Genotype × Environment Interaction Insights for Yield and Yield Components in Castor via AMMI and GGE Biplot Models

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Sadaiah, G. Eswara Reddy, K. Parimala, A. Saritha, G. Madhuri, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8233933/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Despite its industrial potential, productivity in castor remains constrained by genotype × environment interactions (GEI), which obscure the true genetic potential of hybrids across variable production ecologies. The present investigation sought to elucidate the magnitude and pattern of GEI and to identify stable, high-performing hybrids for yield and yield-contributing traits across diverse agro-climatic conditions of Telangana, India. Eight elite castor hybrids were evaluated across multi-environment trials using AMMI (Additive Main Effects and Multiplicative Interaction) and GGE (Genotype and Genotype × Environment) biplot models to dissect stability, adaptability, and environmental representativeness. Highly significant GEI effects were detected for seed yield, days to 50% flowering, number of nodes, and hundred-seed weight, underscoring the differential response of genotypes across environments. The AMMI biplot effectively captured interaction patterns, identifying Palem (E1) location as the most representative and least interactive environment for yield performance. “Which-won-where” analysis of the GGE biplot delineated mega-environment groupings, with PCH-596 excelling under Tandur (E2) and Tornala (E3) locations, while ICH-5 demonstrated superior adaptability to E1. Yield Stability Index (YSI) and GGE ranking analyses consistently recognized PCH-596 and ICH-5 as the most stable and high-yielding hybrids across the environments. The integration of AMMI and GGE biplot methodologies proved highly effective in unravelling complex GEI patterns, facilitating the identification of genotypes with broad and specific adaptability. These findings provide a quantitative basis for environment-specific hybrid recommendations and contribute to accelerating genetic gains in castor breeding programs targeting enhanced productivity and resilience. Biological sciences/Ecology Earth and environmental sciences/Ecology Biological sciences/Genetics Biological sciences/Plant sciences Castor Genotype x Environment Interaction AMMI GGE Biplot yield Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Castor ( Ricinus communis L.), a member of the family Euphorbiaceae , is an economically significant non-edible oilseed crop cultivated across tropical and subtropical regions of the world. Believed to have originated in East Africa, particularly Ethiopia owing to the existence of vast genetic diversity in the region (Rukhsar et al. 2017; Xu et al. 2021 ) castor has since gained global prominence for its industrial versatility. The species is a diploid (2n = 20) C₃ plant, well adapted to semi-arid ecosystems, and valued for its remarkable ability to produce oil under limited moisture conditions. India is the world’s leading producer of castor, followed by Brazil and China, contributing substantially to global castor oil exports. During 2024-25, castor was cultivated over 7.87 lakh hectares in India, with a total production of 15.53 lakh tonnes and an average productivity of 1.93 t ha⁻¹ (Indiastat 2025 ). Gujarat remains the dominant producer, accounting for more than 70% of the national output, with a productivity level (2.27 t ha⁻¹) exceeding the national average, followed by Rajasthan, Andhra Pradesh, Karnataka, Tamil Nadu, and Telangana. Together, Gujarat and Rajasthan contribute nearly 90% of India’s castor acreage and production. Castor seeds contain 46–55% oil by weight (Elhadi 2018 ), and the unique fatty acid composition particularly ricinoleic acid, which constitutes over 80% of the total fatty acids imparts distinct physicochemical properties not found in other vegetable oils (Vieira et al. 2000 ). The hydroxyl functionality of ricinoleic acid makes castor oil an indispensable raw material for over 700 industrial applications, including lubricants, coatings, cosmetics, surfactants, biodiesel, and pharmaceutical formulations (Maiti et al., 1988 ; Sujatha et al. 2008 ; Memon et al. 2023 ). Its renewability, biodegradability, and non-edible nature further enhance its relevance in the bio-based economy and sustainable industrial development frameworks worldwide. Despite its importance, productivity gains in castor remain limited, largely due to the influence of genotype × environment interactions (GEI), which complicate selection for yield stability and adaptability across diverse production environments. Yield and its component traits are complex quantitative characters governed by polygenic inheritance and are strongly modulated by environmental factors such as soil fertility, temperature, rainfall, and biotic or abiotic stress conditions (Joshi et al. 2002 ; Baker 1988 ). Understanding the nature and magnitude of GEI is therefore essential for designing efficient breeding strategies and for recommending genotypes suited to specific agro-ecological zones (Asungre et al. 2021 ). Multi-environment trials (METs) serve as a powerful tool to assess genotypic performance across variable environments, enabling quantification of stability and adaptability. Among the statistical approaches available, the Additive Main Effects and Multiplicative Interaction (AMMI) model and the Genotype plus Genotype × Environment (GGE) biplot analysis have emerged as robust methodologies for dissecting GEI (Yan et al. 2000 ; Ajay et al. 2018 ). The AMMI model partitions the total variation into additive and multiplicative components, thereby providing insights into genotype stability and environmental responsiveness. However, AMMI alone does not account for the relationship between mean performance and stability. The GGE biplot, by integrating both genotype main effects (G) and genotype × environment interaction (GE), effectively identifies superior genotypes and discriminative environments (Miranda et al. 2009 ; Ding et al. 2007 ). Complementary use of AMMI and GGE models enhances the precision of genotype evaluation, facilitating the delineation of mega-environments and the identification of broadly or specifically adapted cultivars. Although castor is a crop of high industrial and economic value, studies employing multi-environment testing and modern GEI analysis tools remain limited compared to other major oilseeds. Therefore, the present investigation was undertaken to assess the magnitude of GEI and to identify high-yielding, stable, and widely adaptable castor hybrids across diverse agro-climatic locations of Telangana using AMMI and GGE biplot analyses. The outcomes are expected to provide a scientific basis for targeted hybrid deployment, environment-specific recommendations, and accelerated genetic improvement in castor breeding programs. 2. Material and Methods 2.1 Study locations The field experiments were conducted during the rabi season of 2022-23 across three distinct agro-ecological locations in Telangana, India, representing diverse soil types and rainfall regimes. The second site, the Regional Agricultural Research Station (RARS), Palem (E1) represents a semi-arid ecology with shallow, moderately fertile soils and an average annual rainfall of 600–650 mm. The second site, the Agricultural Research Station (ARS), Tandur (E2) is characterized by deep, fertile black soils and receives an average annual rainfall of 800–900 mm. The third site, ARS, Tornala (E3) situated in the central zone of Telangana, is typified by resource-poor red soils with limited fertility and receives an average annual rainfall of 800–850 mm. These sites were strategically selected to capture a broad spectrum of environmental variability, enabling a comprehensive assessment of genotype × environment interaction (GEI) for yield and yield-contributing traits in castor ( Ricinus communis L.). 2.2 Soil sampling and analysis A representative composite soil sample was collected from each experimental site prior to sowing by sampling the topsoil at a depth of 0–15 cm using a soil auger. The collected samples were air-dried under shade, gently crushed, and passed through a 2 mm sieve for subsequent physico-chemical analysis. The analyses included determination of soil texture (sand, silt, and clay), pH, organic carbon (OC), available nitrogen (N), phosphorus (P), and potassium (K) contents, as well as particle size distribution. These baseline assessments were conducted at the initiation of the experiment to characterize the soil properties of each site and to support interpretation of genotype × environment interaction (GEI) effects. The study was conducted as per the approved technical programme by the administration of the Professor Jayashankar Telangana Agricultural University (PJTAU). Further, the necessary permissions to collect the soil samples were also obtained from the university administration as per the laid-out regulations. 2.3 Plant materials and data collection The experimental material comprised eight castor ( Ricinus communis L.) genotypes, namely PCH-612 (G₁), PCH-596 (G₂), ICH-1160 (G₃), ICH-66 (G₄), PCH-111 (G₅), ICH-5 (G₆), PCH-574 (G₇), and ICH-1024 (G₈). Among these, PCH-111 (G₅) and ICH-5 (G₆) were included as standard checks. The hybrids viz ., PCH-612, PCH-596, PCH-111 and PCH-574 were developed and sourced from Regional Agricultural Research Station (RARS), Professor Jayashankar Telangana Agricultural University (PJTAU), Palem, Nagarkurnool district, Telangana. Similarly, the hybrids viz ., ICH-1160, ICH-66, ICH-5 and ICH-1024 were developed at ICAR-Indian Institute of Oilseeds Research, Rajendranagar, Hyderabad, Telangana and the seed of these hybrids for the study was sourced from RARS, PJTAU, Palem, Nagarkurnool district, Telangana. The genotypes were evaluated across three locations using a randomized complete block design (RCBD) with three replications. Each gross plot measured 32.4 m² (5.4 m × 6.0 m) and consisted of six rows, while data were recorded from a net plot area of 17.28 m² (3.6 m × 4.8 m). A uniform spacing of 90 cm between rows and 60 cm between plants within a row was maintained. Sowing was carried out during the second week of October 2022 across all experimental locations. Observations were recorded for key yield and yield-contributing traits, namely days to 50% flowering (DFF), number of nodes to primary spike (NN), hundred-seed weight (HSW), and seed yield per hectare (YLD). Days to 50% flowering were determined as the number of days taken for 50% of the plants in a plot to reach flowering. The number of nodes up to the primary spike, indicative of earliness, was recorded manually. Hundred-seed weight was determined from the first harvest by drying the mature capsules, threshing the seeds, cleaning them thoroughly, and weighing a random sample of 100 seeds. Seed yield was computed from three consecutive pickings obtained from the net plot, excluding the border rows and plants on all sides to minimize border effects. The cumulative seed yield from the three pickings was bulked, weighed, and converted to kilograms per hectare for statistical analysis. 2.4 Statistical analysis Data analysis was carried out using R statistical software (version 4.2.1; R Core Team 2022). A combined analysis of variance (ANOVA) was performed to assess the significance of genotype, environment, and genotype × environment interaction (GEI) effects for seed yield and associated traits. Prior to analysis, data were subjected to logarithmic transformation to achieve normalization and stabilize variance. Each experimental location was treated as an independent environment for the purpose of GEI assessment. AMMI (Additive Main Effects and Multiplicative Interaction) and GGE (Genotype plus Genotype × Environment) biplot analyses were employed to elucidate the magnitude and pattern of GEI, identify stable and high-performing genotypes, and determine discriminative and representative test environments. The AMMI analysis was conducted using the “agricolae” package (Mendiburu 2017 ), while the GGE biplot was generated using the “GGEBiplotGUI” package (Bernal and Villardón 2016) in R. The results were interpreted following the standard procedures outlined by and Yan et al. ( 2000 ). 