Research Articles
Vol. 13 No. sp5 (2026): Recent Advances in Agriculture
Multi-environment evaluation of yield stability in finger millet (Eleusine coracana (L.) Gaertn.) using AMMI, GGE, WAASBY and MTSI
Department of Genetics and Plant Breeding, Dr. Rajendra Prasad Central Agricultural University, Pusa, Samastipur 848 125, Bihar, India
Department of Genetics and Plant Breeding, Dr. Rajendra Prasad Central Agricultural University, Pusa, Samastipur 848 125, Bihar, India
Department of Genetics and Plant Breeding, Dr. Rajendra Prasad Central Agricultural University, Pusa, Samastipur 848 125, Bihar, India
Department of Plant Physiology and Biochemistry, Dr. Rajendra Prasad Central Agricultural University, Pusa, Samastipur 848 125, Bihar, India
Department of Genetics and Plant Breeding, Dr. Rajendra Prasad Central Agricultural University, Pusa, Samastipur 848 125, Bihar, India
Department of Genetics and Plant Breeding, Dr. Rajendra Prasad Central Agricultural University, Pusa, Samastipur 848 125, Bihar, India
Abstract
Genotype × environment interaction (GEI) strongly influences finger millet yield and agronomic performance, making multi-environment testing essential for identifying stable, high-yielding genotypes. This study aims to identify high-yielding and stable finger millet genotypes using complementary stability analyses. A total of 57 finger millet genotypes were evaluated in a randomised complete block design (RCBD) with 2 replications across three environments at Dr. Rajendra Prasad Central Agricultural University, Pusa (Dholi and Pusa), Bihar, India during 2024–25. The GEI was significant for grain yield and for all agronomic traits (p < 0.001). Therefore, genotype performance and stability were assessed using additive main effects and multiplicative interaction (AMMI), the genotype plus genotype × environment (GGE) biplot, the weighted average of absolute scores of the best linear unbiased predictions of the GEI effects (WAASB) with its superiority index (WAASBY) and the multi-trait stability index (MTSI). Based on the WAASBY, PR 202 (17.82 g per plant), IE 2082 (17.08 g), IE 96 (16.92 g), IE 2097 (16.57 g), GPU 26 (16.15 g), IE 510 (14.49 g) and IE 2062 (11.02 g) genotypes were identified as high yielding and most stable genotypes. Considering all nine traits jointly, MTSI selected IE 2097, MR 6, IE 817, IE 2082, IE 510, PR 202, IE 2062, IE 886 and GPU 28, with a selection differential of +29.82 % for grain high-yielding and stable performers. These genotypes represent promising candidates for multi-environment cultivation and may serve as valuable parental material in finger millet breeding programs.
References
- 1. Tadele Z, Assefa K. Increasing food production in Africa by boosting the productivity of understudied crops. Agronomy. 2012;2(4):240–83. http://doi.org/10.3390/agronomy2040240
- 2. Ishwarya MC, Swapnil, Rout S, Singh D, Panda KK, Imam Z, et al. Yield stability of finger millet genotypes assessed by AMMI and GGE biplot analysis across diverse environments. Sci Rep. 2025;15:39042. http://doi.org/10.1038/s41598-025-25696-9
- 3. Kuru B, Abera N, Mulualem T. Genotype × environment interaction and yield stability of finger millet (Eleusine coracana) genotypes based on AMMI, GGE and MTSI analysis in humid lowland areas of Ethiopia. Field Crops Res. 2025;322:109707. http://doi.org/10.1016/j.fcr.2024.109707
- 4. Chetana, Nagaraja TE, Meenakshi J, Vinutha DN, Manjunatha M, Kavya S, et al. Ameliorating the stability and yield potential in finger millet (Eleusine coracana (L) Gaertn) through genotype × environment interaction studies. Indian J Genet Plant Breed. 2026;86(1):17–28. http://doi.org/10.1007/s44489-026-00004-5
