Assessing Yield Stability and Environmental Adaptation of Hybrid Rice (Oryza sativa L.) Across Tropical Field Conditions

Assessing Yield Stability and Environmental Adaptation of Hybrid Rice (Oryza sativa L.) 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Across Tropical Field Conditions Bayu Pramono Wibowo, Trias Sitaresmi, Nita Kartina, Swisci Margaret, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8185743/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Understanding hybrid rice performance across diverse field environments is essential for improving productivity under tropical conditions. This study aimed to determine the yield potential, adaptability, and stability of promising hybrid rice across eight agroecological zones in Indonesia using parametric, nonparametric, and multivariate methods to aid in genotype selection. Eight promising hybrid rice varieties and four standard check varieties from the Indonesian Center for Rice Research were tested during 2018–2019. The experiment was conducted using a randomized complete block design with four replications in each environment. The results showed significant effects of genotype, environment, and genotype × environment interaction on grain yield. Environmental factors contributed the largest proportion of yield variation (33.5%), indicating the importance of local growing conditions. Univariate parametric and nonparametric analyses identified four hybrid rice genotypes (G2, G4, G7, and G8) with good stability and broad adaptation. Conversely, AMMI and GGE biplot analyses showed that two hybrid rice genotypes (G5 and G7) were broadly adaptable and stable across environments, producing significantly higher yields than the average. The MGIDI analysis identified the ideal genotypes were G2, G4, and G5 considering yield agronomic traits, and broad adapted based on regression stability analysis. From these approaches, we conclude that rice hybrids G2, G4, G5, and G7 were consistent as superior hybrids across all environments, while G1 showed specific adaptation to favorable sites. This study demonstrated the utilization of multi-trait selection and multi-measure stability analysis was effective to identify hybrid rice genotypes with high yield and adaptive in tropical environments of Indonesia. AMMI GGE MGIDI Stability analysis Hybrid rice Tropical agroecosystems Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction In tropical regions, hybrid rice productivity faces significant vulnerabilities due to its high dependence on fluctuating environmental conditions, including unpredictable water availability, variable soil fertility, and extreme temperature regimes. Therefore, comprehensive evaluation of genotypic responses in diverse field environments is indispensable, offering critical insights into the physiological adaptation mechanisms and inherent production stability of hybrid rice under stress. The strategic integration of advanced stability analysis with multi-trait assessment is crucial for supporting hybrid rice development. Multi-environment trials (METs) are an important stage in hybrid rice breeding. It allows across various environments identification of genotypes with broad adaptability and stable performance. The METs are needed because the genotype and environment interaction (GEI) often complicates the relationship between genetic potential and observed phenotype (Crossa 1990 ; Maulana et al. 2023 ). Therefore, a profound understanding of GE interactions is crucial for plant breeders to select and develop hybrids that consistently perform under variable agroecosystems (Pour-Aboughadareh and Poczai 2021 ). It is important to use appropriate statistical methods to select high-yielding and stable hybrid rice genotypes. Non-parametric and parametric methods have been widely used to evaluate GEI. Non-parametric methods are less limited by the assumptions of normality and homogeneity of variance, as well as the impact of outliers (Liu et al. 2010 ; Shahbazi 2019 a). However, non-parametric measures focus on static stability and consistent performance, often without adequately representing a genotype’s adaptability to environmental changes (Karimizadeh et al. 2012 ). Non-parametric indices have been reported for genotypic selection of stable performance across environments. Nassar and Huhn rank tests have shown application for selecting stable wheat and barley genotypes in heterogeneous environments (Nassar and Huhn 1987 ; Thennarasu 1995). Yan and Kang ( 2002 ) yield-stability index identified maize hybrids with strong yield potential and environmental adaptability. Therefore, while non-parametric procedures may not fully capture adaptability, they remain useful tools for initial screening of stability under environmental variation. Parametric methods using single variate approaches such as regression coefficients, deviations from regression, and stability variance provide quantitative data on yield stability and high-yielding genotypes (Francis and Kannenberg 1978 ). However, these methods depend on assumptions of linearity, normal distribution, and homogeneity of variance, which makes them sensitive to outliers and less precise for multilocation data sets (Liu et al. 2010 ). Multivariate approaches such as the AMMI (Additive Main Effects and Multiplicative Interaction) model and GGE (Genotype and Genotype × Environment) biplots are particularly valuable for visualizing the interaction of genotypes and environments. For instance, researchers can use the GGE biplot to separate mega-environments and to evaluate the effectiveness of test locations (Sitaresmi et al. 2019). Both AMMI and GGE rely heavily on visual interpretation and are sensitive to balanced data. Furthermore, AMMI addresses only a single G×E component, while GGE may provide less accurate estimations within a single mega-environment (Gauch 2006 ; Gauch et al. 2019 ). A more recent analysis is the Multi-trait Genotype–Ideotype Distance Index (MGIDI), which aggregates multiple agronomic traits within a single index. MGIDI ranks genotypes based on their distance and enables breeders to handle multicollinearity while minimizing bias in multi-trait selection (Olivoto and Nardino 2021 ). Its application, however, depends on how researchers define the “ideal genotype,” and this definition may vary across breeding targets and environments (Debnath et al. 2024 ). Various studies have indicated that the combination of diverse stability analysis approaches leads to regular and trustworthy results for selecting genotypes for high yield potential and broad adaptability. Estimation of yield stability using WAAS, WAASB, WAASBY, and GGE biplot analyses led to successful estimations of yield stability under different environments (IRRI 2014 ; Rahman et al. 2025 ), multivariate approaches such as AMMI and GGE biplot are widely recognized methods for multi-environment evaluations, providing concise summaries of rice yield stability and its primary components (Priyanto et al. 2024; Singh et al. 2023 ). The combination of AMMI, GGE, and MTSI stability analysis methods has produced selected genotypes with stable performance across various agroecological zones (Kuru et al. 2025 ). The combination of parametric and non-parametric analyses further validates the selection results for high-yielding genotypes with broad stability and adaptability (Herawati et al. 2021 ). Although AMMI and GGE biplot analyses are widely used for evaluating hybrid rice performance across diverse environments, few studies have systematically integrated them with non-parametric, parametric, and MGIDI indices. This combined approach is innovative because MGIDI, a recently developed multivariate index, allows for the simultaneous assessment of yield, stability, and resistance. These factors are not fully captured by AMMI and GGE alone. Our study, by incorporating MGIDI with established stability methods, offers a comprehensive evaluation of hybrid rice in tropical multi-environment settings. Therefore, this study aimed to assess the integration of parametric, non-parametric, and multivariate approaches for selecting hybrid rice genotypes based on yield and stability. This integrated framework is expected to enhance understanding of GEI and facilitate the identification of high-yielding, stable, and widely adapted hybrid rice lines suitable for breeding programs in tropical environments. Materials and methods Experimental Locations, Materials, and Design Field trials were conducted on a multi-location basis in Indonesia. Table 1 gives information on soil, climatic types, and altitude for multi-locations. The genotypes were evaluated across eight irrigation fields (E1 to E8) from the 2018 dry season (January to May). Table 1 Characteristic of multilocation environments Locations Latitude Longitude Climate types* Soil types Altitude (m asl) E1 -7.617.434 109.182.890 B2 Alluvial 30 E2 -6.584.536 107.443.469 C2 Brown Latosol 172 E3 -7.068.621 112.224.681 C2 Alluvial 50 E4 -6.354.718 107.646.785 D3 Alluvial 16 E5 -7.218.889 111.946.389 D3 Alluvial 55 E6 -6.272.782 107.480.395 B1 Alluvial 14 E7 -6.810.453 107.271.710 C2 Latosol 300 E8 -7.813.937 110.471.495 C3 Regosol 117 Locations: Cilacap (E1), Purwakarta (E2), Lamongan (E3), Subang (E4), Bojonegoro (E5), Karawang (E6), Cianjur (E7), Sleman (E8), *Climate types based on Oldeman; m asl = meters above sea level The hybrid rice breeding materials developed at the Indonesian Center for Rice Research (ICRR), Ministry of Agriculture, Republic of Indonesia, were used as test materials. In this study, the hybrid materials used were eight advanced experimental hybrids (G1 to G8), three commercial hybrid varieties (G9, G10, G11), and one common inbred variety (G12), which was included as a standard check (Table 2 ). Table 2 List of genotypes tested at multi-location trials No Genotypes Code Source 1 H 263 G1 ICRR 2 H 264 G2 ICRR 3 H 265 G3 ICRR 4 H 266 G4 ICRR 5 H 267 G5 ICRR 6 H 273 G6 ICRR 7 H 274 G7 ICRR 8 H 275 G8 ICRR 9 Arize H6444 Gold G9 Bayer 10 Sembada 989 G10 Biogene Plantation 11 Hipa Jatim2 G11 ICRR 12 Inpari 30 Ciherang Sub1 G12 ICRR The experimental design involved organizing genotypes into a four-replicate randomized complete block design, with each plot size 20 m² (4 m x 5 m). Twenty-one-day-old seedlings were transplanted at a density of 1–2 seedlings per hill spacing, maintaining a spacing of 20 x 20 cm. A total of 120 kg ha⁻¹ of nitrogen was applied in three splits: 30% as a basal application, 40% at maximal tillering, and 30% at heading. Furthermore, 45 kg ha⁻¹ of P₂O₅ and 60 kg ha⁻¹ of KCl were utilized as basal fertilizers. Rice reactions to pests and diseases brown planthopper (Bph), bacterial leaf blight (BLB), blast, and rice tungro virus) were evaluated with the Rice Resistance Standard Evaluation System protocol (IRRI 2014 ). Grain quality assessment was conducted at the ICRR from July to December 2018. Parameters recorded included plant height, tiller numbers, filled grain numbers, percentage of seed formation, 1000-grain weight, days to maturity, and grain yield per plot (kg plot⁻¹), which were then converted to grain yield per hectare (ton ha − 1 ) at 14% moisture content. Statistical Analysis Grain yield was analyzed using analysis of variance (ANOVA) to evaluate the variation of phenotype affected by genotypes, environments, and GEI. Genotypes were considered as fixed factors, but environments served as random factors. A further statistical analysis was done to examine how the environment and genotype interact, with the goal of finding out how stable the 12 genotypes are in eight environments. There are two forms of stability analyses, i.e., non-parametric and parametric (univariate and multivariate). The effect of GEI with the adjusted mean grain yield was analyzed using the stability analysis package in PBSTAT-GE 3.7 (Suwarno et al. 2025 ). MGIDI, an innovative index derived from factor analysis for selecting superior genotypes based on multiple traits across one or more environments (Olivoto and Nardino 2021 ). The MGIDI was used for traits including yield, 1000-grain weight, days to maturity, days to flowering, number of productive tillers, number of filled grains, number of grains, plant height, length of panicle, number of unfilled grains, and seed set. The MGIDI was perform using in R.4.4.1 (Olivoto et al. 2019 ), with a selection differential (SD) of 25% intensity applied for all traits. A list of stability analysis used weres presented in Table 3 . Table 3 Non-parametric and parametric models of stability parameters Approach Stability parameter Interpretation References Non-parametric - Univariate Kang’s yield-stability index (S¹, S², S³, S⁶) Combines yield and stability; a lower index value indicates more stable (static) (Nassar R & Huhn M, 1987 ) NP i 1 , NP i 2 , NP i 3 , NP i 4 Lower values indicate more stable genotypes (static) (Thennarasu K 1995) Parametric - Univariate Coefficient of variation (CV%) Stability (static) if CV i \(\:\stackrel{-}{\text{Y}}\) (Francis & Kannenberg 1978 ) Regression coefficient to environmental index (b i ) b i = 1 (stable); b i >1 (responsive but less stable); b i < 1 (unresponsive and less stable) (Finlay & Wilkinson 1963 ) Deviation from regression (S²d i ) Smaller S²d i values indicate more stable genotypes (Eberhart & Russell 1966 ) Wricke’s ecovalence (W i ²) Lower W i ² values indicate higher stability (dynamic) (Wrickle 1962 ) Hanson’s parameter for genotype stability Lower values indicate greater stability (Hanson 1970 ) Shukla’s stability variance (σ²) Lower σ² values indicate more stable genotypes (dynamic) (Shukla 1972 ) Parametric - Multivariate AMMI (Additive Main Effect and Multiplicative Interaction) Integrates ANOVA and PCA; stability visualized in biplot (Zobel et al. 1988 ) GGE Biplot Combines genotype and GEI; visual selection of stable and high-yielding genotypes (Yan 2002 ) Results and Discussion Analysis of Variance and Descriptive Analysis The results of the combined variance analysis for grain yield of eight different environments showed that the effects of genotype (G), environment (E), and GE interaction were all statistically significant (Table 4 ). These findings indicate that genotypic performance was variable across locations, and their response was considerably influenced to a great extent by environmental factors. Among the sources of variation, the environment had the highest percentage of the total sum of squares (33.50%), showing the large variations in agro-ecological conditions (Table 4 ). The significant environmental contribution to yield variance reflects the heterogeneity of the test sites, especially in soil fertility and microclimatic conditions. These findings underscore the role of agroecosystems in performing hybrid performance and indicate that site-specific management strategies could enhance overall production stability. The genotype’s main effect explained 17.53% of the variation, confirming the existence of gene differences among the genotypes. In addition, GEI was responsible for explaining a total of 19.79% of the variation. Table 4 Combined analysis of variance for yield evaluated under multi-environment trials Source of variance df Sum of squares (SS) Mean square F value SS proportion (%) Environment (E) 7 317.65 45.38 13.58 *** 33.50 Replication / E 24 80.20 3.34 4.49 *** 8.46 Genotype (G) 11 166.26 15.11 6.20 *** 17.53 G x E 77 187.69 2.44 3.28 *** 19.79 Error 264 196.47 0.74 Corrected total 383 948.28 df: degrees of freedom, ** highly significant at < 0.001 Table 5 shows that the location effect was significant, indicating that the site selection was effective for this experiment. Some genotypes consistently exhibited high yields in all locations, while others showed lower yields. The genotypes ranged from 3.82 to 11.45 t ha − 1 . The overall mean yield of the locations was 8.31 t ha − 1 . There were four hybrid rice that showed genotype mean yield higher than the environment mean yield, i.e., G1, G3, G5, and G7. The differences in yield between genotypes were statistically significant at all test locations. The strong environmental effect (33.5% of total variance) indicates that agroecological factors, such as rainfall distribution, soil fertility, and temperature regimes, played a dominant role in determining yield outcomes. Locations with fertile alluvial soils (e.g., Cilacap-E1 and Karawang-E6) and favorable irrigation conditions produced higher yields, while Cianjur (7) had lowest productivity because during METs, rainfall was quite high and some genotypes lodging easily. These findings emphasize that hybrid rice yield potential depends not only on genetic capacity but also on the physiological response to local resource availability and microclimatic. Integrating genotype selection with site-specific management could therefore enhance productivity stability under variable field conditions. In addition, the genotype-versus-environment plot of rice hybrids during the 2018 trials is shown in Fig. 1 . Table 5 Grain yield of hybrid rice evaluated under multi-environment trials Genotype Grain yield (t ha − 1 ) Mean E1 E2 E3 E4 E5 E6 E7 E8 G1 11.45 9.04 9.47 9.73 7.71 12.08 8.98 7.89 9.54 G2 9.42 7.39 8.83 8.07 7.11 8.70 7.27 7.49 8.03 G3 11.17 9.06 9.05 7.25 8.28 11.02 8.23 6.97 8.88 G4 9.10 8.44 8.50 7.36 7.76 8.18 5.61 5.72 7.58 G5 11.11 10.08 9.02 8.22 7.89 10.31 8.27 7.02 8.99 G6 9.19 9.41 9.44 7.93 8.44 7.91 5.72 5.55 7.95 G7 9.80 8.33 8.70 7.72 7.82 9.10 8.41 7.35 8.40 G8 9.01 8.37 8.71 7.92 8.08 8.43 7.01 6.34 7.98 G9 9.99 9.72 9.08 8.57 7.47 9.08 8.63 8.41 8.87 G10 8.39 9.29 8.08 7.57 6.87 7.54 3.82 6.21 7.22 G11 10.05 8.73 8.98 7.93 7.53 9.03 8.10 8.37 8.59 G12 8.98 7.84 6.13 7.87 6.88 8.95 7.64 6.72 7.63 Mean 9.80 8.81 8.67 8.01 7.65 9.19 7.31 7.00 8.31 LSD 0.05 0.83 1.08 0.79 0.67 1.28 1.22 1.00 1.22 0.36 CV (%) 7.09 10.2 7.63 7.00 13.94 11.07 11.38 14.59 10.39 LSD 0.05: least significant difference test at 5% significance level; coefficient of variation (CV). Location: Cilacap (E1), Purwakarta (E2), Lamongan (E3), Subang (E4), Bojonegoro (E5), Karawang (E6), Cianjur (E7), Sleman (E8) Non-Parametric Stability Analysis Non-parametric stability analysis was conducted to complement the parametric results and to assess genotype performance without relying on assumptions of normality or homogeneity of variance. This approach included Nassar and Huhn’s rank-based statistics S i (1) , S i (2) , S i (3) , and S i (6) (Nassar and Huhn 1987 ); meanwhile, Thennarasu’s NP indices NP i (1) – NP i (4) parameter. These methods are particularly suitable for datasets from multilocation trials with heterogeneous conditions (Thennarasu 1995). The Nassar and Hühn method uses four parameters to determine the stability of a genotype (Nassar and Huhn 1987 ). A smaller stability index value indicates a genotype to be considered stable. Parameters of stability S i (1) and S i (2) provided further confirmation. S i (1) provides the average absolute deviation of genotype ranks position in all environments, while S (2) represents the variance of these ranks. Using both parameters, hybrid rice genotypes G2, G4, G7, and G8, and G11 (check variety) stood as the first five most stable, implying consistent adaptability across diverse testing environments. Additional information was obtained from S i (3) and S i (6) . Parameter S i (3) estimates the absolute deviation of a rank of a genotype from the mean rank across all environments, and S (6) covers the squared deviations. According to these parameters, hybrid rice genotypes G1, G5, and G7, G9 registered the best rank for stability. These results suggest that even though G1 was ranked less stable through some parametric indices, it demonstrated reliable performance ranks through non-parametric evaluation, especially under favorable environments (Roy et al. 2024 ). The adjusted mean rank of genotypes across each environment provides the test indicator utilized in Thennarasu’s method. The lowest value of NP i 1 , NP i 2 , NP i 3 , and NP i 4 obtained using this method identifies a genotype to be considered stable. For all genotypes, G8 had the lowest value of stability index (Table 6 ). G8 showed consistently low values throughout all Thennarasu indices. Following G8, the next most stable genotypes included hybrid rice (G2, G4, and