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  • 1 Ahmadu Bello University, Zaria, Kaduna State, Nigeria
  • | 2 IITA (UK) Ltd, Carolyn House, 26 Dingwall Road, Croydon CR9 3EE, UK
  • | 3 Ahmadu Bello University, Zaria, Kaduna State, Nigeria
  • | 4 University of Ilorin, Kwara State, Nigeria
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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.

  • 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 48:93104.

    • Search Google Scholar
    • Export Citation
  • 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. 48:99112.

    • Search Google Scholar
    • Export Citation
  • 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. 150:111.

    • Search Google Scholar
    • Export Citation
  • 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. 19:7996.

    • Search Google Scholar
    • Export Citation
  • Byerlee, D., Eicher, C.K. 1971. Africa’s Emerging Maize Revolution. Lynne Rienner Publishers. Boulder, CO, USA.

  • Chang, L., Chai, S.X. 2006. Application of AMMI model in the stability analysis of spring wheat in rainfed area. Acta Ecol. Sin. 26:36773684.

    • Search Google Scholar
    • Export Citation
  • Crossa, J. 1990. Statistical analyses of multilocation trials. Adv. Agron. 44:5585.

  • 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. 81:2737.

    • Search Google Scholar
    • Export Citation
  • Ejeta, G. 2010. African Green Revolution needn’t be a mirage. Sci. 327:831832.

  • 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. 1:4346.

    • Search Google Scholar
    • Export Citation
  • 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. 99:220228.

    • Search Google Scholar
    • Export Citation
  • 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).

    • Search Google Scholar
    • Export Citation
  • Gauch, H.G., Zobel, R.W. 1997. Identifying mega-environments and targeting genotypes. Crop Sci. 37:311326.

  • Gauch, H.G. 1988. Model selection and validation for yield trials with interaction. Biometrics 44:705715.

  • Gauch, H.G., Zobel, R.W. 1988. Predictive and postdictive success of statistical analysis of yield trials. Theor. Appl. Genet. 76:110.

    • Search Google Scholar
    • Export Citation
  • Gauch, H.G. 1992. Statistical Analysis of Regional Yield Trials: AMMI Analysis of Factorial Designs. Elsevier. Amsterdam, The Netherlands.

    • Search Google Scholar
    • Export Citation
  • 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. 6996.

    • Search Google Scholar
    • Export Citation
  • 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. 28:569573.

    • Search Google Scholar
    • Export Citation
  • Moghaddam, M.J., Pourdad, S.S. 2009. Comparison of parametric and non-parametric methods for analyzing genotype × environment interactions in safflower (Carthamus tinctorius L.). J. Agric. Sci. 147:601612.

    • Search Google Scholar
    • Export Citation
  • 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. 147:537545.

    • Search Google Scholar
    • Export Citation
  • 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. 42:314325.

    • Search Google Scholar
    • Export Citation
  • 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. 81:438444.

    • Search Google Scholar
    • Export Citation
  • 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. 146:571581.

    • Search Google Scholar
    • Export Citation
  • SAS Institute 2002. SAS User’s Guide. Version 9.2. SAS Institute Inc. Cary, NC, USA.

  • Westcoff, B. 1987. A method of analysis of the yield stability of crops. J. Agric. Sci. 108:267274.

  • 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. 24:771776.

    • Search Google Scholar
    • Export Citation
  • Yan, W.K. 2010. Optimal use of biplots in the analysis of multi-environment variety trial data. Acta Agron. Sin. 36:116.

  • 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. 93:11111118.

    • Search Google Scholar
    • Export Citation
  • Zobel, R.W., Wright, M.J., Gauch, H.G. 1988. Statistical analysis of a yield trial. Agron. J. 80:388393.

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Cereal Research Communications
Language English
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ISSN 0133-3720 (Print)
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