1Department of Genetics and Plant Breeding, College of Agriculture, Gandhi Krishi Vigyana Kendra, University of Agricultural Sciences, Bengaluru-560 065, Karnataka, India
2AICRP on Seed (Crops) NSP, College of Agriculture, Gandhi Krishi Vigyana Kendra, University of Agricultural Sciences, Bengaluru-560 065, Karnataka, India
3All India Co-ordinated Research Project on Potential Crops, Gandhi Krishi Vigyana Kendra, University of Agricultural Sciences, Bengaluru-560 065, Karnataka, India
*Corresponding Author: Priyanka, Department of Genetics and Plant Breeding, College of Agriculture, Gandhi Krishi Vigyana Kendra, University of Agricultural Sciences, Bengaluru-560 065, Karnataka, India, Email: priyanka.karankoti0198@gmail.com
Mungbean yellow mosaic virus (MYMV) is recognized as a major viral disease severely limiting greengram [Vigna radiata (L.) Wilczek] production resulting in severe yield reduction. Identification of resistant and consistently performing genotypes under varying environments is essential for improving productivity in MYMV-endemic regions.
Fifty MYMV-resistant greengram genotypes were assessed during Kharif 2024–25 across three locations-Mandya, GKVK and Chamarajanagar, using a randomized complete block design with two replications. AMMI and GGE biplot models were utilized to evaluate genotype × environment interactions and assess yield stability among the test genotypes.
The pooled ANOVA indicated significant effects of genotypes, environments and their interactions, indicating substantial genetic variability and differential environmental responses. AMMI analysis resulted that IPCA1 and IPCA2 explained 84.5% and 15.5% of the G × E interaction, respectively. Genotypes G8, G9 and G10 were recognized as stable and high-yielding across environments, while G39, G40, G43 and G44 exhibited specific adaptation to favorable sites (E1 and E2). Genotypes G6, G5 and G25 performed well under E3 conditions. The identified stable and resistant genotypes can be productively used for MYMV-prone regions to enhance greengram yield stability and productivity. The integration of multi-environment evaluation with advanced statistical models further strengthens the identification of widely adaptable genotypes. Such approaches can significantly contribute to the development of resilient greengram varieties suited for diverse agro-climatic conditions.
AMMI, Genotype × environment interaction, GGE biplot, Greengram, MYMV, Stability