G7), and check variety (G12) registered the lowest NP i 2 , NP i 3 , and NP i 4 values after G8. Differences in assumptions, data sensitivity, and the specific stability metrics emphasized by non-parametric models can explain variations in genotype rankings (Roy et al. 2024 ). The observed rank changes across the different approaches in this study underscore the importance of incorporating diverse parameters. Such methodologies are crucial in breeding programs for selecting genotypes that demonstrate both high yield and consistent stability across various environmental conditions. Relying solely on one method often leads to inconsistent results and reduces the accuracy of selecting broadly adaptable genotypes (Maulana et al. 2023 ). Therefore, selection platforms that incorporate dynamic stability are essential for improving hybrid varieties in suitable agroecological regions. The non-parametric results revealed that hybrid rice genotypes G2, G4, G7, and G8 maintained consistent yields across contrasting environments, indicating strong environmental buffering capacity. These genotypes likely possess physiological mechanisms supporting stable grain filling and biomass allocation under varying thermal and soil conditions. Such stability is valuable for farmers in regions with unpredictable rainfall or temperature shifts, as it reduces production risk and ensures more reliable harvests under fluctuating tropical environments. Table 6 Non-parametric stability analysis of yield in hybrid rice evaluated under multi-environment trials Genotype Grain yield (t ha − 1 ) S i (1) S i 2) S i (3) S i (6) NP i (1) NP i (2) NP i (3 ) NP i (4) G1 9.54 5.14 18.50 4.23 1.57 3.75 3.75 1.53 1.96 G2 8.03 3.43 8.57 9.45 2.91 2.25 0.30 0.37 0.46 G3 8.88 4.57 14.5 9.95 2.34 3.00 0.67 0.73 0.94 G4 7.58 3.68 9.41 6.29 3.14 2.63 0.26 0.30 0.39 G5 8.99 4.43 14.21 1.69 0.96 3.25 0.93 0.97 1.22 G6 7.95 5.82 24.27 20.06 4.55 4.38 0.63 0.70 0.88 G7 8.40 3.11 7.13 7.23 2.68 1.88 0.34 0.39 0.49 G8 7.98 3.21 7.43 6.80 2.40 2.00 0.22 0.32 0.40 G9 8.87 4.32 13.41 5.11 1.73 2.89 1.15 0.95 1.19 G10 7.22 5.14 18.79 19.76 5.43 3.50 0.30 0.39 0.50 G11 8.59 3.43 9.14 3.20 1.47 2.50 0.42 0.51 0.62 G12 7.63 4.36 13.43 8.24 4.14 3.00 0.32 0.37 0.47 S i( 1) , S i (2) , S i (3) , S i (6) : Nassar and Hühn’s non-parametric stability parameters. NP i 1 , NP i 2 , NP i 3 , NP i 4 : Thennarasu’s non-parametric stability statistics parameters Univariate Parametric Stability Analysis The yield performance of 12 rice genotypes was evaluated across eight environments using a parametric stability analysis. The indices evaluated included mean yield (Y i ), coefficient of variation (CV i ), regression coefficient (b i ), deviation from regression (S²di), Wricke’s ecovalence (Wi²), Hanson’s stability index (D i ), and Shukla’s stability variance (σ²). These indices evaluated both productivity and stability across different environments (Eberhart and Russell 1966 ). According to Francis and Kannenberg ( 1978 ), a genotype is considered stable when it has a mean yield above the grand mean and a CV i below the population mean. This methodology categorizes genotypes into four distinct groups through the intersection of the CV mean line and the yield mean line: Group 1, characterized by high yield and low CV; Group 2, exhibiting high yield and high CV; Group 3, demonstrating low yield and low CV; and Group 4, defined by low yield and high CV. From this analysis, genotypes G7, G9, and G11 were classified as both high-yielding and stable. On the other hand, hybrid rice G1, G3, and G5 were considered to have lower stability, as, despite yielding above the grand mean, high values of CVi were recorded, likely indicating specific adaptability to more favorable environments (Fig. 2 ). The regression coefficient (b i ) and the deviation from regression (s²di), as proposed by Eberhart and Russell ( 1966 ), were used to further assess dynamic stability. Figure 3 A illustrates this relationship, showing how each genotype performs across different environments. Genotype G1 consistently performed best. Although G1's bi value was significantly greater than 1, suggesting high responsiveness to better environments, it still showed broad adaptation potential, aligning with its identification as the "ideal genotype" in the GGE biplot analysis. The plot of regression coefficient and response value (yield) (Fig. 3 B) shows the position of each genotype based on its regression coefficient and response value. Genotypes G4 and G8 exhibited bi values close to 1 and non-significant s²di. This means they were stable, responding predictably and consistently across various environments, indicating both linear and non-linear stability. In contrast, genotypes G1 and G3 had bi values considerably above 1 and large s²di values. This implies they are highly responsive to environmental changes but are less predictable, making them more suitable for specific target environments rather than general deployment. Wrickle ( 1962 ) developed the W i ² method to determine the extent of a genotype's influence on yield variation across different environments. Genotypes with low W i ² values are considered stable because their yields do not change much even when grown in different environments (Kebede et al. 2023 ; Vanisri et al. 2023). In this study, the hybrid rice genotypes G2, G5, G7, and G8 were categorized as stable. Hanson ( 1970 ) introduced the Di parameter as a tool to assess genotype stability, particularly under trial conditions involving a limited number of genotypes and testing locations. Genotypes with low Di values are considered stable because they show relatively consistent results across various test environments. Based on this parameter, the stable hybrid rice genotypes are G2, G7, G8, while the stable check variety are G9 and G11. The variance of stability, Shukla’s (σ²), also assigned G2, G5, G7, G8, and G11 as the most stable genotypes. The parametric analyses suggest that G7, G9, and G11 are stable and high-yielding, suitable for large-scale deployment in irrigated lowland systems where environmental conditions are relatively uniform. In contrast, hybrid rice G1 and G5, which exhibited high responsiveness (bi > 1), are better suited to favorable, high-input environments where management practices can optimize yield potential. These findings imply that stability classification can guide targeted cultivar placement: broadly adapted hybrids for general cultivation, and highly responsive ones for intensive or high-fertility zones. Table 7 Parametric stability analysis of grain yield of hybrid rice evaluated under multi-environment trials Genotype Y i CV i b i s 2 d i W i 2 D i Stab.var G1 9.54 16.21 1.31 ** 0.71 *** 6.01 8.42 0.97 G2 8.03 10.68 0.71 ** 0.12 ns 2.37 1.86 0.35 G3 8.88 17.54 1.43 *** 0.38 ** 4.62 7.61 0.73 G4 7.58 17.01 1.19 ns 0.18 ns 2.47 4.30 0.36 G5 8.99 15.51 1.38 *** -0.02 ns 1.96 4.70 0.28 G6 7.95 19.53 1.23 * 0.96 *** 7.21 9.22 1.18 G7 8.40 9.47 0.71 ** -0.01 ns 1.61 1.11 0.21 G8 7.98 11.22 0.83 ns 0.00 ns 1.30 1.35 0.16 G9 8.87 8.91 0.63 ** 0.10 ns 2.61 1.72 0.39 G10 7.22 22.98 1.24 * 1.33 *** 9.47 11.53 1.56 G11 8.59 9.16 0.67 ** 0.04 ns 2.08 1.37 0.30 G12 7.63 13.41 0.66 ** 0.55 *** 5.22 4.45 0.83 Y i : grain yield (t ha − 1 ), grand mean = 8.31 t ha − 1 ; LSD at 5% = 0.17. CV i : coefficient of variation, average of CV = 14.38. b i : regression coefficient to environmental index. s²di: deviation from regression. W i ²: Wricke’s ecovalence. D i : Hanson’s parameter for genotype stability. StabVar: Shukla’s stability variance Multivariate Parametric Stability Analysis Multivariate analyses with the AMMI and GGE biplots were carried out to expand the information about GEI. AMMI model partitions the main effects and interaction components using principal component (PC) analysis, whereas GGE biplot visualizes genotype and GEI effects, removing environmental main effects to focus on genotype evaluation across environments (Gauch et al. 2008 ; Yan 2002 ). The AMMI biplot illustrates the GEI through its first two principal components, PC1 and PC2 (Fig. 4 A). These components account for 53.2% and 19.1% of the GEI variability, respectively, totaling 72.3%. Consequently, 27.7% of the GxE variability remains unexplained by this biplot. Nevertheless, this biplot can still offer some insight into the GxE interactions. Genotype G8, positioned near the origin, demonstrated minimal genotype-environment interaction effects. This suggests high stability and broad adaptation across various environments, supporting findings by who emphasized that genotypes with low or near-zero scores are less influenced by environmental fluctuations and thus more stable across diverse conditions. Genotypes situated near the end of an environmental vector are typically considered well-adapted to that specific environment. G5 exhibited a positive interaction with E1 but indicated a negative interaction with E4. The environmental vectors in the AMMI biplot also provide critical information. E6, E7, and E8 exhibited the longest vectors, suggesting these environments highly contributed to the expression of G×E interaction (Fig. 4 A). According to Reshma et al. ( 2024 ), environments with longer vectors are more effective in identifying genotype performance differences and are thus considered valuable for selection stages in breeding programs. In contrast, environments such as E1 and E5, which had short vectors and were located near the origin, exerted minimal discriminatory power. These environments are less useful for differentiating genotypes but are still important in stability evaluation, as they represent environments with low interaction influence (Yan and Tinker 2006 ). The AMMI biplot illustrates genotypic stability and yield response (Fig. 4 B). Genotypic stability is represented by the horizontal line at Y = 0, and the vertical line at X = 8.31 denotes the average yield. Genotypes located furthest to the right on this biplot indicate superior yield performance. Conversely, genotypes situated close to the Y = 0 horizontal axis demonstrate higher stability across environments. This analysis identifies a hybrid rice genotype exhibiting both optimal yield and stability simultaneously, G8; however, genotypes G5 and G7 are the closest approximations. Environmentally, E1 is positioned on the far right of the biplot, signifying an environment with a high average yield. It is crucial to note that PC1 accounts for only 27.7% of the Genotype-by-Environment interaction variability, implying that a substantial portion of this variability is not captured by this specific biplot. Figure 5 presents the GGE biplot, a visual representation of how genotypes interact with different environments. This biplot is constructed using its first two principal components, which collectively explain 79.6% of the total variation attributed to both genetic effects (G) and genotype-environment interaction. This high cumulative variance suggests that the biplot offers a robust approximation of the data gathered from multi-environment trials. However, with 36.8% of the G + GE variance remaining unexplained, the interpretation of this biplot should be approached with caution. As suggested by Yan and Tinker ( 2006 ), such a level of explanation is sufficient for meaningful conclusions about genotype performance and environmental discriminative capacity. The "which-won-where " feature of the GGE biplot revealed that the eight environments were divided into two mega environments (Fig. 5 A): sector I contains E2, E3, and E5, while sector II contains E1, E4, E6, E7, and E8. The vertex genotype at the polygon’s corners represented the best performer within each sector, with G1 excelling in most environments except E2, E3, and E5. Meanwhile, G8 was positioned near the origin, indicating broad adaptability and relative insensitivity to environmental fluctuations. The mean-versus-stability pattern showed that genotypes located closest to the average environment (AE) axis—such as G1, G3, G5, G7, G9, and G11—were stable across environments, while those to the right of the AE axis combined high yield with consistent performance. Complementary results from the AMMI and GGE analyses showed that environments E6, E7, and E8 contributed most to genotype differentiation, reflecting environmental contrasts in altitude, soil type, and climatic stress. These locations can serve as key test sites for identifying genotypes resilient to suboptimal growing conditions. The identification of two mega-environments underscores the need for regional stratification of hybrid rice cultivation based on climate and soil characteristics. Notably, hybrid rice G8 demonstrated consistent yield and stability across both mega-environments, confirming their physiological adaptability to varying temperature and fertility regimes. These findings provide valuable guidance for site-specific hybrid deployment, efficient resource use, and improved production stability in tropical rice-growing systems. The combined application of AMMI and GGE biplot analyses offered a complementary perspective on genotype adaptability. Specifically, AMMI proved effective in delineating interaction patterns, whereas GGE elucidated both the average yield and stability across diverse environmental conditions. Multivariate stability analysis for grain yield has proven effective in selecting genotypes with consistent performance across diverse conditions (Kartina et al. 2021 ; Sharifi et al. 2017 ; Sitaresmi et al. 2019). This integrated methodology contributes to improved selection precision and facilitates the informed deployment of genotypes according to their specific adaptation profiles. Selection of Hybrid Rice Genotypes Based on The MGIDI To complement the univariate and multivariate stability analyses, the Multi-Trait Genotype–Ideotype Distance Index (MGIDI) was employed to identify superior hybrid rice genotypes based on multiple agronomic traits. Unlike traditional approaches that evaluate genotypes based on single traits, MGIDI enables simultaneous selection by integrating all traits into a single index, facilitating the identification of genotypes closest to an ideotype, defined as the theoretical genotype exhibiting optimal values for all traits (Olivoto et al. 2019 ; Olivoto and Nardino 2021 ). Variation in agronomic traits is influenced by multiple sources, including environmental conditions, genetic differences among genotypes, G×E interactions, replication effects within environments, and residual (random) errors. Grain yield was greatly affected by environment (32.1%), followed by genotype (15.1%) and genotype x environment (16.2%), which were relatively equal (Fig. 6 ). Yield is a complex trait that is strongly influenced by the environment and its interaction (GEI) (Huang et al. 2021 ). The genotypes identified by the MGIDI index were G2, G4, and G5 as closest to the ideal type among the tested genotypes (Fig. 7 ). G5 has higher yield than G2 and G4, but all three genotypes were widely adapted based on regression stability analysis (Table 7 ). These genotypes have combining yield stability with favorable agronomic traits such as balanced plant height, productive tillers, and filled grains. These traits reflect efficient resource use and sink–source coordination, which are critical for sustaining yield under tropical conditions where temperature and radiation fluctuate. The balanced performance of G5 indicates its suitability for diverse agroecosystems, supporting the concept of ecological intensification by enhancing yield without increasing input demand. Thus, MGIDI-based selection contributes to identifying genotypes that optimize physiological efficiency and production resilience. Olivoto and Nardino ( 2021 ) emphasized that researchers are urged to carefully examine genotypes at the cut point. The MGIDI index integrates multiple desirable traits into a single selection criterion. However, it showed the power of MGDI analysis to guide genotype selection and advance crop improvement programs. In this study, MGIDI was computed using 14 traits. These traits were rescaled to a 0–100 scale, where higher values represented more desirable trait performance. The ideotype was constructed using the maximum (or minimum, depending on breeding goals) rescaled value for each trait. Figure 8 offers an in-depth assessment of the strengths and weaknesses exhibited by different genotypes, delineated by the contribution of each factor to MGIDI. According Ridara et al. ( 2025 ), the radar plot delineates the strengths and weaknesses of the selected genotypes by examining the factor contributions to the MGIDI indices. The strengths and weaknesses analysis indicated that G2 and G5 have a relatively balanced contribution pattern and are closer to the center, indicating that these two genotypes are more stable and have better overall performance than the other genotypes. Meanwhile, G4 showed a high contribution value to FA1, indicating that this genotype has a prominent weakness in the trait group represented by this factor. On the other hand, FA2 appeared to make the largest contribution to the total variation of MGIDI, so it can be said that the trait group contained by FA2 is the main determinant of performance differences between genotypes (Fig. 8 ). FA1 was consist of yield, weight of 1000 grains, days of maturity and days to flowering, so the high contribution value of FA1 to G4 indicates that G4 has relatively low productivity. FA2 was correlated with number of productive tillers, number filled grains and number of grains. FA3 was build from plant height and length of panicles, and FA4 was correlated with number of unfilled grains and seed set (Table 8 ). The use of factor analysis in this study helped to organize complex multivariate data and support ideotype-based selection. This approach also highlights the importance of multi-trait selection tools in modern breeding programs, where simultaneous improvement in multiple correlated traits is essential. Understanding the strengths and weaknesses of each genotype provides valuable guidance for selecting parental lines in future hybrid breeding efforts. Agronomic traits were grouped into four main factors, along with the selection direction (sense), selection target (goal), and selection differential (SD). Selection diffential is the average percentage change in traits after selection based on the MGIDI index. Positive SD indicates an increase in the desired trait, while a negative SD indicates a decrease in the trait according to the predetermined selection direction. Characters that have a positive linear relationship with the economic value of rice are selected in the same direction (increase), while characters that tend to reduce the economic value are selected in the opposite direction (decrease). The positive SD for the number of filled grains (2.08%) and number of grains (5.03%) indicate that MGIDI selection succeeded in increasing yield potential by increasing the main yield components. The direction of selection for plant height was a decrease with SD 0.63%, indicating the success of selection in reducing plant height is expected to reduce the potential to lodging, while a positive SD for panicle length (1.04%) indicates an increase in panicle length which is expected to support grain yield. The high SD for the number of unfilled grains (6.03%) indicates that this trait still has significant variation between genotypes, while the negative SD for seed set (-1.29%) indicates a slight decrease but still in the direction of increasing selection. Overall, these results demonstrate that MGIDI-based selection can produce positive genetic progress for most important traits without sacrificing performance balance between traits. These findings demonstrate that MGIDI is an effective and integrative tool for multi-trait selection, enabling breeders to make informed decisions in hybrid development by balancing yield and other agronomic traits. This approach is able to identify superior genotypes with optimal trait combinations and provide clear selection directions for character improvement in the next generation. Its use complements traditional methods and aligns with current demands for more resilient, high-performing cultivars suited to diverse agroecological