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Plant Science Today (PST)

Research Articles

Vol. 12 No. 2 (2025)

Evaluating stability of corn (Zea mays L.) hybrids for fresh cob weight and quality across environments

DOI
https://doi.org/10.14719/pst.6876
Submitted
23 December 2024
Published
10-03-2025 — Updated on 01-04-2025
Versions

Abstract

Corn (Zea mays L.) is a major commercial crop cultivated worldwide.. Recognising its economic importance, corn breeding has gained considerable momentum, with a key focus on understanding how the environment influences genotype performance. This interaction, known as Genotype × Environment (G × E), plays a crucial role in identifying stable and high-performing varieties. This study analysed three critical quality traits, such as cob weight, total soluble solids, and total sugars using advanced statistical tools, including AMMI (Additive Model and Multiplicative Index), GGE (Genotype and Genotype-Environment), and WAASB (Weighted Average of Absolute Scores) models. These approaches were applied to evaluate the performance of 40 sweet-field corn hybrids across multiple environments. Based on the study two promising hybrids 45530×UMI 1230?+ and 45679×UMI 1200?+, that consistently performed well across different seasons in Coimbatore were identified. The Which-Won-Where plot further characterized the mega-environment, identifying the most suitable genotype for each environment based on all traits. This comprehensive analysis provides valuable insights into the G × E interaction effects on key quality parameters of fresh corn.  The findings of this study would help in focused breeding efforts for developing stable and better performing corn hybrids, ensuring they meet both production and quality demands across varying environmental conditions.

 