3. Results and Discussion 3.1 Soil analysis The physico-chemical characteristics of the soils at the three experimental locations are presented in Table 1 . Among the sites, E2 exhibited comparatively higher concentrations of organic carbon, nitrogen, phosphorus, and potassium, indicating greater soil fertility. E1 recorded moderate levels of available N, P, and K, which can be considered optimal for balanced crop growth and favorable yield performance. In contrast, E3, characterized by sandy clay loam texture and acidic pH, showed lower levels of organic carbon and available nutrients, which likely contributed to reduced plant vigor and lower productivity at this site. Although the high nutrient status and organic carbon content at E2 promoted luxuriant vegetative growth, this appeared to occur at the expense of reproductive development, resulting in relatively lower seed yields compared with E1. These results underscore the influence of soil fertility and pH on crop performance and highlight the differential environmental effects contributing to genotype × environment interaction (GEI) in castor. Further these inferences were found true as E1 was found to be more discriminating and representative environment among the three locations studied. Table 1 Soil nutrient status at the experimental locations Location pH Texture OC (%) N (Kg/ha) P (Kg/ha) K (Kg/ha) RARS, Palem 6.94 Sandy loam 0.23 139 52.24 316 ARS, Tornala 5.1 Sandy clay loam 0.14 104 32.4 255 ARS, Tandur 7.49 Clay 0.66 213.3 68.23 569 3.2 Combined analysis of variance: A combined analysis of variance (ANOVA) was performed to assess the contribution of different sources of variation and their interactions (Table 2 ) (Peterson 1939 ). The results revealed highly significant differences among genotypes, environments, and genotype × environment (G×E) interactions for seed yield (YIELD) and its related traits, namely days to 50% flowering (DFF) and hundred seed weight (HSW). In contrast, for the number of nodes to the primary spike (NN), significant variation was observed only among genotypes and the replication × environment component, while the environmental effect alone was non-significant. These findings indicate that substantial genetic variability existed among the genotypes, and their performance was differentially influenced by environmental conditions. Table 2 Combined pooled Analysis of variance for yield and yield related traits for the trial conducted during rabi , 2022-23. Effect Df DFF NN HSW Yield SS MSS %SS SS MSS %SS SS MSS %SS SS MSS %SS Environment 2 4405.5 2202.76** 88.18 5.35 2.68 2.91 248.5 124.2** 26.74 10342507 5171254** 47.89 Rep (Env) 6 10.7 1.79** 0.21 18.94 3.16* 10.30 13.1 2.19** 1.40 464879 77480** 2.15 Genotype 7 123.9 17.70** 2.48 54.90 7.84** 29.88 204.9 29.28** 22.04 2826215 403745** 13.08 Env: Gen 14 170.3 12.16** 3.40 22.52 1.61 12.25 146.4 10.46** 15.75 2484420 177459** 11.50 Residual 42 115.2 2.74** 2.30 59.49 1.42 32.38 169.8 4.04** 18.27 2989671 71183** 13.84 Total 85 4995.9 58.78 183.72 2.16 929.3 10.93** 21592111 254025** CV 15.11 CV 13.21 CV 10.53 CV 21.78 ( DF = Degree of freedom. SS = sum of squares. MSS . = mean square. *Significant at p ≤ 0.05. **Significant at p < 0.001) Among the environments, E1 emerged as the most favorable site for the expression of genotypic potential, resulting in superior yields, followed by E2, whereas E3 was relatively less conducive for seed yield. This pattern reflects the strong environmental influence compared to G×E interaction, suggesting that soil fertility, pH, and nutrient status at E1 provided optimal conditions for growth and yield formation (Yan et al. 2003 ; Andrade et al. 2016 ; Khan 2021; Ajay et al. 2022 ). The mean performance of genotypes across locations (Table 3 ) further supported these observations. At E1, genotype ICH-1160 (G 3 ) was the earliest to flower, while at E2, G 3 , PCH-111 (G 5 ), and PCH-574 (G 7 ) exhibited earliness, and G 5 was the earliest at E3. Delayed flowering was consistently observed at E3 across genotypes, possibly due to suboptimal soil conditions and lower nutrient availability (Akhila et al. 2022 ). The trait NN was relatively uniform across sites; however, variations in internodal duration likely contributed to differences in flowering time. Location E3 recorded the highest HSW, probably due to prolonged maturity that favored better seed filling, although genotype ICH-1024 (G 8 ) exhibited notably poor seed set at E2. For seed yield, the E1 location recorded the highest mean performance among environments (Fig. 1 ). Overall, genotype PCH-596 (G 2 ) demonstrated consistent and superior performance across all three locations, with an average yield of 2,689 kg ha⁻¹, highlighting its adaptability and potential stability across diverse environments. Table 3 Average performance of genotypes for yield related traits at Palem, Tandur and Tornala locations. Genotype code Genotype DFF NN HSW Yield Palem Tandur Tornala Palem Tandur Tornala Palem Tandur Tornala Palem Tandur Tornala G1 PCH 612 47.6 54.3 64 11.3 11.6 11.6 31.3 32.9 35.7 2692.3 2620.9 2102 G2 PCH 596 48 57 66 12.6 13.3 14 33.6 30.4 34.1 3165.3 2530.4 2371.6 G3 ICH 1160 43.6 51.6 65 10 11.6 11.6 31 27.3 35.5 2869.6 1666.6 2006 G4 ICH 66 47.6 47.6 63.3 9.6 10.6 11 30.3 27.9 35.8 2508.3 2075.1 1842.3 G5 PCH 111 44.6 51.6 62.6 10.6 12.6 12 30.6 30 31.2 3356 2061.7 1970.3 G6 ICH 5 44 53.6 67.6 11.6 11.6 10 31.5 32.5 35.1 3376.3 2263.3 2136.6 G7 PCH 574 47.3 51.6 66.6 12.3 11.6 10.6 31 31.1 34.2 2871 2108.8 1988 G8 ICH 1024 47.6 50.6 65.3 10.3 10.6 10 30.3 23 29.8 2422.3 1942.4 2048 3.3 Additive main effects and multiplicative interaction1 (AMMI1) biplot: The Additive Main Effects and Multiplicative Interaction (AMMI) model was employed to dissect genotype × environment interaction (GEI) and assess the stability of castor genotypes across the three test locations. The AMMI1 biplot (Fig. 2 ) depicts the first interaction principal component axis (IPCA1) plotted against the main effect, providing insight into the magnitude and pattern of GEI for the traits studied: days to 50% flowering (DFF; Fig. 2 A), number of nodes to primary spike (NN; Fig. 2 B), hundred-seed weight (HSW; Fig. 2 C), and seed yield (YIELD; Fig. 2 D). The proportion of variation explained by IPCA1 was 66.7% for DFF, 87.7% for NN, 67.5% for HSW, and 79.7% for YIELD, indicating substantial contribution of the interaction component to total variation (Ebdon and Gauch 2002 ). For DFF (Fig. 2 A), genotypes G 1 , G 7 , and G 8 were positioned near the origin with IPCA1 values close to zero, indicating high stability and consistent flowering across environments. In contrast, G 2 and G 4 exhibited environment-specific performance, with G 2 showing superior performance at E2 and G 4 at E1. Among environments, E1 and E2 were highly discriminative for DFF, as reflected by long vectors with narrow angles between them (Vargas and Crossa 2000 ; Yan and Hunt 2001 ). Regarding NN (Fig. 2 B), G 1 was closest to the origin, indicating stable node development across environments. Genotypes G 6 and G 7 showed moderate stability with relatively low IPCA1 values, whereas G 2 , G 3 , G 4 , and G 5 exhibited higher IPCA1 scores, reflecting pronounced interaction with environments. G 3 and G 5 performed better at E3, while G 4 and G 8 , located on the left of the plot, showed fewer nodes and thus earlier flowering. Among environments, E3 was the most discriminative for NN, whereas E2 exhibited minimal interaction, providing stable conditions for node development. For HSW (Fig. 2 C), G 2 had the highest seed weight and low IPCA1 score, indicating both high performance and stability. Genotypes G 1 and G 6 also showed relatively high HSW but experienced moderate interaction across environments, suggesting less stability. Among environments, E2 exhibited strong negative interaction, potentially constraining high-performing genotypes, while E1, positioned near the origin, provided stable conditions for HSW expression. For YIELD (Fig. 2 D), G 1 achieved the highest yield but displayed high IPCA1 values, indicating lower stability across environments. Conversely, G 2 combined high yield with a low IPCA1 score, reflecting both superior performance and moderate stability. G 7 , although moderate yielding, was located near the origin, demonstrating consistent performance across environments. Environmental vectors further emphasized differential discrimination: E1, with a long vector and high IPCA1, favored genotypes such as G 1 , whereas E2, with negative interaction, appeared less favorable for high-yielding genotypes. E3, positioned near the origin with a short vector, represented a relatively stable testing environment suitable for evaluating genotypic performance (Gauch & Zobel, 1997 ; Narasimhulu et al., 2023 ). 3.4 Additive main effects and multiplicative interaction2 (AMMI2) biplot: In addition to IPCA1, the second interaction principal component (IPCA2) provides deeper insights into genotype × environment interaction (GEI) and enables precise identification of genotypic adaptability across diverse environments. The AMMI2 biplot (Fig. 3 ) plots IPCA1 against IPCA2, where the distance from the origin reflects the magnitude of interaction (Purchase, 1997 ; Kilic, 2014 ). IPCA2 accounted for 33.3%, 12.3%, 32.5%, and 20.3% of the GEI variance in DFF, NN, HSW, and YIELD, respectively, indicating that the first two IPCAs captured nearly all interaction effects. Environments positioned near the origin exhibit minimal influence on GEI, suggesting conditions that support broad genotypic adaptability. In DFF (Fig. 3 A), G 8 and G 4 were closest to the origin, demonstrating high stability across environments, while G 1 , G 2 , and G 6 showed stronger interaction and variable performance. E3, located near the origin, represented a stable environment, whereas E1 and E2 acted as discriminating sites. Clustering patterns indicated that G 1 and G 2 adapted well to E2, whereas G 3 and G 6 performed better in E3. Regarding NN (Fig. 3 B), G 8 , G 4 , and G 1 maintained consistent node development across locations, while G 3 , G 5 , and G 6 exhibited greater sensitivity to environmental variation. E1 and E3 emerged as highly discriminating environments, whereas E2 provided stable conditions. For HSW (Fig. 3 C), G 1 , G 6 , and G 7 demonstrated stable seed weight across locations, while G 3 , G 5 , and G 8 showed higher interaction with environments. E1 and E3 effectively differentiated genotype performance, whereas E2 exhibited stability with minimal interaction. In YIELD (Fig. 3 D), G 2 , G 4 , and G 7 combined high productivity with stability, whereas G 1 , G 3 , and G 5 experienced greater variation across environments. E1 and E2 offered strong differentiation among genotypes, while E3, positioned near the origin in the negative quadrant, represented a stable but low-yielding environment (Kilic, 2014 ). AMMI Stability Value (ASV) analysis ranked PCH-574 (G 7 ), PCH-596 (G 2 ), ICH-66 (G 4 ), ICH-1024 (G 8 ), and ICH-5 (G 6 ) as the most stable genotypes across locations (Table 4 ). Yield Stability Index (YSI), incorporating both stability and mean performance, identified PCH-596 (G 2 ), ICH-5 (G 6 ), PCH-612 (G 1 ), PCH-111 (G 5 ), and PCH-574 (G 7 ) as top performers. These findings highlight that ASV captures only stability, whereas YSI provides a comprehensive evaluation integrating stability and yield potential (Singamsetti et al. 2021 ). Table 4 Ranking of genotypes using AMMI Stability value (ASV) and Yield Stability Index (YSI) based on yield Genotype ASV rASV YSI rYSI Means (Yield) PCH 612 63.476 8 11 3 2471.77 PCH 596 10.913 2 3 1 2689.13 ICH 1160 40.273 6 12 6 2180.77 ICH 66 30.805 3 10 7 2141.94 PCH 111 58.509 7 11 4 2462.67 ICH 5 39.948 5 7 2 2592.10 PCH 574 2.109 1 6 5 2322.60 ICH 1024 35.423 4 12 8 2137.57 3.5 GGE biplot Site regression genotype × environment (G×E) interaction (GGE) biplot analysis is a powerful approach for interpreting multi-environment trial data in plant breeding and for identifying genotypes with specific environmental adaptations (Yan et al. 2000 ; Yan 2001 ). This method recognizes that genotypes may not perform optimally across all conditions and emphasizes the combined effect of genotype and G×E interaction, which primarily determines yield performance. GGE biplot analysis is particularly effective for datasets spanning multiple environments and facilitates visualization of complex interactions. Tools such as the “which-won-where” pattern allow clear identification of discriminating and representative environments, aiding in the selection of superior and broadly adaptable genotypes (Ajay et al. 2021 ). 