zones. Table 8 Factorial loadings, communality, and differential selection based on the multi-trait genotype-ideotype distance index for 12 hybrid rice genotypes No Variables Factor Sense Goal SD (%) 1 Yield FA1 increase 0 -1.04 2 Weigth 1000 grains FA1 increase 0 -0.437 3 Days to maturity FA1 decrease 100 -1.05 4 Days to flowering FA1 decrease 100 -0.61 5 Number of productive tillers FA2 increase 0 -0.726 6 Number of filled grains FA2 increase 100 2.08 7 Number of grains FA2 increase 100 5.03 8 Plant height FA3 decrease 100 -0.634 9 Length of panicle FA3 increase 100 1.04 10 Number of unfilled grains FA4 increase 100 6.03 11 Seed set FA4 increase 0 -1.29 Implication for Breeding and Future Research Stability and adaptability are critical factors in hybrid rice breeding. They ensure consistent yield, resilience to environmental stresses, and support sustainable agricultural practices. By leveraging advanced breeding techniques and conducting extensive multi-environment trials, researchers can develop rice varieties that meet the demands of a growing population and changing climate. From a breeding point of view, the estimation of genotypic variance, phenotypic variance, environmental variance, interaction of G×E by analysis of variance is very important in terms of gene expression controlling complex traits like grain yield over a broad range of environmental conditions. Based on information about the variance contribution by genotypes and environment, an ideal genotype can be selected with less influence by environment Non-parametric methods are particularly valuable in multi-environment trials, especially when heterogeneity of variance is present. They effectively discriminate among genotypes even with variability across environments (Ferreira et al. 2016 ). These methods are also advantageous for large datasets because they do not rely on strict distributional assumptions (Akbari and Darvishzadeh 2024 ). Furthermore, non-parametric methods can handle both crossover and non-crossover types of genotype × environment interactions, providing breeders with a reliable tool to assess stability and adaptability across diverse and complex testing conditions (Hladni et al. 2011 ; Mohammadi and Amri 2008 ). Consequently, non-parametric methods are useful in the preliminary yield testing of hybrid rice for screening stable and high-yielding genotypes, thereby helping to identify promising hybrids for further testing and development. Parametric analysis is fundamental for evaluating stability in multi-environment trials by providing a systematic framework for elucidating genotype-environment interactions, confirming statistical assumptions, and establishing a connection between stability and yield (Shahbazi 2019 b). Parametric models are frequently employed in multi-environment trials to divide variation into genotype, environment, and genotype x environment interactions. Parametric analysis resulted in genotypes G5, G7, G9, and G11 as high-yielding genotypes and stable. The Additive Main Effects and Multiplicative Interaction (AMMI) and Genotype plus Genotype-by-Environment Interaction (GGE) biplot analyses are powerful statistical tools for evaluating yield stability across multi-environment trials. AMMI combines analysis of variance to capture additive effects and principal component analysis to model multiplicative interactions, allowing breeders to identify genotypes that are both high yielding and stable (Bocianowski et al., 2024 ; Pramanik et al., 2024 ). The GGE biplot focuses on genotype and GE interaction effects and graphically represents performance using the first two principal components (Tripodi et al., 2025 ). AMMI provides statistical precision for stability interpretation, whereas GGE offers a clear visualization of yield performance and adaptability across diverse environments. Although both analyses provide many advantages, they also have several limitations, including high computational complexity, challenges in handling genotype × environment (G×E) interactions, issues with data quality and missing data, difficulties in interpreting results, model selection and validation, as well as scalability problems when the number of traits and environments increases (Akinwale et al. 2014 ; Hadasch et al. 2017 ; Montesinos-López et al. 2019 ). Furthermore, improving interpretability and scalability will ensure that multi-environment trial data can be more effectively utilized in practical breeding pipelines, particularly in hybrid rice breeding, where simultaneous evaluation of multiple traits and environments is essential. The MGIDI represents an important advance in genotype selection because it allows breeders to simultaneously evaluate multiple traits relative to an ideotype (Debnath et al. 2024 ). This is particularly relevant for hybrid rice, where high grain yield must be balanced with other traits such as grain quality, biotic resistance, and tolerance to abiotic stress. MGIDI has been effectively used to identify high-performing rice genotypes by evaluating multiple traits such as spikelet fertility, seedling dry weight (Pallavi et al. 2024 ), nitrogen use efficiency (Duc et al. 2025 ), and resistance to biotic stresses (Dewi et al. 2025). Furthermore, studies have shown significant genetic gains using MGIDI, with improvements in traits like grain yield (Mohanty et al., 2025 ), grain quality (Feizi et al. 2025 ). The use of MGIDI analysis can strongly support hybrid rice breeding because it integrates multiple traits simultaneously, reduces issues of multicollinearity, and achieves high selection gains. These advantages make MGIDI an invaluable tool for modern rice breeding programs, ensuring that selected genotypes are not only high-yielding but also exhibit a balanced performance in terms of grain quality, biotic resistance, and abiotic stress tolerance. The integration of multi-environment and multi-trait analyses provides a strong framework for enhancing rice production efficiency across heterogeneous agroecosystems. This approach enables the identification of stable and high-performing genotypes, such as G2, G4, and G5, that maintain yield reliability under varying conditions of water availability, soil fertility, and temperature. By combining physiological understanding with stability modeling, hybrid recommendations can be tailored to specific agroecological zones, ensuring optimal performance under local environmental constraints. To further refine genotype adaptability assessment, future studies should integrate environmental covariates—such as soil moisture, temperature, and rainfall patterns—into predictive models of hybrid performance. Additionally, linking stability analysis with genomic information will strengthen the identification of genes or quantitative trait loci (QTLs) associated with yield stability, supporting the development of molecular markers for marker-assisted selection. The integration of genomic and phenotypic stability data, as demonstrated by Sayed et al. ( 2025 ), has shown promise in identifying genotypes combining high productivity with drought resilience. Moreover, advances in machine learning can facilitate modeling of complex genotype × environment interactions and enhance predictive accuracy under untested conditions. Collectively, these interdisciplinary approaches will accelerate the development of high-performing, environmentally resilient, and widely adaptable hybrid rice varieties to meet the challenges of climate variability and resource limitation. Conclusion The study demonstrated that environmental variation strongly influenced hybrid rice performance across field sites. Integrating multiple stability analyses allowed the identification of hybrids with consistent yield and adaptability under diverse tropical agroecosystems. Univariate parametric and non-parametric methods highlighted four hybrid rice genotypes G2, G4, G7, G8 as stable performers, while AMMI and GGE biplots (multivariate) confirmed G5, and G7 as broadly adapted and high-yielding genotypes. The MGIDI analysis identified the ideal genotypes were G2, G4, and G5 considering yield agronomic traits, and broad adapted based on regression stability analysis. From these approaches, we conclude that G2, G4, G5, and G7 were consistent as superior hybrids. This integrative approach provides a framework for hybrid rice breeding in tropical environments, ensuring the development of cultivars that combine high yield potential with adaptability and resilience to variable growing conditions. Declarations Funding The breeding activities were funded by the Indonesian Center for Rice Research (ICRR), while the multilocation trials were financially supported by Bayer Indonesia. Author Contribution Conceptualization: Y.W., T.S., B.P.W.Validation: I.A.R., T.S., Y.W.Methodology: B.P.W., T.S., Y.W., I.A.R., S.SWriting – original draft: B.P.W., Y.W., T.S., I.A.R., A.H., U.S.Writing – review & editing: Y.W., I.A.R., W.B.S.Field evaluation: B.P.W., N.K., B.K., R.R., S.S.Data curation: B.P.W., Y.W., T.S., S.M, I.A.R.Statistical analysis: B.P.W., Y.W., T.S., S.M.All co-authors reviewed the final version and approved the manuscript before submission. Acknowledgement We are grateful to the Indonesian Ministry of Agriculture for providing research facilities and resources. Data Availability The datasets generated and analyzed during this study are available from the corresponding author on reasonable request. References Akbari N, Darvishzadeh R (2024) A Study on the Yield Stability of Oilseed Sunflower Genotypes under Drought Stress. J Crop Breed 16(4):129–141. https://doi.org/10.61186/jcb.16.4.129 Akinwale RO, Fakorede MAB, Badu-Apraku B, Oluwaranti A (2014) Assessing the usefulness of GGE biplot as a statistical tool for plant breeders and agronomists. Cereal Res Commun 42(3):534–546. https://doi.org/10.1556/CRC.42.2014.3.16 Bambang Priyanto S, Neni Iriany R, Takdir Makkulawu A, Jumadi O (2024) A comprehensive evaluation of the adaptability and stability of promising maize hybrids in Indonesia using different stability approaches. Chil J Agricultural Res 84(3):338–348. https://doi.org/10.4067/S0718-58392024000300338 Bocianowski J, Nowosad K, Rejek D (2024) Genotype-environment interaction for grain yield in maize (Zea mays L.) using the additive main effects and multiplicative interaction (AMMI) model. J Appl Genet 65(4):653–664. https://doi.org/10.1007/s13353-024-00899-4 Crossa J (1990) Statistical analyses of multilocation trials. Advances in agronomy. Elsevier, pp 55–85. https://doi.org/10.1016/S0065-2113(08)60818-4 Debnath P, Chakma K, Bhuiyan MSU, Thapa R, Pan R, Akhter D (2024) A novel multi trait genotype ideotype distance index (MGIDI) for genotype selection in plant breeding: Application, prospects, and limitations. Crop Des 3:100074. https://doi.org/10.1016/j.cropd.2024.100074 Duc NT, Harika A, Raju D, Kumar S, Pandey R, Sathee L, Alimuthu E, Singh B, Vishwakarma C, Ellur RK, Iquebal MA, Pandey R, Rajendran A, Sakhare A, Sahoo RN, Chirravuri NN, Senthil A, Chinnusamy V (2025) Identification of superior rice donors with enhanced nitrogen use efficiency using a comprehensive multivariate genotype selection strategy. iScience 28(9):113280. https://doi.org/10.1016/j.isci.2025.113280 Eberhart SA, Russell WA (1966) Stability parameters for comparing varieties. Crop Sci 6:36–40. https://doi.org/10.2135/cropsci1966.0011183X000600010011x Feizi N, Sabouri A, Bakhshipour A, Abedi A (2025) Combinatorial Approaches to Image Processing and MGIDI for the Efficient Selection of Superior Rice Grain Quality Lines. Agriculture 15(6):615. https://doi.org/10.3390/agriculture15060615 Ferreira DF, Fernandes SB, Bruzi AT, Ramalho MAP (2016) Non-parametric approach to the study of phenotypic stability. Genet Mol Res 15(1). https://doi.org/10.4238/gmr.15017517 Finlay K, Wilkinson G (1963) The analysis of adaptation in a plant-breeding programme. Aust J Agric Res 14:742. https://doi.org/10.1071/AR9630742 Francis TR, Kannenberg LW (1978) Yield stability studies In: Short-season Maize IA descriptive method for grouping genotypes. Can J Plant Sci 58:1029–1034. https://doi.org/10.4141/cjps78-157 Gauch HG (2006) Statistical analysis of yield trials by AMMI and GGE. Crop Sci 46:1488–1500. https://doi.org/10.2135/cropsci2005.07-0193 Gauch HG, Piepho H, Annicchiarico P (2008) Statistical analysis of yield trials by AMMI and GGE: Further considerations. Crop Sci 48:866–889. https://doi.org/10.2135/cropsci2007.09.0513 Gauch HG, Qian S, Piepho HP, Zhou L, Chen R (2019) Consequences of PCA graphs, SNP codings, and PCA variants for elucidating population structure. PLoS ONE 14:e0218306. https://doi.org/10.1371/journal.pone.0218306 Hadasch S, Forkman J, Piepho H (2017) Cross-Validation in AMMI and GGE Models: A Comparison of Methods. Crop Sci 57(1):264–274. https://doi.org/10.2135/cropsci2016.07.0613 Hanson WD (1970) Genotypic stability. Theoret Appl Genet 40:226–231. https://doi.org/10.1007/BF00285245 Herawati R, Lestari AP, Nurmegawati, Ganefianti DW, Romeida A (2021) Comparative study on the stability and adaptability of different models to develop a high-yield inbred line from landrace rice varieties. Annals Agricultural Sci 66(2):184–192. https://doi.org/10.1016/j.aoas.2021.12.004 Hladni N, Jocic S, Miklic V, Saftic-Pankovic D, Kraljevic-Balalic M (2011) Interdependence of yield and yield components of confectionary sunflower hybrids. Genetika 43(3):583–594. https://doi.org/10.2298/GENSR1103583H Huang X, Jang S, Kim B, Piao Z, Redona E, Koh HJ (2021) Evaluating genotype × environment interactions of yield traits and adaptability in rice cultivars grown under temperate, subtropical and tropical environments. Agriculture 11 , 558. https://doi.org/10.3390/agriculture11060558 IRRI (2014) Standar Evaluation System (SES) for Rice, 5th edn. International Rice Research Institute Dewi IS, Purwoko BS Ratna Kartika Putri, & Iskandar Lubis. (2025). Brown Plant Hopper Resistance in Promising Doubled Haploid Rice Lines Selected by MGIDI and FAI-BLUP Index. Pertanika J Trop Agric Sci, 48 (3). https://doi.org/10.47836/pjtas.48.3.20 Karimizadeh R, Mohammadi M, Sabaghnia N, Shefazadeh MK (2012) Using huehn's nonparametric stability statistics to investigate genotype x environment interaction. Not Bot Hort Agrobot Cluj 40:293. https://doi.org/10.15835/nbha4017593 Kartina N, Widyastuti Y, Rumanti IA, Wibowo P, B., Satoto, Mardiana (2021) Hybrid rice stability studies in Indonesia. SABRAO Journal of Breeding and Genetics . https://sabraojournal.org/sabrao-journal-of-breeding-and-genetics-issn-1029-7073-eissn-2224-8978-volume-53-number-3-september-2021 / Kebede G, Worku W, Jifar H, Feyissa F (2023) GGE biplot analysis of genotype by environment interaction and grain yield stability of oat ( Avena sativa L.) in Ethiopia. Agrosystems Geosci Env 6:e20410. https://doi.org/10.1002/agg2.20410 Kuru B, Abera N, Mulualem T (2025) Genotype X environment interaction and yield stability of finger millet (Eleusine Coracana L.) genotypes based on AMMI, GGE and MTSI analysis in humid lowland areas of Ethiopia. Field Crops Res 322:109707. https://doi.org/10.1016/j.fcr.2024.109707 Liu Y, Duan C, Tian M, Hu E, Huang Y (2010) Yield stability of maize hybrids evaluated in maize regional trials in Southwestern China using nonparametric methods. Agricultural Sci China 9:1413–1422. https://doi.org/10.1016/S1671-2927(09)60232-7 Maulana H, Solihin E, Trimo L, Hidayat S, Wijaya AA, Hariadi H, Amien S, Ruswandi D, Karuniawan A (2023) Genotype-by-environment interactions (GEIs) and evaluate superior sweet potato (Ipomoea batatas [L.] Lam) using combined analysis and GGE biplot. Heliyon 9 , e20203. https://doi.org/10.1016/j.heliyon.2023.e20203 Mohammadi R, Amri A (2008) Comparison of parametric and non-parametric methods for selecting stable and adapted durum wheat genotypes in variable environments. Euphytica 159 (3), 419–432. https://doi.org/10.1007/s10681-007-9600-6 Mohanty SK, Mahapatra S, Batabyal S, Carlson M, Kanungo G, Ayyagari A, Tchedre K, Franco JA, Singer M, Barone SB, Chavala S, Mahajan VB (2025) A synthetic opsin restores vision in patients with severe retinal degeneration. Mol Ther 33(5):2279–2290. https://doi.org/10.1016/j.ymthe.2025.03.031 Montesinos-López OA, Montesinos‐López A, Hernández MV, Ortiz‐Monasterio I, Pérez‐Rodríguez P, Burgueño J, Crossa J (2019) Multivariate Bayesian Analysis of On‐Farm Trials with Multiple‐Trait and Multiple‐Environment Data. Agron J 111(6):2658–2669. https://doi.org/10.2134/agronj2018.06.0362 Nassar R, Huhn M (1987) Studies on estimation of phenotypic stability: Tests of significance for nonparametric measures of phenotypic stability. Biom J 29:433–452 Olivoto T, Lúcio ADC, Da Silva JAG, Sari BG, Diel MI (2019) Mean performance and stability In: Multi-Environment trials II: Selection based on multiple traits. Agron J 111:2961–2969. https://doi.org/10.2134/agronj2019.03.0221 Olivoto T, Nardino M (2021) MGIDI: toward an effective multivariate selection in biological experiments. Bioinformatics 37:1383–1389. https://doi.org/10.1093/bioinformatics/btaa981 Pallavi M, Maruthi Prasad BP, Shanthi P, Reddy VLN, Nirmal Kumar AR (2024) Multi trait genotype- ideotype distance index (MGIDI) for early seedling vigour and yield related traits to identify elite lines in rice (Oryza sativa L). Electron J Plant Breed 15(1). https://doi.org/10.37992/2024.1501.020 Pour-Aboughadareh A, Poczai P (2021) Dataset on the use of MGIDI index in screening drought-tolerant wild wheat accessions at the early growth stage. Data Brief 36:107096. https://doi.org/10.1016/j.dib.2021.107096 Pramanik K, Sahu GS, Acharya C, Tripathy G, Dash P, Koundinya M, Jena AVV, Kumar C, Mohapatra DS, Pradhan PP, Karubakee J, S., Moharana DP (2024) Estimating phenotypic stability for relevant yield and quality traits in French bean (Phaseolus vulgaris L.) using AMMI analysis. Heliyon 10(5):e26918. https://doi.org/10.1016/j.heliyon.2024.e26918 Rahman MA, Alam Z, Akm SI, Hossain MM, Munna NA, Islam MR, Kamruzzaman M, Rabbi SMH (2025) Evaluating Genotype × Environment Interactions for Selecting High-Yielding Stable Rice Varieties Under Water Stagnant Conditions. Ecol Genet Genomics 37. https://doi.org/10.2139/ssrn.5174079 Reshma OSP, Reddy SB, Shivaprasad KM (2024) Evaluation of rice genotypes for yield stability and adaptability across multiple environments using AMMI and GGE biplot analysis. J Adv Biol Biotechnol 27:1164–1176. https://doi.org/10.9734/jabb/2024/v27i81239 Ridara F, Ustari D, Wicaksono AA, Algina A, Amien S, Susanto GWA, Koerniati S, Hastilestari BR, Concibido V, Karuniawan A (2025) Integrating multi-trait selection and environmental stability for Soybean ( Glycine max L. Merr.) genotype improvement in Indonesia. Plant Cell Environ 1–13. https://doi.org/10.1111/pce.15662 Roy D, Gaur A, Pandey ID, Barman M, Ahmed B (2024) Designing an index for multi-location yield stability analysis involving univariate and multivariate methods in rice (Oryza sativa L). Braz arch biol technol 67:e24230303. https://doi.org/10.1590/1678-4324-2024230303 Sayed LM, Khaled KAM, Samaha GM, Saber AA (2025) Integrating phenotypic, molecular, and bioinformatics approaches for developing drought-tolerant sesame. Sci Rep 15(1):21897. https://doi.org/10.1038/s41598-025-06891-0 Shahbazi E (2019) Genotype selection and stability analysis for seed yield of Nigella sativa using parametric and non-parametric statistics. Sci Hort 253:172–179. https://doi.org/10.1016/j.scienta.2019.04.047 Shahbazi E (2019b) Genotype selection and stability analysis for seed yield of Nigella sativa using parametric and non-parametric statistics. Sci Hort 253:172–179. https://doi.org/10.1016/j.scienta.2019.04.047 Sharifi P, Aminpanah H, Erfani R, Mohaddesi A, Abbasian A (2017) Evaluation of Genotype × Environment Interaction in Rice Based on AMMI Model in Iran. Rice Sci 24(3):173–180. https://doi.org/10.1016/j.rsci.2017.02.001 Shukla GK (1972) Some statistical aspects of partitioning genotype-environmental components of variability. Heredity 29:237–245. https://doi.org/10.1038/hdy.1972.87 Singh G, Khanna R, Kaur R, Kaur K, Kaur R, Sharma N, Mangat GS (2023) Performance under multi-environment trial for quantitative traits of rice (Oryza sativa L.) genotypes in North-West India (Punjab). Ecol Genet Genomics 28:100190. https://doi.org/10.1016/j.egg.2023.100190 