Genotype-by-environment Interaction and Yield Stability Analysis of Egyptian Cotton (Gossypium Barbadense L.)

Genotype-by-environment Interaction and Yield Stability Analysis of Egyptian Cotton (Gossypium Barbadense L.) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Genotype-by-environment Interaction and Yield Stability Analysis of Egyptian Cotton ( Gossypium Barbadense L. ) Fathi E. Elfeki, Badeaa A. Mahmoud, Mohab W. Elshazly, Usama A. Abd El-Razek This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8818924/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Genotype-by-environment interaction (GEI) poses a significant challenge to achieving consistent cotton yield across diverse agro-ecologies. This study aimed to quantify the components of yield variation, identify stable high-yielding genotypes, and delineate mega-environments within the Nile Delta for efficient cultivar deployment. Seven cotton genotypes (four advanced lines and three commercial cultivars) were evaluated across six environments, generated from three locations over two growing seasons (2023 and 2024). Data were analyzed using combined analysis of variance, Additive Main Effects and Multiplicative Interaction (AMMI), and Genotype plus Genotype-by-Environment (GGE) biplot models. The combined ANOVA revealed that GEI was the predominant source of variation, accounting for 47.12% of the total sum of squares, followed by environmental (32.91%) and genotypic (19.97%) effects. AMMI analysis identified 'Line 1' and 'Line 4' as highly stable genotypes. The GGE biplot effectively categorized the test environments into three distinct mega-environments (MEs): a favorable ME (Kafr El-Sheikh) where 'Line 1' performed best; a moderate-stress ME (Damietta) where 'Line 4' excelled; and a high-stress ME (El-Sharkia) where the commercial cultivar 'Giza 94' showed superior stability. These findings provide a robust framework for strategic cultivar recommendation and breeding targeted at specific agro-ecological zones in the Nile Delta, which can enhance yield stability and regional productivity. AMMI GGE biplot Gossypium barbadense L. genotype-by-environment interaction mega-environment stability cotton yield Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Egyptian long-staple cotton ( Gossypium barbadense L.) is a strategic crop renowned for its premium fiber quality, commanding high prices in international markets and supporting agricultural communities throughout the Nile Delta (El-Sayed, Abdel-Ghani and Sultan, 2021 ). However, maximizing its genetic potential is constrained by the heterogeneity of the production landscape. Variations in soil composition, water availability, thermal conditions, and pest pressure within the Nile Delta create significant genotype-by-environment interactions (GEI), leading to fluctuations in the relative performance of cultivars (Annicchiarico, 2002 ). Substantial GEI complicates breeding efforts and hinders the formulation of universal cultivar recommendations. The necessity to understand and manage GEI is well-established in crop science. Differential genotypic responses to environmental variables are a major determinant of final yield (Yan and Kang, 2003 ). This has prompted a paradigm shift from developing widely adapted "super varieties" towards identifying cultivars suited to specific agro-ecological zones or "mega-environments" (Gauch and Zobel, 1997 ). To decipher complex GEI patterns, advanced statistical models such as the Additive Main Effects and Multiplicative Interaction (AMMI) model and the Genotype plus Genotype-by-Environment (GGE) biplot have proven invaluable. The AMMI model partitions variation into additive and multiplicative components, providing quantitative stability estimates (Gauch, 2006 ). The GGE biplot offers intuitive visualization of genotype performance and facilitates the detection of "which-won-where" patterns, aiding in mega-environment delineation (Yan et al., 2007 ). These tools have been successfully applied in cotton research to identify stable genotypes and specific adaptation zones (Abdel-Moneam et al., 2021 ; Hinze et al., 2017 ). This study was conducted with the following objectives: (1) to quantify the effects of GEI on seed cotton yield in the Nile Delta; (2) to identify broadly adapted and specifically adapted stable genotypes; (3) to delineate primary cotton-growing areas into distinct mega-environments; and (4) to develop actionable cultivar deployment recommendations. To our knowledge, this represents a comprehensive and contemporary analysis of GEI and mega-environments for Egyptian long-staple cotton. 2. Materials and Methods 2.1. Plant Materials and Experimental Sites Seven cotton genotypes were used in this study: four advanced breeding lines ('Line 1', 'Line 2', 'Line 3', 'Line 4') and three commercial cultivars ('Giza 86', 'Giza 94', 'Giza 96'). Field trials were conducted during the 2023 and 2024 growing seasons at three locations representing a gradient of environmental conditions in the Nile Delta: Kafr El-Sheikh (favorable, low-stress), Damietta (moderate-stress), and El-Sharkia (high-stress). The combination of three locations over two years resulted in six distinct test environments (E1–E6). 2.2. Experimental Design and Data Collection The experiment was arranged in a randomized complete block design (RCBD) with three replications at each location. All standard agronomic practices for the region were followed uniformly. At physiological maturity, seed cotton yield was harvested from the central rows of each plot. Yield was recorded and standardized to Kentar per Feddan (1 Kentar = 157.5 kg; 1 Feddan = 4200 m²) at a 12% moisture content. 