ABSTRACT

Groundnut (Arachis hypogaea L.) is an annual legume of Fabaceae family. A study on genetic variability and stability analysis was conducted during kharif 2023 on fifteen groundnut genotypes across four environments in zone 8 of Karnataka viz., Dharwad, Bailhongal, Hanamanamatti and Nippani. The objective was to identify stable genotypes and evaluate correlation and path analysis for yield and its attributes. The analysis of variance showed significant differences across genotypes, indicating high variability among the genotypes. Traits such as primary branches, mature pods, hundred kernel weight, total pods, shelling per cent, pod yield per plant, kernel yield (kg/ha) and oil yield exhibited moderate to high variability, heritability and genetic advance over mean. Disease screening revealed that most genotypes were moderately resistant to late leaf spot and rust, highlighting their potential in breeding for disease resistance. The environments significantly impacted all traits, confirming the study's relevance across locations. Genotype × environment interactions were significant for most traits, except days to 50% flowering and days to maturity, indicating varied genotype responses. The environment index revealed that Dharwad was the most favorable environment for most traits evaluated. In this study Eberhart and Russell (1966) model was used to analyse genotype by environment (G × E) interactions in groundnut. ICGV-15090 was stable across all environments, whereas Dh-299 was stable only in favourable conditions. Key traits like pod yield per plant, oil yield, hundred kernel weight and shelling per cent positively influenced kernel yield across different environments, making these traits essential for selection during breeding. Conversely, days to 50% flowering and maturity exhibited, negative association with kernel yield. Path coefficient analysis identified shelling percentage, total pods, pod yield per plant and oil yield as direct positive contributors to kernel yield per plant. Prioritizing these traits in selection is crucial for improving kernel yield.