3.5.1 ‘ Discriminativeness vs. representativeness’ pattern of GGE biplot Identifying optimal test environments is crucial for effective breeding programs aimed at selecting and cultivating superior genotypes. The ‘Discriminativeness vs. Representativeness’ view of the GGE biplot (Fig. 4 ) provides a visual assessment of each environment's ability to differentiate among genotypes and its representativeness of the target region. In this view, the length of the environment vectors indicates discriminating ability, while the angle with the average environment coordinate (AEC) abscissa reflects representativeness. Longer vectors suggest greater potential to distinguish among genotypes, whereas shorter vectors indicate limited discrimination (Yan et al. 2007 ). In the present study, the shortest vector was observed for E1 in HSW, whereas the longest appeared for E3 in NN. Angle measurements showed that E1 for HSW had the smallest angle, indicating high representativeness, while E1 for NN formed the largest angle relative to the abscissa. An environment combining a long vector with a narrow angle is considered ideal for genotype selection (Samyuktha et al. 2020 ; Yan 2002 ). Among the locations, E2 displayed both a long vector and narrow angle, demonstrating strong discriminative power and representativeness. Overall, the analysis suggested that E2 was optimal for DFF and NN, E1 for HSW, and E3 for YIELD, highlighting these environments as most suitable for evaluating and selecting superior castor genotypes across traits. 3.5.2 Genotype ranking: best genotype assessment GGE biplot analysis provides a powerful framework to identify superior and stable castor genotypes across multiple environments. The ideal genotype combines high mean performance with minimal interaction and is typically positioned near the center of the biplot, close to the arrowhead within the concentric circles (Yan et al. 2007 ). In the case of days to 50% flowering (DFF), genotype G 7 occupied a position near the origin, reflecting remarkable stability across environments with negligible G×E interaction. Genotypes G 2 , G 4 , and G 6 , positioned farther from the origin, exhibited pronounced interactions, indicating environment-specific performance. Notably, G 2 aligned closely with E2 and the concentric circles, suggesting high performance and specific adaptation to this environment. E2 demonstrated the strongest discriminative capacity, while E1, near the origin, allowed limited differentiation among genotypes (Fig. 5 ). For the number of nodes to the primary spike (NN), G 1 and G 5 maintained proximity to the origin, signalling high stability across environments. In contrast, G 3 , G 4 , and G 6 showed greater sensitivity to environmental variation, positioned farther from the origin. G 5 , located farthest along the ranking axis, achieved superior overall performance, with G 3 and G 4 demonstrating adaptability due to alignment in the same direction. G 2 and G 6 remained stable with moderate performance, whereas G 1 and G 7 combined high stability with average performance. Among environments, E1 exhibited maximal discrimination, with E2 and E3 providing moderate differentiation (Fig. 5 ). Hundred-seed weight (HSW) analysis revealed that G 8 projected farthest along the ranking axis, emerging as the top performer with high seed weight and favorable interaction patterns. G 5 followed, demonstrating strong seed weight but increased sensitivity. Genotypes G 1 , G 2 , G 6 , and G 7 clustered near the origin, indicating moderate yet consistent performance across environments. E1, located near the origin, showed limited capacity to differentiate genotypes, whereas E2 (top-left) and E3 (bottom-left) were more discriminating, although no genotype closely aligned with these environments, suggesting the absence of a dominant environmental “winner” (Fig. 5 ). In seed yield (YIELD), G 6 and G 5 exhibited high and stable performance, positioned closest to the ranking axis. G 2 and G 3 displayed intermediate projections, indicative of moderate yield potential, while G 1 and G 4 , situated off-axis, experienced strong G×E interactions coupled with lower mean performance. E1, positioned at the bottom-right, strongly discriminated among genotypes, favoring G 6 and G 5 and highlighting their specific adaptability. E2 also contributed to genotype differentiation, with G 1 and G 2 partially adapted, whereas E3, near the origin, represented a stable but less discriminating environment, yielding relatively uniform performance across genotypes (Yan et al. 2003 ) (Fig. 5 ). 3.5.3 Which-Won-Where: The polygon or “which-won-where” view of the GGE biplot provides a robust visualization of genotype × environment interactions, crossover GEI, mega-environment differentiation, and specific genotype adaptations (Fig. 8 ). In this representation, test environments were distributed across two of five sectors for DFF, one of six sectors for NN, two of six sectors for HSW, and two of six sectors for YIELD. For DFF, two distinct mega-environments were observed: E1 and E2 formed the first, while E3 represented the second. Vertex genotypes G 2 and G 1 dominated the first mega-environment, indicating strong responsiveness, whereas G6 emerged as the vertex genotype for the second mega-environment. In NN, G 2 was the most responsive genotype across all environments (E1, E2, and E3). For HSW, G 1 and G 6 defined the first mega-environment (E1 & E2), while G 3 and G 4 were vertex genotypes for the second (E3). In terms of YIELD, G 2 exhibited superior responsiveness in the mega-environment comprising E2 and E3, whereas G6 showed enhanced performance in E1 (Olanrewaju et al., 2021 ). Several vertex genotypes were positioned in sectors without any test environments, including G 3 and G 4 for DFF; G 3 , G 4 , G 6 , G 7 , and G 8 for NN; G 8 , G 5 , and G 7 for HSW; and G 1 , G 8 , G 3 , and G 5 for YIELD. These genotypes demonstrated poor performance across environments and are considered undesirable. In contrast, non-vertex genotypes, located within the polygon, exhibited moderate responsiveness, reflecting higher stability under varying environmental conditions. Vertex genotypes are indicative of superior performance and specific adaptation to particular mega-environments, whereas genotypes without associated environments are less desirable due to lower stability (Bernal and Villardon 2016 ; Ajay et al. 2012 ). Clustering of environmental vectors within a sector indicates that a single genotype excels across all encompassed environments, while distribution across multiple sectors reveals differential genotype superiority depending on environmental context (Hashim et al. 2021 ). 4. Conclusion The evaluation of eight castor genotypes across three diverse locations revealed significant genotype × environment interactions, highlighting the influence of environment-specific gene expression on trait performance. Among the test locations, Tandur (E2) was identified as the most discriminating and representative environment, ideal for selecting superior genotypes. Integrating insights from both AMMI and GGE biplot analyses, ICH-5 (G 6 ), PCH-111 (G 5 ), and PCH-596 (G 2 ) consistently demonstrated high yield and stability across environments. Notably, ICH-5 and PCH-596 combined superior seed yield with higher hundred-seed weight, reflecting their agronomic potential and broad adaptability. Although PCH-574 (G 7 ) showed stability in days to 50% flowering, its performance for seed yield and hundred-seed weight remained average, emphasizing the importance of multi-trait evaluation. The “which-won-where” GGE biplot further highlighted PCH-596 (G 2 ) as a vertex genotype, confirming its superior adaptability and performance across the tested environments. Overall, this study underscores the effectiveness of combining AMMI and GGE analyses for dissecting GEI and provides a robust framework for selecting high-yielding, stable castor genotypes for targeted breeding programs. Declarations Funding: Authors would like thank Professor Jayashankar Telangana Agricultural University, Rajendranagar, Hyderabad, Telangana, India – 500 030 for financial support. Competing interest: Authors declare that they do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted. Author contributions: Planning and conducting the experiment and contributed significantly to writing the manuscripts [K. Sadaiah and G. Eswara Reddy], Conducted the experiment & data collection [K. Parimala, A. Saritha and T. Rajeshwar Reddy], crop management and preparation of manuscript [G. Madhuri, V. Divya Rani and N. Nalini], Planned and conducted the experiment and review & editing [S. Vanisri and M. 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08:39:57","extension":"html","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":137409,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8233933/v1/eec54d52eebd86081c4921a9.html"},{"id":98582037,"identity":"e82ca6e8-540e-406b-84e5-9123b53bc326","added_by":"auto","created_at":"2025-12-19 08:39:57","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":120743,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMean performance of the studied traits across using box plots at three environments during \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003erabi, \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e2022-23. DFF: \u003c/strong\u003eDays to 50% flowering, \u003cstrong\u003eNN: \u003c/strong\u003eNumber of Nodes\u003cstrong\u003e, HSW: \u003c/strong\u003eHundred Seed Weight (g)\u003cstrong\u003e, YIELD: \u003c/strong\u003eSeed Yield (kg/ha)\u003cstrong\u003e, ENV: \u003c/strong\u003eEnvironments, \u003cstrong\u003eE1\u003c/strong\u003e: RARS, Palem, \u003cstrong\u003eE2\u003c/strong\u003e: ARS, Tandur, \u003cstrong\u003eE3\u003c/strong\u003e: ARS, Tornala.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8233933/v1/a0a4a07305467996da0c5461.jpeg"},{"id":98628236,"identity":"9234e58e-941a-4011-ab9b-5afa518643af","added_by":"auto","created_at":"2025-12-19 17:11:12","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":454864,"visible":true,"origin":"","legend":"\u003cp\u003eAdditive main effects and multiplicative interaction 1 (AMMI 1) biplots based on PC1 illustrating G × E interactions of the 8 castor genotypes in three environments: (\u003cstrong\u003eA\u003c/strong\u003e) DFF (\u003cstrong\u003eB\u003c/strong\u003e) NN (\u003cstrong\u003eC\u003c/strong\u003e) HSW (\u003cstrong\u003eD\u003c/strong\u003e) YIELD.