Sitaresmi, Suwarno WB, Gunarsih C, Nafisah, Nugraha Y, Sasmita P, Daradjat AA (2019) Comprehensive stability analysis of rice genotypes through multi-location yield trials using PBSTAT-GE. SABRAO J Breed Genet 51:355 Suwarno WB, Aswidinnoor H, Syukur M, Ritonga AW, Sitaresmi T (n.d.). Stability Analysis in Plant Breeding Using PBSTAT-GE. (2025) Thennarasu K (19950 On certain non-parametric procedures for studying genotype-environment interactions and yield stability. Indian J Genet 60 , 9 Tripodi P, Soler S, Campanelli G, Figàs MR, Casanova C, Soler E, Sestili S, Bertone A, Cardi T, Prohens J (2025) GGE analysis and stability of traits in tomato cultivars grown under organic farming conditions: A two-year study. Hortic Plant J 11(2):721–736. https://doi.org/10.1016/j.hpj.2023.09.009 Vanisri VIL, Rahul SMS (2023) Parametric stability analysis of cold tolerant rice genotypes for grain yield. J Theoretical Biology Forum 12:471–475. https://doi.org/10.61739/TBF.2023.12.2.471 Wrickle (1962) Über eine Methode zur Erfassung der ökologischen Streubreite in Feldversuchen. Z für Pflanzenzüchtung 47:92–96 Yan W (2002) Singular-value partitioning in biplot analysis of multienvironment trial data. Agr J 94(5):990–996. https://doi.org/10.2134/agronj2002.9900 Yan W, Kang MS (2002) GGE biplot analysis: A graphical tool for breeders, geneticists, and agronomists, 1st edn. CRC. https://doi.org/10.1201/9781420040371 Yan W, Tinker NA (2006) Biplot analysis of multi-environment trial data: Principles and applications. Can J Plant Sci 86:623–645. https://doi.org/10.4141/P05-169 Zobel RW, Wright MJ, Gauch HG (1988) Statistical analysis of a yield trial. Agron J 80:388–393. https://doi.org/10.2134/agronj1988.00021962008000030002x Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8185743","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":553117935,"identity":"57fe738a-72c2-4c82-aeea-ddca82c6ab71","order_by":0,"name":"Bayu Pramono Wibowo","email":"","orcid":"","institution":"National Research and Innovation Agency (BRIN)","correspondingAuthor":false,"prefix":"","firstName":"Bayu","middleName":"Pramono","lastName":"Wibowo","suffix":""},{"id":553117936,"identity":"5ec6c083-fdd0-48cb-b849-7a657d04b8c6","order_by":1,"name":"Trias Sitaresmi","email":"","orcid":"","institution":"National Research and Innovation Agency (BRIN)","correspondingAuthor":false,"prefix":"","firstName":"Trias","middleName":"","lastName":"Sitaresmi","suffix":""},{"id":553117937,"identity":"85046609-7afb-40c7-a84f-30219471d850","order_by":2,"name":"Nita Kartina","email":"","orcid":"","institution":"Ministry of Agriculture","correspondingAuthor":false,"prefix":"","firstName":"Nita","middleName":"","lastName":"Kartina","suffix":""},{"id":553117938,"identity":"9695637f-07df-478b-8d54-040bed539ded","order_by":3,"name":"Swisci Margaret","email":"","orcid":"","institution":"National Research and Innovation Agency (BRIN)","correspondingAuthor":false,"prefix":"","firstName":"Swisci","middleName":"","lastName":"Margaret","suffix":""},{"id":553117939,"identity":"e1c3e2a8-52fb-4a9b-9303-a9eafe762d1a","order_by":4,"name":"Untung Susanto","email":"","orcid":"","institution":"National Research and Innovation Agency 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1","display":"","copyAsset":false,"role":"figure","size":4484,"visible":true,"origin":"","legend":"\u003cp\u003eGenotype versus environment plot of 12 genotypes in eight environments\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8185743/v1/28377c67ecc5267bd808c2c4.png"},{"id":97289213,"identity":"308e62e0-d775-4b88-b93b-057b2828c8e0","added_by":"auto","created_at":"2025-12-02 19:08:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":7938,"visible":true,"origin":"","legend":"\u003cp\u003eStability analysis based on Francis-Kannenberg\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8185743/v1/a7be6276a66346ab5b8c149c.png"},{"id":97370417,"identity":"61d8e0c4-56b8-461f-9eb8-9949cc1bc1c0","added_by":"auto","created_at":"2025-12-03 16:27:19","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":58050,"visible":true,"origin":"","legend":"\u003cp\u003eStability analysis based on Eberhart-Russell\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8185743/v1/93d55b9123bbc74b351a9409.png"},{"id":97289211,"identity":"af4dab94-9178-485c-b57b-d5aad7b41e46","added_by":"auto","created_at":"2025-12-02 19:08:35","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":43862,"visible":true,"origin":"","legend":"\u003cp\u003eAMMI analysis of rice genotypes stability.\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8185743/v1/58dde8b7df3c92fbe6cfa9fe.png"},{"id":97370048,"identity":"9ee839be-ca15-45b7-8b9a-d887d0788f72","added_by":"auto","created_at":"2025-12-03 16:26:35","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":44520,"visible":true,"origin":"","legend":"\u003cp\u003eGGE biplot of grain yield of 12 genotypes in eight environments\u003c/p\u003e","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8185743/v1/82116da872b70ddc7185d041.png"},{"id":97289223,"identity":"b903d549-e343-48be-8ac6-3f494b6bea06","added_by":"auto","created_at":"2025-12-02 19:08:35","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":22289,"visible":true,"origin":"","legend":"\u003cp\u003eProportion of phenotypic variance of hybrid rice in multi-location yield trial\u003c/p\u003e","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8185743/v1/35b67150ad1effacec23df01.png"},{"id":97289227,"identity":"095fc31f-4cb8-4fec-9308-a363f56f28d3","added_by":"auto","created_at":"2025-12-02 19:08:35","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":32532,"visible":true,"origin":"","legend":"\u003cp\u003eHybrid rice genotypes rankings showing selected accessions using the MGIDI. The selected accessions were shown as red dots, while the unselected genotypes were shown as black dots. The red circle represented the cut point according to the selection pressure\u003c/p\u003e","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8185743/v1/ba64104307d9d1a82b677605.png"},{"id":97368246,"identity":"9a1930e8-2793-4598-b66c-f1675d44b9d3","added_by":"auto","created_at":"2025-12-03 16:21:51","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":18157,"visible":true,"origin":"","legend":"\u003cp\u003eThe strengths and weaknesses of the selected genotypes were shown as the proportion of each factor on the computed MGIDI. The smaller the proportion explained by a factor (closer to the external edge), the closer the traits within that factor are to the ideotype. The black broken circle at the center shows the theoretical value if all the factors contributed equally\u003c/p\u003e","description":"","filename":"Onlinefloatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-8185743/v1/1a0ed0f6b1db0c8ae9f10c9c.png"},{"id":97373071,"identity":"a07dc902-d8ed-4381-85b8-43bf54f97fc9","added_by":"auto","created_at":"2025-12-03 16:34:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1662035,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8185743/v1/73bd43c4-6ece-4481-8040-2e31db9b95ae.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessing Yield Stability and Environmental Adaptation of Hybrid Rice (Oryza sativa L.) Across Tropical Field Conditions","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn tropical regions, hybrid rice productivity faces significant vulnerabilities due to its high dependence on fluctuating environmental conditions, including unpredictable water availability, variable soil fertility, and extreme temperature regimes. Therefore, comprehensive evaluation of genotypic responses in diverse field environments is indispensable, offering critical insights into the physiological adaptation mechanisms and inherent production stability of hybrid rice under stress. The strategic integration of advanced stability analysis with multi-trait assessment is crucial for supporting hybrid rice development.\u003c/p\u003e\u003cp\u003eMulti-environment trials (METs) are an important stage in hybrid rice breeding. It allows across various environments identification of genotypes with broad adaptability and stable performance. The METs are needed because the genotype and environment interaction (GEI) often complicates the relationship between genetic potential and observed phenotype (Crossa \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Maulana et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, a profound understanding of GE interactions is crucial for plant breeders to select and develop hybrids that consistently perform under variable agroecosystems (Pour-Aboughadareh and Poczai \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). It is important to use appropriate statistical methods to select high-yielding and stable hybrid rice genotypes.\u003c/p\u003e\u003cp\u003eNon-parametric and parametric methods have been widely used to evaluate GEI. Non-parametric methods are less limited by the assumptions of normality and homogeneity of variance, as well as the impact of outliers (Liu et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Shahbazi \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003ea). However, non-parametric measures focus on static stability and consistent performance, often without adequately representing a genotype\u0026rsquo;s adaptability to environmental changes (Karimizadeh et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Non-parametric indices have been reported for genotypic selection of stable performance across environments. Nassar and Huhn rank tests have shown application for selecting stable wheat and barley genotypes in heterogeneous environments (Nassar and Huhn \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1987\u003c/span\u003e; Thennarasu 1995). Yan and Kang (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) yield-stability index identified maize hybrids with strong yield potential and environmental adaptability. Therefore, while non-parametric procedures may not fully capture adaptability, they remain useful tools for initial screening of stability under environmental variation.\u003c/p\u003e\u003cp\u003eParametric methods using single variate approaches such as regression coefficients, deviations from regression, and stability variance provide quantitative data on yield stability and high-yielding genotypes (Francis and Kannenberg \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1978\u003c/span\u003e). However, these methods depend on assumptions of linearity, normal distribution, and homogeneity of variance, which makes them sensitive to outliers and less precise for multilocation data sets (Liu et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Multivariate approaches such as the AMMI (Additive Main Effects and Multiplicative Interaction) model and GGE (Genotype and Genotype \u0026times; Environment) biplots are particularly valuable for visualizing the interaction of genotypes and environments. For instance, researchers can use the GGE biplot to separate mega-environments and to evaluate the effectiveness of test locations (Sitaresmi et al. 2019). Both AMMI and GGE rely heavily on visual interpretation and are sensitive to balanced data. Furthermore, AMMI addresses only a single G\u0026times;E component, while GGE may provide less accurate estimations within a single mega-environment (Gauch \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Gauch et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). A more recent analysis is the Multi-trait Genotype\u0026ndash;Ideotype Distance Index (MGIDI), which aggregates multiple agronomic traits within a single index. MGIDI ranks genotypes based on their distance and enables breeders to handle multicollinearity while minimizing bias in multi-trait selection (Olivoto and Nardino \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Its application, however, depends on how researchers define the \u0026ldquo;ideal genotype,\u0026rdquo; and this definition may vary across breeding targets and environments (Debnath et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eVarious studies have indicated that the combination of diverse stability analysis approaches leads to regular and trustworthy results for selecting genotypes for high yield potential and broad adaptability. Estimation of yield stability using WAAS, WAASB, WAASBY, and GGE biplot analyses led to successful estimations of yield stability under different environments (IRRI \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Rahman et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), multivariate approaches such as AMMI and GGE biplot are widely recognized methods for multi-environment evaluations, providing concise summaries of rice yield stability and its primary components (Priyanto et al. 2024; Singh et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The combination of AMMI, GGE, and MTSI stability analysis methods has produced selected genotypes with stable performance across various agroecological zones (Kuru et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The combination of parametric and non-parametric analyses further validates the selection results for high-yielding genotypes with broad stability and adaptability (Herawati et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Although AMMI and GGE biplot analyses are widely used for evaluating hybrid rice performance across diverse environments, few studies have systematically integrated them with non-parametric, parametric, and MGIDI indices. This combined approach is innovative because MGIDI, a recently developed multivariate index, allows for the simultaneous assessment of yield, stability, and resistance. These factors are not fully captured by AMMI and GGE alone. Our study, by incorporating MGIDI with established stability methods, offers a comprehensive evaluation of hybrid rice in tropical multi-environment settings. Therefore, this study aimed to assess the integration of parametric, non-parametric, and multivariate approaches for selecting hybrid rice genotypes based on yield and stability. This integrated framework is expected to enhance understanding of GEI and facilitate the identification of high-yielding, stable, and widely adapted hybrid rice lines suitable for breeding programs in tropical environments.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eExperimental Locations, Materials, and Design\u003c/h2\u003e\u003cp\u003eField trials were conducted on a multi-location basis in Indonesia. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e gives information on soil, climatic types, and altitude for multi-locations. The genotypes were evaluated across eight irrigation fields (E1 to E8) from the 2018 dry season (January to May).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCharacteristic of multilocation environments\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLocations\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLatitude\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLongitude\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eClimate types*\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSoil types\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAltitude (m asl)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eE1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-7.617.434\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e109.182.890\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eB2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAlluvial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eE2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-6.584.536\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e107.443.469\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBrown Latosol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e172\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eE3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-7.068.621\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e112.224.681\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAlluvial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eE4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-6.354.718\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e107.646.785\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eD3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAlluvial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eE5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-7.218.889\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e111.946.389\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eD3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAlluvial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eE6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-6.272.782\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e107.480.395\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eB1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAlluvial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eE7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-6.810.453\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e107.271.710\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLatosol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eE8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-7.813.937\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e110.471.495\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRegosol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e117\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eLocations: Cilacap (E1), Purwakarta (E2), Lamongan (E3), Subang (E4), Bojonegoro (E5), Karawang (E6), Cianjur (E7), Sleman (E8), *Climate types based on Oldeman; m asl\u0026thinsp;=\u0026thinsp;meters above sea level\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe hybrid rice breeding materials developed at the Indonesian Center for Rice Research (ICRR), Ministry of Agriculture, Republic of Indonesia, were used as test materials. In this study, the hybrid materials used were eight advanced experimental hybrids (G1 to G8), three commercial hybrid varieties (G9, G10, G11), and one common inbred variety (G12), which was included as a standard check (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eList of genotypes tested at multi-location trials\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGenotypes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCode\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSource\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eH 263\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eG1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eICRR\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eH 264\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eG2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eICRR\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eH 265\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eG3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eICRR\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eH 266\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eG4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eICRR\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eH 267\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eG5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eICRR\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eH 273\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eG6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eICRR\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eH 274\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eG7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eICRR\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eH 275\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eG8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eICRR\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eArize H6444 Gold\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eG9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBayer\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSembada 989\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eG10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBiogene Plantation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHipa Jatim2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eG11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eICRR\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInpari 30 Ciherang Sub1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eG12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eICRR\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe experimental design involved organizing genotypes into a four-replicate randomized complete block design, with each plot size 20 m\u0026sup2; (4 m x 5 m). Twenty-one-day-old seedlings were transplanted at a density of 1\u0026ndash;2 seedlings per hill spacing, maintaining a spacing of 20 x 20 cm. A total of 120 kg ha⁻\u0026sup1; of nitrogen was applied in three splits: 30% as a basal application, 40% at maximal tillering, and 30% at heading. Furthermore, 45 kg ha⁻\u0026sup1; of P₂O₅ and 60 kg ha⁻\u0026sup1; of KCl were utilized as basal fertilizers.