Skip to main navigation menu Skip to main content Skip to site footer
Plant Science Today (PST)

Research Articles

Vol. 13 No. sp2 (2026): Recent Advances in Agriculture

Genotype × environment interactions for resistance to northern corn leaf blight and sorghum downy mildew in maize

DOI
https://doi.org/10.14719/pst.13282
Submitted
19 December 2025
Published
06-05-2026

Abstract

Stability of disease resistance is critical in maize breeding because genotype × environment interaction (GEI) can substantially influence trait expression. In the present study, 31 multi-parent advanced generation intercross (MAGIC) recombinant inbred lines (RILs), along with 2 checks, were evaluated for northern corn leaf blight (NCLB) across 3 locations, while 32 RILs were assessed for their reaction to sorghum downy mildew (SDM) across 3 rainy seasons. Pooled analysis of variance (ANOVA) revealed a non-significant GEI for SDM, indicating stable genotype performance across seasons, whereas NCLB showed significant GEI. Correlation analysis further supported these results: SDM exhibited very high inter-season correlations, suggesting minimal genotype re-ranking, while NCLB showed moderate to low correlations among environments, reflecting environmental divergence and crossover interactions. Unweighted pair group method with arithmetic mean (UPGMA) clustering grouped the genotypes into three major clusters for both diseases but did not distinctly separate resistant and susceptible classes, suggesting quantitative inheritance and continuous phenotypic variation. Further stability analysis using additive main effects and multiplicative interaction (AMMI) and genotype plus genotype-by-environment interaction (GGE) biplot models identified G13, G15 and G2 as stable and resistant for NCLB, while G15, G10, G16 and G32 exhibited broad adaptation across environments. These stable MAGIC RILs for NCLB disease represent valuable genetic resources for breeding durable resistance in NCLB prone maize production regions.