2.3. Statistical Analysis A combined analysis of variance (ANOVA) was performed to partition the total variation into contributions from genotypes (G), environments (E), and their interaction (GEI). The AMMI model was applied to assess genotype stability using the AMMI Stability Value (ASV). The GGE biplot methodology was employed for visual analysis of mega-environments and for genotype evaluation based on the model: Yij - µ - Ej = Gi + GEij , where the environment mean effect is removed to focus on genotype (G) and interaction (GE) effects (Yan and Kang, 2003 ). All analyses were performed using R software (version 4.3.1) with the 'metan' and 'GGEBiplots' packages. 3. Results 3.1. Analysis of Variance and Environmental Variation The six test environments exhibited a wide range of productivity, with mean seed cotton yield varying from 9.42 Kentar/Feddan (E6, El-Sharkia 2024) to 14.18 Kentar/Feddan (E1, Kafr El-Sheikh 2023), as detailed in Table 1 . Table 1 Environmental characterization and mean cotton yield across six test environments. Environment Location Year Mean Yield (Kentar/Feddan) ± SE Rainfall (mm) Stress Level E1 Kafr El-Sheikh 2023 14.18 ± 0.42 78 Low E2 Kafr El-Sheikh 2024 13.42 ± 0.38 65 Low-Moderate E3 Damietta 2023 12.87 ± 0.35 52 Moderate E4 Damietta 2024 12.31 ± 0.41 48 Moderate E5 El-Sharkia 2023 10.96 ± 0.39 35 High E6 El-Sharkia 2024 9.42 ± 0.44 28 High The combined ANOVA (Table 2 ) indicated that all sources of variation (G, E, and GEI) were highly significant (p < 0.01). The interaction effect (GEI) contributed the largest proportion (47.12%) to the total sum of squares, followed by environmental (32.91%) and genotypic (19.97%) effects. Table 2 Combined analysis of variance for seed cotton yield of seven cotton genotypes across six environments. Source of Variation df Sum of Squares Mean Square F-value p-value Genotypes (G) 6 142.8 23.80 8.42 < 0.01 Environments (E) 5 235.9 47.18 16.71 < 0.01 G × E Interaction 30 337.4 11.25 3.96 < 0.01 Error 126 356.8 2.83 Total 167 1072.9 3.2. AMMI Stability Analysis The AMMI analysis provided stability estimates via the AMMI Stability Value (ASV) (Table 3 ). 'Line 4' (ASV = 0.27) and 'Line 1' (ASV = 0.30) were classified as highly stable. The commercial cultivar 'Giza 94' also showed high stability (ASV = 0.28). In contrast, 'Line 3' was the least stable genotype with a markedly high ASV of 2.18. Table 3 Mean yield and AMMI Stability Value (ASV) for the seven cotton genotypes. Genotype IPCA1 Score IPCA2 Score ASV Stability Classification Mean Yield (Kentar/Feddan) ± SE Line 1 -0.18 0.38 0.30 Highly Stable 13.30 ± 0.31 Line 4 0.15 0.24 0.27 Highly Stable 12.84 ± 0.28 Giza 94 0.08 -0.41 0.28 Highly Stable 11.95 ± 0.35 Giza 86 -0.27 0.65 0.60 Moderately Stable 11.42 ± 0.42 Line 2 0.42 -0.52 0.68 Moderately Stable 11.18 ± 0.38 Giza 96 -0.35 0.78 0.74 Moderately Stable 10.87 ± 0.44 Line 3 0.98 1.22 2.18 Unstable 9.76 ± 0.52 3.3. Mega-Environment Delineation and Genotype Evaluation via GGE Biplot The GGE biplot analysis effectively captured the patterns of genotype performance and interaction, explaining 79.1% of the total G + GEI variation (Principal Component 1, PC1 = 51.0%; PC2 = 28.1%). Mega-Environment Identification The "which-won-where" polygon view of the biplot (Fig. 1 ) clearly delineated the six test environments into three distinct mega-environments (MEs) based on the winning vertex genotype in each sector ME1 (Favorable) : Encompassed the two Kafr El-Sheikh environments (E1, E2). ME2 (Moderate Stress) : Encompassed the two Damietta environments (E3, E4). ME3 (High Stress) : Encompassed the two El-Sharkia environments (E5, E6). Environments are represented by red markers (E1-E6); genotypes are represented by blue markers. The polygon connects the vertex genotypes, defining the sectors for the three mega-environments. Genotype Performance and Stability The Average Environment Coordination (AEC) view (Fig. 2 ) was used to simultaneously evaluate genotypes for their mean yield (projection onto the Average Environment Axis, AEA) and stability (deviation from this axis). Genotypes located closer to the AEA (indicated by the single-arrow line) have higher mean yield, while those with shorter projections onto the perpendicular stability line (double-arrowed line) are more stable. Ideal Genotype Ranking To synthesize both yield and stability, an ideal genotype ranking biplot was constructed (Fig. 3 ). A theoretical ideal genotype, defined by the highest mean yield and absolute stability, is placed at the center of the concentric circles. The proximity of a genotype to this center indicates its overall desirability. The ideal genotype is positioned at the center. Genotypes closer to the center are considered more desirable. Relationships among Environments and Genotypes The relationships among the six test environments are visualized in Fig. 4 . Environments connected by vectors with small angles are positively correlated. Figure 5 illustrates the interrelationships among the seven cotton genotypes. 4. Discussion The substantial contribution of genotype-by-environment interaction to total yield variation (47.12%) constitutes the pivotal finding of this investigation. This magnitude exceeds environmental main effects and substantially outweighs genotypic differences, confirming that relative cultivar performance in the Nile Delta is inherently context-dependent. A genotype delivering exceptional yields under favorable conditions may perform only moderately—or even poorly—when confronted with environmental constraints. This reality renders pursuit of a single universally adapted cultivar for the entire Delta region biologically unrealistic and agronomically inefficient (Annicchiarico, 2002 ). Instead, these findings strongly support adoption of spatially explicit cultivar deployment strategies that acknowledge and leverage environmental heterogeneity. The delineation of three distinct mega-environments through GGE biplot analysis provides an actionable framework for implementing such strategies. The clustering of locations into separate adaptation zones aligns