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Performance of extra-early maize cultivars based on GGE biplot and AMMI analysis

Published online by Cambridge University Press:  05 October 2011

B. BADU-APRAKU ,
M. OYEKUNLE ,
K. OBENG-ANTWI ,
A. S. OSUMAN ,
S. G. ADO ,
N. COULIBAY ,
C. G. YALLOU ,
M. ABDULAI ,
G. A. BOAKYEWAA  and
A. DIDJEIRA
B. BADU-APRAKU*
Affiliation:
International Institute of Tropical Agriculture, IITA (UK) Ltd, Carolyn House, 26 Dingwall Road, Croydon CR9 3EE, UK
M. OYEKUNLE
Affiliation:
International Institute of Tropical Agriculture, IITA (UK) Ltd, Carolyn House, 26 Dingwall Road, Croydon CR9 3EE, UK
K. OBENG-ANTWI
Affiliation:
Crops Research Institute (CRI), Kumasi, Ghana
A. S. OSUMAN
Affiliation:
Crops Research Institute (CRI), Kumasi, Ghana
S. G. ADO
Affiliation:
Institute for Agricultural Research (IAR), Zaria, Nigeria
N. COULIBAY
Affiliation:
Institut d'Economie Rurale, Bamako, Mali
C. G. YALLOU
Affiliation:
Institut Nationale de Recherches Agricoles du Benin, Cotonou, Benin
M. ABDULAI
Affiliation:
Savanna Agricultural Research Institute (SARI), Tamale, Ghana
G. A. BOAKYEWAA
Affiliation:
Savanna Agricultural Research Institute (SARI), Tamale, Ghana
A. DIDJEIRA
Affiliation:
Institu Togolais de Recherches Agricoles, Lome, Togo
*
*To whom all correspondence should be addressed. Email: [email protected]
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Summary

Multi-environment trials (METs) in West Africa have demonstrated the existence of genotype×environment interactions (G×E), which complicate the selection of superior cultivars and the best testing sites for identifying superior and stable genotypes. Two powerful statistical tools available for MET analysis are the additive main effects and multiplicative interaction (AMMI) and the genotype main effect+G×E (known as GGE) biplot. The objective of the present study was to compare their effectiveness in identifying maize mega-environments and stable and superior maize cultivars with good adaptation to West Africa. Twelve extra-early maturing maize cultivars were evaluated at 17 locations in four countries in West Africa from 2006 to 2009. The effects of genotype (G), environments (E) and G×E were significant (P<0 01) for grain yield. Differences between E accounted for 0 75 of the total variation in the sum of squares for grain yield, whereas the G effects accounted for 0 03 and G×E for 0 22. The GGE biplot explained 0 74 of total variations in the sum of squares for grain yield and revealed three mega-environments and seven cultivar groups. The AMMI graph explained 0 13 and revealed four groups each of environments and cultivars. The two procedures provided similar results in terms of stability and performance of the cultivars. Both methods identified the cultivars 2004 TZEE-W Pop STR C4 and TZEE-W Pop STR C4 as superior across environments. Cultivar 2004 TZEE-W Pop STR C4 was the most stable. The GGE biplot was more versatile and flexible, and provided a better understanding of G×E than the AMMI graph. It identified Zaria, Ilorin, Ikenne, Ejura, Kita, Babile, Ina and Angaredebou as the core testing sites of the three mega-environments for testing the Regional Uniform Variety Trials-extra-early.

