An investigation was carried out on fifteen rice genotypes to identify stable rice hybrids across six different agroclimatic zones in Telangana state using AMMI and GGE bi-plot analyses during July to November, 2020. Analysis of variance clearly showed that environments contributed highest (65.47%) in total sum of squares followed by genotypes×environments (21.19%) indicating very greater role played by environments and their interactions in realizing final grain yield. AMMI analysis revealed that rice hybrids viz., RNRH 39 (G6), 27P31 (G14) and RNRH 15 (G1) were recorded higher mean grain yield with positive IPCA1 scores. The hybrids, JGLH 275 (G11) and JGLH 365 (G15) were plotted near to zero IPCA1 axis indicating that these hybrids are relatively more stable across locations. GGE bi-plot genotype view depicts that the hybrids, JGLH 365 (G15) and US 314 (G8) were inside the first concentric circle and found to be more stable across environments. GGE bi-plot environment view showed that Rudrur (E4) location was the most ideal environment. However, Warangal (E6) and Jagtial (E1) locations were poor and most discriminating. Depending on dispersion of environments in different directions, six locations were partitioned into three mega zones as first zone comprised of four locations viz., Kunaram (E2), Kampasagar (E3), Rudrur (E4) and Rajendranagar (E5) whereas highly dispersed Jagtial (E1) and Warangal (E6) were identified as two separate mega environments. The bi-plot view identified that 27P31 (G14), JGL 24423 (G2) and RNRH 39 (G6) were the best performing genotypes in first zone comprising four locations.
The experiment was carried out under three seasons with 15 genotypes at Agricultural Research Station, Kunaram, Telangana state, India during rabi season (December to April) 2014–15 (E1), kharif season (July to November) 2015 (E2) and rabi season (December to April) 2015–16 (E3). The objective of the study was to assess the stability and adaptability of 15 rice genotypes of the various maturity groups over three seasons. The GGE biplot tool of these 15 rice genotypes of various maturity durations expressed a significant genotype, environment and G×E interaction for yield and days to 50% flowering. Genotype and environment interaction effect was responsible for the greatest part of the variation, followed by genotypes and environment effects for grain yield. Days to 50% flowering of genotypes was highly affected by environments followed by genotypes, and genotype and environment interaction. It also detected that rabi season 2014–15 (E1) was identified as the best suited season for the potential expression of the grain yield, while kharif season 2015 (E2) was the right season for the expression of reduced days to 50% flowering. Further, the what–won–where model indicated that short duration rice genotype G14 (KNM 1690) and medium duration genotype G9 (KNM 1632) in the environments rabi season 2014–15 (E1) and kharif season 2015 (E2), respectively and the early line G11 (KNM 1684) in the environment rabi season 2015–16 (E3) were the winning genotypes and suitable for their respective environments for grain yield. G7 (KNM 1616) was the vertex early genotype and closer to the ideal genotype expressed high yield and stability for all the environments. G13 (KNM 1689) and G14 (KNM 1690) were found to be stable for earliness across all the seasons and could be utilized for the development of early duration varieties. The rice genotype, G15 (BPT 5204) was found to be stable for lateness for all the seasons.
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