Skip to main navigation menu Skip to main content Skip to site footer

Research Articles

Vol. 13 No. sp1 (2026): Recent Advances in Agriculture

AMMI and GGE biplot-based evaluation of yield and protein percentage in nutrient-rich rice genotypes across diverse environments

DOI
https://doi.org/10.14719/pst.11268
Submitted
13 August 2025
Published
13-02-2026

Abstract

Stable, high-yielding and nutritionally enriched rice genotypes are essential to ensure food and nutritional security under changing climatic conditions. In this study, 12 nutrient-rich rice genotypes, along with standard checks, were evaluated for grain yield and protein content across four diverse and contrasting environments in Chhattisgarh, India, during kharif season of 2024–25, using a randomized complete block design (RCBD) with two replications. Although based on a single year, the use of multiple test sites and robust genotype × environment (G × E) interaction analysis through additive main effects and multiplicative interaction (AMMI) and genotype (G) plus genotype × environment interaction (GE) biplot (GGE biplot) models enabled reliable genotype assessment. Combined analysis of variance (ANOVA) based on the AMMI model revealed significant effects of environmental factors and G × E interactions (GEIs) on both traits. In the GGE model, the first two principal components explained 91.6 % of the variation in yield and 81.5 % of the variation in protein content. The “which-won-where” biplot identified G9 as superior for yield across environments, while G9 and G3 excelled in protein content in E1 and E2 respectively. The most discriminating environment for yield was E3, whereas E1 and E3 were highly discriminating for protein content. The most representative environments were E1 for yield and E4 for protein content. The ideal environments identified were E4 for yield and E2 for protein content. Genotypes G9 and G3 emerged as promising candidates for commercial cultivation. These nutrient-rich lines may also serve as valuable parental material for breeding programs targeting enhanced yield and nutritional quality, contributing to the development and release of biofortified rice varieties.

