomes 2, 3, 4, 5, 6, 8, and 9, while protein content markers were found on chromosomes 1, 3, 5, and 6. Stability analysis using Additive Main Effect and Multiplicative Interaction (AMMI) and Genotype plus Genotype-Environment (GGE) biplot models identified genotypes 130, 105, and 20 as the most stable for yield, while genotype 130 exhibited superior protein content stability. Multi-location trials further confirmed that Uyo (rainy season) was the most favorable environment for optimizing both yield and protein accumulation. These findings provide a comprehensive framework for mungbean breeding in Nigeria, highlighting the genetic potential of specific genotypes for adaptation to diverse climatic conditions. The integration of phenotypic, genotypic, and stability analyses offers crucial insights for marker-assisted selection, aiding in the development of high-yielding, climate-resilient mungbean varieties. This research lays a foundation for enhancing smallholder farmer productivity, soil fertility, and dietary diversity, aligning with national and global efforts toward sustainable agriculture and food security. These findings provide valuable insights into the genetic control of key agronomic traits, laying a foundation for marker-assisted selection in mungbean breeding programs. This study highlights the potential of mungbean to enhance food security, soil fertility, and nutritional balance in Nigeria’s diverse agro-ecological zones.