logically with known environmental gradients across the Delta. Kafr El-Sheikh environments formed a high-potential mega-environment where Line 1 not only achieved the highest mean yield but also demonstrated remarkable stability (ASV = 0.30). This combination of high productivity and reliability offers particular value to farmers seeking consistent returns under favorable conditions. In the moderate-stress mega-environment represented by Damietta, Line 4—the most stable genotype overall—emerged as the optimal choice. Its performance pattern suggests that stability mechanisms may confer greater advantage than maximum yield potential in environments experiencing moderate but variable stress levels, reflecting the concept of biological buffering where certain genotypes maintain relatively consistent performance across fluctuating conditions (Gauch, 2006 ). For the high-stress mega-environment (El-Sharkia), the established cultivar Giza 94 demonstrated superior adaptation, validating historical breeding emphasis on resilience traits in commercial varieties. This outcome suggests that cultivar selection for marginal environments should prioritize specific adaptive mechanisms—potentially including root architecture modifications or physiological stress tolerance—over absolute yield potential under ideal conditions. Methodologically, the complementary application of AMMI and GGE biplot proved particularly valuable. AMMI provided rigorous quantitative stability metrics that objectively ranked genotypes, while GGE biplot translated these statistical insights into intuitive visual representations facilitating practical interpretation. The strong concordance between methods in identifying Lines 1 and 4 as superior performers enhances confidence in these selections. Critically, the integrated approach prevented potential misinterpretation that might arise from considering yield potential alone; genotypes exhibiting high mean performance but substantial instability would have been inappropriately recommended without stability assessment. These findings carry several practical implications for Egyptian cotton improvement programs. First, breeding networks should establish testing protocols that evaluate advanced lines across representative sites from each mega-environment early in selection cycles. Such spatially distributed testing enables identification of both broadly adapted genotypes and those with specific adaptation to particular zones. Second, crossing strategies could deliberately combine parents with complementary adaptation patterns—for instance, pairing Line 1 (high yield potential in favorable environments) with Giza 94 (stress resilience)—to generate segregating populations potentially exhibiting transgressive segregation for both yield potential and stability. Third, seed distribution systems should adopt zone-specific recommendations rather than uniform cultivar deployment, thereby enhancing farmer profitability through better genotype-environment matching. This investigation possesses limitations warranting acknowledgment. The two-year evaluation period, while capturing seasonal variation, would benefit from extension across additional growing seasons to strengthen mega-environment definitions and account for inter-annual climatic fluctuations. Furthermore, future research should investigate physiological mechanisms underpinning the stability of Lines 1 and 4—potentially examining root system architecture, stomatal regulation under water deficit, or membrane thermostability—to develop trait-based selection criteria complementing empirical yield evaluation. 5. Conclusions This study quantified the dominant role of genotype-by-environment interaction in shaping cotton yield variation across the Nile Delta and successfully delineated three distinct mega-environments corresponding to favorable, moderate-stress, and high-stress production zones. The integrated application of AMMI and GGE biplot methodologies proved highly effective for identifying stable high-performing genotypes and mapping their zones of optimal adaptation. Line 1 demonstrated superior performance in favorable environments (Kafr El-Sheikh), Line 4 excelled under moderate stress conditions (Damietta), and Giza 94 maintained reliable yields in high-stress environments (El-Sharkia). We recommend adoption of these spatially targeted cultivar deployment strategies by Egyptian extension services and seed companies. Furthermore, breeding programs should institutionalize mega-environment-based testing protocols to accelerate development of genotypes with defined adaptation patterns. Implementation of these evidence-based recommendations should enhance yield stability, reduce production risk for farmers, and contribute to sustained productivity across Egypt's cotton-growing regions. Declarations Institutional Review Board Statement Not applicable. Informed Consent Statement : Not applicable. Conflicts of Interest: The authors declare no conflict of interest. Funding: This research received no external funding. Author Contribution F.E.E. and B.A.M. conceptualized the research objectives and designed the field experiments; M.W.E. coordinated field trial implementation across the three locations and supervised data collection; U.A.A. performed the statistical analyses using AMMI and GGE biplot methodologies, prepared all figures and tables, and led manuscript drafting; F.E.E. and U.A.A. jointly interpreted the results and developed cultivar deployment recommendations; B.A.M. and M.W.E. contributed to methodological refinement and validation of environmental characterizations; U.A.A. prepared the original draft of the manuscript; all authors (F.E.E., B.A.M., M.W.E., and U.A.A.) critically reviewed, edited, and approved the final version of the manuscript. Acknowledgments: The authors thank the technical staff of the Cotton Research Institute for their assistance in field management and data collection. Data Availability The raw data supporting the conclusions of this article will be made available by the corresponding author upon reasonable request. References Abdel-Moneam MA, Sultan MS, Abdel-Ghani AH, El-Sayed AA. GGE biplot and AMMI analysis for mega-environment investigation and genotype evaluation of Egyptian cotton. J Crop Improv. 