Advertisement

Cereal Research Communications

, Volume 45, Issue 2, pp 336–345 | Cite as

Stability Analysis of Maize Cultivars Adapted to Tropical Environments Using AMMI Analysis

  • M. OyekunleEmail author
  • A. Menkir
  • H. Mani
  • G. Olaoye
  • I. S. Usman
  • S. G. Ado
  • U. S. Abdullahi
  • H. O. Ahmed
  • L. B. Hassan
  • R. O. Abdulmalik
  • H. Abubakar
Article

Abstract

Genotype × environment interactions complicate selection of superior genotypes for narrow and wide adaptation. Eighteen tropically-adapted maize cultivars were evaluated at six locations in Nigeria for 2 yrs to (i) identify superior and stable cultivars across environments and (ii) assess relationships among test environments. Environment and genotype × environment interactions (GEI) were significant (P < 0·05) for grain yield. Environments accounted for 63.5% of the total variation in the sum of squares for grain yield, whereas the genotype accounted for 3.5% and GEI for 32.8%. Grain yield of the cultivars ranged from 2292 kg ha–1 for DTSTR-W SYN2 to 2892 kg ha−1 for TZL COMP4 C3 DT C2 with an average of 2555 kg ha−1. Cultivar DT SYN2-Y had the least additive main effect and multiplicative interaction (AMMI) stability value of 7.4 and hence the most stable but low-yielding across environments. AMMI biplot explained 90.5% and classified cultivars and environments into four groups each. IWD C3 SYN F3 was identified as the high-yielding and stable cultivar across environments. ZA15, ZA14, BK14, BK15 and IL15 had environment mean above the grand mean, while BG14, BG15, LE14, LE15, IL14, LA14 and LA15 had mean below the grand mean. ZA, BK, BG, LE and LA were found to be consistent in ranking the maize cultivars. However, Zaria, Birnin Kudu, and Ilorin were identified as the best test locations and could be used for selecting the superior maize cultivars. The identified high-yielding and stable cultivar could be further tested and promoted for adoption to contribute to food insecurity in Nigeria.