\u003c/p\u003e\u003cp\u003eRice reactions to pests and diseases brown planthopper (Bph), bacterial leaf blight (BLB), blast, and rice tungro virus) were evaluated with the Rice Resistance Standard Evaluation System protocol (IRRI \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Grain quality assessment was conducted at the ICRR from July to December 2018. Parameters recorded included plant height, tiller numbers, filled grain numbers, percentage of seed formation, 1000-grain weight, days to maturity, and grain yield per plot (kg plot⁻\u0026sup1;), which were then converted to grain yield per hectare (ton ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) at 14% moisture content.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eGrain yield was analyzed using analysis of variance (ANOVA) to evaluate the variation of phenotype affected by genotypes, environments, and GEI. Genotypes were considered as fixed factors, but environments served as random factors. A further statistical analysis was done to examine how the environment and genotype interact, with the goal of finding out how stable the 12 genotypes are in eight environments. There are two forms of stability analyses, i.e., non-parametric and parametric (univariate and multivariate). The effect of GEI with the adjusted mean grain yield was analyzed using the stability analysis package in PBSTAT-GE 3.7 (Suwarno et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). MGIDI, an innovative index derived from factor analysis for selecting superior genotypes based on multiple traits across one or more environments (Olivoto and Nardino \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The MGIDI was used for traits including yield, 1000-grain weight, days to maturity, days to flowering, number of productive tillers, number of filled grains, number of grains, plant height, length of panicle, number of unfilled grains, and seed set. The MGIDI was perform using in R.4.4.1 (Olivoto et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), with a selection differential (SD) of 25% intensity applied for all traits. A list of stability analysis used weres presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eNon-parametric and parametric models of stability parameters\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eApproach\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStability parameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInterpretation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReferences\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-parametric - Univariate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKang\u0026rsquo;s yield-stability index (S\u0026sup1;, S\u0026sup2;, S\u0026sup3;, S⁶)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCombines yield and stability; a lower index value indicates more stable (static)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(Nassar R \u0026amp; Huhn M, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1987\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNP\u003csub\u003ei\u003c/sub\u003e\u003csup\u003e1\u003c/sup\u003e, NP\u003csub\u003ei\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e, NP\u003csub\u003ei\u003c/sub\u003e\u003csup\u003e3\u003c/sup\u003e, NP\u003csub\u003ei\u003c/sub\u003e\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLower values indicate more stable genotypes (static)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(Thennarasu K 1995)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParametric - Univariate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoefficient of variation (CV%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStability (static) if CV\u003csub\u003ei\u003c/sub\u003e \u0026lt; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{\\text{C}\\text{V}}\\)\u003c/span\u003e\u003c/span\u003e and \u0026#119884;\u003csub\u003e\u0026#119894;\u003c/sub\u003e \u0026gt;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{\\text{Y}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(Francis \u0026amp; Kannenberg \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1978\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRegression coefficient to environmental index (b\u003csub\u003ei\u003c/sub\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eb\u003csub\u003ei\u003c/sub\u003e = 1 (stable); b\u003csub\u003ei\u003c/sub\u003e \u0026gt;1 (responsive but less stable); b\u003csub\u003ei\u003c/sub\u003e \u0026lt; 1 (unresponsive and less stable)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(Finlay \u0026amp; Wilkinson \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1963\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDeviation from regression (S\u0026sup2;d\u003csub\u003ei\u003c/sub\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSmaller S\u0026sup2;d\u003csub\u003ei\u003c/sub\u003e values indicate more stable genotypes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(Eberhart \u0026amp; Russell \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1966\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWricke\u0026rsquo;s ecovalence (W\u003csub\u003ei\u003c/sub\u003e\u0026sup2;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLower W\u003csub\u003ei\u003c/sub\u003e\u0026sup2; values indicate higher stability (dynamic)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(Wrickle \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e1962\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHanson\u0026rsquo;s parameter for genotype stability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLower values indicate greater stability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(Hanson \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1970\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eShukla\u0026rsquo;s stability variance (σ\u0026sup2;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLower σ\u0026sup2; values indicate more stable genotypes (dynamic)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(Shukla \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e1972\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParametric - Multivariate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAMMI (Additive Main Effect and Multiplicative Interaction)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIntegrates ANOVA and PCA; stability visualized in biplot\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(Zobel et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e1988\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGGE Biplot\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCombines genotype and GEI; visual selection of stable and high-yielding genotypes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(Yan \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2002\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Results and Discussion","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eAnalysis of Variance and Descriptive Analysis\u003c/h2\u003e\u003cp\u003eThe results of the combined variance analysis for grain yield of eight different environments showed that the effects of genotype (G), environment (E), and GE interaction were all statistically significant (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). These findings indicate that genotypic performance was variable across locations, and their response was considerably influenced to a great extent by environmental factors. Among the sources of variation, the environment had the highest percentage of the total sum of squares (33.50%), showing the large variations in agro-ecological conditions (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The significant environmental contribution to yield variance reflects the heterogeneity of the test sites, especially in soil fertility and microclimatic conditions. These findings underscore the role of agroecosystems in performing hybrid performance and indicate that site-specific management strategies could enhance overall production stability. The genotype\u0026rsquo;s main effect explained 17.53% of the variation, confirming the existence of gene differences among the genotypes. In addition, GEI was responsible for explaining a total of 19.79% of the variation.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCombined analysis of variance for yield evaluated under multi-environment trials\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"12\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSource of variance\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003edf\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eSum of squares (SS)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eMean square\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u003cp\u003eF value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003eSS proportion (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEnvironment (E)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e317.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e45.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e13.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e33.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReplication / E\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e80.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e3.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e4.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e8.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGenotype (G)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e166.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e15.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e6.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e17.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG x E\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e187.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e2.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e3.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e19.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eError\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e264\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e196.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCorrected total\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e383\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e948.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"12\"\u003edf: degrees of freedom, ** highly significant at \u0026lt;\u0026thinsp;0.001\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows that the location effect was significant, indicating that the site selection was effective for this experiment. Some genotypes consistently exhibited high yields in all locations, while others showed lower yields. The genotypes ranged from 3.82 to 11.45 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. The overall mean yield of the locations was 8.31 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. There were four hybrid rice that showed genotype mean yield higher than the environment mean yield, i.e., G1, G3, G5, and G7. The differences in yield between genotypes were statistically significant at all test locations. The strong environmental effect (33.5% of total variance) indicates that agroecological factors, such as rainfall distribution, soil fertility, and temperature regimes, played a dominant role in determining yield outcomes. Locations with fertile alluvial soils (e.g., Cilacap-E1 and Karawang-E6) and favorable irrigation conditions produced higher yields, while Cianjur (7) had lowest productivity because during METs, rainfall was quite high and some genotypes lodging easily. These findings emphasize that hybrid rice yield potential depends not only on genetic capacity but also on the physiological response to local resource availability and microclimatic. Integrating genotype selection with site-specific management could therefore enhance productivity stability under variable field conditions. In addition, the genotype-versus-environment plot of rice hybrids during the 2018 trials is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGrain yield of hybrid rice evaluated under multi-environment trials\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eGenotype\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"8\" nameend=\"c9\" namest=\"c2\"\u003e\u003cp\u003eGrain yield (t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eE1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eE2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eE3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eE4\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eE5\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eE6\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eE7\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eE8\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e12.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e8.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e7.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e9.54\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e7.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e8.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e11.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e8.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e6.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e8.88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e7.58\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e8.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e7.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e8.99\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e7.95\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e8.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e7.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e8.40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e6.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e7.98\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e8.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e8.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e8.87\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e6.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e7.22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e8.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e8.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e8.59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e6.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e7.63\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e7.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e8.31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLSD 0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCV (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e11.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e11.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e14.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e10.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eLSD 0.05: least significant difference test at 5% significance level; coefficient of variation (CV).\u003c/p\u003e\u003cp\u003eLocation: Cilacap (E1), Purwakarta (E2), Lamongan (E3), Subang (E4), Bojonegoro (E5), Karawang (E6), Cianjur (E7), Sleman (E8)\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eNon-Parametric Stability Analysis\u003c/h3\u003e\n\u003cp\u003eNon-parametric stability analysis was conducted to complement the parametric results and to assess genotype performance without relying on assumptions of normality or homogeneity of variance. This approach included Nassar and Huhn\u0026rsquo;s rank-based statistics S\u003csub\u003ei\u003c/sub\u003e\u003csup\u003e(1)\u003c/sup\u003e, S\u003csub\u003ei\u003c/sub\u003e\u003csup\u003e(2)\u003c/sup\u003e, S\u003csub\u003ei\u003c/sub\u003e\u003csup\u003e(3)\u003c/sup\u003e, and S\u003csub\u003ei\u003c/sub\u003e\u003csup\u003e(6)\u003c/sup\u003e (Nassar and Huhn \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1987\u003c/span\u003e); meanwhile, Thennarasu\u0026rsquo;s NP indices NP\u003csub\u003ei\u003c/sub\u003e\u003csup\u003e(1)\u003c/sup\u003e \u0026ndash; NP\u003csub\u003ei\u003c/sub\u003e\u003csup\u003e(4)\u003c/sup\u003e parameter. These methods are particularly suitable for datasets from multilocation trials with heterogeneous conditions (Thennarasu 1995).\u003c/p\u003e\u003cp\u003eThe Nassar and H\u0026uuml;hn method uses four parameters to determine the stability of a genotype (Nassar and Huhn \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1987\u003c/span\u003e). A smaller stability index value indicates a genotype to be considered stable. Parameters of stability S\u003csub\u003ei\u003c/sub\u003e\u003csup\u003e(1)\u003c/sup\u003e and S\u003csub\u003ei\u003c/sub\u003e\u003csup\u003e(2)\u003c/sup\u003e provided further confirmation. S\u003csub\u003ei\u003c/sub\u003e\u003csup\u003e(1)\u003c/sup\u003e provides the average absolute deviation of genotype ranks position in all environments, while S\u003csup\u003e(2)\u003c/sup\u003e represents the variance of these ranks. Using both parameters, hybrid rice genotypes G2, G4, G7, and G8, and G11 (check variety) stood as the first five most stable, implying consistent adaptability across diverse testing environments. Additional information was obtained from S\u003csub\u003ei\u003c/sub\u003e\u003csup\u003e(3)\u003c/sup\u003e and S\u003csub\u003ei\u003c/sub\u003e\u003csup\u003e(6)\u003c/sup\u003e. Parameter S\u003csub\u003ei\u003c/sub\u003e\u003csup\u003e(3)\u003c/sup\u003e estimates the absolute deviation of a rank of a genotype from the mean rank across all environments, and S\u003csup\u003e(6)\u003c/sup\u003e covers the squared deviations. According to these parameters, hybrid rice genotypes G1, G5, and G7, G9 registered the best rank for stability.\u003c/p\u003e\u003cp\u003eThese results suggest that even though G1 was ranked less stable through some parametric indices, it demonstrated reliable performance ranks through non-parametric evaluation, especially under favorable environments (Roy et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The adjusted mean rank of genotypes across each environment provides the test indicator utilized in Thennarasu\u0026rsquo;s method. The lowest value of NP\u003csub\u003ei\u003c/sub\u003e\u003csup\u003e1\u003c/sup\u003e, NP\u003csub\u003ei\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e, NP\u003csub\u003ei\u003c/sub\u003e\u003csup\u003e3\u003c/sup\u003e, and NP\u003csub\u003ei\u003c/sub\u003e\u003csup\u003e4\u003c/sup\u003e obtained using this method identifies a genotype to be considered stable. For all genotypes, G8 had the lowest value of stability index (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). G8 showed consistently low values throughout all Thennarasu indices. Following G8, the next most stable genotypes included hybrid rice (G2, G4, and G7), and check variety (G12) registered the lowest NP\u003csub\u003ei\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e, NP\u003csub\u003ei\u003c/sub\u003e\u003csup\u003e3\u003c/sup\u003e, and NP\u003csub\u003ei\u003c/sub\u003e\u003csup\u003e4\u003c/sup\u003e values after G8.