Integrating data from asymmetric multi-models can identify drought-resistant groundnut genotypes for drought hot-spot locations

Abstract

Abstract Water/drought stress experiments are frequently conducted under imposed stress or rainout shelters, while natural drought hot-spot investigations are rare. The “drought hot spot” in Anantapur, Andhra Pradesh, India, is appropriate for drought stress evaluation due to its hot, arid environment, limited rainfall, with over 50% rainfall variability. According to reports, 30 out of 200 groundnut cultivars in India are supposed to possess drought-tolerant characteristics. However, these cultivars are yet to be evaluated in areas that are prone to drought. This study tested these drought-tolerant genotypes in naturally drought-prone areas of Anantapur under rainfed conditions from Kharif 2017 to 2019. Pod yield and rainfall-use-efficiency (RUE) were measured for these genotypes. Genotype and genotype*environment interactions affected pod yield and RUE (GEI). The AMMI model exhibits significant season-to-season variability within the same area with environmental vectors > 90° angles. GGE biplot suggested the 2018 wet season for drought-resistant cultivar identification. Kadiri5 and GPBD5 were the most drought-tolerant cultivars for cultivation in Anantapur and adjacent regions. These types could also be used to generate drought-tolerant groundnut variants for drought-prone regions.