Keywords

AMMI stability value genotype × environment interaction grain yield maize cultivars multiple environments 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Badu-Apraku, B., Abamu, F.J., Menkir, A., Fakorede, M.A.B., Obeng-Antwi, K., The, C. 2003. Genotype by environment interactions in the regional early variety trials in West and Central Africa. Maydica 48:93–104.Google Scholar
  2. Badu-Apraku, B., Lum, A.F., Fakorede M.A.B., Menkir, A., Chabi, Y., The, C., Abdulai, M., Jacob, S., Agbaje, S. 2008. Performance of cultivars derived from recurrent selection for grain yield and striga resistance in early maize. Crop Sci. 48:99–112.CrossRefGoogle Scholar
  3. Badu-Apraku, B., Oyekunle, M., Obeng-Antwi, K., Osuman, A., Ado, S.G., Coulibaly, N., Yallou, C., Abdulai, M.S., Boakyewaa, G.A., Didjeira, A. 2011a. Performance of extra-early maize cultivars based on GGE biplot and AMMI analysis. J. Agric. Sci. 150:1–11.Google Scholar
  4. Badu-Apraku, B., Akinwale, R.O., Menkir, A., Obeng-Antwi, K., Osuman, A.S., Coulibaly, N., Onyibe, J.E. Yallou, G.C., Abdullai, M.S., Didjera, A. 2011b. Use of GGE biplot for targeting early maturing maize cultivars to mega-environments in West Africa. African Crop Sci. J. 19:79–96.CrossRefGoogle Scholar
  5. Byerlee, D., Eicher, C.K. 1971. Africa’s Emerging Maize Revolution. Lynne Rienner Publishers. Boulder, CO, USA.Google Scholar
  6. Chang, L., Chai, S.X. 2006. Application of AMMI model in the stability analysis of spring wheat in rainfed area. Acta Ecol. Sin. 26:3677–3684.Google Scholar
  7. Crossa, J. 1990. Statistical analyses of multilocation trials. Adv. Agron. 44:55–85.CrossRefGoogle Scholar
  8. Crossa, J., Fox, P.N., Pfeiffer, W.H., Rajaram, S., Gauch, H.G. 1991. AMMI adjustment for statistical analysis of an international wheat yield trial. Theor. Appl. Genet. 81:27–37.CrossRefGoogle Scholar
  9. Ejeta, G. 2010. African Green Revolution needn’t be a mirage. Sci. 327:831–832.CrossRefGoogle Scholar
  10. Fakorede, M.A.B., Adeyemo, M.O. 1986. Genotype × environment components of variance for three types of maize varieties in the rainforest zone of S.W. Nigeria. Nigerian J. Agron. 1:43–46.Google Scholar
  11. Fan, X.M., Kang, M.S., Chen, H., Zhang, Y., Tan, J., Xu, C. 2007. Yield stability of maize hybrids evaluated in multi-environment trials in Yunnan, China. Agron. J. 99:220–228.CrossRefGoogle Scholar
  12. FAOSTAT 2016. Food and Agriculture Organization of the United Nation Statistics Division. Available at: http://faostat3.fao.org/ download/Q/QC/E (accessed on May 4, 2016).Google Scholar
  13. Gauch, H.G., Zobel, R.W. 1997. Identifying mega-environments and targeting genotypes. Crop Sci. 37:311–326.CrossRefGoogle Scholar
  14. Gauch, H.G. 1988. Model selection and validation for yield trials with interaction. Biometrics 44:705–715.CrossRefGoogle Scholar
  15. Gauch, H.G., Zobel, R.W. 1988. Predictive and postdictive success of statistical analysis of yield trials. Theor. Appl. Genet. 76:1–10.CrossRefGoogle Scholar
  16. Gauch, H.G. 1992. Statistical Analysis of Regional Yield Trials: AMMI Analysis of Factorial Designs. Elsevier. Amsterdam, The Netherlands.Google Scholar
  17. Kang, M.S., Balzarini, M.G., Guerra, J.L.L. 2004. Genotype-by-environment interaction. In: Saxton, A.M. (ed.), Genetic Analysis of Complex Traits Using SAS. SAS Publ. SAS Inst. Cary, NC, USA. pp. 69–96.Google Scholar
  18. Liu, W.J., Li, H.J., Wang, X.D., Zhou, K.D. 2002. Stability analysis for elementary characters of hybrid rice by AMMI model. Acta Agron. Sin. 28:569–573.Google Scholar
  19. Moghaddam, M.J., Pourdad, S.S. 2009. Comparison of parametric and non-parametric methods for analyzing genotype × environment interactions in safflower (Carthamus tinctoriusL.). J. Agric. Sci. 147:601–612.CrossRefGoogle Scholar
  20. Mohammadi, R., Amri, A., Haghparast, R., Sadeghzadeh, D., Armion, M., Ahmadi, M.M. 2009. Pattern analysis of genotype-by-environment interaction for grain yield in durum wheat. J. Agric. Sci. 147:537–545.CrossRefGoogle Scholar
  21. Oyekunle, M., Badu-Apraku, B. 2014. Hybrid performance and inbred-hybrid relationship of early maturing tropical maize under drought and well-watered conditions. Cereal Res. Commun. 42:314–325.CrossRefGoogle Scholar
  22. Reddy, P.S., Rathore, A., Reddy, B.V.S., Panwar, S. 2011. Application GGE biplot and AMMI model to evaluate sweet sorghum (Sorghum bicolor) hybrids for genotype × environment interaction and seasonal adaptation. Indian J. Agri. Sci. 81:438–444.Google Scholar
  23. Sabaghnia, N., Sabaghpour, S.H., Dehghani, H. 2008. The use of an AMMI model and its parameters to analyse yield stability in multi-environment trials. J. Agric. Sci. 146:571–581.CrossRefGoogle Scholar
  24. SAS Institute 2002. SAS User’s Guide. Version 9.2. SAS Institute Inc. Cary, NC, USA.Google Scholar
  25. Westcoff, B. 1987. A method of analysis of the yield stability of crops. J. Agric. Sci. 108:267–274.CrossRefGoogle Scholar
  26. Xu, N.Y., Zhang, G.W., Li, J., Zhou, Z.G. 2013. Ecological regionalization of cotton varieties based on GGE biplot. Chin. J. Appl. Ecol. 24:771–776.Google Scholar
  27. Yan, W.K. 2010. Optimal use of biplots in the analysis of multi-environment variety trial data. Acta Agron. Sin. 36:1–16.Google Scholar
  28. Yan, W. 2001. GGE biplot: a Windows application for graphical analysis of multi-environment trial data and other types of two-way data. Agron. J. 93:1111–1118.CrossRefGoogle Scholar
  29. Zobel, R.W., Wright, M.J., Gauch, H.G. 1988. Statistical analysis of a yield trial. Agron. J. 80:388–393.CrossRefGoogle Scholar

Copyright information

© Akadémiai Kiadó, Budapest 2017

Authors and Affiliations

  • M. Oyekunle
    • 1
    Email author
  • A. Menkir
    • 2
  • H. Mani
    • 3
  • G. Olaoye
    • 4
  • I. S. Usman
    • 1
  • S. G. Ado
    • 1
  • U. S. Abdullahi
    • 1
  • H. O. Ahmed
    • 1
  • L. B. Hassan
    • 1
  • R. O. Abdulmalik
    • 1
  • H. Abubakar
    • 1
  1. 1.Department of Plant ScienceAhmadu Bello UniversityZariaNigeria
  2. 2.International Institute of Tropical Agriculture (IITA)IITA (UK) LtdCroydonUK
  3. 3.Department of AgronomyAhmadu Bello UniversityZariaNigeria
  4. 4.Department of AgronomyUniversity of IlorinKwara StateNigeria

Personalised recommendations