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8233933/v1/a63fc64b412ca6f618fd5974.jpeg"},{"id":98627916,"identity":"640d5b8b-c0d6-435e-ad37-09455ed85c1a","added_by":"auto","created_at":"2025-12-19 17:10:47","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":457419,"visible":true,"origin":"","legend":"\u003cp\u003eAdditive main effects and multiplicative interaction 2 (AMMI 2) biplots based on PC2 illustrating G × E interactions of the 8 castor genotypes for four traits in three environments.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8233933/v1/6ba79c4ecf791f96d591fd48.jpeg"},{"id":98582038,"identity":"d2562f72-af99-4fb5-a0a5-4f1cbca60d2f","added_by":"auto","created_at":"2025-12-19 08:39:57","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":589338,"visible":true,"origin":"","legend":"\u003cp\u003eDiscriminativeness Vs Representativeness based on PC1 and PC2 showing G × E interactions of the 8 castor genotypes for four traits under three locations:\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8233933/v1/0356806ba1fc61aec6e4969d.jpeg"},{"id":98627860,"identity":"4be5362d-ef39-48ac-a621-0bb12bc492cb","added_by":"auto","created_at":"2025-12-19 17:10:43","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":676087,"visible":true,"origin":"","legend":"\u003cp\u003eRanking genotypes based on PC1 and PC2 showing G × E interactions of the 8 castor genotypes for four traits under three locations:\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8233933/v1/98bf3f5d2537f96c62ac2e01.jpeg"},{"id":98628400,"identity":"2d7d37c3-633d-4373-98a3-5fe35d2e25fa","added_by":"auto","created_at":"2025-12-19 17:11:27","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":555937,"visible":true,"origin":"","legend":"\u003cp\u003eRanking environments based on PC1 and PC2 showing G × E interactions of the 8 castor genotypes for four traits under three locations:\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8233933/v1/faeb7ff8b3f41144df370220.jpeg"},{"id":98582046,"identity":"62a80611-b7cc-4c71-82e7-94e66e0933f8","added_by":"auto","created_at":"2025-12-19 08:39:57","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":469864,"visible":true,"origin":"","legend":"\u003cp\u003ePatterns (\u003cstrong\u003eA–D\u003c/strong\u003e). Polygon views of GGE biplot of 8 castor genotypes under the effects of genotypes-by environment interactions for four traits under three locations.\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8233933/v1/c02cd9f5e2af06357554e929.jpeg"},{"id":98627338,"identity":"110ffc2e-7237-4f17-8486-458e249bd48c","added_by":"auto","created_at":"2025-12-19 17:10:17","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":603930,"visible":true,"origin":"","legend":"\u003cp\u003ePatterns (\u003cstrong\u003eA–D\u003c/strong\u003e). Polygon views of GGE biplot for which-won-where view of 8 castor genotypes under the effects of genotypes-by environment interactions for four traits under three locations.\u003c/p\u003e","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8233933/v1/28c65cd9424c8ce3778980c0.jpeg"},{"id":107928974,"identity":"0578e379-4571-4b23-bae6-dd791a1de0e3","added_by":"auto","created_at":"2026-04-27 16:13:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4453816,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8233933/v1/e63c4c31-bd23-41de-9ee1-ed8f5659da6b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genotype × Environment Interaction Insights for Yield and Yield Components in Castor via AMMI and GGE Biplot Models","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCastor (\u003cem\u003eRicinus communis\u003c/em\u003e L.), a member of the family \u003cem\u003eEuphorbiaceae\u003c/em\u003e, is an economically significant non-edible oilseed crop cultivated across tropical and subtropical regions of the world. Believed to have originated in East Africa, particularly Ethiopia owing to the existence of vast genetic diversity in the region (Rukhsar et al. 2017; Xu et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) castor has since gained global prominence for its industrial versatility. The species is a diploid (2n\u0026thinsp;=\u0026thinsp;20) C₃ plant, well adapted to semi-arid ecosystems, and valued for its remarkable ability to produce oil under limited moisture conditions. India is the world\u0026rsquo;s leading producer of castor, followed by Brazil and China, contributing substantially to global castor oil exports. During 2024-25, castor was cultivated over 7.87 lakh hectares in India, with a total production of 15.53 lakh tonnes and an average productivity of 1.93 t ha⁻\u0026sup1; (Indiastat \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Gujarat remains the dominant producer, accounting for more than 70% of the national output, with a productivity level (2.27 t ha⁻\u0026sup1;) exceeding the national average, followed by Rajasthan, Andhra Pradesh, Karnataka, Tamil Nadu, and Telangana. Together, Gujarat and Rajasthan contribute nearly 90% of India\u0026rsquo;s castor acreage and production.\u003c/p\u003e \u003cp\u003eCastor seeds contain 46\u0026ndash;55% oil by weight (Elhadi \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and the unique fatty acid composition particularly ricinoleic acid, which constitutes over 80% of the total fatty acids imparts distinct physicochemical properties not found in other vegetable oils (Vieira et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). The hydroxyl functionality of ricinoleic acid makes castor oil an indispensable raw material for over 700 industrial applications, including lubricants, coatings, cosmetics, surfactants, biodiesel, and pharmaceutical formulations (Maiti et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Sujatha et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Memon et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Its renewability, biodegradability, and non-edible nature further enhance its relevance in the bio-based economy and sustainable industrial development frameworks worldwide.\u003c/p\u003e \u003cp\u003eDespite its importance, productivity gains in castor remain limited, largely due to the influence of genotype \u0026times; environment interactions (GEI), which complicate selection for yield stability and adaptability across diverse production environments. Yield and its component traits are complex quantitative characters governed by polygenic inheritance and are strongly modulated by environmental factors such as soil fertility, temperature, rainfall, and biotic or abiotic stress conditions (Joshi et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Baker \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). Understanding the nature and magnitude of GEI is therefore essential for designing efficient breeding strategies and for recommending genotypes suited to specific agro-ecological zones (Asungre et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Multi-environment trials (METs) serve as a powerful tool to assess genotypic performance across variable environments, enabling quantification of stability and adaptability. Among the statistical approaches available, the Additive Main Effects and Multiplicative Interaction (AMMI) model and the Genotype plus Genotype \u0026times; Environment (GGE) biplot analysis have emerged as robust methodologies for dissecting GEI (Yan et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Ajay et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The AMMI model partitions the total variation into additive and multiplicative components, thereby providing insights into genotype stability and environmental responsiveness. However, AMMI alone does not account for the relationship between mean performance and stability. The GGE biplot, by integrating both genotype main effects (G) and genotype \u0026times; environment interaction (GE), effectively identifies superior genotypes and discriminative environments (Miranda et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Ding et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Complementary use of AMMI and GGE models enhances the precision of genotype evaluation, facilitating the delineation of mega-environments and the identification of broadly or specifically adapted cultivars.\u003c/p\u003e \u003cp\u003eAlthough castor is a crop of high industrial and economic value, studies employing multi-environment testing and modern GEI analysis tools remain limited compared to other major oilseeds. Therefore, the present investigation was undertaken to assess the magnitude of GEI and to identify high-yielding, stable, and widely adaptable castor hybrids across diverse agro-climatic locations of Telangana using AMMI and GGE biplot analyses. The outcomes are expected to provide a scientific basis for targeted hybrid deployment, environment-specific recommendations, and accelerated genetic improvement in castor breeding programs.\u003c/p\u003e"},{"header":"2. Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study locations\u003c/h2\u003e \u003cp\u003eThe field experiments were conducted during the \u003cem\u003erabi\u003c/em\u003e season of 2022-23 across three distinct agro-ecological locations in Telangana, India, representing diverse soil types and rainfall regimes. The second site, the Regional Agricultural Research Station (RARS), Palem (E1) represents a semi-arid ecology with shallow, moderately fertile soils and an average annual rainfall of 600\u0026ndash;650 mm. The second site, the Agricultural Research Station (ARS), Tandur (E2) is characterized by deep, fertile black soils and receives an average annual rainfall of 800\u0026ndash;900 mm. The third site, ARS, Tornala (E3) situated in the central zone of Telangana, is typified by resource-poor red soils with limited fertility and receives an average annual rainfall of 800\u0026ndash;850 mm. These sites were strategically selected to capture a broad spectrum of environmental variability, enabling a comprehensive assessment of genotype \u0026times; environment interaction (GEI) for yield and yield-contributing traits in castor (\u003cem\u003eRicinus communis\u003c/em\u003e L.).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Soil sampling and analysis\u003c/h2\u003e \u003cp\u003eA representative composite soil sample was collected from each experimental site prior to sowing by sampling the topsoil at a depth of 0\u0026ndash;15 cm using a soil auger. The collected samples were air-dried under shade, gently crushed, and passed through a 2 mm sieve for subsequent physico-chemical analysis. The analyses included determination of soil texture (sand, silt, and clay), pH, organic carbon (OC), available nitrogen (N), phosphorus (P), and potassium (K) contents, as well as particle size distribution. These baseline assessments were conducted at the initiation of the experiment to characterize the soil properties of each site and to support interpretation of genotype \u0026times; environment interaction (GEI) effects. The study was conducted as per the approved technical programme by the administration of the Professor Jayashankar Telangana Agricultural University (PJTAU). Further, the necessary permissions to collect the soil samples were also obtained from the university administration as per the laid-out regulations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Plant materials and data collection\u003c/h2\u003e \u003cp\u003eThe experimental material comprised eight castor (\u003cem\u003eRicinus communis\u003c/em\u003e L.) genotypes, namely PCH-612 (G₁), PCH-596 (G₂), ICH-1160 (G₃), ICH-66 (G₄), PCH-111 (G₅), ICH-5 (G₆), PCH-574 (G₇), and ICH-1024 (G₈). Among these, PCH-111 (G₅) and ICH-5 (G₆) were included as standard checks. The hybrids \u003cem\u003eviz\u003c/em\u003e., PCH-612, PCH-596, PCH-111 and PCH-574 were developed and sourced from Regional Agricultural Research Station (RARS), Professor Jayashankar Telangana Agricultural University (PJTAU), Palem, Nagarkurnool district, Telangana.