References

  1. 1. Patel R, Parmar DJ, Patel DA, Memon J, Patel MB, Patel JK. 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). Indian J Genet. 2023;83(1):59–68.
  2. 2. Hooda KS, Bagaria PK, Khokhar M, Kaur H, Rakshit S. Mass screening techniques for resistance to maize diseases. ICAR-Indian Inst Maize Res, PAU Campus, Ludhiana. 2018;93 pp.
  3. 3. Leonard KJ, Suggs EG. Setosphaeria turcica n. comb., the ascigerous state of Helminthosporium turcicum Pass. Phytopathol. 1974;64(9):1130–1.
  4. 4. Pant SK, Kumar P, Chauhan VS. Effect of turcicum leaf blight on photosynthesis in maize. Indian Phytopathol. 2000;54:251–2.
  5. 5. Nwanosike MR, Mabagala R, Kusolwa PM. Effect of northern corn leaf blight (Exserohilum turcicum) severity on yield of maize (Zea mays L.) in Morogoro, Tanzania. Int J Sci Res. 2015;4(9):466–75.
  6. 6. Singh R, Mani VP, Koranga KS, Khendelwal RS, Bhandari P, Pant SK. Identification of additional sources of resistance to Exserohilum turcicum in maize (Zea mays L.). SABRAO J Breed Genet. 2004;36:45–7.
  7. 7. Schuh W, Frederiksen R, Jeger M. Analysis of spatial patterns in sorghum downy mildew with Morisita’s index of dispersion. Phytopathology. 1986;76:446–50. https://doi.org/10.1094/phyto-76-446
  8. 8. Renfro BL. Introduction and decision taken on downy mildew issue. Indian Phytopathol. 1973;23:177–9.
  9. 9. Zhao L, Wu Y, Yu X, Shi J. Genetic approaches and breeding strategies for enhancing northern corn leaf blight resistance in maize (Zea mays L.). Maize Genom Genet. 2024;15(6):282–92. https://doi.org/10.5376/mgg.2024.15.0027
  10. 10. Mackay I, Powell W. Methods for linkage disequilibrium mapping in crops. Trends Plant Sci. 2007;12:57–63.
  11. 11. Huang BE, Verbyla KL, Verbyla AP, Raghavan C, Singh VK, Gaur P. Magic populations in crops: current status and future prospects. Theor Appl Genet. 2015;128:999–1017. https://doi.org/10.1007/s00122-015-2506-0
  12. 12. Scott MF, Ladejobi O, Amer S, Bentley AR, Biernaskie J, Boden SA, et al. Multi-parent populations in crops: a toolbox integrating genomics and genetic mapping with breeding. Heredity. 2020;125(6):396–416. https://doi.org/10.1038/s41437-020-0336-6
  13. 13. Desai SP, Lohithaswa HC, Mallikarjuna N. Genetic characterization and comparative assessment of response of biparental and multi-parental populations to northern corn leaf blight in maize (Zea mays L.). Indian J Genet. 2022;82(3):304–10.
  14. 14. Mwendo MM, Mbega ER, Ndunguru J, Philipo M. AMMI and GGE biplot analysis of maize genotypes for seed yield across different maize growing location in Tanzania. Cogent Food Agric. 2025;11(1):2438909. https://doi.org/10.1080/23311932.2024.2438909
  15. 15. Yan W, Hunt LA, Sheng Q, Szlavnics Z. Cultivar evaluation and mega-environment investigation based on the GGE biplot. Crop Sci. 2000;40:597–605.
  16. 16. Supriadi D, Bimantara YM, Zendrato YM. Assessment of genotype × environment and yield performance of tropical maize hybrids using stability statistics and graphical biplots. PeerJ. 2024;12:e18624. https://doi.org/10.7717/peerj.18624
  17. 17. Gollob HF. A statistical model which combines features of factor analytic and analysis of variance techniques. Psychometrika. 1968;33:73–115.
  18. 18. Ajay BC, Ramya KT, Fiyaz AR, Govindaraj G, Bera SK, Kumar N, et al. R-AMMI-LM: Linear-fit Robust-AMMI model to analyze genotype-by environment interactions. Indian J Genet Plant Breed. 2021;81(1):87–92. https://doi.org/10.31742/IJGPB.81.1.9
  19. 19. Crossa J, Gauch HG, Zobel RW. Additive main effects and multiplicative interaction analysis of two international maize cultivar trials. Crop Sci. 1990;30(3):493–500. https://doi.org/10.2135/cropsci1990.0011183X003000030003x
  20. 20. Gauch HG Jr. Model selection and validation for yield trials with interaction. Biometrics. 1988;44:705–15. https://doi.org/10.2307/2531585
  21. 21. Ma C, Liu C, Ye Z. Influence of genotype × environment interaction on yield stability of maize hybrids with AMMI model and GGE biplot. Agron. 2024;14(5):1000. https://doi.org/10.3390/agronomy14051000
  22. 22. Bocianowski J, Nowosad K, Rejek D. Genotype-environment interaction for grain yield in maize (Zea mays L.) using the additive main effects and multiplicative interaction (AMMI) model. J Appl Genet. 2024;65(4):653–64. https://doi.org/10.1007/s13353-024-00899-4
  23. 23. Chung C, Longfellow JM, Walsh EK, Kerdieh Z, Esbroeck GV, Balint-Kurti P, et al. Resistance loci affecting distinct stages of fungal pathogenesis: use of introgression lines for QTL mapping and characterization in the maize-Setosphaeria turcica pathosystem. BMC Plant Biol. 2010;10:103.
  24. 24. Mitiku M, Eshte Y, Shiferaw W. Evaluation of maize variety for northern leaf blight (Trichometasphaeria turcica) in South Omo zone. World J Agric Res. 2014;2(5):237–9.
  25. 25. Lal S, Singh IS. Breeding for resistance to downy mildews and stalk rots in maize. Theor Appl Genet. 1984;69:111–9.
  26. 26. Zobel RW. A powerful statistical model for understanding genotype-by-environment interaction. In: Kang MS, editor. Genotype-by-environment interaction and plant breeding. Louisiana State University; 1990. p.126–40.