Type
Crops and Soils Research Papers
Information
The Journal of Agricultural Science , Volume 150 , Issue 4 , August 2012 , pp. 473 - 483
Copyright
Copyright © Cambridge University Press 2011

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References

REFERENCES

Badu-Apraku, B. & Lum, A. F. (2007). Agronomic performance of Striga resistant early-maturing maize varieties and inbred lines in the savannas of West and Central Africa. Crop Science 47, 737–750.CrossRefGoogle Scholar
Badu-Apraku, B., Fajemisin, J. M. & Diallo, A. O. (1995). The performance of early and extra-early varieties across environments in West and Central Africa. In Contributing to Food Self-sufficiency: Maize Research and Development in West and Central Africa. Proceedings of a Regional Maize Workshop, 29 May–2 June 1995 (Eds Badu-Apraku, B., Akoroda, M. O., Ouedraogo, M. & Quin, F. M.), pp. 149–159. Cotonou, Benin Republic: IITA.Google Scholar
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
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 Science 48, 99–112.CrossRefGoogle Scholar
Badu-Apraku, B., Fakorede, M. A. B., Lum, A. F. & Akinwale, R. (2009). Improvement of yield and other traits of extra-early maize under stress and nonstress environments. Agronomy Journal 101, 381–389.Google Scholar
Badu-Apraku, B., Ewool, M. & Yallou, C. G. (2010). Registration of Striga-resistant tropical extra-early maize population. Journal of Plant Registrations 4, 60–66.Google Scholar
Byerlee, D. & Eicher, C. K. (1971). Africa's Emerging Maize Revolution. Boulder, CO: Lynne Rienner Publishers.Google Scholar
Cornelius, P. L., Van Sanford, D. A. & Seyedsadr, M. S. (1993). Clustering cultivars into groups without rank-change interactions. Crop Science 33, 1193–1200.CrossRefGoogle Scholar
Crossa, J. (1990). Statistical analyses of multilocation trials. Advances in Agronomy 44, 55–85.Google Scholar
Crossa, J. & Cornelius, P. L. (1997). Sites regression and shifted multiplicative model clustering of cultivar trial sites under heterogeneity of error variances. Crop Science 37, 406–415.Google Scholar
Eberhart, S. A. & Russell, W. A. (1966). Stability parameters for comparing varieties. Crop Science 6, 36–40.CrossRefGoogle Scholar
Ejeta, G. (2010). African Green Revolution needn't be a mirage. Science 327, 831–832.CrossRefGoogle ScholarPubMed
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 Journal of Agronomy 1, 43–46.Google Scholar
FAO (2008). West Africa Catalogue of Plant Species and Varieties. Rome, Italy: FAO.Google Scholar
Finlay, K. W. & Wilkinson, G. N. (1963). The analysis of adaptation in a plant-breeding programme. Australian Journal of Agricultural Research 14, 742–754.CrossRefGoogle Scholar
Gauch, H. G. (1988). Model selection and validation for yield trials with interaction. Biometrics 44, 705–715.CrossRefGoogle Scholar
Gauch, H. G. (2006). Statistical Analysis of Regional Yield Trial: AMMI Analysis of Factorial Designs. Amsterdam: Elsevier.Google Scholar
Gauch, H. G. & Zobel, R. W. (1988). Predictive and postdictive success of statistical analyses of yield trials. Theoretical and Applied Genetics 76, 1–10.Google Scholar
Gauch, H. G. & Zobel, R. W. (1997). Identifying mega-environments and targeting genotypes. Crop Science 37, 311–326.Google Scholar
Gauch, H. G., Piepho, H.-P. & Annicchiarico, P. (2008). Statistical analysis of yield trials by AMMI and GGE: further considerations. Crop Science 48, 866–889.CrossRefGoogle Scholar
Gollob, H. (1968). A stastistical model which combines features of factor analytic and analysis of variance techniques. Psychometrika 33, 73–115.CrossRefGoogle Scholar
IITA (1992). Sustainable Food Production in Sub-Saharan Africa. 1. IITA's Contribution. Ibadan, Nigeria: IITA.Google Scholar
