Country-wide, multi-location trails of Green Super Rice lines for yield performance and stability in Pakistan using genetic and stability parameters

Abstract

Abstract Background: Rice (Oryza sativa L.) is an important member of the family Poaceae and more than half of world population dependent for their dietary nutrition on rice. Rice cultivars with high yield, resilient to stresses and wider environmental adoptability are essential to ensure the yield stability and food security. The fundamental objective of this study was to identify yielding rice genotypes with stability performance and wider adoptability in a rice growing areas in Pakistan. Methodology & Results: A triplicate RCBD design experiment containing 20 Green Super Rice (GSR) advanced lines was conducted at 12 rice growing ecologies in four Provinces in Pakistan. Grain yield stability performance was assessed by using mean performance, analysis of variance (ANOVA), genetic components (genotypic, phenotypic, environmental variances and heritability), univariate and multivariate statistics i.e. regression coefficient, AMMI stability value, AMMI stability index, Wricke’s Ecovalence, Shukla’s Stability Variance and Weighted Average of Absolute Score, AMMI model and GGE biplot analysis respectively. Analysis of variance revealed significant differences among genotypes, locations, and G x E interaction for mean squares (p < 0.05) of PH, NT, PL, GPP, TGW and PY. all the traits except NT revealed higher genotypic variance than environmental variance. Broad sense heritability was estimated in the range of 44.36% to 98.60%. Based on ASV, ASI, bi, Wi2, σ2i and WAAS statistics, the genotypes G1, G4, G5, G8, G11 and G12 revealed lowest values for parametric statistics and considered more stable genotypes on the bases of paddy yield. The additive main effects and multiplicative interaction (AMMI) model revealed significant variation (p< 0.05) for genotypes and non-signification for environment and highly significant for G × E interaction. The variation proportion of PC1 and PC2 from interaction revealed 67.2% variability for paddy yield. Based on ‘mean verses stability analysis of GGE biplot’, ‘Which-won-where’ GGE Biplot, ‘discriminativeness vs. representativeness’ pattern of stability, ‘IPCA and WAASB/GY’ ratio-based stability Heat-map, and ranking of genotypes, the genotypes G1, G2, G3, G5, G8, G10, G11 and G13 were considered best performing and stable genotypes with yield potential more than 8 tons ha-1. Discriminativeness vs. representativeness’ pattern of stability identifies two environments, E5 (D.I Khan, KPK) and E6 (Usta Muhammad, Baluchistan) were best suited for yield performance. Conclusions: Based on these findings we have concluded that the genotypes (G1, G2, G3, G5, G8, G10, G11 and G13) can be recommended for commercial variety development process and future breeding program.