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Research Articles

Early Access

Assessment of genotype × environment interaction in horsegram genotypes across Telangana using AMMI and GGE biplot analysis

DOI
https://doi.org/10.14719/pst.14724
Submitted
26 March 2026
Published
10-11-2026

Abstract

Horsegram, a hardy and climate-resilient pulse crop belonging to the family Fabaceae, is widely recognised for its adaptability to adverse environmental conditions. However, its productivity remains limited by significant genotype × environment interaction (GEI) effects. The present study was undertaken to assess the magnitude and pattern of GEI and to identify high-yielding genotypes with stable performance for yield and yield-attributing traits under the agro-climatic conditions of Telangana. Eight genotypes were evaluated in a randomised block design with three replications across three late kharif seasons (2021-22, 2022-23 and 2023-24). The analysis revealed highly significant GEI effects for all the traits studied, indicating differential genotype performance across the three environments. The Additive Main effects and Multiplicative Interaction (AMMI) biplot effectively captured different interaction patterns and identified environment E1 (2021-22) as the most representative and least interactive environment for yield performance. The “which-won-where” pattern of the Genotype Plus Genotype × Environment (GGE) biplot revealed distinct mega-environments, with genotype PHG-202 performing best under the environments E1(2021-22) and E2 (2022-23), whereas PHG-196 showed superior adaptability to E3 (2023-24). Furthermore, the Average Environment Coordinate (AEC) view indicated that E1 was both highly discriminative and representative. GGE ranking analysis revealed that genotype PHG-112 was positioned closest to the ideal genotype, indicating high mean yield coupled with superior stability across environments. The integrated analysis using AMMI and GGE biplots identified PHG-112, PHG-196 and PHG-202 as high-yielding and stable genotypes with broad adaptability across the tested environments, highlighting their potential for cultivation across a wide range of agro-climatic conditions and their usefulness in future horsegram improvement programmes.