2021;35(2):209–25. https://doi.org/10.1080/15427528.2020.1858467 . Annicchiarico P. Genotype x environment interaction: Challenges and opportunities for plant breeding and cultivar recommendations. FAO Plant Production and Protection Paper No. 174. Food and Agriculture Organization of the United Nations; 2002. El-Sayed AA, Abdel-Ghani AH, Sultan MS. Egyptian cotton: A legacy of excellence and a future of promise. Agronomy. 2021;11(7):1432. https://doi.org/10.3390/agronomy11071432 . Gauch HG. Statistical analysis of yield trials by AMMI and GGE. Crop Sci. 2006;46(4):1488–500. https://doi.org/10.2135/cropsci2005.07-0193 . Gauch HG, Zobel RW. Identifying mega-environments and targeting genotypes. Crop Sci. 1997;37(2):311–26. https://doi.org/10.2135/cropsci1997.0011183X003700020002x . Hinze LL, Hulse-Kemp AM, Wilson IW, Stelly DM. A GGE biplot analysis of genotype-by-environment interaction in the US National Cotton Variety Test. Crop Sci. 2017;57(3):1339–53. https://doi.org/10.2135/cropsci2016.08.0694 . Yan W, Kang MS. GGE biplot analysis: A graphical tool for breeders, geneticists, and agronomists. CRC; 2003. Yan W, Kang MS, Ma B, Woods S, Cornelius PL. GGE biplot vs. AMMI analysis of genotype-by-environment data. Crop Sci. 2007;47(2):643–55. https://doi.org/10.2135/cropsci2006.06.0374 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8818924","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":588271441,"identity":"c15417a6-3df7-4c30-8737-fc064adca8b3","order_by":0,"name":"Fathi E. 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Abd El-Razek","email":"data:image/png;base64,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","orcid":"","institution":"Tanta university","correspondingAuthor":true,"prefix":"","firstName":"Usama","middleName":"A. 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Introduction","content":"\u003cp\u003eEgyptian long-staple cotton (\u003cem\u003eGossypium barbadense\u003c/em\u003e L.) is a strategic crop renowned for its premium fiber quality, commanding high prices in international markets and supporting agricultural communities throughout the Nile Delta (El-Sayed, Abdel-Ghani and Sultan, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, maximizing its genetic potential is constrained by the heterogeneity of the production landscape. Variations in soil composition, water availability, thermal conditions, and pest pressure within the Nile Delta create significant genotype-by-environment interactions (GEI), leading to fluctuations in the relative performance of cultivars (Annicchiarico, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Substantial GEI complicates breeding efforts and hinders the formulation of universal cultivar recommendations.\u003c/p\u003e \u003cp\u003eThe necessity to understand and manage GEI is well-established in crop science. Differential genotypic responses to environmental variables are a major determinant of final yield (Yan and Kang, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). This has prompted a paradigm shift from developing widely adapted \"super varieties\" towards identifying cultivars suited to specific agro-ecological zones or \"mega-environments\" (Gauch and Zobel, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). To decipher complex GEI patterns, advanced statistical models such as the Additive Main Effects and Multiplicative Interaction (AMMI) model and the Genotype plus Genotype-by-Environment (GGE) biplot have proven invaluable. The AMMI model partitions variation into additive and multiplicative components, providing quantitative stability estimates (Gauch, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The GGE biplot offers intuitive visualization of genotype performance and facilitates the detection of \"which-won-where\" patterns, aiding in mega-environment delineation (Yan et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). These tools have been successfully applied in cotton research to identify stable genotypes and specific adaptation zones (Abdel-Moneam et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hinze et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study was conducted with the following objectives: (1) to quantify the effects of GEI on seed cotton yield in the Nile Delta; (2) to identify broadly adapted and specifically adapted stable genotypes; (3) to delineate primary cotton-growing areas into distinct mega-environments; and (4) to develop actionable cultivar deployment recommendations. To our knowledge, this represents a comprehensive and contemporary analysis of GEI and mega-environments for Egyptian long-staple cotton.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Plant Materials and Experimental Sites\u003c/h2\u003e \u003cp\u003eSeven cotton genotypes were used in this study: four advanced breeding lines ('Line 1', 'Line 2', 'Line 3', 'Line 4') and three commercial cultivars ('Giza 86', 'Giza 94', 'Giza 96'). Field trials were conducted during the 2023 and 2024 growing seasons at three locations representing a gradient of environmental conditions in the Nile Delta: \u003cb\u003eKafr El-Sheikh\u003c/b\u003e (favorable, low-stress), \u003cb\u003eDamietta\u003c/b\u003e (moderate-stress), and \u003cb\u003eEl-Sharkia\u003c/b\u003e (high-stress). The combination of three locations over two years resulted in six distinct test environments (E1\u0026ndash;E6).