\u003c/p\u003e \u003cp\u003eSimilarly, the hybrids \u003cem\u003eviz\u003c/em\u003e., ICH-1160, ICH-66, ICH-5 and ICH-1024 were developed at ICAR-Indian Institute of Oilseeds Research, Rajendranagar, Hyderabad, Telangana and the seed of these hybrids for the study was sourced from RARS, PJTAU, Palem, Nagarkurnool district, Telangana. The genotypes were evaluated across three locations using a randomized complete block design (RCBD) with three replications. Each gross plot measured 32.4 m\u0026sup2; (5.4 m \u0026times; 6.0 m) and consisted of six rows, while data were recorded from a net plot area of 17.28 m\u0026sup2; (3.6 m \u0026times; 4.8 m). A uniform spacing of 90 cm between rows and 60 cm between plants within a row was maintained. Sowing was carried out during the second week of October 2022 across all experimental locations.\u003c/p\u003e \u003cp\u003eObservations were recorded for key yield and yield-contributing traits, namely days to 50% flowering (DFF), number of nodes to primary spike (NN), hundred-seed weight (HSW), and seed yield per hectare (YLD). Days to 50% flowering were determined as the number of days taken for 50% of the plants in a plot to reach flowering. The number of nodes up to the primary spike, indicative of earliness, was recorded manually. Hundred-seed weight was determined from the first harvest by drying the mature capsules, threshing the seeds, cleaning them thoroughly, and weighing a random sample of 100 seeds. Seed yield was computed from three consecutive pickings obtained from the net plot, excluding the border rows and plants on all sides to minimize border effects. The cumulative seed yield from the three pickings was bulked, weighed, and converted to kilograms per hectare for statistical analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical analysis\u003c/h2\u003e \u003cp\u003eData analysis was carried out using R statistical software (version 4.2.1; R Core Team 2022). A combined analysis of variance (ANOVA) was performed to assess the significance of genotype, environment, and genotype \u0026times; environment interaction (GEI) effects for seed yield and associated traits. Prior to analysis, data were subjected to logarithmic transformation to achieve normalization and stabilize variance. Each experimental location was treated as an independent environment for the purpose of GEI assessment.\u003c/p\u003e \u003cp\u003eAMMI (Additive Main Effects and Multiplicative Interaction) and GGE (Genotype plus Genotype \u0026times; Environment) biplot analyses were employed to elucidate the magnitude and pattern of GEI, identify stable and high-performing genotypes, and determine discriminative and representative test environments. The AMMI analysis was conducted using the \u0026ldquo;agricolae\u0026rdquo; package (Mendiburu \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), while the GGE biplot was generated using the \u0026ldquo;GGEBiplotGUI\u0026rdquo; package (Bernal and Villard\u0026oacute;n 2016) in R. The results were interpreted following the standard procedures outlined by and Yan et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Soil analysis\u003c/h2\u003e \u003cp\u003eThe physico-chemical characteristics of the soils at the three experimental locations are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Among the sites, E2 exhibited comparatively higher concentrations of organic carbon, nitrogen, phosphorus, and potassium, indicating greater soil fertility. E1 recorded moderate levels of available N, P, and K, which can be considered optimal for balanced crop growth and favorable yield performance. In contrast, E3, characterized by sandy clay loam texture and acidic pH, showed lower levels of organic carbon and available nutrients, which likely contributed to reduced plant vigor and lower productivity at this site. Although the high nutrient status and organic carbon content at E2 promoted luxuriant vegetative growth, this appeared to occur at the expense of reproductive development, resulting in relatively lower seed yields compared with E1. These results underscore the influence of soil fertility and pH on crop performance and highlight the differential environmental effects contributing to genotype \u0026times; environment interaction (GEI) in castor. Further these inferences were found true as E1 was found to be more discriminating and representative environment among the three locations studied.\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\u003eSoil nutrient status at the experimental locations\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTexture\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOC (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN (Kg/ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP (Kg/ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eK (Kg/ha)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRARS, Palem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSandy loam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e52.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e316\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eARS, Tornala\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSandy clay loam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e32.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e255\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eARS, Tandur\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eClay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e213.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e68.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e569\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 \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Combined analysis of variance:\u003c/h2\u003e \u003cp\u003eA combined analysis of variance (ANOVA) was performed to assess the contribution of different sources of variation and their interactions (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) (Peterson \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1939\u003c/span\u003e). The results revealed highly significant differences among genotypes, environments, and genotype \u0026times; environment (G\u0026times;E) interactions for seed yield (YIELD) and its related traits, namely days to 50% flowering (DFF) and hundred seed weight (HSW). In contrast, for the number of nodes to the primary spike (NN), significant variation was observed only among genotypes and the replication \u0026times; environment component, while the environmental effect alone was non-significant. These findings indicate that substantial genetic variability existed among the genotypes, and their performance was differentially influenced by environmental conditions.\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\u003eCombined pooled Analysis of variance for yield and yield related traits for the trial conducted during \u003cem\u003erabi\u003c/em\u003e, 2022-23.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"14\"\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=\"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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEffect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eDFF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eNN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003eHSW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c14\" namest=\"c12\"\u003e \u003cp\u003eYield\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e%SS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e%SS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eMSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e%SS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eMSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e%SS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnvironment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4405.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2202.76**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e88.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e248.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e124.2**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e26.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e10342507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e5171254**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e47.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRep (Env)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.79**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.16*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.19**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e464879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e77480**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e2.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenotype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e123.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.70**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e54.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.84**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e29.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e204.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e29.28**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e22.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2826215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e403745**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e13.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnv: Gen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e170.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.16**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e146.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e10.46**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e15.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2484420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e177459**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e11.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e115.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.74**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e59.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e32.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e169.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.04**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e18.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2989671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e71183**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e13.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4995.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e183.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e929.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e10.93**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e21592111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e254025**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e10.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eCV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e21.