\u003c/p\u003e\u003cp\u003eDifferences in assumptions, data sensitivity, and the specific stability metrics emphasized by non-parametric models can explain variations in genotype rankings (Roy et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The observed rank changes across the different approaches in this study underscore the importance of incorporating diverse parameters. Such methodologies are crucial in breeding programs for selecting genotypes that demonstrate both high yield and consistent stability across various environmental conditions. Relying solely on one method often leads to inconsistent results and reduces the accuracy of selecting broadly adaptable genotypes (Maulana et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, selection platforms that incorporate dynamic stability are essential for improving hybrid varieties in suitable agroecological regions.\u003c/p\u003e\u003cp\u003eThe non-parametric results revealed that hybrid rice genotypes G2, G4, G7, and G8 maintained consistent yields across contrasting environments, indicating strong environmental buffering capacity. These genotypes likely possess physiological mechanisms supporting stable grain filling and biomass allocation under varying thermal and soil conditions. Such stability is valuable for farmers in regions with unpredictable rainfall or temperature shifts, as it reduces production risk and ensures more reliable harvests under fluctuating tropical environments.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eNon-parametric stability analysis of yield in hybrid rice evaluated under multi-environment trials\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGenotype\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGrain yield (t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eS\u003csub\u003ei\u003c/sub\u003e\u003csup\u003e(1)\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eS\u003csub\u003ei\u003c/sub\u003e\u003csup\u003e2)\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eS\u003csub\u003ei\u003c/sub\u003e\u003csup\u003e(3)\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eS\u003csub\u003ei\u003c/sub\u003e\u003csup\u003e(6)\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNP\u003csub\u003ei\u003c/sub\u003e\u003csup\u003e(1)\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNP\u003csub\u003ei\u003c/sub\u003e\u003csup\u003e(2)\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eNP\u003csub\u003ei\u003c/sub\u003e\u003csup\u003e(3\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eNP\u003csub\u003ei\u003c/sub\u003e\u003csup\u003e(4)\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.96\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.46\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003eS\u003csub\u003ei(\u003c/sub\u003e\u003csup\u003e1)\u003c/sup\u003e, S\u003csub\u003ei\u003c/sub\u003e\u003csup\u003e(2)\u003c/sup\u003e, S\u003csub\u003ei\u003c/sub\u003e\u003csup\u003e(3)\u003c/sup\u003e, S\u003csub\u003ei\u003c/sub\u003e\u003csup\u003e(6)\u003c/sup\u003e: Nassar and H\u0026uuml;hn\u0026rsquo;s non-parametric stability parameters. NP\u003csub\u003ei\u003c/sub\u003e\u003csup\u003e1\u003c/sup\u003e, NP\u003csub\u003ei\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e, NP\u003csub\u003ei\u003c/sub\u003e\u003csup\u003e3\u003c/sup\u003e, NP\u003csub\u003ei\u003c/sub\u003e\u003csup\u003e4\u003c/sup\u003e: Thennarasu\u0026rsquo;s non-parametric stability statistics parameters\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eUnivariate Parametric Stability Analysis\u003c/h2\u003e\u003cp\u003eThe yield performance of 12 rice genotypes was evaluated across eight environments using a parametric stability analysis. The indices evaluated included mean yield (Y\u003csub\u003ei\u003c/sub\u003e), coefficient of variation (CV\u003csub\u003ei\u003c/sub\u003e), regression coefficient (b\u003csub\u003ei\u003c/sub\u003e), deviation from regression (S\u0026sup2;di), Wricke\u0026rsquo;s ecovalence (Wi\u0026sup2;), Hanson\u0026rsquo;s stability index (D\u003csub\u003ei\u003c/sub\u003e), and Shukla\u0026rsquo;s stability variance (σ\u0026sup2;). These indices evaluated both productivity and stability across different environments (Eberhart and Russell \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1966\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAccording to Francis and Kannenberg (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1978\u003c/span\u003e), a genotype is considered stable when it has a mean yield above the grand mean and a CV\u003csub\u003ei\u003c/sub\u003e below the population mean. This methodology categorizes genotypes into four distinct groups through the intersection of the CV mean line and the yield mean line: Group 1, characterized by high yield and low CV; Group 2, exhibiting high yield and high CV; Group 3, demonstrating low yield and low CV; and Group 4, defined by low yield and high CV. From this analysis, genotypes G7, G9, and G11 were classified as both high-yielding and stable. On the other hand, hybrid rice G1, G3, and G5 were considered to have lower stability, as, despite yielding above the grand mean, high values of CVi were recorded, likely indicating specific adaptability to more favorable environments (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe regression coefficient (b\u003csub\u003ei\u003c/sub\u003e) and the deviation from regression (s\u0026sup2;di), as proposed by Eberhart and Russell (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1966\u003c/span\u003e), were used to further assess dynamic stability. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA illustrates this relationship, showing how each genotype performs across different environments. Genotype G1 consistently performed best. Although G1's bi value was significantly greater than 1, suggesting high responsiveness to better environments, it still showed broad adaptation potential, aligning with its identification as the \"ideal genotype\" in the GGE biplot analysis.\u003c/p\u003e\u003cp\u003eThe plot of regression coefficient and response value (yield) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB) shows the position of each genotype based on its regression coefficient and response value. Genotypes G4 and G8 exhibited bi values close to 1 and non-significant s\u0026sup2;di. This means they were stable, responding predictably and consistently across various environments, indicating both linear and non-linear stability. In contrast, genotypes G1 and G3 had bi values considerably above 1 and large s\u0026sup2;di values. This implies they are highly responsive to environmental changes but are less predictable, making them more suitable for specific target environments rather than general deployment.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWrickle (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e1962\u003c/span\u003e) developed the W\u003csub\u003ei\u003c/sub\u003e\u0026sup2; method to determine the extent of a genotype's influence on yield variation across different environments. Genotypes with low W\u003csub\u003ei\u003c/sub\u003e\u0026sup2; values are considered stable because their yields do not change much even when grown in different environments (Kebede et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Vanisri et al. 2023). In this study, the hybrid rice genotypes G2, G5, G7, and G8 were categorized as stable. Hanson (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1970\u003c/span\u003e) introduced the Di parameter as a tool to assess genotype stability, particularly under trial conditions involving a limited number of genotypes and testing locations. Genotypes with low Di values are considered stable because they show relatively consistent results across various test environments. Based on this parameter, the stable hybrid rice genotypes are G2, G7, G8, while the stable check variety are G9 and G11. The variance of stability, Shukla\u0026rsquo;s (σ\u0026sup2;), also assigned G2, G5, G7, G8, and G11 as the most stable genotypes.\u003c/p\u003e\u003cp\u003eThe parametric analyses suggest that G7, G9, and G11 are stable and high-yielding, suitable for large-scale deployment in irrigated lowland systems where environmental conditions are relatively uniform. In contrast, hybrid rice G1 and G5, which exhibited high responsiveness (bi\u0026thinsp;\u0026gt;\u0026thinsp;1), are better suited to favorable, high-input environments where management practices can optimize yield potential. These findings imply that stability classification can guide targeted cultivar placement: broadly adapted hybrids for general cultivation, and highly responsive ones for intensive or high-fertility zones.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eParametric stability analysis of grain yield of hybrid rice evaluated under multi-environment trials\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGenotype\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eY\u003csub\u003ei\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCV\u003csub\u003ei\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" 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colname=\"c4\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e9.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e11.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.56\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003eY\u003csub\u003ei\u003c/sub\u003e: grain yield (t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), grand mean\u0026thinsp;=\u0026thinsp;8.31 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e; LSD at 5% = 0.17. CV\u003csub\u003ei\u003c/sub\u003e: coefficient of variation, average of CV\u0026thinsp;=\u0026thinsp;14.38. b\u003csub\u003ei\u003c/sub\u003e: regression coefficient to environmental index. s\u0026sup2;di: deviation from regression. W\u003csub\u003ei\u003c/sub\u003e\u0026sup2;: Wricke\u0026rsquo;s ecovalence. D\u003csub\u003ei\u003c/sub\u003e: Hanson\u0026rsquo;s parameter for genotype stability. StabVar: Shukla\u0026rsquo;s stability variance\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eMultivariate Parametric Stability Analysis\u003c/h3\u003e\n\u003cp\u003eMultivariate analyses with the AMMI and GGE biplots were carried out to expand the information about GEI. AMMI model partitions the main effects and interaction components using principal component (PC) analysis, whereas GGE biplot visualizes genotype and GEI effects, removing environmental main effects to focus on genotype evaluation across environments (Gauch et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Yan \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe AMMI biplot illustrates the GEI through its first two principal components, PC1 and PC2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). These components account for 53.2% and 19.1% of the GEI variability, respectively, totaling 72.3%. Consequently, 27.7% of the GxE variability remains unexplained by this biplot. Nevertheless, this biplot can still offer some insight into the GxE interactions. Genotype G8, positioned near the origin, demonstrated minimal genotype-environment interaction effects. This suggests high stability and broad adaptation across various environments, supporting findings by who emphasized that genotypes with low or near-zero scores are less influenced by environmental fluctuations and thus more stable across diverse conditions.\u003c/p\u003e\u003cp\u003eGenotypes situated near the end of an environmental vector are typically considered well-adapted to that specific environment. G5 exhibited a positive interaction with E1 but indicated a negative interaction with E4. The environmental vectors in the AMMI biplot also provide critical information. E6, E7, and E8 exhibited the longest vectors, suggesting these environments highly contributed to the expression of G\u0026times;E interaction (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). According to Reshma et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), environments with longer vectors are more effective in identifying genotype performance differences and are thus considered valuable for selection stages in breeding programs. In contrast, environments such as E1 and E5, which had short vectors and were located near the origin, exerted minimal discriminatory power. These environments are less useful for differentiating genotypes but are still important in stability evaluation, as they represent environments with low interaction influence (Yan and Tinker \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe AMMI biplot illustrates genotypic stability and yield response (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Genotypic stability is represented by the horizontal line at Y\u0026thinsp;=\u0026thinsp;0, and the vertical line at X\u0026thinsp;=\u0026thinsp;8.31 denotes the average yield. Genotypes located furthest to the right on this biplot indicate superior yield performance. Conversely, genotypes situated close to the Y\u0026thinsp;=\u0026thinsp;0 horizontal axis demonstrate higher stability across environments. This analysis identifies a hybrid rice genotype exhibiting both optimal yield and stability simultaneously, G8; however, genotypes G5 and G7 are the closest approximations. Environmentally, E1 is positioned on the far right of the biplot, signifying an environment with a high average yield. It is crucial to note that PC1 accounts for only 27.7% of the Genotype-by-Environment interaction variability, implying that a substantial portion of this variability is not captured by this specific biplot.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the GGE biplot, a visual representation of how genotypes interact with different environments. This biplot is constructed using its first two principal components, which collectively explain 79.6% of the total variation attributed to both genetic effects (G) and genotype-environment interaction. This high cumulative variance suggests that the biplot offers a robust approximation of the data gathered from multi-environment trials. However, with 36.8% of the G\u0026thinsp;+\u0026thinsp;GE variance remaining unexplained, the interpretation of this biplot should be approached with caution. As suggested by Yan and Tinker (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), such a level of explanation is sufficient for meaningful conclusions about genotype performance and environmental discriminative capacity.\u003c/p\u003e\u003cp\u003eThe \"which-won-where\u003cb\u003e\"\u003c/b\u003e feature of the GGE biplot revealed that the eight environments were divided into two mega environments (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA): sector I contains E2, E3, and E5, while sector II contains E1, E4, E6, E7, and E8. The vertex genotype at the polygon\u0026rsquo;s corners represented the best performer within each sector, with G1 excelling in most environments except E2, E3, and E5. Meanwhile, G8 was positioned near the origin, indicating broad adaptability and relative insensitivity to environmental fluctuations. The mean-versus-stability pattern showed that genotypes located closest to the average environment (AE) axis\u0026mdash;such as G1, G3, G5, G7, G9, and G11\u0026mdash;were stable across environments, while those to the right of the AE axis combined high yield with consistent performance. Complementary results from the AMMI and GGE analyses showed that environments E6, E7, and E8 contributed most to genotype differentiation, reflecting environmental contrasts in altitude, soil type, and climatic stress. These locations can serve as key test sites for identifying genotypes resilient to suboptimal growing conditions. The identification of two mega-environments underscores the need for regional stratification of hybrid rice cultivation based on climate and soil characteristics. Notably, hybrid rice G8 demonstrated consistent yield and stability across both mega-environments, confirming their physiological adaptability to varying temperature and fertility regimes. These findings provide valuable guidance for site-specific hybrid deployment, efficient resource use, and improved production stability in tropical rice-growing systems.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe combined application of AMMI and GGE biplot analyses offered a complementary perspective on genotype adaptability. Specifically, AMMI proved effective in delineating interaction patterns, whereas GGE elucidated both the average yield and stability across diverse environmental conditions. Multivariate stability analysis for grain yield has proven effective in selecting genotypes with consistent performance across diverse conditions (Kartina et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Sharifi et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Sitaresmi et al. 2019). This integrated methodology contributes to improved selection precision and facilitates the informed deployment of genotypes according to their specific adaptation profiles.\u003c/p\u003e\n\u003ch3\u003eSelection of Hybrid Rice Genotypes Based on The MGIDI\u003c/h3\u003e\n\u003cp\u003eTo complement the univariate and multivariate stability analyses, the Multi-Trait Genotype\u0026ndash;Ideotype Distance Index (MGIDI) was employed to identify superior hybrid rice genotypes based on multiple agronomic traits. Unlike traditional approaches that evaluate genotypes based on single traits, MGIDI enables simultaneous selection by integrating all traits into a single index, facilitating the identification of genotypes closest to an ideotype, defined as the theoretical genotype exhibiting optimal values for all traits (Olivoto et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Olivoto and Nardino \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eVariation in agronomic traits is influenced by multiple sources, including environmental conditions, genetic differences among genotypes, G\u0026times;E interactions, replication effects within environments, and residual (random) errors. Grain yield was greatly affected by environment (32.1%), followed by genotype (15.1%) and genotype x environment (16.2%), which were relatively equal (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Yield is a complex trait that is strongly influenced by the environment and its interaction (GEI) (Huang et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe genotypes identified by the MGIDI index were G2, G4, and G5 as closest to the ideal type among the tested genotypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). G5 has higher yield than G2 and G4, but all three genotypes were widely adapted based on regression stability analysis (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). These genotypes have combining yield stability with favorable agronomic traits such as balanced plant height, productive tillers, and filled grains. These traits reflect efficient resource use and sink\u0026ndash;source coordination, which are critical for sustaining yield under tropical conditions where temperature and radiation fluctuate. The balanced performance of G5 indicates its suitability for diverse agroecosystems, supporting the concept of ecological intensification by enhancing yield without increasing input demand. Thus, MGIDI-based selection contributes to identifying genotypes that optimize physiological efficiency and production resilience. Olivoto and Nardino (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) emphasized that researchers are urged to carefully examine genotypes at the cut point. The MGIDI index integrates multiple desirable traits into a single selection criterion. However, it showed the power of MGDI analysis to guide genotype selection and advance crop improvement programs.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn this study, MGIDI was computed using 14 traits. These traits were rescaled to a 0\u0026ndash;100 scale, where higher values represented more desirable trait performance. The ideotype was constructed using the maximum (or minimum, depending on breeding goals) rescaled value for each trait. Figure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e offers an in-depth assessment of the strengths and weaknesses exhibited by different genotypes, delineated by the contribution of each factor to MGIDI. According Ridara et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), the radar plot delineates the strengths and weaknesses of the selected genotypes by examining the factor contributions to the MGIDI indices.