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Experimental Design and Data Collection\u003c/h2\u003e \u003cp\u003eThe experiment was arranged in a randomized complete block design (RCBD) with three replications at each location. All standard agronomic practices for the region were followed uniformly. At physiological maturity, seed cotton yield was harvested from the central rows of each plot. Yield was recorded and standardized to \u003cb\u003eKentar per Feddan\u003c/b\u003e (1 Kentar\u0026thinsp;=\u0026thinsp;157.5 kg; 1 Feddan\u0026thinsp;=\u0026thinsp;4200 m\u0026sup2;) at a 12% moisture content.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Statistical Analysis\u003c/h2\u003e \u003cp\u003eA combined analysis of variance (ANOVA) was performed to partition the total variation into contributions from genotypes (G), environments (E), and their interaction (GEI). The AMMI model was applied to assess genotype stability using the AMMI Stability Value (ASV). The GGE biplot methodology was employed for visual analysis of mega-environments and for genotype evaluation based on the model: \u003cem\u003eYij - \u0026micro; - Ej\u0026thinsp;=\u0026thinsp;Gi\u0026thinsp;+\u0026thinsp;GEij\u003c/em\u003e, where the environment mean effect is removed to focus on genotype (G) and interaction (GE) effects (Yan and Kang, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). All analyses were performed using R software (version 4.3.1) with the 'metan' and 'GGEBiplots' packages.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Analysis of Variance and Environmental Variation\u003c/h2\u003e \u003cp\u003eThe six test environments exhibited a wide range of productivity, with mean seed cotton yield varying from 9.42 Kentar/Feddan (E6, El-Sharkia 2024) to 14.18 Kentar/Feddan (E1, Kafr El-Sheikh 2023), as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\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\u003eEnvironmental characterization and mean cotton yield across six test environments.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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=\"\u0026plusmn;\" 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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnvironment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLocation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean Yield (Kentar/Feddan)\u0026thinsp;\u0026plusmn;\u0026thinsp;SE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRainfall (mm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStress Level\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKafr El-Sheikh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e14.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKafr El-Sheikh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e13.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLow-Moderate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDamietta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e12.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDamietta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e12.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEl-Sharkia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e10.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEl-Sharkia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e9.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe combined ANOVA (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) indicated that all sources of variation (G, E, and GEI) were highly significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The interaction effect (GEI) contributed the largest proportion (47.12%) to the total sum of squares, followed by environmental (32.91%) and genotypic (19.97%) effects.\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 analysis of variance for seed cotton yield of seven cotton genotypes across six environments.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"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\u003eSource of Variation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSum of Squares\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean Square\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenotypes (G)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e142.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnvironments (E)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e235.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG \u0026times; E Interaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e337.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eError\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e356.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\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\u003e\u003cb\u003e167\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1072.9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\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=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2. AMMI Stability Analysis\u003c/h2\u003e \u003cp\u003eThe AMMI analysis provided stability estimates via the AMMI Stability Value (ASV) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). 'Line 4' (ASV\u0026thinsp;=\u0026thinsp;0.27) and 'Line 1' (ASV\u0026thinsp;=\u0026thinsp;0.30) were classified as highly stable. The commercial cultivar 'Giza 94' also showed high stability (ASV\u0026thinsp;=\u0026thinsp;0.28). In contrast, 'Line 3' was the least stable genotype with a markedly high ASV of 2.18.\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\u003eMean yield and AMMI Stability Value (ASV) for the seven cotton genotypes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" 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\u003eIPCA1 Score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIPCA2 Score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eASV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStability Classification\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMean Yield (Kentar/Feddan)\u0026thinsp;\u0026plusmn;\u0026thinsp;SE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLine 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHighly Stable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e13.