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Genotype × environment interaction insights for yield and yield components in castor via AMMI and GGE biplot models


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 (G2) and ICH-5 (G6) 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. Further, Multi-Trait Stability Index (MTSI) has proven ICH-1160 (G3) as the most stable genotype across the environments. 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.

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Data availability

Data will be made available on request.

References

  1. Rukhsar Patel, M. P., Parmar, D. J., Kalola, A. D. & Kumar, S. Morphological and molecular diversity patterns in castor germplasm accessions. Ind. Crops Prod. 97, 316–323 (2017).

    Google Scholar 

  2. Xu, W. et al. Genomic insights into the origin, domestication and genetic basis of agronomic traits of castor bean. Genome Biol. 22(1), 1–27 (2021).

    Google Scholar 

  3. Indiastat. https://www.indiastat.com (2025).

  4. Elhadi, M. Y. Fruits and vegetable Phyto Chemicals: Chemistry and Human Health 2nd edn, 1 (Wiley, 2018).

    Google Scholar 

  5. Vieira, C. et al. Effect of ricinoleic acid in acute and subchronic experimental models of inflammation. Mediat. Inflamm. 9(5), 223–228 (2000).

    Google Scholar 

  6. Maiti, S., Hegde, M. R. & Chattopadhyay, S. B. Handbook of Oil Seed Crops 317 (Oxford and IBH Publishing Co. (PVt). Ltd., 1988).

    Google Scholar 

  7. Memon, J. et al. Deployment of AMMI, GGE-biplot and MTSI to select elite genotypes of castor (Ricinus communis L.). Heliyon 9, e13515 (2023).

    Google Scholar 

  8. Sujatha, M., Reddy, T. P. & Mahasi, M. J. Role of biotechnological interventions in the improvement of castor (Ricinus communis L.) and Jatropha curcas L.. Biotechnol. Adv. 26(5), 424–435 (2008).

    Google Scholar 

  9. Baker, R. J. Differential response to environmental stress. https://agris.fao.org/agris-search/search.do?recordID=US8863668 (1988).

  10. Joshi, H. J., Maheta, D. R. & Jadon, B. S. Phenotypic stability and adaptability of castor hybrids. Indian J. Agric. Res. 36(4), 269–273 (2002).

    Google Scholar 

  11. Asungre, P. A., Akromah, R., Kena, A. W. & Gangashetty, P. Genotype by environment interaction on grain yield stability and iron and zinc content in OPV of pearl millet in Ghana using the AMMI method. Int. J. Agron. https://doi.org/10.1155/2021/9656653 (2021).

    Google Scholar 

  12. Ajay, B. C. et al. Modified AMMI stability index (MASI) for stability analysis. Groundnut Newsl. 18, 4–5 (2018).

    Google Scholar 

  13. Yan, W., Hunt, L. A., Sheng, Q. & Szlavnics, Z. Cultivar evaluation and mega-environment investigation based on the GGE biplot. Crop Sci. 40(3), 597–605. https://doi.org/10.2135/cropsci2000.403597x (2000).

    Google Scholar 

  14. Ding, M., Tier, B. & Yan, W. K. Application of GGE biplot analysis to evaluate genotype (G), environment (E) and G×E interaction on P. radiata: case study. In Australasian Forest Genetics Conference 11–14 (2007).

  15. Miranda, G. V. et al. Multivariate analyses of genotype x environment interaction of popcorn. Pesqui. Agropecu. Bras. 44, 45–50 (2009).

    Google Scholar 

  16. Olivoto, T., Lúcio, A. D., da Silva, J. A., Sari, B. G. & Diel, M. I. Mean performance and stability in multi-environment trials II: Selection based on multiple traits. J. Agron. 111(6), 2961–2969. https://doi.org/10.2134/agronj2019.03.0221 (2019).

    Google Scholar 

  17. De Mendiburu, F. Agricolae. Statistical Procedures for Agricultural Research. Available from https://cran.r-project.org/web/packages/agricolae/index.html (2017).

  18. Bernal, E. F. & Villardon, P. G. GGE Biplot GUI: Interactive GGE Biplots in R. https://cran.r-project.org/web/packages/GGEBiplotGUI/index.html (2016).

  19. Olivoto, T. & Lúcio, A. D. C. Metan: An R package for multi-environment trial analysis. Methods Ecol. Evol. 11, 783–789 (2020).

    Google Scholar 

  20. Peterson, D. D. Statistical Techniques in Agricultural Research: A Simple Exposition of Practice and Procedure in Biometry (McGraw-Hill Book Company Inc, 1939).

    Google Scholar 

  21. Akhila, S. R., Kumar, S., Sakure, A. A., Patel, D. A. & Patel, M. P. Integration of morpho-physico-biochemical traits with SSR and SRAP markers for characterization of castor genotypes of Indian origin. Oil Crop Sci. 7(1), 22–30 (2022).

    Google Scholar 

  22. Baye, T. M., Abebe, T. & Wilke, R. A. Genotype–environment interactions and their translational implications. Pers. Med. 8(1), 59–70 (2011).