References

  1. 1. Kesh H, Kharb R, Ram K, Munjal R, Kaushik P, Kumar D. Adaptability and AMMI biplot analysis for yield and agronomical traits in scented rice genotypes under diverse production environments. Ind J Tradit Knowl. 2021;20(2):550–62. https://doi.org/10.56042/ijtk.v20i2.29903
  2. 2. Jayaprakash G, Bains A, Chawla P, Fogarasi M, Fogarasi S. A narrative review on rice proteins: current scenario and food industrial application. 2022;14(15):3003. https://doi.org/10.3390/polym14153003
  3. 3. Balindong JL, Liu L, Ward RM, Barkla BJ, Waters DLE. Optimisation and standardisation of extraction and HPLC analysis of rice grain protein. J Cereal Sci. 2016;72:124–30. https://doi.org/10.1016/j.jcs.2016.10.005
  4. 4. Wang T, Xu P, Chen Z, Zhou X, Wang R. Alteration of the structure of rice proteins by their interaction with soy protein isolates to design novel protein composites. Food Funct. 2018;9:4282–91. https://doi.org/10.1039/C8FO00661J
  5. 5. Behera PP, Singh SK, Sivasankarreddy K, Majhi PK, Reddy BJ, Singh DK. Yield attributing traits of high zinc rice (Oryza sativa L.) genotypes with special reference to principal component analysis. Environ Conserv J. 2022;23(3):458–70. https://doi.org/10.36953/ECJ.10302233
  6. 6. Muthuramu S, Ragavan T. AMMI analysis for yield and stability in direct seeded rainfed rice. Bangladesh J Bot. 2022;51(3):469–75. https://doi.org/10.3329/bjb.v51i3.61993
  7. 7. Yan W, Tinker NA. Biplot analysis of multi environment trial data: principles and applications. Can J Pl Sci. 2006;86(3):623–45. https://doi.org/10.4141/P05-169
  8. 8. Kendal E, Sener O. Examination of genotype × environment interactions by GGE biplot analysis in spring durum wheat. Ind J Genet Plant Breed. 2015;75(3):341–8. https://doi.org/10.5958/0975-6906.2015.00054.1
  9. 9. Solonechnyi P, Kozachenko M, Vasko N, Gudzenko V, Ishenko V, Kozelets G, et al. AMMI and GGE biplot analysis of yield performance of spring barley (Hordeum vulgare L.) varieties in multi environment trials. Agricult Forest. 2018;64(1):121–32. https://doi.org/10.17707/AgricultForest.64.1.15
  10. 10. Olanrewaju OS, Oyatomi O, Babalola OO, Abberton M. GGE biplot analysis of genotype × environment interaction and yield stability in Bambara groundnut. Agron. 2021;11(9):1839. https://doi.org/10.3390/agronomy11091839
  11. 11. Vinu V, Alarmelu S, Elayaraja K, Appunu C, Hemaprabha G, Parthiban S, et al. Multi-environment analysis of yield and quality traits in sugarcane (Saccharum sp.) through AMMI and GGE biplot analysis. Sugar Tech. 2025;27(2):540–8. https://doi.org/10.1007/s12355-024-01498-7
  12. 12. Juliano BO. Rice in human nutrition. Rome: Food and Agriculture Organization of the United Nations; Manila: International Rice Research Institute. 1993. p. 162
  13. 13. Wickham H, François R, Henry L, Müller K, Vaughan D. dplyr: a grammar of data manipulation. R package version 1.1.4. 2025. https://dplyr.tidyverse.org
  14. 14. Wickham H, Chang W, Wickham MH. ggplot2: create elegant data visualisations using the grammar of graphics. R package version 2.2.1. 2016. https://doi.org/10.32614/CRAN.package.ggplot2
  15. 15. Wickham H, Vaughan D, Girlich M. tidyr: tidy messy data. R package version 1.3.1.9000. 2025. https://github.com/tidyverse/tidyr
  16. 16. Mendiburu FD. agricolae: statistical procedures for agricultural research. R package version 1.3-7. 2023.
  17. 17. Olivoto T, Lúcio AD. metan: an R package for multi environment trial analysis. Methods Ecol Evol. 2020;11(6):783–9. https://doi.org/10.1111/2041-210x.13384
  18. 18. Purchase JL, Hatting H, van Deventer CS. Genotype × environment interaction of winter wheat (Triticum aestivum L.) in South Africa: II. stability analysis of yield performance. S Afr J Plant Soil. 2000;17(3):101–7. https://doi.org/10.1080/02571862.2000.10634878
  19. 19. Zali H, Farshadfar E, Sabaghpour SH, Karimizadeh R. Evaluation of genotype × environment interaction in chickpea using measures of stability from AMMI model. Ann Biol Res. 2012;3(7):3126–36.
  20. 20. Ajay BC, Aravind J, Fiyaz RA. Ammistability: R package for ranking genotypes based on stability parameters derived from AMMI model. Indian J Genet Plant Breed. 2019;79(2):460–6. https://doi.org/10.31742/IJGPB.79.2.10