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLine 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHighly Stable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e12.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGiza 94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHighly Stable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e11.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGiza 86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModerately Stable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e11.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLine 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModerately Stable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e11.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGiza 96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModerately Stable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e10.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLine 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUnstable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e9.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\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\u003e3.3. Mega-Environment Delineation and Genotype Evaluation via GGE Biplot\u003c/h2\u003e \u003cp\u003eThe GGE biplot analysis effectively captured the patterns of genotype performance and interaction, explaining 79.1% of the total G\u0026thinsp;+\u0026thinsp;GEI variation (Principal Component 1, PC1\u0026thinsp;=\u0026thinsp;51.0%; PC2\u0026thinsp;=\u0026thinsp;28.1%).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eMega-Environment Identification\u003c/strong\u003e \u003cp\u003eThe \"which-won-where\" polygon view of the biplot (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) clearly delineated the six test environments into three distinct mega-environments (MEs) based on the winning vertex genotype in each sector\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eME1 (Favorable)\u003c/b\u003e: Encompassed the two Kafr El-Sheikh environments (E1, E2).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eME2 (Moderate Stress)\u003c/b\u003e: Encompassed the two Damietta environments (E3, E4).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eME3 (High Stress)\u003c/b\u003e: Encompassed the two El-Sharkia environments (E5, E6).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eEnvironments are represented by red markers (E1-E6); genotypes are represented by blue markers. The polygon connects the vertex genotypes, defining the sectors for the three mega-environments.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eGenotype Performance and Stability\u003c/strong\u003e \u003cp\u003eThe Average Environment Coordination (AEC) view (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) was used to simultaneously evaluate genotypes for their mean yield (projection onto the Average Environment Axis, AEA) and stability (deviation from this axis). Genotypes located closer to the AEA (indicated by the single-arrow line) have higher mean yield, while those with shorter projections onto the perpendicular stability line (double-arrowed line) are more stable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eIdeal Genotype Ranking\u003c/strong\u003e \u003cp\u003eTo synthesize both yield and stability, an ideal genotype ranking biplot was constructed (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). A theoretical ideal genotype, defined by the highest mean yield and absolute stability, is placed at the center of the concentric circles. The proximity of a genotype to this center indicates its overall desirability.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe ideal genotype is positioned at the center. Genotypes closer to the center are considered more desirable.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eRelationships among Environments and Genotypes\u003c/strong\u003e \u003cp\u003eThe relationships among the six test environments are visualized in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Environments connected by vectors with small angles are positively correlated. Figure\u0026nbsp;5 illustrates the interrelationships among the seven cotton genotypes.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe substantial contribution of genotype-by-environment interaction to total yield variation (47.12%) constitutes the pivotal finding of this investigation. This magnitude exceeds environmental main effects and substantially outweighs genotypic differences, confirming that relative cultivar performance in the Nile Delta is inherently context-dependent. A genotype delivering exceptional yields under favorable conditions may perform only moderately\u0026mdash;or even poorly\u0026mdash;when confronted with environmental constraints. This reality renders pursuit of a single universally adapted cultivar for the entire Delta region biologically unrealistic and agronomically inefficient (Annicchiarico, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Instead, these findings strongly support adoption of spatially explicit cultivar deployment strategies that acknowledge and leverage environmental heterogeneity.