Mandel, J. (1969). The partitioning of interaction in analysis of variance. Journal of Research of the National Bureau of Standards Section B: Mathematical Sciences 73, 309–328.CrossRefGoogle Scholar
Mandel, J. (1971). A new analysis of variance model for non-additive data. Technometrics 13, 1–18.Google Scholar
Menkir, A. (2003). The role of GIS in the development and targeting of maize germplasm to farmers’ needs in West and Central Africa. In Contributing to Food Self-sufficiency: Maize Research and Development in West and Central Africa. Proceedings of a Regional Maize Workshop, IITA-Cotonou, Benin Republic, 14–18 May 2001 (Ed. Badu-Apraku, B.), pp. 16–30. Ibadan, Nigeria: WECAMAN/IITA.Google Scholar
Moghaddam, M. J. & Pourdad, S. S. (2009). Comparison of parametric and non-parametric methods for analysing genotype×environment interactions in safflower (Carthamus tinctorius L.). Journal of Agricultural Science, Cambridge 147, 601–612.CrossRefGoogle Scholar
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. Journal of Agricultural Science, Cambridge 147, 537–545.Google Scholar
Pinstrup-Anderson, R., Pandya-Lorch, R. & Rosegrant, M. (1999). World Food Prospects: Critical Issues for the Early 21st Century. 2020 Food Policy Report. Washington, DC: IFPRI.Google Scholar
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. Journal of Agricultural Science, Cambridge 146, 571–581.Google Scholar
Sadeghi, S. M., Samizadeh, H., Amiri, E. & Ashouri, M. (2011). Additive main effects and multiplicative interactions (AMMI) analysis of dry leaf yield in tobacco hybrids across environments. African Journal of Biotechnology 10, 4358–4364.Google Scholar
SAS INSTITUTE. (2002). SAS User's Guide. Version 9.2. Cary, NC: SAS Institute Inc.Google Scholar
Williams, E. J. (1952). The interpretation of interactions in factorial experiments. Biometrika 39, 65–81.CrossRefGoogle Scholar
Yan, W. (2001). GGE biplot: a Windows application for graphical analysis of multi-environment trial data and other types of two-way data. Agronomy Journal 93, 1111–1118.Google Scholar
Yan, W. & Kang, M. S. (2003). GGE Biplot Analysis: A Graphical Tool for Breeders, Genetics, and Agronomist. Boca Raton, FL: CRC Press.Google Scholar
Yan, W. & Rajcan, I. (2002). Biplot evaluation of test locations and trait relations for breeding superior soybean cultivars in Ontario. Crop Science 42, 11–20.Google Scholar
Yan, W. & Tinker, N. A. (2006). Biplot analysis of multi-environment trial data; principles and application. Canadian Journal of Plant Science 86, 623–645.CrossRefGoogle Scholar
Yan, W., Hunt, L. A., Sheng, Q. & Szlavnics, Z. (2000). Cultivar evaluation and mega-environment investigation based on the GGE biplot. Crop Science 40, 597–605.CrossRefGoogle Scholar
Yan, W., Cornelius, P. L., Crossa, J. & Hunt, L. A. (2001). Two types of GGE biplots for analyzing multi-environment trial data. Crop Science 41, 656–663.Google Scholar
Yan, W., Kang, M. S., Ma, B., Woods, S. & Cornelius, P. L. (2007). GGE biplot vs. AMMI analysis of genotype-by-environment data. Crop Science 47, 643–655.CrossRefGoogle Scholar
Yan, W., Fregeau-Reid, J., Pageau, D., Martin, R., Mitchell-Fetch, J., Etienne, M., Rowsell, J., Scott, P., Price, M., De Haan, B., Cummiskey, A., Lajeunesse, J., Durand, J. & Sparry, E. (2010). Identifying essential test locations for oat breeding in Eastern Canada. Crop Science 50, 504–515.CrossRefGoogle Scholar
Yang, R., Crossa, J., Cornelius, P. L. & Burgueno, J. (2009). Biplot analysis of genotype×environment interaction: proceed with caution. Crop Science 49, 1564–1576.Google Scholar
Yates, F. & Cochran, W. G. (1938). The analysis of group experiments. Journal of Agricultural Science 28, 556–580.CrossRefGoogle Scholar
Zobel, R. W., Wright, M. J. & Gauch, H. G. (1988). Statistical analysis of a yield trial. Agronomy Journal 80, 388–393.CrossRefGoogle Scholar