References

  1. 1. Prasad SK, Singh MK. Horse gram-an underutilized nutraceutical pulse crop: a review. J Food Sci Technol. 2015;52(5):2489–99. https://doi.org/10.1007/s13197-014-1312-z
  2. 2. Kumar D. Horsegram: forgotten legume of dry land agriculture. Indian Farming. 2006;56(4):25–27.
  3. 3. Chahota RK, Sharma TR, Sharma SK, Kumar N, Rana JC. Horsegram. In: Singh M, Upadhyaya HD, Bisht IS, editors. Genetic and Genomic Resources of Grain Legume Improvement. London: Elsevier; 2013. p. 293–305. https://doi.org/10.1016/B978-0-12-397935-3.00012-8
  4. 4. Indiastat. State and Season wise of Area, Production and Productivity of Horsegram in India, 2021: New Delhi
  5. 5. Jayashree GC, Krishnamma PN. Studies on physical properties of different horse gram varieties suitable for machine sowing. Int J Curr Microbiol Appl Sci. 2020;9(11):864–68. https://doi.org/10.20546/ijcmas.2020.911.103
  6. 6. Bhartiya A, Aditya JP, Kant L. Nutritional and remedial potential of an underutilized food legume horsegram (Macrotyloma uniflorum): a review. J Anim Plant Sci. 2015;25(4):908–20.
  7. 7. Fuller D, Murphy C. The origins and early dispersal of horsegram (Macrotyloma uniflorum), a major crop of ancient India. Genet Resour Crop Evol. 2018;65:285–305. https://doi.org/10.1007/s10722-017-0532-2
  8. 8. Frutos E, Galindo MP, Leiva V. An interactive biplot implementation in R for modeling genotype-by-environment interaction. Stoch Environ Res Risk Assess. 2014;28:1629–41. https://doi.org/10.1007/s00477-013-0821-z
  9. 9. Peterson DD. Statistical techniques in agricultural research: a simple exposition of practice and procedure in biometry. New York and London: McGraw-Hill Book Company, Inc.; 1939.
  10. 10. Sulthana JS, Saravanan NA, Sivakumar R, Thirumalraj S, Vanniarajan C, Raveendran M, et al. Identification and validation of stable horse gram [Macrotyloma uniflorum (Lam.) Verdc.] genotypes using multi-trait based selection indices in multi-environment experiments. Legume Res. 2025;48(4):559–67. https://doi.org/10.18805/LR-5437
  11. 11. Maruthi V, Raghuram Reddy P, Reddy KS, Reddy BMK, Salini, Saroja DGM. Agronomic management of CRIDA-18R-a new variety of horse gram (Macrotyloma uniflorum) for South India matching monsoon patterns of rainfall. Legume Res.2018;41(1):102–07. https://doi.org/10.18805/lr.v0iOF.9105
  12. 12. Prasath S, Menaka C, Geetha R, Yuvaraja A. Pattern of seed development and maturation in horse gram (Macrotyloma uniflorum Lam.). Legume Res. 2024;47(6):922–26. https://doi.org/10.18805/LR-4592
  13. 13. Vaishnavi V, Sulthana JS, Anandhi K, Sivakumar R, Kannan B, Saravanan NA, et al. Deciphering G × E interaction of photo-insensitive horsegram mutants using AMMI and GGE biplot models. Legume Res. 2025;48(1):86–93. https://doi.org/10.18805/LR-5211
  14. 14. Vargas HM, Crossa J. The AMMI analysis and graphing the biplot. Mexico City: Biometrics and Statistics Unit, CIMMYT; 2000. 39 p.
  15. 15. Yan W, Hunt LA. Interpretation of genotype × environment interaction for winter wheat yield in Ontario. Crop Sci. 2001;41:19–25. https://doi.org/10.2135/cropsci2001.41119x
  16. 16. Reddy KV, Ashwini, Ramesh S, Sunitha NC. Comparative BLUP, YREM-based performance and AMMI model-based stability of horse gram [Macrotyloma uniflorum (Lam.) Verdc.] genotypes differing in growth habit. Genet Resour Crop Evol. 2021;68(3):457–67. https://doi.org/10.1007/s10722-020-01089-x
  17. 17. Gauch HG Jr, Zobel RW. Identifying mega-environments and targeting genotypes. Crop Sci. 1997;37(2):311–26. https://doi.org/10.2135/cropsci1997.0011183X003700020002x
  18. 18. 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. 2023;44:185–92. https://doi.org/10.22438/jeb/44/2/MRN-4095
  19. 19. Kilic H. Additive main effects and multiplicative interactions (AMMI) analysis of grain yield in barley genotypes across environments. J Agric Sci. 2014;20(4):337–44. https://doi.org/10.15832/tbd.44431
  20. 20. Jafarullakhan SS, Vijayakumar V, Singh KV, Saravanan NA, Giridhari VA, Rathinavelu S, et al. Assessing the yield and nutrient potential of horse gram mutants (Macrotyloma uniflorum Lam. Verdc.), an underutilized legume through a multi-environment-based experiment. Sci Rep. 2024;14(1):16305. https://doi.org/10.1038/s41598-024-67282-5
  21. 21. KiranKumar R, Ramesh S, Chandana BR, Basanagouda G, Gazala P, Siddu CB, et al. AMMI model and YREM-based grain yield stability of horse gram [Macrotyloma uniflorum (Lam.) Verdc.] YMV disease resistant genotypes. Mysore J Agric Sci. 2023;57(2):136–46.
  22. 22. Kumar R, Dhansu P, Kulshreshtha N, Meena MR, Kumaraswamy MH, Appunu C, et al. Identification of salinity tolerant stable sugarcane cultivars using AMMI, GGE and some other stability parameters under multi environments of salinity stress. Sustainability. 2023;15(2):1119. https://doi.org/10.3390/su15021119
  23. 23. Samyuktha SM, Malarvizhi D, Karthikeyan A, Dhasarathan M, Hemavathy AT, Vanniarajan C, et al. Delineation of genotype × environment interaction for identification of stable genotypes to grain yield in mungbean. Front Agron. 2020;2:577911. https://doi.org/10.3389/fagro.2020.577911
  24. 24. Enyew M, Feyissa T, Geleta M, Tesfaye K, Hammenhag C, Carlsson AS. Genotype by environment interaction, correlation, AMMI, GGE biplot and cluster analysis for grain yield and other agronomic traits in sorghum (Sorghum bicolor L. Moench). PLoS One. 2021;16(10):e0258211. https://doi.org/10.1371/journal.pone.0258211
  25. 25. Mohammadi R, Haghparast R, Amri A, Ceccarelli S. Yield stability of rainfed durum wheat and GGE biplot analysis of multi-environment trials. Crop Pasture Sci. 2010;61(1):92–101. https://doi.org/10.1071/CP09151
  26. 26. Hashim N, Rafii MY, Oladosu Y, Ismail MR, Ramli A, Arolu F, et al. Integrating multivariate and univariate statistical models to investigate genotype-environment interaction of advanced fragrant rice genotypes under rainfed condition. Sustainability. 2021;13(8):4555. https://doi.org/10.3390/su13084555

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