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"14\"\u003e(\u003cem\u003eDF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;Degree of freedom. \u003cem\u003eSS\u003c/em\u003e\u0026thinsp;=\u0026thinsp;sum of squares. \u003cem\u003eMSS\u003c/em\u003e. = mean square. *Significant at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05. **Significant at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAmong the environments, E1 emerged as the most favorable site for the expression of genotypic potential, resulting in superior yields, followed by E2, whereas E3 was relatively less conducive for seed yield. This pattern reflects the strong environmental influence compared to G\u0026times;E interaction, suggesting that soil fertility, pH, and nutrient status at E1 provided optimal conditions for growth and yield formation (Yan et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Andrade et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Khan 2021; Ajay et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The mean performance of genotypes across locations (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) further supported these observations. At E1, genotype ICH-1160 (G\u003csub\u003e3\u003c/sub\u003e) was the earliest to flower, while at E2, G\u003csub\u003e3\u003c/sub\u003e, PCH-111 (G\u003csub\u003e5\u003c/sub\u003e), and PCH-574 (G\u003csub\u003e7\u003c/sub\u003e) exhibited earliness, and G\u003csub\u003e5\u003c/sub\u003e was the earliest at E3. Delayed flowering was consistently observed at E3 across genotypes, possibly due to suboptimal soil conditions and lower nutrient availability (Akhila et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The trait NN was relatively uniform across sites; however, variations in internodal duration likely contributed to differences in flowering time. Location E3 recorded the highest HSW, probably due to prolonged maturity that favored better seed filling, although genotype ICH-1024 (G\u003csub\u003e8\u003c/sub\u003e) exhibited notably poor seed set at E2. For seed yield, the E1 location recorded the highest mean performance among environments (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Overall, genotype PCH-596 (G\u003csub\u003e2\u003c/sub\u003e) demonstrated consistent and superior performance across all three locations, with an average yield of 2,689 kg ha⁻\u0026sup1;, highlighting its adaptability and potential stability across diverse environments.\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\u003eAverage performance of genotypes for yield related traits at Palem, Tandur and Tornala locations.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"14\"\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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGenotype code\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGenotype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eDFF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eNN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003eHSW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c14\" namest=\"c12\"\u003e \u003cp\u003eYield\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePalem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTandur\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTornala\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePalem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTandur\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTornala\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePalem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eTandur\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eTornala\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003ePalem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eTandur\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003eTornala\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\u003ePCH 612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e31.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e32.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e35.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2692.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e2620.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e2102\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\u003ePCH 596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e33.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e30.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e34.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3165.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e2530.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e2371.6\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\u003eICH 1160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e27.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e35.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2869.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1666.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e2006\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\u003eICH 66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e63.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e30.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e27.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e35.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2508.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e2075.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1842.3\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\u003ePCH 111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e62.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e30.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e31.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e2061.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1970.3\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\u003eICH 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e67.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e31.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e32.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e35.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3376.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e2263.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e2136.6\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\u003ePCH 574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e31.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e34.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e2108.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1988\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\u003eICH 1024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e65.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e30.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e29.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2422.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1942.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e2048\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\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Additive main effects and multiplicative interaction1 (AMMI1) biplot:\u003c/h2\u003e \u003cp\u003eThe Additive Main Effects and Multiplicative Interaction (AMMI) model was employed to dissect genotype \u0026times; environment interaction (GEI) and assess the stability of castor genotypes across the three test locations. The AMMI1 biplot (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) depicts the first interaction principal component axis (IPCA1) plotted against the main effect, providing insight into the magnitude and pattern of GEI for the traits studied: days to 50% flowering (DFF; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), number of nodes to primary spike (NN; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), hundred-seed weight (HSW; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC), and seed yield (YIELD; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). The proportion of variation explained by IPCA1 was 66.7% for DFF, 87.7% for NN, 67.5% for HSW, and 79.7% for YIELD, indicating substantial contribution of the interaction component to total variation (Ebdon and Gauch \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). For DFF (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), genotypes G\u003csub\u003e1\u003c/sub\u003e, G\u003csub\u003e7\u003c/sub\u003e, and G\u003csub\u003e8\u003c/sub\u003e were positioned near the origin with IPCA1 values close to zero, indicating high stability and consistent flowering across environments. In contrast, G\u003csub\u003e2\u003c/sub\u003e and G\u003csub\u003e4\u003c/sub\u003e exhibited environment-specific performance, with G\u003csub\u003e2\u003c/sub\u003e showing superior performance at E2 and G\u003csub\u003e4\u003c/sub\u003e at E1. Among environments, E1 and E2 were highly discriminative for DFF, as reflected by long vectors with narrow angles between them (Vargas and Crossa \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Yan and Hunt \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRegarding NN (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), G\u003csub\u003e1\u003c/sub\u003e was closest to the origin, indicating stable node development across environments. Genotypes G\u003csub\u003e6\u003c/sub\u003e and G\u003csub\u003e7\u003c/sub\u003e showed moderate stability with relatively low IPCA1 values, whereas G\u003csub\u003e2\u003c/sub\u003e, G\u003csub\u003e3\u003c/sub\u003e, G\u003csub\u003e4\u003c/sub\u003e, and G\u003csub\u003e5\u003c/sub\u003e exhibited higher IPCA1 scores, reflecting pronounced interaction with environments. G\u003csub\u003e3\u003c/sub\u003e and G\u003csub\u003e5\u003c/sub\u003e performed better at E3, while G\u003csub\u003e4\u003c/sub\u003e and G\u003csub\u003e8\u003c/sub\u003e, located on the left of the plot, showed fewer nodes and thus earlier flowering. Among environments, E3 was the most discriminative for NN, whereas E2 exhibited minimal interaction, providing stable conditions for node development. For HSW (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC), G\u003csub\u003e2\u003c/sub\u003e had the highest seed weight and low IPCA1 score, indicating both high performance and stability. Genotypes G\u003csub\u003e1\u003c/sub\u003e and G\u003csub\u003e6\u003c/sub\u003e also showed relatively high HSW but experienced moderate interaction across environments, suggesting less stability. Among environments, E2 exhibited strong negative interaction, potentially constraining high-performing genotypes, while E1, positioned near the origin, provided stable conditions for HSW expression.\u003c/p\u003e \u003cp\u003eFor YIELD (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD), G\u003csub\u003e1\u003c/sub\u003e achieved the highest yield but displayed high IPCA1 values, indicating lower stability across environments. Conversely, G\u003csub\u003e2\u003c/sub\u003e combined high yield with a low IPCA1 score, reflecting both superior performance and moderate stability. G\u003csub\u003e7\u003c/sub\u003e, although moderate yielding, was located near the origin, demonstrating consistent performance across environments. Environmental vectors further emphasized differential discrimination: E1, with a long vector and high IPCA1, favored genotypes such as G\u003csub\u003e1\u003c/sub\u003e, whereas E2, with negative interaction, appeared less favorable for high-yielding genotypes. E3, positioned near the origin with a short vector, represented a relatively stable testing environment suitable for evaluating genotypic performance (Gauch \u0026amp; Zobel, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Narasimhulu et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Additive main effects and multiplicative interaction2 (AMMI2) biplot:\u003c/h2\u003e \u003cp\u003eIn addition to IPCA1, the second interaction principal component (IPCA2) provides deeper insights into genotype \u0026times; environment interaction (GEI) and enables precise identification of genotypic adaptability across diverse environments. The AMMI2 biplot (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) plots IPCA1 against IPCA2, where the distance from the origin reflects the magnitude of interaction (Purchase, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Kilic, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). IPCA2 accounted for 33.3%, 12.3%, 32.5%, and 20.3% of the GEI variance in DFF, NN, HSW, and YIELD, respectively, indicating that the first two IPCAs captured nearly all interaction effects. Environments positioned near the origin exhibit minimal influence on GEI, suggesting conditions that support broad genotypic adaptability.