References

  1. USDA- National Agricultural Statistics Service [Internet]. USA; 2024 [Cited 2024 Dec 02]. Available from: https://www.nass.usda.gov/Statistics_by_Subject
  2. Williams RM, O’Brien L, Eagles HA, Solah VA, Jayasena V. The influences of genotype, environment and genotype × environment interaction on wheat quality. Aust J Agric Res. 2008;59(2):95–111. https://doi.org/10.1071/AR07185
  3. Yan W, Tinker NA. Biplot analysis of multi-environment trial data: Principles and applications. Can J Plant Sci. 2006;86(3):623–45. https://doi.org/10.4141/P05-169
  4. Olivoto T, Lúcio AD, da Silva JA, Marchioro VS, de 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. https://doi.org/10.2134/agronj2019.03.0220
  5. Shahriari Z, Heidari B, Dadkhodaie A. Dissection of genotype × environment interactions for mucilage and seed yield in Plantago species: Application of AMMI and GGE biplot analyses. PloS One. 2018;13(5):e0196095. https://doi.org/10.1371/journal.pone.0196095
  6. Patel R, Parmar DJ, Kumar S, Patel DA, Memon J, Patel MB, et al. Dissection of genotype × environment interaction for green cob yield using AMMI and GGE biplot with MTSI for selection of elite genotype of sweet corn (Zea mays conva. saccharata var. rugosa). Ind J Genet. 2023;83(01):59–68. https://doi.org/10.31742/ISGPB.83.1.8
  7. Mousavi SMN, Illés A, Szabó A, Shojaei SH, Demeter C, Bakos Z, et al. Stability yield indices on different sweet corn hybrids based on AMMI analysis. Braz J Biol. 2023;84:e270680. https://doi.org/10.1590/1519-6984.270680
  8. Stansluos AAL, Öztürk A, Niedba?a G, Türko?lu A, Halilo?lu K, Szulc P, et al. Genotype–Trait (GT) biplot analysis for yield and quality stability in some sweet corn (Zea mays L. saccharata Sturt.) genotypes. Agron. 2023;13(6):1538. https://doi.org/10.3390/agronomy13061538
  9. Kempthrone O. An introduction to genetic statistics. New York: John Wiley and sons; 1957
  10. Natesan S, Singh TS, Duraisamy T, Chandrasekharan N, Chandran S, Adhimoolam K, et al. Characterization of crtRB1 gene polymorphism and ?-carotene content in maize landraces originated from north eastern himalayan region (NEHR) of India. Front Sustain Food Syst. 2020;4:78. https://doi.org/10.3389/fsufs.2020.00078
  11. Olsen JK, Blight GW, Gillespie D. Comparison of yield, cob characteristics and sensory quality of six supersweet (sh2) corn cultivars grown in a subtropical environment. Aust J Exp Agric. 1990;30(3):387–93. https://doi.org/10.1071/EA9900387
  12. Hedge JE, Hofreiter BT. Estimation of carbohydrate. Methods in carbohydrate chemistry. New York: Academic Press. 1962;17–22.
  13. Somogyi M. Notes on sugar determination. J Biol Chem. 1952;195(1):19–23. https://doi.org/10.1016/S0021-9258(19)50870-5
  14. Gauch Jr 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
  15. Movahedi H, Mostafavi K, Shams M, Golparvar AR. AMMI analysis of genotype × environment interaction on grain yield of sesame (Sesamum indicum L.) genotypes in Iran. Biotechnol Equip [Internet]. 2020 Jan 1 [cited 2024 Dec 10];34(1):1013–18. Available from: https://www.tandfonline.com/doi/full/10.1080/13102818.2020.1816216
  16. Purchase JL. Parametric analysis to describe genotype x environment interaction and yield stability in winter wheat [Thesis]. Bloemfontein: University of the Free State; 1997
  17. Gebeyehu C, Bulti T, Dagnachew L, Kebede D. Additive main effect and multiplicative interactions (AMMI) and regression analysis in sorghum [Sorghum bicolor (L). Moench] varieties. J Appl Biosci. 2019;136:13877–86. https://doi.org/10.4314/jab.v136i1.4
  18. Baraki F, Tsehaye Y, Abay F. Grain yield performance and stability analysis of sesame (Sesamum indicum L.) genotypes in Western Tigray, Ethiopia. J Exp Agric Int. 2019;29(6):1–9. http://dx.doi.org/10.9734/JEAI/2019/45744t
  19. Farshadfar E, Vaisi Z, Yaghotipoor A. Non parametric estimation of phenotypic stability in wheat-barley disomic addition lines. Ann Biol Res. 2011;2(6):586–98.
  20. Olivoto T. Stability analysis in R using the WAASB index; 2019 Available from: https://doi.org/10.13140/RG.2.2.32425.75369/1
  21. Wang R, Wang H, Huang S, Zhao Y, Chen E, Li F. Assessment of yield performances for grain sorghum varieties by AMMI and GGE biplot analyses. Front Plant Sci. [Internet]. 2023 Oct 30 [cited 2024 Dec 10];14:1261323. Available from: https://www.frontiersin.org/articles/10.3389/fpls.2023.1261323/full.https://doi.org/10.3389/fpls.2023.126132322.
  22. WeiKai YW, Kang MS. GGE biplot analysis: a graphical tool for breeders, geneticists and agronomists. Boca Raton: CRC Press Inc; 2003 https://www.cabidigitallibrary.org/doi/full/10.5555/20053076730
  23. Bocianowski J, Waligóra H, Majchrzak L. Genotype by year interaction for selected traits in sweet maize (Zea mays L.) hybrids using AMMI model. Euphytica. 2024;220(6):1–14. https://doi.org/10.1007/s10681-024-03352-z
  24. Behera PP, Singode A, Bhat BV, Sarma R. Selection of high yielding stable forage sorghum genotypes using WAASB and MGIDI methods. Ind J Genet. 2024;84(02):224–31. https://doi.org/10.31742/ISGPB.84.2.10
  25. Kumar R, Kumar S, Chikkappa GK, Das AK, Dhonde S, Kaur Y, et al. Unveiling genotype × environment dynamics for grain yield in QPM hybrids through AMMI, GGE Biplot and MTSI approach. Ind J Genet. 2024;84(03):449–60. https://doi.org/10.31742/ISGPB.84.3.16
  26. Verma A, Singh GP. Wheat genotypes evaluated for Stability, adaptability analysis for Northern Hills by AMMI and BLUP Tools. Int J Agric Sci. 2020;5. Available from: https://www.iaras.org/home/caijas/wheat-genotypes-evaluated-for-stability-adaptability-analysis-for-northern-hills-by-ammi-blup-tools
  27. Arshad W, ALEEM S, Khan MI, Saqib M, Zeeshan M, Mohsin MU, et al. AMMI, BLUP and WAASB stability models for the selection of stable chickpea (Cicer arietinum L.) genotypes in different agroecological regions of Pakistan. J Food and Agri Technol Res. 2024;3(02):66–81.
  28. Nataraj V, Bhartiya A, Singh CP, Devi HN, Deshmukh MP, Verghese P, et al. WAASB?based stability analysis and simultaneous selection for grain yield and early maturity in soybean. Agron J. 2021;113(4):3089–99. https://doi.org/10.1002/agj2.20750
  29. Vineeth TV, Prasad I, Chinchmalatpure AR, Lokeshkumar BM, Kumar S, Ravikiran KT, et al. Weighted average absolute scores of BLUPs (WAASB) based selection of stable Asiatic cotton genotypes for the salt affected Vertisols of India. Ind J Genet. 2022;82(01):104–08. https://doi.org/10.31742/IJGPB.82.1.15
  30. Pranati J, Kumar CS, Gangashetty PI, Sharma M, Pandey MK, Sajja SB. Genetic stability analysis of early maturing pigeon pea genotypes using AMMI and WAASB models. J Food Legum. 2024;37(3):255–61. https://doi.org/10.59797/jfl.v37.i3.204

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