Estimation of Genotype×Environment Interaction in Some Elite Pigeonpea [Cajanus cajan (L.) Millspaugh] Genotypes by AMMI and GGE Biplot Models - Kishore, N. Sandhya; Rao, P. Jagan Mohan; Veeranna, D.; Pallavi, Ch.; Madhu, M.; Padmaja, G.; Reddy, R. Uma; Lingaiah, N. (International Journal of Bio-resource and Stress Management, 2022)
The study was conducted with 14 elite genotypes of pigeon pea during kharif (June‒January) of 2018, 2019 and 2020 at Regional Agricultural Research Station, Warangal, Telangana state, India. The objective of the study was to find out the stable genotypes and genotype by environment crossovers using AMMI and GGE Biplot stability models. Analysis of variance clearly showed that environments contributed highest (26.04%) in total sum of squares followed by genotypes × environments (21.34%) indicating very greater role played by environments and their interactions in realizing final grain yield and when the interaction was partitioned among the first two interaction principal component axis (IPCA) as they were significant in predictive assessment and capturing 80.50% and 19.50% of the total variation in the G×E interaction sum of squares, respectively. GGE biplot revealed that the environments E1 (kharif, 2018) and E3 (kharif, 2020) are the most discriminating. The What-Won-Where GGE Biplot for yield revealed that in E3 (kharif, 2020), G5 (WRG-437) was the winner and in E2, the genotype G13 (WRGE-134) followed by G11 (WRGE-143) and G8 (WRGE-138). In another mega environment E1, the genotype G9 (WRGE-141) was the winning genotype. AMMI and GGE bi plots analysis revealed that among environments, E1, E2 exerted strong interaction forces while, the environmentE3 did less and in the genotypes WRGE-138, WRGE-134, WRGE-136 and WRGE-143 identified as the most adapted lines with stable performance coupled with negligible G×E interaction and can be considered as the potential genotypes, which can improve the Pigeon pea productivity.