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn DFF (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), G\u003csub\u003e8\u003c/sub\u003e and G\u003csub\u003e4\u003c/sub\u003e were closest to the origin, demonstrating high stability across environments, while G\u003csub\u003e1\u003c/sub\u003e, G\u003csub\u003e2\u003c/sub\u003e, and G\u003csub\u003e6\u003c/sub\u003e showed stronger interaction and variable performance. E3, located near the origin, represented a stable environment, whereas E1 and E2 acted as discriminating sites. Clustering patterns indicated that G\u003csub\u003e1\u003c/sub\u003e and G\u003csub\u003e2\u003c/sub\u003e adapted well to E2, whereas G\u003csub\u003e3\u003c/sub\u003e and G\u003csub\u003e6\u003c/sub\u003e performed better in E3. Regarding NN (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB), G\u003csub\u003e8\u003c/sub\u003e, G\u003csub\u003e4\u003c/sub\u003e, and G\u003csub\u003e1\u003c/sub\u003e maintained consistent node development across locations, while G\u003csub\u003e3\u003c/sub\u003e, G\u003csub\u003e5\u003c/sub\u003e, and G\u003csub\u003e6\u003c/sub\u003e exhibited greater sensitivity to environmental variation. E1 and E3 emerged as highly discriminating environments, whereas E2 provided stable conditions. For HSW (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC), G\u003csub\u003e1\u003c/sub\u003e, G\u003csub\u003e6\u003c/sub\u003e, and G\u003csub\u003e7\u003c/sub\u003e demonstrated stable seed weight across locations, while G\u003csub\u003e3\u003c/sub\u003e, G\u003csub\u003e5\u003c/sub\u003e, and G\u003csub\u003e8\u003c/sub\u003e showed higher interaction with environments. E1 and E3 effectively differentiated genotype performance, whereas E2 exhibited stability with minimal interaction.\u003c/p\u003e \u003cp\u003eIn YIELD (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD), G\u003csub\u003e2\u003c/sub\u003e, G\u003csub\u003e4\u003c/sub\u003e, and G\u003csub\u003e7\u003c/sub\u003e combined high productivity with stability, whereas G\u003csub\u003e1\u003c/sub\u003e, G\u003csub\u003e3\u003c/sub\u003e, and G\u003csub\u003e5\u003c/sub\u003e experienced greater variation across environments. E1 and E2 offered strong differentiation among genotypes, while E3, positioned near the origin in the negative quadrant, represented a stable but low-yielding environment (Kilic, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). AMMI Stability Value (ASV) analysis ranked PCH-574 (G\u003csub\u003e7\u003c/sub\u003e), PCH-596 (G\u003csub\u003e2\u003c/sub\u003e), ICH-66 (G\u003csub\u003e4\u003c/sub\u003e), ICH-1024 (G\u003csub\u003e8\u003c/sub\u003e), and ICH-5 (G\u003csub\u003e6\u003c/sub\u003e) as the most stable genotypes across locations (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Yield Stability Index (YSI), incorporating both stability and mean performance, identified PCH-596 (G\u003csub\u003e2\u003c/sub\u003e), ICH-5 (G\u003csub\u003e6\u003c/sub\u003e), PCH-612 (G\u003csub\u003e1\u003c/sub\u003e), PCH-111 (G\u003csub\u003e5\u003c/sub\u003e), and PCH-574 (G\u003csub\u003e7\u003c/sub\u003e) as top performers. These findings highlight that ASV captures only stability, whereas YSI provides a comprehensive evaluation integrating stability and yield potential (Singamsetti et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\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\u003eRanking of genotypes using AMMI Stability value (ASV) and Yield Stability Index (YSI) based on yield\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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\u003eGenotype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eASV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003erASV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYSI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003erYSI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMeans (Yield)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCH 612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e63.476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2471.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCH 596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2689.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICH 1160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40.273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2180.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICH 66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30.805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2141.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCH 111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58.509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2462.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICH 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39.948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2592.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCH 574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2322.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICH 1024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35.423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2137.57\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 \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.5 GGE biplot\u003c/h2\u003e \u003cp\u003eSite regression genotype \u0026times; environment (G\u0026times;E) interaction (GGE) biplot analysis is a powerful approach for interpreting multi-environment trial data in plant breeding and for identifying genotypes with specific environmental adaptations (Yan et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Yan \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). This method recognizes that genotypes may not perform optimally across all conditions and emphasizes the combined effect of genotype and G\u0026times;E interaction, which primarily determines yield performance. GGE biplot analysis is particularly effective for datasets spanning multiple environments and facilitates visualization of complex interactions. Tools such as the \u0026ldquo;which-won-where\u0026rdquo; pattern allow clear identification of discriminating and representative environments, aiding in the selection of superior and broadly adaptable genotypes (Ajay et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.5.1 \u0026lsquo;\u003cb\u003eDiscriminativeness vs. representativeness\u0026rsquo; pattern of GGE biplot\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eIdentifying optimal test environments is crucial for effective breeding programs aimed at selecting and cultivating superior genotypes. The \u0026lsquo;Discriminativeness vs. Representativeness\u0026rsquo; view of the GGE biplot (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) provides a visual assessment of each environment's ability to differentiate among genotypes and its representativeness of the target region. In this view, the length of the environment vectors indicates discriminating ability, while the angle with the average environment coordinate (AEC) abscissa reflects representativeness. Longer vectors suggest greater potential to distinguish among genotypes, whereas shorter vectors indicate limited discrimination (Yan et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the present study, the shortest vector was observed for E1 in HSW, whereas the longest appeared for E3 in NN. Angle measurements showed that E1 for HSW had the smallest angle, indicating high representativeness, while E1 for NN formed the largest angle relative to the abscissa. An environment combining a long vector with a narrow angle is considered ideal for genotype selection (Samyuktha et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Yan \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Among the locations, E2 displayed both a long vector and narrow angle, demonstrating strong discriminative power and representativeness. Overall, the analysis suggested that E2 was optimal for DFF and NN, E1 for HSW, and E3 for YIELD, highlighting these environments as most suitable for evaluating and selecting superior castor genotypes across traits.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.5.2 Genotype ranking: best genotype assessment\u003c/h2\u003e \u003cp\u003eGGE biplot analysis provides a powerful framework to identify superior and stable castor genotypes across multiple environments. The ideal genotype combines high mean performance with minimal interaction and is typically positioned near the center of the biplot, close to the arrowhead within the concentric circles (Yan et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the case of days to 50% flowering (DFF), genotype G\u003csub\u003e7\u003c/sub\u003e occupied a position near the origin, reflecting remarkable stability across environments with negligible G\u0026times;E interaction. Genotypes G\u003csub\u003e2\u003c/sub\u003e, G\u003csub\u003e4\u003c/sub\u003e, and G\u003csub\u003e6\u003c/sub\u003e, positioned farther from the origin, exhibited pronounced interactions, indicating environment-specific performance. Notably, G\u003csub\u003e2\u003c/sub\u003e aligned closely with E2 and the concentric circles, suggesting high performance and specific adaptation to this environment. E2 demonstrated the strongest discriminative capacity, while E1, near the origin, allowed limited differentiation among genotypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). For the number of nodes to the primary spike (NN), G\u003csub\u003e1\u003c/sub\u003e and G\u003csub\u003e5\u003c/sub\u003e maintained proximity to the origin, signalling high stability across environments. In contrast, G\u003csub\u003e3\u003c/sub\u003e, G\u003csub\u003e4\u003c/sub\u003e, and G\u003csub\u003e6\u003c/sub\u003e showed greater sensitivity to environmental variation, positioned farther from the origin. G\u003csub\u003e5\u003c/sub\u003e, located farthest along the ranking axis, achieved superior overall performance, with G\u003csub\u003e3\u003c/sub\u003e and G\u003csub\u003e4\u003c/sub\u003e demonstrating adaptability due to alignment in the same direction. G\u003csub\u003e2\u003c/sub\u003e and G\u003csub\u003e6\u003c/sub\u003e remained stable with moderate performance, whereas G\u003csub\u003e1\u003c/sub\u003e and G\u003csub\u003e7\u003c/sub\u003e combined high stability with average performance. Among environments, E1 exhibited maximal discrimination, with E2 and E3 providing moderate differentiation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Hundred-seed weight (HSW) analysis revealed that G\u003csub\u003e8\u003c/sub\u003e projected farthest along the ranking axis, emerging as the top performer with high seed weight and favorable interaction patterns. G\u003csub\u003e5\u003c/sub\u003e followed, demonstrating strong seed weight but increased sensitivity. Genotypes G\u003csub\u003e1\u003c/sub\u003e, G\u003csub\u003e2\u003c/sub\u003e, G\u003csub\u003e6\u003c/sub\u003e, and G\u003csub\u003e7\u003c/sub\u003e clustered near the origin, indicating moderate yet consistent performance across environments. E1, located near the origin, showed limited capacity to differentiate genotypes, whereas E2 (top-left) and E3 (bottom-left) were more discriminating, although no genotype closely aligned with these environments, suggesting the absence of a dominant environmental \u0026ldquo;winner\u0026rdquo; (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn seed yield (YIELD), G\u003csub\u003e6\u003c/sub\u003e and G\u003csub\u003e5\u003c/sub\u003e exhibited high and stable performance, positioned closest to the ranking axis. G\u003csub\u003e2\u003c/sub\u003e and G\u003csub\u003e3\u003c/sub\u003e displayed intermediate projections, indicative of moderate yield potential, while G\u003csub\u003e1\u003c/sub\u003e and G\u003csub\u003e4\u003c/sub\u003e, situated off-axis, experienced strong G\u0026times;E interactions coupled with lower mean performance. E1, positioned at the bottom-right, strongly discriminated among genotypes, favoring G\u003csub\u003e6\u003c/sub\u003e and G\u003csub\u003e5\u003c/sub\u003e and highlighting their specific adaptability. E2 also contributed to genotype differentiation, with G\u003csub\u003e1\u003c/sub\u003e and G\u003csub\u003e2\u003c/sub\u003e partially adapted, whereas E3, near the origin, represented a stable but less discriminating environment, yielding relatively uniform performance across genotypes (Yan et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e3.5.3 Which-Won-Where:\u003c/h2\u003e \u003cp\u003eThe polygon or \u0026ldquo;which-won-where\u0026rdquo; view of the GGE biplot provides a robust visualization of genotype \u0026times; environment interactions, crossover GEI, mega-environment differentiation, and specific genotype adaptations (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). In this representation, test environments were distributed across two of five sectors for DFF, one of six sectors for NN, two of six sectors for HSW, and two of six sectors for YIELD.