  21. 21. Esan VI, Oke GO, Ogunbode TO, Obisesan IA. AMMI and GGE biplot analyses of Bambara groundnut [Vigna subterranea (L.) Verdc.] for agronomic performances under three environmental conditions. Front Plant Sci. 2023;13:997429. https://doi.org/10.3389/fpls.2022.997429
  22. 22. Spoorthi V, Ramesh S, Sunitha NC, Vaijayanthi PV. Are genotypes’ single-year YREMs and BLUPs good predictors of their performance in future years? an empirical analysis in dolichos bean [Lablab purpureus (L.) Sweet var. Lignosus]. Genet Resour Crop Evol. 2021;68:1401–9. https://doi.org/10.1007/s10722-020-01070-8
  23. 23. Muthuramu S, Gnanasekaran M, Thiyagu K, Sheeba A, Thangaraj K, Gunasekaran M, et al. GGE biplot analysis in rice landraces grown under rainfed ecosystem. Plant Sci Today. 2025;12(sp3):1–6. https:/doi.org/10.14719/pst.8324
  24. 24. Mohan YC, Krishna L, Sreedhar S, Chandra BS, Raju CD, Madhukar P, et al. Stability analysis of rice hybrids for grain yield in Telangana through AMMI and GGE bi-plot model. Int J Bio-res Stress Manag.2021;12(6):687–95. https://doi.org/10.23910/1.2021.2575
  25. 25. Devi KR, Venkanna V, Lingaiah N, Prasad KR, Chandra BS, Hari Y, et al. AMMI biplot analysis for genotype × environment interaction and stability for yield in hybrid rice (Oryza sativa L.) under different production seasons. Curr J Appl Sci Tech. 2020;39(48):169–75. https://doi.org/10.9734/cjast/2020/v39i4831214
  26. 26. Mahant RD, Sahu H, Mannade AK, Premi V, Sahu RK. Deciphering the interactions between genetic elements and environmental factors in rice (Oryza sativa L.) genotypes: valuable perspectives unveiled through AMMI modeling and GGE biplots analysis. Int J Adv Biochem Res. 2024;8(3):31–8. https://doi.org/10.33545/26174693.2024.v8.i3a.672
  27. 27. Sahu H, Premi V, Bhariya SK, Mannade AK, Mahant RD. Identifying mega-environments and evaluating grain yield stability in bio-fortified rice using AMMI and GGE approaches. Int J Bio-resour Stress Manag. 2025;16(5):1–11. https://doi.org/10.23910/1.2025.5991
  28. 28. Joshi P, Vandemark G. AMMI and GGE biplot analysis of seed protein concentration, yield and 100-seed weight for chickpea cultivars and breeding lines in the US Pacific Northwest. Crop Sci. 2022;65:e21417. https://doi.org/10.1002/csc2.21417
  29. 29. Rakotondramanana M, Wissuwa M, Ramanankaja L, Razafimbelo T, Stangoulis J, Grenier C. Stability of grain zinc concentrations across lowland rice environments favors zinc biofortification breeding. Front Plant Sci. 2024;15:1293831. https://doi.org/10.3389/fpls.2024.1293831
  30. 30. Susanto U, Rohaeni WR, Johnson SB, Jamil A. GGE biplot analysis for genotype × environment interaction on yield trait of high Fe content rice genotypes in Indonesian irrigated environments. AGRIVITA J Agric Sci. 2015;37(3):265–75. https://doi.org/10.17503/Agrivita-2015-37-3-p265-275
  31. 31. Utami DW, Maruapey A, Maulana, H, Sinaga PH, Basith S, Karuniawan A. The sustainability index and other stability analyses for evaluating superior Fe-tolerant rice (Oryza sativa L.). Sustainability 2023;15(16):12233. https://doi.org/10.3390/su151612233
  32. 32. Oladosu Y, Rafii MY, Abdullah N, Magaji U, Miah G, Hussin G, et al. Genotype × environment interaction and stability analyses of yield and yield components of established and mutant rice genotypes tested in multiple locations in Malaysia. Acta Acta Agric Scand Sect B Soil Plant Sci. 2017;67(7):590–606. https://doi.org/10.1080/09064710.2017.1321138
  33. 33. Matongera N, Ndhlela T, Biljon AV, Labuschagne M. Genotype × environment interaction and yield stability of normal and biofortified maize inbred lines in stress and non-stress environments. Cogent Food Agric. 2023;9(1):2163868. https://doi.org/10.1080/23311932.2022.2163868
  34. 34. Endalamaw C, Tsegaye D, van Biljon A, Herselman L, Labuschagne M. Kernel composition in sorghum landraces revealed via analyses of genotype-by-environment interactions. PLoS ONE. 2025;20(4):e0320513. https://doi.org/10.1371/journal.pone.0320513
  35. 35. Gore PG, Das A, Bhardwaj R, Tripathi K, Pratap A, Dikshit HK, et al. Understanding G × E interaction for nutritional and antinutritional factors in a diverse panel of Vigna stipulacea (Lam.) Kuntz germplasm tested over the locations. Front Plant Sci. 2021;12:766645. https://doi.org/10.3389/fpls.2021.766645