\u003c/p\u003e \u003cp\u003eThe delineation of three distinct mega-environments through GGE biplot analysis provides an actionable framework for implementing such strategies. The clustering of locations into separate adaptation zones aligns logically with known environmental gradients across the Delta. Kafr El-Sheikh environments formed a high-potential mega-environment where Line 1 not only achieved the highest mean yield but also demonstrated remarkable stability (ASV\u0026thinsp;=\u0026thinsp;0.30). This combination of high productivity and reliability offers particular value to farmers seeking consistent returns under favorable conditions. In the moderate-stress mega-environment represented by Damietta, Line 4\u0026mdash;the most stable genotype overall\u0026mdash;emerged as the optimal choice. Its performance pattern suggests that stability mechanisms may confer greater advantage than maximum yield potential in environments experiencing moderate but variable stress levels, reflecting the concept of biological buffering where certain genotypes maintain relatively consistent performance across fluctuating conditions (Gauch, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). For the high-stress mega-environment (El-Sharkia), the established cultivar Giza 94 demonstrated superior adaptation, validating historical breeding emphasis on resilience traits in commercial varieties. This outcome suggests that cultivar selection for marginal environments should prioritize specific adaptive mechanisms\u0026mdash;potentially including root architecture modifications or physiological stress tolerance\u0026mdash;over absolute yield potential under ideal conditions.\u003c/p\u003e \u003cp\u003eMethodologically, the complementary application of AMMI and GGE biplot proved particularly valuable. AMMI provided rigorous quantitative stability metrics that objectively ranked genotypes, while GGE biplot translated these statistical insights into intuitive visual representations facilitating practical interpretation. The strong concordance between methods in identifying Lines 1 and 4 as superior performers enhances confidence in these selections. Critically, the integrated approach prevented potential misinterpretation that might arise from considering yield potential alone; genotypes exhibiting high mean performance but substantial instability would have been inappropriately recommended without stability assessment.\u003c/p\u003e \u003cp\u003eThese findings carry several practical implications for Egyptian cotton improvement programs. First, breeding networks should establish testing protocols that evaluate advanced lines across representative sites from each mega-environment early in selection cycles. Such spatially distributed testing enables identification of both broadly adapted genotypes and those with specific adaptation to particular zones. Second, crossing strategies could deliberately combine parents with complementary adaptation patterns\u0026mdash;for instance, pairing Line 1 (high yield potential in favorable environments) with Giza 94 (stress resilience)\u0026mdash;to generate segregating populations potentially exhibiting transgressive segregation for both yield potential and stability. Third, seed distribution systems should adopt zone-specific recommendations rather than uniform cultivar deployment, thereby enhancing farmer profitability through better genotype-environment matching.\u003c/p\u003e \u003cp\u003eThis investigation possesses limitations warranting acknowledgment. The two-year evaluation period, while capturing seasonal variation, would benefit from extension across additional growing seasons to strengthen mega-environment definitions and account for inter-annual climatic fluctuations. Furthermore, future research should investigate physiological mechanisms underpinning the stability of Lines 1 and 4\u0026mdash;potentially examining root system architecture, stomatal regulation under water deficit, or membrane thermostability\u0026mdash;to develop trait-based selection criteria complementing empirical yield evaluation.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis study quantified the dominant role of genotype-by-environment interaction in shaping cotton yield variation across the Nile Delta and successfully delineated three distinct mega-environments corresponding to favorable, moderate-stress, and high-stress production zones. The integrated application of AMMI and GGE biplot methodologies proved highly effective for identifying stable high-performing genotypes and mapping their zones of optimal adaptation. Line 1 demonstrated superior performance in favorable environments (Kafr El-Sheikh), Line 4 excelled under moderate stress conditions (Damietta), and Giza 94 maintained reliable yields in high-stress environments (El-Sharkia). We recommend adoption of these spatially targeted cultivar deployment strategies by Egyptian extension services and seed companies. Furthermore, breeding programs should institutionalize mega-environment-based testing protocols to accelerate development of genotypes with defined adaptation patterns. Implementation of these evidence-based recommendations should enhance yield stability, reduce production risk for farmers, and contribute to sustained productivity across Egypt's cotton-growing regions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eInstitutional Review Board Statement\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eStatement\u003c/strong\u003e: Not applicable.\u003c/p\u003e\n\u003ch2\u003eConflicts of Interest:\u003c/h2\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003ch2\u003eFunding:\u003c/h2\u003e\n\u003cp\u003eThis research received no external funding.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eF.E.E. and B.A.M. conceptualized the research objectives and designed the field experiments; M.W.E. coordinated field trial implementation across the three locations and supervised data collection; U.A.A. performed the statistical analyses using AMMI and GGE biplot methodologies, prepared all figures and tables, and led manuscript drafting; F.E.E. and U.A.A. jointly interpreted the results and developed cultivar deployment recommendations; B.A.M. and M.W.E. contributed to methodological refinement and validation of environmental characterizations; U.A.A. prepared the original draft of the manuscript; all authors (F.E.E., B.A.M., M.W.E., and U.A.A.) critically reviewed, edited, and approved the final version of the manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgments:\u003c/h2\u003e\n\u003cp\u003eThe authors thank the technical staff of the Cotton Research Institute for their assistance in field management and data collection.