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor DFF, two distinct mega-environments were observed: E1 and E2 formed the first, while E3 represented the second. Vertex genotypes G\u003csub\u003e2\u003c/sub\u003e and G\u003csub\u003e1\u003c/sub\u003e dominated the first mega-environment, indicating strong responsiveness, whereas G6 emerged as the vertex genotype for the second mega-environment. In NN, G\u003csub\u003e2\u003c/sub\u003e was the most responsive genotype across all environments (E1, E2, and E3). For HSW, G\u003csub\u003e1\u003c/sub\u003e and G\u003csub\u003e6\u003c/sub\u003e defined the first mega-environment (E1 \u0026amp; E2), while G\u003csub\u003e3\u003c/sub\u003e and G\u003csub\u003e4\u003c/sub\u003e were vertex genotypes for the second (E3). In terms of YIELD, G\u003csub\u003e2\u003c/sub\u003e exhibited superior responsiveness in the mega-environment comprising E2 and E3, whereas G6 showed enhanced performance in E1 (Olanrewaju et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Several vertex genotypes were positioned in sectors without any test environments, including G\u003csub\u003e3\u003c/sub\u003e and G\u003csub\u003e4\u003c/sub\u003e for DFF; G\u003csub\u003e3\u003c/sub\u003e, G\u003csub\u003e4\u003c/sub\u003e, G\u003csub\u003e6\u003c/sub\u003e, G\u003csub\u003e7\u003c/sub\u003e, and G\u003csub\u003e8\u003c/sub\u003e for NN; G\u003csub\u003e8\u003c/sub\u003e, G\u003csub\u003e5\u003c/sub\u003e, and G\u003csub\u003e7\u003c/sub\u003e for HSW; and G\u003csub\u003e1\u003c/sub\u003e, G\u003csub\u003e8\u003c/sub\u003e, G\u003csub\u003e3\u003c/sub\u003e, and G\u003csub\u003e5\u003c/sub\u003e for YIELD. These genotypes demonstrated poor performance across environments and are considered undesirable. In contrast, non-vertex genotypes, located within the polygon, exhibited moderate responsiveness, reflecting higher stability under varying environmental conditions.\u003c/p\u003e \u003cp\u003eVertex genotypes are indicative of superior performance and specific adaptation to particular mega-environments, whereas genotypes without associated environments are less desirable due to lower stability (Bernal and Villardon \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Ajay et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Clustering of environmental vectors within a sector indicates that a single genotype excels across all encompassed environments, while distribution across multiple sectors reveals differential genotype superiority depending on environmental context (Hashim et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThe evaluation of eight castor genotypes across three diverse locations revealed significant genotype \u0026times; environment interactions, highlighting the influence of environment-specific gene expression on trait performance. Among the test locations, Tandur (E2) was identified as the most discriminating and representative environment, ideal for selecting superior genotypes. Integrating insights from both AMMI and GGE biplot analyses, ICH-5 (G\u003csub\u003e6\u003c/sub\u003e), PCH-111 (G\u003csub\u003e5\u003c/sub\u003e), and PCH-596 (G\u003csub\u003e2\u003c/sub\u003e) consistently demonstrated high yield and stability across environments. Notably, ICH-5 and PCH-596 combined superior seed yield with higher hundred-seed weight, reflecting their agronomic potential and broad adaptability. Although PCH-574 (G\u003csub\u003e7\u003c/sub\u003e) showed stability in days to 50% flowering, its performance for seed yield and hundred-seed weight remained average, emphasizing the importance of multi-trait evaluation. The \u0026ldquo;which-won-where\u0026rdquo; GGE biplot further highlighted PCH-596 (G\u003csub\u003e2\u003c/sub\u003e) as a vertex genotype, confirming its superior adaptability and performance across the tested environments. Overall, this study underscores the effectiveness of combining AMMI and GGE analyses for dissecting GEI and provides a robust framework for selecting high-yielding, stable castor genotypes for targeted breeding programs.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors would like thank Professor Jayashankar Telangana Agricultural University, Rajendranagar, Hyderabad, Telangana, India \u0026ndash; 500 030 for financial support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors declare that they do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePlanning and conducting the experiment and contributed significantly to writing the manuscripts [K. Sadaiah and G. Eswara Reddy], Conducted the experiment \u0026amp; data collection [K. Parimala, A. Saritha and T. Rajeshwar Reddy], crop management and preparation of manuscript [G. Madhuri, V. Divya Rani and N. Nalini], Planned and conducted the experiment and review \u0026amp; editing [S. Vanisri and M. Sreedhar], Supervision, review \u0026amp; editing and administration [L. Krishna].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll authors have read and approved the final manuscript\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval consent to pub:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u0026nbsp;\u003c/strong\u003eData will be made available on request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAjay BC, Aravind J, Abdul Fiyaz R, Bera SK, Narendra Kumar Gangadhar K, Praveen Kona (2018) Modified AMMI Stability Index (MASI) for stability analysis. 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Crop Sci 40 (3): 597\u0026ndash;605.\u003c/li\u003e\n\u003cli\u003eYan W (2002) Singular-value partitioning in biplot analysis of multi environment trial data.\u003cem\u003e \u003c/em\u003eAgronomy J 94: 990\u0026ndash;996\u003cem\u003e. \u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eYan W (2001) GGE biplot- a window application for graphical analysis of multi-environmental data and other types of two-way data. Agronomy J 93: 1111\u0026ndash;1118.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Castor, Genotype x Environment Interaction, AMMI, GGE Biplot, yield","lastPublishedDoi":"10.21203/rs.3.rs-8233933/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8233933/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDespite its industrial potential, productivity in castor remains constrained by genotype \u0026times; environment interactions (GEI), which obscure the true genetic potential of hybrids across variable production ecologies. The present investigation sought to elucidate the magnitude and pattern of GEI and to identify stable, high-performing hybrids for yield and yield-contributing traits across diverse agro-climatic conditions of Telangana, India. Eight elite castor hybrids were evaluated across multi-environment trials using AMMI (Additive Main Effects and Multiplicative Interaction) and GGE (Genotype and Genotype \u0026times; Environment) biplot models to dissect stability, adaptability, and environmental representativeness. Highly significant GEI effects were detected for seed yield, days to 50% flowering, number of nodes, and hundred-seed weight, underscoring the differential response of genotypes across environments. The AMMI biplot effectively captured interaction patterns, identifying Palem (E1) location as the most representative and least interactive environment for yield performance. \u0026ldquo;Which-won-where\u0026rdquo; analysis of the GGE biplot delineated mega-environment groupings, with PCH-596 excelling under Tandur (E2) and Tornala (E3) locations, while ICH-5 demonstrated superior adaptability to E1. Yield Stability Index (YSI) and GGE ranking analyses consistently recognized PCH-596 and ICH-5 as the most stable and high-yielding hybrids across the environments. The integration of AMMI and GGE biplot methodologies proved highly effective in unravelling complex GEI patterns, facilitating the identification of genotypes with broad and specific adaptability. These findings provide a quantitative basis for environment-specific hybrid recommendations and contribute to accelerating genetic gains in castor breeding programs targeting enhanced productivity and resilience.\u003c/p\u003e","manuscriptTitle":"Genotype × Environment Interaction Insights for Yield and Yield Components in Castor via AMMI and GGE Biplot Models","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-19 08:39:52","doi":"10.21203/rs.3.rs-8233933/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-27T16:44:41+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-27T09:27:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-24T18:46:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"47375592867051405026580883437634942133","date":"2026-01-19T14:28:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"228443308551047939501198660077450750846","date":"2026-01-14T07:45:29+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-17T10:22:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-15T12:45:30+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-15T12:20:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-11T03:49:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-12-11T03:45:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7fc74436-504b-4682-8379-872bd54e887a","owner":[],"postedDate":"December 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":59808997,"name":"Biological sciences/Ecology"},{"id":59808998,"name":"Earth and environmental sciences/Ecology"},{"id":59808999,"name":"Biological sciences/Genetics"},{"id":59809000,"name":"Biological sciences/Plant sciences"}],"tags":[],"updatedAt":"2026-04-27T16:10:32+00:00","versionOfRecord":{"articleIdentity":"rs-8233933","link":"https://doi.org/10.1038/s41598-026-44030-5","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-04-20 15:58:12","publishedOnDateReadable":"April 20th, 2026"},"versionCreatedAt":"2025-12-19 08:39:52","video":"","vorDoi":"10.1038/s41598-026-44030-5","vorDoiUrl":"https://doi.org/10.1038/s41598-026-44030-5","workflowStages":[]},"version":"v1","identity":"rs-8233933","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8233933","identity":"rs-8233933","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}