  27. 27. Farshadfar E, Sutka J. Biplot analysis of genotype-environment interaction in durum wheat using the AMMI model. Acta Agron Hung. 2006;54:459–67.
  28. 28. Olivoto T, Lúcio ADC. Metan: an R package for multi-environment trial analysis. Methods Ecol Evol. 2020;11(6):783–9.
  29. 29. Purchase JL, Hatting H, van Deventer CS. Genotype × environment interaction of winter wheat (Triticum aestivum L.) in South Africa: II. Stability of cultivars and recommendations for stable cultivars. Euphytica. 2000;112:1–12.
  30. 30. Sokal RR, Michener CD. A statistical method for evaluating systematic relationships. Univ Kans Sci Bull. 1958;38:1409–38.
  31. 31. Sowmya MS, Lohithaswa HC, Jadesha G, Mahadevu P, Mallikarjuna N, Mohan Rao A. Genetic variability studies on resistance to sorghum downy mildew disease in MAGIC population of maize (Zea mays L.). Mysore J Agric Sci. 2024;58(4):110–4.
  32. 32. Lohithaswa HC, Balasundara DC, Mallikarjuna MG, Sowmya MS, Mallikarjuna N, Kulkarni RS, et al. Experimental evaluation of effectiveness of genomic selection for resistance to northern corn leaf blight in maize. J Appl Genet. 2024;66(3):493–521.
  33. 33. Ranganatha HM, Lohithaswa HC, Pandravada A. Mapping and validation of major quantitative trait loci for resistance to northern corn leaf blight along with the determination of the relationship between resistances to multiple foliar pathogens of maize (Zea mays L.). Front Genet. 2021;11:548407.
  34. 34. Rashid Z, Sofi M, Harlapur SI, Kachapur RM, Dar ZA, Singh PK, et al. Genome-wide association studies in tropical maize germplasm reveal novel and known genomic regions for resistance to northern corn leaf blight. Sci Rep. 2020;10:21949.
  35. 35. Yan W, Cornelius PL, Crossa J, Hunt LA. Two types of GGE biplots for analyzing multi-environment trial data. Crop Sci. 2001;41(3):656–63. https://doi.org/10.2135/cropsci2001.413656x
  36. 36. Gauch HG Jr. A simple protocol for AMMI analysis of yield trials. Crop Sci. 2013;53(5):1860–9. https://doi.org/10.2135/cropsci2013.04.0241
  37. 37. Kang MS. Simultaneous selection for yield and stability in crop performance trials: consequences for growers. Agron J. 1993;85:1860–9.
  38. 38. Gauch HG, Piepho HP, Annicchiarico P. Statistical analysis of yield trials by AMMI and GGE. Crop Sci. 2008;48:866–89.
  39. 39. Bose LK, Jambhulkar NN, Pande K, Singh ON. Stability analysis of rice genotypes for grain yield and its components under irrigated ecosystem. African J Agric Res. 2014;9(26):2044–50.
  40. 40. Ebdon JS, Gauch HG Jr. Additive main effect and multiplicative interaction analysis of national turfgrass performance trials: I. Interpretation of genotype × environment interaction. Crop Sci. 2002;42(2):489–96. https://doi.org/10.2135/cropsci2002.4890
  41. 41. Bozovic D, Zivanovic T, Popovic V, Tatic M, Gospavic Z, Miloradovic Z, et al. Assessment stability of maize lines yield by GGE-biplot analysis. Genetika. 2018;50(3):755–70.
  42. 42. Elayaraja K, Govindaraj P, Mahadevwamy HK, Appunu C, Hemaprabha G, Rajesh V, et al. Stability analysis for identification of stable genotypes of sugarcane (Saccharum spp.) through AMMI model. Indian J Genet Plant Breed. 2022;82(4):480–9. https://doi.org/10.31742/ISGPB.82.4.12
  43. 43. Samonte SOP, Wilson LT, McClung AM, Medley JC. Targeting cultivars onto rice growing environments using AMMI and SREG GGE biplot analyses. Crop Sci. 2005;45(6):2414–24. https://doi.org/10.2135/cropsci2004.0627
  44. 44. Kumar A, Sharma A, Kumar R, Rana M, Sharma S, Kumar P, et al. 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
  45. 45. Zhang H, Liu Z, Li X, Wang Q, Yang Y, Zhang W, et al. Genotype by environment interaction for grain yield in foxtail millet (Setaria italica) using AMMI model and GGE biplot. Plant Growth Regul. 2023;99(1):101–12. https://doi.org/10.1007/s10725-022-00885-y
  46. 46. Yan W, Tinker NA. Biplot analysis of multienvironment trial data: principles and applications. Can J Plant Sci. 2006;86:623–45. https://doi.org/10.4141/P05-169
  47. 47. Ohunakin AO, Odiyi AC, Akinyele BO. Genetic variance components and GGE interaction of tropical maize genotypes under northern leaf blight disease infection. Cereal Res Commun. 2020;49(2):277–83. https://doi.org/10.1007/s42976-020-00100-6
  48. 48. Reddy PS, Satyavathi CT, Khandelwal Patil HT, Narasimhulu R, Bhadarge HH, Iyanar K, et al. GGE biplot analysis for identification of ideal cultivars and testing locations of pearl millet (Pennisetum glaucum L.R. Br.) for peninsular India. Indian J Genet Plant Breed. 2022;82(2):167–76. https://doi.org/10.31742/IJGPB.82.2.5
  49. 49. Kottawa-Arachchi JD, Ranatunga MAB, Abeysiriw Ardena DS. Utilization of within location non-replicated multi-locational yield trials and GGE biplot methods for testing adaptability of tea (Camellia sinensis (L.) O. Kuntze) over diverse environments. Indian J Genet Plant Breed. 2022;82(3):342–9. https://doi.org/10.31742/ISGPB.82.3.9

Downloads

Download data is not yet available.