    Google Scholar 

  23. Haldane, J. B. S. The interaction of nature and nature. Ann. Eugen. 13, 197–205 (1946).

    Google Scholar 

  24. Ebdon, J. S. & Gauch, H. G. Jr. Additive main effect and multiplicative interaction analysis of national turfgrass performance trials: Interpretation of genotype × environment interaction. Crop Sci. 42(2), 489–496 (2002).

    Google Scholar 

  25. Vargas, M. & Crossa, J. The AMMI analysis and graphing the biplot. Biometrics and statistics unit, CIMMYT combining features of AMMI and BLUP techniques. Agron. J. 111(6), 2949–2960 (2000).

    Google Scholar 

  26. Yan, W. & Hunt, L. A. Interpretation of genotype environment interaction for winter wheat yield in Ontario. Crop Sci. 41, 19–25 (2001).

    Google Scholar 

  27. Gauch, H. G. Jr. & Zobel, R. W. Identifying mega-environments and targeting genotypes. Crop Sci. 37(2), 311–326 (1997).

    Google Scholar 

  28. Narasimhulu, R., Veeraraghavaiah, R., Sahadeva Reddy, B., Tara Satyavathi, C. & Ajay BC Sanjana Reddy, P. Yield stability analysis of pearl millet genotypes in arid region of India using AMMI and GGE biplot. J. Environ. Biol. 44, 185–192 (2023).

    Google Scholar 

  29. Kilic, H. Additive main effects and multiplicative interactions (AMMI) analysis of grain yield in barley genotypes across environments. J. Agric. Sci. 20(4), 337–344 (2014).

    Google Scholar 

  30. Purchase, J. L. Parametric Stability to Describe G x E Interactions and Yield Stability in Winter Wheat. Ph.D. Thesis, Department of Agronomy, Faculty of Agricultural University of Orange Free State, Bloemfontein, South Africa (1997).

  31. Akter, A. et al. AMMI biplot analysis for stability of grain yield in hybrid rice (Oryza sativa L.). J. Rice Res. 2, 1–4 (2014).

    Google Scholar 

  32. Singamsetti, A. et al. Genotype × environment interaction and selection of maize (Zea mays L.) hybrids across moisture regimes. Field Crops Res. 270, 108224 (2021).

    Google Scholar 

  33. Yan, W. GGE biplot: A window application for graphical analysis of multi-environmental data and other types of two-way data. Agron. J. 93, 1111–1118 (2001).

    Google Scholar 

  34. Ajay, B. C. et al. Identification of stable sources for low phosphorus conditions from groundnut (Arachis hypogaea L.) germplasm accessions using GGE biplot analysis. Indian J. Genet. Plant Breed. 81, 300–306 (2021).

    Google Scholar 

  35. Yan, W., Kang, M. S., Ma, B., Woods, S. & Cornelius, P. L. GGE biplot vs. AMMI analysis of genotype-by-environment data. Crop Sci. 47(2), 643–653 (2007).

    Google Scholar 

  36. Samyuktha, S. M. et al. Delineation of genotype × environment interaction for identification of stable genotypes to grain yield in mungbean. Front. Agron. https://doi.org/10.3389/fagro.2020.577911 (2020).

    Google Scholar 

  37. Yan, W. Singular-value partitioning in biplot analysis of multi environment trial data. Agron. J. 94, 990–996 (2002).

    Google Scholar 

  38. Olanrewaju, O. S., Oyatom, O., Babalola, O. O. & Abberton, M. GGE biplot analysis of genotype × environment interaction and yield stability in bambara groundnut. Agronomy 11, 1839 (2021).

    Google Scholar 

  39. Ajay, B. C. et al. Improving genetic attributes of confectionary traits in peanut (Arachis hypogaea L.) using multivariate analytical tools. J. Agric. Sci. 4(3), 247–258 (2012).

    Google Scholar 

  40. Hashim, N. et al. Integrating multivariate and univariate statistical models to investigate genotype environment interaction of advanced fragrant rice genotypes under rainfed condition. Sustainability 13(8), 4555 (2021).

    Google Scholar 

  41. Hussain, T., Akram, Z., Shabbir, G., Manaf, A. & Ahmed, M. Identification of drought tolerant chickpea genotypes through multi trait stability index. Saudi J. Biol. Sci. 28, 6818–6828 (2021).

    Google Scholar 

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Funding

Authors would like thank Professor Jayashankar Telangana Agricultural University, Rajendranagar, Hyderabad, Telangana, India – 500 030 for financial support.

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Authors and Affiliations

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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], Interpretation of data, review & editing of manuscript [S. Vanisri and M. Sreedhar], Supervision and administrative support [L. Krishna].

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Correspondence to
K. Sadaiah.

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Sadaiah, K., Reddy, G.E., Parimala, K. et al. Genotype × environment interaction insights for yield and yield components in castor via AMMI and GGE biplot models.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-44030-5

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  • DOI: https://doi.org/10.1038/s41598-026-44030-5

Keywords

  • Castor
  • Genotype × environment interaction
  • AMMI
  • GGE biplot
  • MTSI
  • Yield


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