RESUMEN
The present investigation was conducted to study the genetic variability, correlation and path coefficient analysis for eight yield and yield attributing traits in 47 (40 improved parental lines + 7 checks) lines of rice. Analysis of variance revealed significant differences among all the treatments under study. High values of PCV and GCV were recorded for traits like number of filled grains per panicle and single plant yield while moderate PCV and GCV recorded for plant height, number of productive tillers per plant, spikelet fertility and 1000 grain weight. High heritability coupled with high genetic advance as percent of mean was observed for the traits namely number of filled grains per panicle, plant height and single plant yield. The traits viz., productive tillers per plant, panicle length, number of filled grains per panicle, spikelet fertility % and 1000-grain weight exhibited significant and positive association with single plant yield. Number of filled grains per panicle, 1000-grain weight, number of productive tillers per plant, plant height and panicle length exhibited positive direct effect on single plant yield indicating that direct selection for these traits would lead to improvement in single plant yield.
RESUMEN
Rice lines need to be grown and evaluated for yield under different agro-ecological locations to identify stable and high-yielding lines for deployment in breeding programs. With this aim, a set of rice germplasm was evaluated for G×E in four different environments (E1-Dadesuguru-Wet 2020, E2-ICAR–IIRR-Dry 2019, E3-ICAR–IIRR-Wet 2020, E4-ICAR–IIRR-Dry 2020). The experimental trial was laid out in a randomized complete block (RCB) design with three replications at each location for 118 rice lines. Data on yield per plant was analyzed using the Additive Main Effect and Multiplicative Interaction (AMMI) and Genotype, and Genotype × Environment Interaction (GGE) models. The combined analysis of variance (ANOVA) manifested significant variations for tested genotypes, locations, years, genotype × year, and genotype × location interactions revealing the influence of environmental factors on yield traits. All four environments showed discrimination power, whereas E2 and E3 were found as the representative environment as they fall near the Average-Environment axis (AEA). The AMMI biplot PC1 contributed 79.20% variability and PC2 contributed 15.18% variability. From the GGE biplot analysis, the rice lines Phouren, JBB-631-1, and JBB-1325 were found to be the best and most stable. The rice lines Phouren, PUP-229, and TI-112 were stable in the first sub-group Dhadesugur-Wet 2020 (E1). The rice lines Langphou, and NPK-45 were stable in the second sub-group ICAR-IIRR-Wet 2020 (E3). Environment ICAR-IIRR-Dry 2019 (E2) was the third subgroup and the rice lines Moirangphou-Yenthik and TI-3 topped for the same. The ICAR IIRR-Dry 2020 (E4) environment formed the fourth subgroup where Phouren-Amubi, TI-128 and JBB-1325 topped the season. In conclusion, this study revealed that G × E interactions are significant for yield variation, and its AMMI and biplots analysis are efficient tools for visualizing the response of genotypes to different locations.