\u003c/p\u003e\u003cp\u003eThe strengths and weaknesses analysis indicated that G2 and G5 have a relatively balanced contribution pattern and are closer to the center, indicating that these two genotypes are more stable and have better overall performance than the other genotypes. Meanwhile, G4 showed a high contribution value to FA1, indicating that this genotype has a prominent weakness in the trait group represented by this factor. On the other hand, FA2 appeared to make the largest contribution to the total variation of MGIDI, so it can be said that the trait group contained by FA2 is the main determinant of performance differences between genotypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). FA1 was consist of yield, weight of 1000 grains, days of maturity and days to flowering, so the high contribution value of FA1 to G4 indicates that G4 has relatively low productivity. FA2 was correlated with number of productive tillers, number filled grains and number of grains. FA3 was build from plant height and length of panicles, and FA4 was correlated with number of unfilled grains and seed set (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe use of factor analysis in this study helped to organize complex multivariate data and support ideotype-based selection. This approach also highlights the importance of multi-trait selection tools in modern breeding programs, where simultaneous improvement in multiple correlated traits is essential. Understanding the strengths and weaknesses of each genotype provides valuable guidance for selecting parental lines in future hybrid breeding efforts.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAgronomic traits were grouped into four main factors, along with the selection direction (sense), selection target (goal), and selection differential (SD). Selection diffential is the average percentage change in traits after selection based on the MGIDI index. Positive SD indicates an increase in the desired trait, while a negative SD indicates a decrease in the trait according to the predetermined selection direction. Characters that have a positive linear relationship with the economic value of rice are selected in the same direction (increase), while characters that tend to reduce the economic value are selected in the opposite direction (decrease). The positive SD for the number of filled grains (2.08%) and number of grains (5.03%) indicate that MGIDI selection succeeded in increasing yield potential by increasing the main yield components. The direction of selection for plant height was a decrease with SD 0.63%, indicating the success of selection in reducing plant height is expected to reduce the potential to lodging, while a positive SD for panicle length (1.04%) indicates an increase in panicle length which is expected to support grain yield. The high SD for the number of unfilled grains (6.03%) indicates that this trait still has significant variation between genotypes, while the negative SD for seed set (-1.29%) indicates a slight decrease but still in the direction of increasing selection. Overall, these results demonstrate that MGIDI-based selection can produce positive genetic progress for most important traits without sacrificing performance balance between traits.\u003c/p\u003e\u003cp\u003eThese findings demonstrate that MGIDI is an effective and integrative tool for multi-trait selection, enabling breeders to make informed decisions in hybrid development by balancing yield and other agronomic traits. This approach is able to identify superior genotypes with optimal trait combinations and provide clear selection directions for character improvement in the next generation. Its use complements traditional methods and aligns with current demands for more resilient, high-performing cultivars suited to diverse agroecological zones.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFactorial loadings, communality, and differential selection based on the multi-trait genotype-ideotype distance index for 12 hybrid rice genotypes\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFactor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSense\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGoal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSD (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYield\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFA1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eincrease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWeigth 1000 grains\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFA1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eincrease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.437\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDays to maturity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFA1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003edecrease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDays to flowering\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFA1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003edecrease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.61\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber of productive tillers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFA2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eincrease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.726\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber of filled grains\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFA2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eincrease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber of grains\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFA2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eincrease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePlant height\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFA3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003edecrease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.634\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLength of panicle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFA3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eincrease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber of unfilled grains\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFA4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eincrease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSeed set\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFA4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eincrease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eImplication for Breeding and Future Research\u003c/h2\u003e\u003cp\u003eStability and adaptability are critical factors in hybrid rice breeding. They ensure consistent yield, resilience to environmental stresses, and support sustainable agricultural practices. By leveraging advanced breeding techniques and conducting extensive multi-environment trials, researchers can develop rice varieties that meet the demands of a growing population and changing climate. From a breeding point of view, the estimation of genotypic variance, phenotypic variance, environmental variance, interaction of G\u0026times;E by analysis of variance is very important in terms of gene expression controlling complex traits like grain yield over a broad range of environmental conditions. Based on information about the variance contribution by genotypes and environment, an ideal genotype can be selected with less influence by environment\u003c/p\u003e\u003cp\u003eNon-parametric methods are particularly valuable in multi-environment trials, especially when heterogeneity of variance is present. They effectively discriminate among genotypes even with variability across environments (Ferreira et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). These methods are also advantageous for large datasets because they do not rely on strict distributional assumptions (Akbari and Darvishzadeh \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Furthermore, non-parametric methods can handle both crossover and non-crossover types of genotype \u0026times; environment interactions, providing breeders with a reliable tool to assess stability and adaptability across diverse and complex testing conditions (Hladni et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Mohammadi and Amri \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Consequently, non-parametric methods are useful in the preliminary yield testing of hybrid rice for screening stable and high-yielding genotypes, thereby helping to identify promising hybrids for further testing and development.\u003c/p\u003e\u003cp\u003eParametric analysis is fundamental for evaluating stability in multi-environment trials by providing a systematic framework for elucidating genotype-environment interactions, confirming statistical assumptions, and establishing a connection between stability and yield (Shahbazi \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003eb). Parametric models are frequently employed in multi-environment trials to divide variation into genotype, environment, and genotype x environment interactions. Parametric analysis resulted in genotypes G5, G7, G9, and G11 as high-yielding genotypes and stable. The Additive Main Effects and Multiplicative Interaction (AMMI) and Genotype plus Genotype-by-Environment Interaction (GGE) biplot analyses are powerful statistical tools for evaluating yield stability across multi-environment trials. AMMI combines analysis of variance to capture additive effects and principal component analysis to model multiplicative interactions, allowing breeders to identify genotypes that are both high yielding and stable (Bocianowski et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Pramanik et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The GGE biplot focuses on genotype and GE interaction effects and graphically represents performance using the first two principal components (Tripodi et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). AMMI provides statistical precision for stability interpretation, whereas GGE offers a clear visualization of yield performance and adaptability across diverse environments. Although both analyses provide many advantages, they also have several limitations, including high computational complexity, challenges in handling genotype \u0026times; environment (G\u0026times;E) interactions, issues with data quality and missing data, difficulties in interpreting results, model selection and validation, as well as scalability problems when the number of traits and environments increases (Akinwale et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Hadasch et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Montesinos-L\u0026oacute;pez et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Furthermore, improving interpretability and scalability will ensure that multi-environment trial data can be more effectively utilized in practical breeding pipelines, particularly in hybrid rice breeding, where simultaneous evaluation of multiple traits and environments is essential.\u003c/p\u003e\u003cp\u003eThe MGIDI represents an important advance in genotype selection because it allows breeders to simultaneously evaluate multiple traits relative to an ideotype (Debnath et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This is particularly relevant for hybrid rice, where high grain yield must be balanced with other traits such as grain quality, biotic resistance, and tolerance to abiotic stress. MGIDI has been effectively used to identify high-performing rice genotypes by evaluating multiple traits such as spikelet fertility, seedling dry weight (Pallavi et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), nitrogen use efficiency (Duc et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), and resistance to biotic stresses (Dewi et al. 2025). Furthermore, studies have shown significant genetic gains using MGIDI, with improvements in traits like grain yield (Mohanty et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), grain quality (Feizi et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The use of MGIDI analysis can strongly support hybrid rice breeding because it integrates multiple traits simultaneously, reduces issues of multicollinearity, and achieves high selection gains. These advantages make MGIDI an invaluable tool for modern rice breeding programs, ensuring that selected genotypes are not only high-yielding but also exhibit a balanced performance in terms of grain quality, biotic resistance, and abiotic stress tolerance.\u003c/p\u003e\u003cp\u003eThe integration of multi-environment and multi-trait analyses provides a strong framework for enhancing rice production efficiency across heterogeneous agroecosystems. This approach enables the identification of stable and high-performing genotypes, such as G2, G4, and G5, that maintain yield reliability under varying conditions of water availability, soil fertility, and temperature. By combining physiological understanding with stability modeling, hybrid recommendations can be tailored to specific agroecological zones, ensuring optimal performance under local environmental constraints. To further refine genotype adaptability assessment, future studies should integrate environmental covariates\u0026mdash;such as soil moisture, temperature, and rainfall patterns\u0026mdash;into predictive models of hybrid performance. Additionally, linking stability analysis with genomic information will strengthen the identification of genes or quantitative trait loci (QTLs) associated with yield stability, supporting the development of molecular markers for marker-assisted selection. The integration of genomic and phenotypic stability data, as demonstrated by Sayed et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), has shown promise in identifying genotypes combining high productivity with drought resilience. Moreover, advances in machine learning can facilitate modeling of complex genotype \u0026times; environment interactions and enhance predictive accuracy under untested conditions. Collectively, these interdisciplinary approaches will accelerate the development of high-performing, environmentally resilient, and widely adaptable hybrid rice varieties to meet the challenges of climate variability and resource limitation.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe study demonstrated that environmental variation strongly influenced hybrid rice performance across field sites. Integrating multiple stability analyses allowed the identification of hybrids with consistent yield and adaptability under diverse tropical agroecosystems. Univariate parametric and non-parametric methods highlighted four hybrid rice genotypes G2, G4, G7, G8 as stable performers, while AMMI and GGE biplots (multivariate) confirmed G5, and G7 as broadly adapted and high-yielding genotypes. The MGIDI analysis identified the ideal genotypes were G2, G4, and G5 considering yield agronomic traits, and broad adapted based on regression stability analysis. From these approaches, we conclude that G2, G4, G5, and G7 were consistent as superior hybrids. This integrative approach provides a framework for hybrid rice breeding in tropical environments, ensuring the development of cultivars that combine high yield potential with adaptability and resilience to variable growing conditions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThe breeding activities were funded by the Indonesian Center for Rice Research (ICRR), while the multilocation trials were financially supported by Bayer Indonesia.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization: Y.W., T.S., B.P.W.Validation: I.A.R., T.S., Y.W.Methodology: B.P.W., T.S., Y.W., I.A.R., S.SWriting \u0026ndash; original draft: B.P.W., Y.W., T.S., I.A.R., A.H., U.S.Writing \u0026ndash; review \u0026amp; editing: Y.W., I.A.R., W.B.S.Field evaluation: B.P.W., N.K., B.K., R.R., S.S.Data curation: B.P.W., Y.W., T.S., S.M, I.A.R.Statistical analysis: B.P.W., Y.W., T.S., S.M.All co-authors reviewed the final version and approved the manuscript before submission.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe are grateful to the Indonesian Ministry of Agriculture for providing research facilities and resources.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and analyzed during this study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAkbari N, Darvishzadeh R (2024) A Study on the Yield Stability of Oilseed Sunflower Genotypes under Drought Stress. J Crop Breed 16(4):129\u0026ndash;141. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.61186/jcb.16.4.129\u003c/span\u003e\u003cspan address=\"10.61186/jcb.16.4.129\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAkinwale RO, Fakorede MAB, Badu-Apraku B, Oluwaranti A (2014) Assessing the usefulness of GGE biplot as a statistical tool for plant breeders and agronomists. Cereal Res Commun 42(3):534\u0026ndash;546. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1556/CRC.42.2014.3.16\u003c/span\u003e\u003cspan address=\"10.1556/CRC.42.2014.3.16\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBambang Priyanto S, Neni Iriany R, Takdir Makkulawu A, Jumadi O (2024) A comprehensive evaluation of the adaptability and stability of promising maize hybrids in Indonesia using different stability approaches. Chil J Agricultural Res 84(3):338\u0026ndash;348. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4067/S0718-58392024000300338\u003c/span\u003e\u003cspan address=\"10.4067/S0718-58392024000300338\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBocianowski J, Nowosad K, Rejek D (2024) Genotype-environment interaction for grain yield in maize (Zea mays L.) using the additive main effects and multiplicative interaction (AMMI) model. J Appl Genet 65(4):653\u0026ndash;664. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s13353-024-00899-4\u003c/span\u003e\u003cspan address=\"10.1007/s13353-024-00899-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCrossa J (1990) Statistical analyses of multilocation trials. Advances in agronomy. Elsevier, pp 55\u0026ndash;85. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0065-2113(08)60818-4\u003c/span\u003e\u003cspan address=\"10.1016/S0065-2113(08)60818-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDebnath P, Chakma K, Bhuiyan MSU, Thapa R, Pan R, Akhter D (2024) A novel multi trait genotype ideotype distance index (MGIDI) for genotype selection in plant breeding: Application, prospects, and limitations. Crop Des 3:100074. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cropd.2024.100074\u003c/span\u003e\u003cspan address=\"10.1016/j.cropd.2024.100074\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDuc NT, Harika A, Raju D, Kumar S, Pandey R, Sathee L, Alimuthu E, Singh B, Vishwakarma C, Ellur RK, Iquebal MA, Pandey R, Rajendran A, Sakhare A, Sahoo RN, Chirravuri NN, Senthil A, Chinnusamy V (2025) Identification of superior rice donors with enhanced nitrogen use efficiency using a comprehensive multivariate genotype selection strategy. iScience 28(9):113280. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.isci.2025.113280\u003c/span\u003e\u003cspan address=\"10.1016/j.isci.2025.113280\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEberhart SA, Russell WA (1966) Stability parameters for comparing varieties. Crop Sci 6:36\u0026ndash;40. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2135/cropsci1966.0011183X000600010011x\u003c/span\u003e\u003cspan address=\"10.2135/cropsci1966.0011183X000600010011x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFeizi N, Sabouri A, Bakhshipour A, Abedi A (2025) Combinatorial Approaches to Image Processing and MGIDI for the Efficient Selection of Superior Rice Grain Quality Lines. Agriculture 15(6):615. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/agriculture15060615\u003c/span\u003e\u003cspan address=\"10.3390/agriculture15060615\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFerreira DF, Fernandes SB, Bruzi AT, Ramalho MAP (2016) Non-parametric approach to the study of phenotypic stability. Genet Mol Res 15(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4238/gmr.15017517\u003c/span\u003e\u003cspan address=\"10.4238/gmr.15017517\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFinlay K, Wilkinson G (1963) The analysis of adaptation in a plant-breeding programme. Aust J Agric Res 14:742. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1071/AR9630742\u003c/span\u003e\u003cspan address=\"10.1071/AR9630742\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFrancis TR, Kannenberg LW (1978) Yield stability studies In: Short-season Maize IA descriptive method for grouping genotypes. Can J Plant Sci 58:1029\u0026ndash;1034. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4141/cjps78-157\u003c/span\u003e\u003cspan address=\"10.4141/cjps78-157\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGauch HG (2006) Statistical analysis of yield trials by AMMI and GGE. Crop Sci 46:1488\u0026ndash;1500. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2135/cropsci2005.07-0193\u003c/span\u003e\u003cspan address=\"10.2135/cropsci2005.07-0193\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGauch HG, Piepho H, Annicchiarico P (2008) Statistical analysis of yield trials by AMMI and GGE: Further considerations. Crop Sci 48:866\u0026ndash;889. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2135/cropsci2007.09.0513\u003c/span\u003e\u003cspan address=\"10.2135/cropsci2007.09.0513\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGauch HG, Qian S, Piepho HP, Zhou L, Chen R (2019) Consequences of PCA graphs, SNP codings, and PCA variants for elucidating population structure. PLoS ONE 14:e0218306. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0218306\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0218306\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHadasch S, Forkman J, Piepho H (2017) Cross-Validation in AMMI and GGE Models: A Comparison of Methods. Crop Sci 57(1):264\u0026ndash;274. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2135/cropsci2016.07.0613\u003c/span\u003e\u003cspan address=\"10.2135/cropsci2016.07.0613\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHanson WD (1970) Genotypic stability. Theoret Appl Genet 40:226\u0026ndash;231. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/BF00285245\u003c/span\u003e\u003cspan address=\"10.1007/BF00285245\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHerawati R, Lestari AP, Nurmegawati, Ganefianti DW, Romeida A (2021) Comparative study on the stability and adaptability of different models to develop a high-yield inbred line from landrace rice varieties. Annals Agricultural Sci 66(2):184\u0026ndash;192. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.aoas.2021.12.004\u003c/span\u003e\u003cspan address=\"10.1016/j.aoas.2021.12.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHladni N, Jocic S, Miklic V, Saftic-Pankovic D, Kraljevic-Balalic M (2011) Interdependence of yield and yield components of confectionary sunflower hybrids. Genetika 43(3):583\u0026ndash;594. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2298/GENSR1103583H\u003c/span\u003e\u003cspan address=\"10.2298/GENSR1103583H\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang X, Jang S, Kim B, Piao Z, Redona E, Koh HJ (2021) Evaluating genotype \u0026times; environment interactions of yield traits and adaptability in rice cultivars grown under temperate, subtropical and tropical environments. \u003cem\u003eAgriculture 11\u003c/em\u003e, 558. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/agriculture11060558\u003c/span\u003e\u003cspan address=\"10.3390/agriculture11060558\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIRRI (2014) Standar Evaluation System (SES) for Rice, 5th edn. International Rice Research Institute\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDewi IS, Purwoko BS Ratna Kartika Putri, \u0026amp; Iskandar Lubis. (2025). Brown Plant Hopper Resistance in Promising Doubled Haploid Rice Lines Selected by MGIDI and FAI-BLUP Index. Pertanika J Trop Agric Sci, \u003cem\u003e48\u003c/em\u003e(3). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.47836/pjtas.48.3.20\u003c/span\u003e\u003cspan address=\"10.47836/pjtas.48.3.20\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKarimizadeh R, Mohammadi M, Sabaghnia N, Shefazadeh MK (2012) Using huehn's nonparametric stability statistics to investigate genotype x environment interaction. Not Bot Hort Agrobot Cluj 40:293. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.15835/nbha4017593\u003c/span\u003e\u003cspan address=\"10.15835/nbha4017593\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKartina N, Widyastuti Y, Rumanti IA, Wibowo P, B., Satoto, Mardiana (2021) Hybrid rice stability studies in Indonesia. \u003cem\u003eSABRAO Journal of Breeding and Genetics\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://sabraojournal.org/sabrao-journal-of-breeding-and-genetics-issn-1029-7073-eissn-2224-8978-volume-53-number-3-september-2021\u003c/span\u003e\u003cspan address=\"https://sabraojournal.org/sabrao-journal-of-breeding-and-genetics-issn-1029-7073-eissn-2224-8978-volume-53-number-3-september-2021\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e/\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKebede G, Worku W, Jifar H, Feyissa F (2023) GGE biplot analysis of genotype by environment interaction and grain yield stability of oat (\u003cem\u003eAvena sativa\u003c/em\u003e L.) in Ethiopia. Agrosystems Geosci Env 6:e20410. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/agg2.20410\u003c/span\u003e\u003cspan address=\"10.1002/agg2.20410\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKuru B, Abera N, Mulualem T (2025) Genotype X environment interaction and yield stability of finger millet (Eleusine Coracana L.) genotypes based on AMMI, GGE and MTSI analysis in humid lowland areas of Ethiopia. Field Crops Res 322:109707. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.fcr.2024.109707\u003c/span\u003e\u003cspan address=\"10.1016/j.fcr.2024.109707\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu Y, Duan C, Tian M, Hu E, Huang Y (2010) Yield stability of maize hybrids evaluated in maize regional trials in Southwestern China using nonparametric methods. Agricultural Sci China 9:1413\u0026ndash;1422. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S1671-2927(09)60232-7\u003c/span\u003e\u003cspan address=\"10.1016/S1671-2927(09)60232-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMaulana H, Solihin E, Trimo L, Hidayat S, Wijaya AA, Hariadi H, Amien S, Ruswandi D, Karuniawan A (2023) Genotype-by-environment interactions (GEIs) and evaluate superior sweet potato (Ipomoea batatas [L.] Lam) using combined analysis and GGE biplot. \u003cem\u003eHeliyon 9\u003c/em\u003e, e20203. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.heliyon.2023.e20203\u003c/span\u003e\u003cspan address=\"10.1016/j.heliyon.2023.e20203\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMohammadi R, Amri A (2008) Comparison of parametric and non-parametric methods for selecting stable and adapted durum wheat genotypes in variable environments. \u003cem\u003eEuphytica 159\u003c/em\u003e(3), 419\u0026ndash;432. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10681-007-9600-6\u003c/span\u003e\u003cspan address=\"10.1007/s10681-007-9600-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMohanty SK, Mahapatra S, Batabyal S, Carlson M, Kanungo G, Ayyagari A, Tchedre K, Franco JA, Singer M, Barone SB, Chavala S, Mahajan VB (2025) A synthetic opsin restores vision in patients with severe retinal degeneration. Mol Ther 33(5):2279\u0026ndash;2290. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ymthe.2025.03.031\u003c/span\u003e\u003cspan address=\"10.1016/j.ymthe.2025.03.031\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMontesinos-L\u0026oacute;pez OA, Montesinos‐L\u0026oacute;pez A, Hern\u0026aacute;ndez MV, Ortiz‐Monasterio I, P\u0026eacute;rez‐Rodr\u0026iacute;guez P, Burgue\u0026ntilde;o J, Crossa J (2019) Multivariate Bayesian Analysis of On‐Farm Trials with Multiple‐Trait and Multiple‐Environment Data. Agron J 111(6):2658\u0026ndash;2669. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2134/agronj2018.06.0362\u003c/span\u003e\u003cspan address=\"10.2134/agronj2018.06.0362\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNassar R, Huhn M (1987) Studies on estimation of phenotypic stability: Tests of significance for nonparametric measures of phenotypic stability. Biom J 29:433\u0026ndash;452\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOlivoto T, L\u0026uacute;cio ADC, Da Silva JAG, Sari BG, Diel MI (2019) Mean performance and stability In: Multi-Environment trials II: Selection based on multiple traits. Agron J 111:2961\u0026ndash;2969. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2134/agronj2019.03.0221\u003c/span\u003e\u003cspan address=\"10.2134/agronj2019.03.0221\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOlivoto T, Nardino M (2021) MGIDI: toward an effective multivariate selection in biological experiments. Bioinformatics 37:1383\u0026ndash;1389. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/bioinformatics/btaa981\u003c/span\u003e\u003cspan address=\"10.1093/bioinformatics/btaa981\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePallavi M, Maruthi Prasad BP, Shanthi P, Reddy VLN, Nirmal Kumar AR (2024) Multi trait genotype- ideotype distance index (MGIDI) for early seedling vigour and yield related traits to identify elite lines in rice (Oryza sativa L). Electron J Plant Breed 15(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.37992/2024.1501.020\u003c/span\u003e\u003cspan address=\"10.37992/2024.1501.020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePour-Aboughadareh A, Poczai P (2021) Dataset on the use of MGIDI index in screening drought-tolerant wild wheat accessions at the early growth stage. Data Brief 36:107096. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.dib.2021.107096\u003c/span\u003e\u003cspan address=\"10.1016/j.dib.2021.107096\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePramanik K, Sahu GS, Acharya C, Tripathy G, Dash P, Koundinya M, Jena AVV, Kumar C, Mohapatra DS, Pradhan PP, Karubakee J, S., Moharana DP (2024) Estimating phenotypic stability for relevant yield and quality traits in French bean (Phaseolus vulgaris L.) using AMMI analysis. Heliyon 10(5):e26918. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.heliyon.2024.e26918\u003c/span\u003e\u003cspan address=\"10.1016/j.heliyon.2024.e26918\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRahman MA, Alam Z, Akm SI, Hossain MM, Munna NA, Islam MR, Kamruzzaman M, Rabbi SMH (2025) Evaluating Genotype \u0026times; Environment Interactions for Selecting High-Yielding Stable Rice Varieties Under Water Stagnant Conditions. Ecol Genet Genomics 37. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2139/ssrn.5174079\u003c/span\u003e\u003cspan address=\"10.2139/ssrn.5174079\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eReshma OSP, Reddy SB, Shivaprasad KM (2024) Evaluation of rice genotypes for yield stability and adaptability across multiple environments using AMMI and GGE biplot analysis. J Adv Biol Biotechnol 27:1164\u0026ndash;1176. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.9734/jabb/2024/v27i81239\u003c/span\u003e\u003cspan address=\"10.9734/jabb/2024/v27i81239\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRidara F, Ustari D, Wicaksono AA, Algina A, Amien S, Susanto GWA, Koerniati S, Hastilestari BR, Concibido V, Karuniawan A (2025) Integrating multi-trait selection and environmental stability for Soybean (\u003cem\u003eGlycine max\u003c/em\u003e L. Merr.) genotype improvement in Indonesia. Plant Cell Environ 1\u0026ndash;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/pce.15662\u003c/span\u003e\u003cspan address=\"10.1111/pce.15662\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRoy D, Gaur A, Pandey ID, Barman M, Ahmed B (2024) Designing an index for multi-location yield stability analysis involving univariate and multivariate methods in rice (Oryza sativa L). Braz arch biol technol 67:e24230303. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1590/1678-4324-2024230303\u003c/span\u003e\u003cspan address=\"10.1590/1678-4324-2024230303\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSayed LM, Khaled KAM, Samaha GM, Saber AA (2025) Integrating phenotypic, molecular, and bioinformatics approaches for developing drought-tolerant sesame. Sci Rep 15(1):21897. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-025-06891-0\u003c/span\u003e\u003cspan address=\"10.1038/s41598-025-06891-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShahbazi E (2019) Genotype selection and stability analysis for seed yield of Nigella sativa using parametric and non-parametric statistics. Sci Hort 253:172\u0026ndash;179. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scienta.2019.04.047\u003c/span\u003e\u003cspan address=\"10.1016/j.scienta.2019.04.047\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShahbazi E (2019b) Genotype selection and stability analysis for seed yield of Nigella sativa using parametric and non-parametric statistics. Sci Hort 253:172\u0026ndash;179. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scienta.2019.04.047\u003c/span\u003e\u003cspan address=\"10.1016/j.scienta.2019.04.047\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSharifi P, Aminpanah H, Erfani R, Mohaddesi A, Abbasian A (2017) Evaluation of Genotype \u0026times; Environment Interaction in Rice Based on AMMI Model in Iran. Rice Sci 24(3):173\u0026ndash;180. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.rsci.2017.02.001\u003c/span\u003e\u003cspan address=\"10.1016/j.rsci.2017.02.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShukla GK (1972) Some statistical aspects of partitioning genotype-environmental components of variability. Heredity 29:237\u0026ndash;245. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/hdy.1972.87\u003c/span\u003e\u003cspan address=\"10.1038/hdy.1972.87\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSingh G, Khanna R, Kaur R, Kaur K, Kaur R, Sharma N, Mangat GS (2023) Performance under multi-environment trial for quantitative traits of rice (Oryza sativa L.) genotypes in North-West India (Punjab). Ecol Genet Genomics 28:100190. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.egg.2023.100190\u003c/span\u003e\u003cspan address=\"10.1016/j.egg.2023.100190\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSitaresmi, Suwarno WB, Gunarsih C, Nafisah, Nugraha Y, Sasmita P, Daradjat AA (2019) Comprehensive stability analysis of rice genotypes through multi-location yield trials using PBSTAT-GE. SABRAO J Breed Genet 51:355\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSuwarno WB, Aswidinnoor H, Syukur M, Ritonga AW, Sitaresmi T (n.d.). Stability Analysis in Plant Breeding Using PBSTAT-GE. (2025)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eThennarasu K (19950 On certain non-parametric procedures for studying genotype-environment interactions and yield stability. Indian J Genet \u003cem\u003e60\u003c/em\u003e, 9\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTripodi P, Soler S, Campanelli G, Fig\u0026agrave;s MR, Casanova C, Soler E, Sestili S, Bertone A, Cardi T, Prohens J (2025) GGE analysis and stability of traits in tomato cultivars grown under organic farming conditions: A two-year study. Hortic Plant J 11(2):721\u0026ndash;736. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.hpj.2023.09.009\u003c/span\u003e\u003cspan address=\"10.1016/j.hpj.2023.09.009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVanisri VIL, Rahul SMS (2023) Parametric stability analysis of cold tolerant rice genotypes for grain yield. J Theoretical Biology Forum 12:471\u0026ndash;475. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.61739/TBF.2023.12.2.471\u003c/span\u003e\u003cspan address=\"10.61739/TBF.2023.12.2.471\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWrickle (1962) \u0026Uuml;ber eine Methode zur Erfassung der \u0026ouml;kologischen Streubreite in Feldversuchen. Z f\u0026uuml;r Pflanzenz\u0026uuml;chtung 47:92\u0026ndash;96\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYan W (2002) Singular-value partitioning in biplot analysis of multienvironment trial data. Agr J 94(5):990\u0026ndash;996. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2134/agronj2002.9900\u003c/span\u003e\u003cspan address=\"10.2134/agronj2002.9900\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYan W, Kang MS (2002) GGE biplot analysis: A graphical tool for breeders, geneticists, and agronomists, 1st edn. CRC. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1201/9781420040371\u003c/span\u003e\u003cspan address=\"10.1201/9781420040371\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYan W, Tinker NA (2006) Biplot analysis of multi-environment trial data: Principles and applications. Can J Plant Sci 86:623\u0026ndash;645. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4141/P05-169\u003c/span\u003e\u003cspan address=\"10.4141/P05-169\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZobel RW, Wright MJ, Gauch HG (1988) Statistical analysis of a yield trial. Agron J 80:388\u0026ndash;393. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2134/agronj1988.00021962008000030002x\u003c/span\u003e\u003cspan address=\"10.2134/agronj1988.00021962008000030002x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"AMMI, GGE, MGIDI, Stability analysis, Hybrid rice, Tropical agroecosystems","lastPublishedDoi":"10.21203/rs.3.rs-8185743/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8185743/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUnderstanding hybrid rice performance across diverse field environments is essential for improving productivity under tropical conditions. This study aimed to determine the yield potential, adaptability, and stability of promising hybrid rice across eight agroecological zones in Indonesia using parametric, nonparametric, and multivariate methods to aid in genotype selection. Eight promising hybrid rice varieties and four standard check varieties from the Indonesian Center for Rice Research were tested during 2018\u0026ndash;2019. The experiment was conducted using a randomized complete block design with four replications in each environment. The results showed significant effects of genotype, environment, and genotype \u0026times; environment interaction on grain yield. Environmental factors contributed the largest proportion of yield variation (33.5%), indicating the importance of local growing conditions. Univariate parametric and nonparametric analyses identified four hybrid rice genotypes (G2, G4, G7, and G8) with good stability and broad adaptation. Conversely, AMMI and GGE biplot analyses showed that two hybrid rice genotypes (G5 and G7) were broadly adaptable and stable across environments, producing significantly higher yields than the average. The MGIDI analysis identified the ideal genotypes were G2, G4, and G5 considering yield agronomic traits, and broad adapted based on regression stability analysis. From these approaches, we conclude that rice hybrids G2, G4, G5, and G7 were consistent as superior hybrids across all environments, while G1 showed specific adaptation to favorable sites. This study demonstrated the utilization of multi-trait selection and multi-measure stability analysis was effective to identify hybrid rice genotypes with high yield and adaptive in tropical environments of Indonesia.\u003c/p\u003e","manuscriptTitle":"Assessing Yield Stability and Environmental Adaptation of Hybrid Rice (Oryza sativa L.) Across Tropical Field Conditions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-02 19:08:30","doi":"10.21203/rs.3.rs-8185743/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e9411fbe-fedd-4813-8a58-19397fa39e69","owner":[],"postedDate":"December 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-02T19:08:30+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-02 19:08:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8185743","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8185743","identity":"rs-8185743","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}