  36. 36. Sankar MS, Singh SP, Prakash G, Satyavathi CT, Soumya SL, Yadav Y, et al. Deciphering genotype-by-environment interaction for target environmental delineation and identification of stable resistant sources against foliar blast disease of pearl millet. Front Plant Sci. 2021;12:656158. https://doi.org/10.3389/fpls.2021.656158
  37. 37. 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
  38. 38. Inabangan-Asilo MA, Swamy BPM, Amparado AF, Iris GL, Descalsota-Empleo, Arocena EC, et al. Stability and G × E analysis of zinc-biofortified rice genotypes evaluated in diverse environments. Euphytica. 2019;215:61. https://doi.org/10.1007/s10681-019-2384-7
  39. 39. Pour-Aboughadareh A, Barati A, Koohkan SA, Jabari M, Marzoghian A, Gholipoor A, et al. Dissection of genotype-by-environment interaction and yield stability analysis in barley using AMMI model and stability statistics. Bull Natl Res Centre. 2022;46:19. https://doi.org/10.1186/s42269-022-00703-5
  40. 40. Danakumara T, Kumar T, Kumar N, Patil BS, Bharadwaj C, Patel U, et al. A multi-model based stability analysis employing multi-environmental trials (METs) data for discerning heat tolerance in chickpea (Cicer arietinum L.) landraces. Plants 2023;12(21):3691.
  41. https://doi.org/10.3390/plants12213691
  42. 41. Gupta S, Das S, Dikshit HK, Mishra GP, Aski MS, Bansal R, et al. Genotype by environment interaction effect on grain iron and zinc concentration of Indian and Mediterranean lentil genotypes. Agronomy. 2021;11(9):1761. https://doi.org/10.3390/agronomy11091761
  43. 42. Kumar A, Jnanesha AC, Lal RK, Chanotiya CS, Venugopal S, Swamy YVVS. Precision agriculture innovation focuses on sustainability using GGE biplot and AMMI analysis to evaluate GE interaction for quality essential oil yield in Eucalyptus citriodora Hook. Biochem Syst Ecol. 2023;107:104603. https://doi.org/10.1016/j.bse.2023.104603
  44. 43. Haider Z, Akhter M, Mahmood A, Khan RAR. Comparison of GGE biplot and AMMI analysis of multi-environment trial (MET) data to assess adaptability and stability of rice genotypes. Afr J Agric Res. 2017;12(51):3542–8. https://doi.org/10.5897/AJAR2017.12528
  45. 44. Chandrashekhar S, Babu R, Jeyaprakash P, Umarani R, Bhuvaneshwari K, Manonmani S. Yield stability analysis in multi-environment trials of hybrid rice (Oryza sativa L.) in northern India using GGE biplot analysis. Electron J Plant Breed. 2020;11:665–73.
  46. 45. Das CK, Bastia DN, Naik BS, Kabat B, Mohanty MR, Mahapatra SS. GGE biplot and AMMI analysis of grain yield stability & adaptability behaviour of paddy (Oryza sativa L.) genotypes under different agroecological zones of Odisha. Oryza–Int J Rice. 2018;55(4):528–42. https://doi.org/10.5958/2249-5266.2018.00076.0
  47. 46. Jadhav S, Balakrishnan D, Shankar GV, Beerelli K, Chandu G, Neelamraju S. Genotype by environment (G × E) interaction study on yield traits in different maturity groups of rice. J Crop Sci Biotechnol. 2019;22:425–49. https://doi.org/10.1007/s12892-018-0082-0
  48. 47. Velu G, Singh RP, Huerta-Espino J, Peña RJ, Arun B, Mahendru-Singh A, et al. Performance of biofortified spring wheat genotypes in target environments for grain zinc and iron concentrations. Field Crops Res. 2012;137:261–7. https://doi.org/10.1016/j.fcr.2012.07.018
  49. 48. Dang X, Hu X, Ma Y, Li Y, Kan W, Dong X. AMMI and GGE biplot analysis for genotype × environment interactions affecting the yield and quality characteristics of sugar beet. PeerJ. 2024;12:e16882. https://doi.org/10.7717/peerj.16882
  50. 49. Dwivedi A, Basandrai D, Sarial AK. AMMI biplot analysis for grain yield of basmati lines (Oryza sativa L.) in northwestern Himalayan hill regions. Indian J Genet Plant Breed. 2020;80(2):140–6. https://doi.org/10.31742/IJGPB.80.2.3
  51. 50. Khan MMH, Rafii MY, Ramlee SI, Jusoh M, Mamun MAI. AMMI and GGE biplot analysis for yield performance and stability assessment of selected Bambara groundnut (Vigna subterranea L. Verdc.) genotypes under the multi-environmental trials (METs). Sci Rep. 2021;11:22791. https://doi.org/10.1038/s41598-021-01411-2

Downloads

Download data is not yet available.