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe raw data supporting the conclusions of this article will be made available by the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbdel-Moneam MA, Sultan MS, Abdel-Ghani AH, El-Sayed AA. GGE biplot and AMMI analysis for mega-environment investigation and genotype evaluation of Egyptian cotton. J Crop Improv. 2021;35(2):209\u0026ndash;25. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/15427528.2020.1858467\u003c/span\u003e\u003cspan address=\"10.1080/15427528.2020.1858467\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnnicchiarico P. Genotype x environment interaction: Challenges and opportunities for plant breeding and cultivar recommendations. FAO Plant Production and Protection Paper No. 174. Food and Agriculture Organization of the United Nations; 2002.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEl-Sayed AA, Abdel-Ghani AH, Sultan MS. Egyptian cotton: A legacy of excellence and a future of promise. Agronomy. 2021;11(7):1432. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/agronomy11071432\u003c/span\u003e\u003cspan address=\"10.3390/agronomy11071432\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGauch HG. Statistical analysis of yield trials by AMMI and GGE. Crop Sci. 2006;46(4):1488\u0026ndash;500. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2135/cropsci2005.07-0193\u003c/span\u003e\u003cspan address=\"10.2135/cropsci2005.07-0193\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGauch HG, Zobel RW. Identifying mega-environments and targeting genotypes. Crop Sci. 1997;37(2):311\u0026ndash;26. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2135/cropsci1997.0011183X003700020002x\u003c/span\u003e\u003cspan address=\"10.2135/cropsci1997.0011183X003700020002x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHinze LL, Hulse-Kemp AM, Wilson IW, Stelly DM. A GGE biplot analysis of genotype-by-environment interaction in the US National Cotton Variety Test. Crop Sci. 2017;57(3):1339\u0026ndash;53. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2135/cropsci2016.08.0694\u003c/span\u003e\u003cspan address=\"10.2135/cropsci2016.08.0694\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYan W, Kang MS. GGE biplot analysis: A graphical tool for breeders, geneticists, and agronomists. CRC; 2003.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYan W, Kang MS, Ma B, Woods S, Cornelius PL. GGE biplot vs. AMMI analysis of genotype-by-environment data. Crop Sci. 2007;47(2):643\u0026ndash;55. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2135/cropsci2006.06.0374\u003c/span\u003e\u003cspan address=\"10.2135/cropsci2006.06.0374\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"AMMI, GGE biplot, Gossypium barbadense L., genotype-by-environment interaction, mega-environment, stability, cotton yield","lastPublishedDoi":"10.21203/rs.3.rs-8818924/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8818924/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGenotype-by-environment interaction (GEI) poses a significant challenge to achieving consistent cotton yield across diverse agro-ecologies. This study aimed to quantify the components of yield variation, identify stable high-yielding genotypes, and delineate mega-environments within the Nile Delta for efficient cultivar deployment. Seven cotton genotypes (four advanced lines and three commercial cultivars) were evaluated across six environments, generated from three locations over two growing seasons (2023 and 2024). Data were analyzed using combined analysis of variance, Additive Main Effects and Multiplicative Interaction (AMMI), and Genotype plus Genotype-by-Environment (GGE) biplot models. The combined ANOVA revealed that GEI was the predominant source of variation, accounting for 47.12% of the total sum of squares, followed by environmental (32.91%) and genotypic (19.97%) effects. AMMI analysis identified 'Line 1' and 'Line 4' as highly stable genotypes. The GGE biplot effectively categorized the test environments into three distinct mega-environments (MEs): a favorable ME (Kafr El-Sheikh) where 'Line 1' performed best; a moderate-stress ME (Damietta) where 'Line 4' excelled; and a high-stress ME (El-Sharkia) where the commercial cultivar 'Giza 94' showed superior stability. These findings provide a robust framework for strategic cultivar recommendation and breeding targeted at specific agro-ecological zones in the Nile Delta, which can enhance yield stability and regional productivity.\u003c/p\u003e","manuscriptTitle":"Genotype-by-environment Interaction and Yield Stability Analysis of Egyptian Cotton (Gossypium Barbadense L.)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-18 09:50:45","doi":"10.21203/rs.3.rs-8818924/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4e90ff01-a44f-48d7-88c0-472fa677b81f","owner":[],"postedDate":"February 18th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-07T06:43:05+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-07T01:31:36+00:00","index":22,"fulltext":""},{"type":"reviewerAgreed","content":"283973329568461298676015284785452216956","date":"2026-05-06T08:56:25+00:00","index":21,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-07T06:58:27+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-18 09:50:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8818924","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8818924","identity":"rs-8818924","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}