- 5. Zobel RW, Wright MJ, Gauch HG. Statistical analysis of a yield trial. Agron J. 1988;80:388–93. http://doi.org/10.2134/agronj1988.00021962008000030002x
- 6. Gabriel KR. The biplot graphic display of matrices with application to principal component analysis. Biometrika. 1971;58(3):453–67. http://doi.org/10.1093/biomet/58.3.453
- 7. Crossa J, Gauch HG, Zobel RW. Additive main effects and multiplicative interaction analysis of two international maize cultivar trials. Crop Sci. 1990;30:493–500. http://doi.org/10.2135/cropsci1990.0011183X003000030003x
- 8. Gauch HG. Statistical analysis of yield trials by AMMI and GGE. Crop Sci. 2006;46:1488–1500. http://doi.org/10.2135/cropsci2005.07-0193
- 9. Yan W. GGE biplot-a Windows application for graphical analysis of multi-environment trial data and other types of two-way data. Agron J. 2001;93(5):1111–8. http://doi.org/10.2134/agronj2001.9351111x
- 10. Yan W, Kang MS. GGE Biplot Analysis: A Graphical Tool for Breeders, Geneticists and Agronomists. Boca Raton (FL): CRC Press; 2002. http://doi.org/10.1201/9781420040371
- 11. Yan W, Kang MS, Ma B, Woods S, Cornelius PL. GGE biplot vs. AMMI analysis of genotype-by-environment data. Crop Sci. 2007;47:643–53. http://doi.org/10.2135/cropsci2006.06.0374
- 12. Yan W. Singular-value partitioning for biplot analysis of multi-environment trial data. Agron J. 2002;94:990–6. http://doi.org/10.2134/agronj2002.0990
- 13. Yan W, Frégeau-Reid J. Genotype by yield*trait (GYT) biplot: a novel approach for genotype selection based on multiple traits. Sci Rep. 2018;8:8242. http://doi.org/10.1038/s41598-018-26688-8
- 14. Olivoto T, Lúcio ADC, da Silva JAG, Marchioro VS, Souza VQ, Jost E. Mean performance and stability in multi-environment trials I: combining features of AMMI and BLUP techniques. Agron J. 2019;111(6):2949–60. http://doi.org/10.2134/agronj2019.03.0220
- 15. Olivoto T, Lúcio ADC, da Silva JAG, Sari BG, Diel MI. Mean performance and stability in multi-environment trials II: selection based on multiple traits. Agron J. 2019;111(6):2961–9. http://doi.org/10.2134/agronj2019.03.0221
- 16. Hartley HO. The maximum F-ratio as a short-cut test for heterogeneity of variance. Biometrika. 1950;37(3-4):308–12. http://doi.org/10.1093/biomet/37.3-4.308
- 17. Gollob HF. A statistical model which combines features of factor analytic and analysis of variance techniques. Psychometrika. 1968;33:73–115. http://doi.org/10.1007/BF02289676
- 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. http://doi.org/10.1080/02571862.2000.10634878
- 19. Farshadfar E. Incorporation of AMMI stability value and grain yield in a single non-parametric index (GSI) in bread wheat. Pak J Biol Sci. 2008;11:1791–6. http://doi.org/10.3923/pjbs.2008.1791.1796
- 20. R Core Team. R: A Language and Environment for Statistical Computing. Vienna (Austria): R Foundation for Statistical Computing; 2024. http://www.R-project.org/
- 21. Olivoto T, Lúcio ADC. metan: an R package for multi-environment trial analysis. Methods Ecol Evol. 2020;11(6):783–9. http://doi.org/10.1111/2041-210X.13384
- 22. Manna P, Rout S, Swapnil, Rashmi K, Sahay S, Susmitha PD, et al. Stability analysis for yield and yield components through AMMI and GGE biplot techniques in germplasm of finger millet (Eleusine coracana). Indian J Agric Sci. 2026;96(2):143–8. http://doi.org/10.56093/ijas.v96i02.173798
- 23. Anuradha N, Patro TSSK, Singamsetti A, Rani YS, Triveni U, Nirmala Kumari A, et al. Comparative study of AMMI- and BLUP-based simultaneous selection for grain yield and stability of finger millet [Eleusine coracana (L.) Gaertn.] genotypes. Front Plant Sci. 2022;12:786839. http://doi.org/10.3389/fpls.2021.786839
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