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Results

Mean Performance and Combined Analysis of Variance

The performance of different genotypes across 12 locations is summarized in Table 3. A combined analysis of variance for GSR genotypes (Table 4) showed significant differences (p < 0.05) in traits such as Plant height (PH), number of tillers per plant (NT), panicle length (PL), grains per panicle (GPP), thousand grain weight (TGW), and paddy yield in Kg per hectare (PY. Kg ha−1). The analysis also revealed significant differences (p < 0.05) in genotypes (G), environments (E), and genotype-environment interaction (G x E) for most of the traits. These findings highlight the need for further testing to assess the stability and adaptability of different genotypes. Estimation of Genotypic and Phenotypic Components of Variances The analysis of genotypic and phenotypic variances for metric traits of GSR lines is presented in Table 5. The phenotypic variance was partitioned into genotypic variance and environmental variance. The study showed that all traits except NT had higher genotypic variance than environmental variance. The broad sense heritability ranged from 44.36% to 98.60%, with maximum heritability value recorded for PH (98.60%) and minimum value for NT (44.36%). These results indicate that the traits under study have additive genes and are more stable for selection in the development of varieties. Univariate Stability Statistics Parametric stability statistics were used to assess the stability of yield and yield-related traits of GSR lines. The results, presented in Table 6, show that genotypes G1, G4, G5, G8, G11, and G12 had the lowest values for parametric stability statistics such as AMMI stability value (ASV), AMMI stability index (ASI), stability value based on regression coefficient (bi), Shukla’s stability variance (σ2 i), Wricke’s ecovalence (Wi2), and weighted average of absolute scores (WAAS). These genotypes can be considered the most stable based on their paddy yield performance. Multivariate Stability Statistics AMMI analysis of variance revealed significant G x E interaction for paddy yield, and the first two Interaction Principal Component Axis (IPC1 and IPC2) showed 39% and 67% variance, respectively (Table 7). This analysis provides a comprehensive understanding of the interaction between genotypes and environments, which is crucial for evaluating stability in rice production. Additionally, the mean vs. stability analysis of GGE biplot showed that genotypes G2, G5, G11, and G18 had higher paddy yield in specific environments (Fig. 2). The 'which-won-where' GGE biplot divided the environments into five sectors based on similar environmental conditions and revealed the best-performing genotypes in each sector (Fig. 4). This analysis identified genotypes that performed well in particular environments, providing valuable insights for variety selection. The 'Discriminativeness vs. representativeness' pattern of stability analysis further categorized genotypes based on their ability to differentiate among environments and stability across different environments (Fig. 5). This analysis identified genotypes with high discriminativeness and representativeness, as well as genotypes suitable for specific environments or management practices. The IPCA and WAASB/GY ratio-based stability heat-map presented a graphical representation of genotype ranking based on the number of principal component axes utilized (Fig. 6). This analysis further highlighted stable genotypes based on IPCAs and WAASB.GY ratio. Ranking Environments and Genotypes The GGE biplot analysis was used to rank environments and genotypes based on their performance. The analysis revealed that the environment E2 (KSK, Lahore) was the best for paddy yield performance, while genotypes G9, G2, G14, and G3 were among the top-ranked genotypes (Figs. 7 and 8). Discussion The findings of this study provide valuable insights into the performance and stability of different genotypes in rice production. The use of both univariate and multivariate statistics allowed for a comprehensive evaluation of genotypes and environments, facilitating the selection of stable and high-yielding varieties. The results of the analysis of variance and estimation of genotypic and phenotypic variances highlighted the importance of genotype-environment interaction in rice production. The higher genotypic variance and heritability observed for most traits indicate the presence of additive genes and stable characters for selection in variety development. The parametric stability statistics and multivariate stability statistics, such as AMMI analysis and GGE biplot analysis, provided valuable insights into the stability and adaptability of genotypes across different environments. The identification of stable genotypes with high paddy yield performance in specific environments can aid in breeding programs and variety development. The ranking of environments and genotypes based on their performance in different analyses further enhanced the understanding of genotype-environment interactions and helped identify the most suitable genotypes and environments. This knowledge can be applied to optimize rice production and ensure stable yields across different regions. Overall, this study demonstrates the importance of evaluating genotype-environment interactions and using advanced statistical techniques to select stable and high-yielding genotypes for variety development. The findings contribute to the ongoing efforts to enhance rice production and ensure food security.