Malaysian Applied Biology Journal

  • Increase font size
  • Default font size
  • Decrease font size


E-mail Print PDF
Malays. Appl. Biol. (2011) 40(2):19–26


MAKINDE, S.C.O.1* and ARIYO, O.J.2

1Department of Botany, Faculty of Science, Lagos State University, Ojo Campus, P.O. Box 001, LASU Post Office Ojo, Lagos, Nigeria
2Department of Plant Breeding and Seed Technology, College of Plant Science, University of Agriculture Abeokuta, P.M.B2240 Abeokuta, Ogun State, Nigeria
*E-mail: This e-mail address is being protected from spambots. You need JavaScript enabled to view it


Twenty two groundnut genotypes collected from International Crops Research Institute for Semi Arid Tropics (ICRISAT) and local sources were cultivated in two different locations during 2003, 2004 and 2005 planting seasons. Data on yield was subjected to the additive main effect and multiplicative interaction (AMMI) and joint regression analysis. Differences between the genotypes and environments accounted for 58% and 28% of the total variance respectively while genotype x environment interaction accounted for 14% of the total variance. The first, second and third interaction axes captured 56%, 16% and 6% respectively of the total variation due to GxE interaction. The AMMI plot accounted for 96% of the total sum of squares. The environment differed in main and interaction effects. The first (E1) and fourth (E4) environments had positive interaction effects while the second (E2), third (E3) and fifth (E5) had negative interaction effects. First environment and second environment had the highest (3.52) and least (-0.15) interaction effects respectively. Groundnut genotypes used in this study exhibited similar response to different environment except for ICG-6402. ICG-4998 was the most favoured genotype in all the environments. However, most genotypes recorded highest yield during the 2003 planting in first environment (E1).
Key words: Additive Main Effect and Multiplicative Interaction model, biplot, joint regression analysis, Principal Component Analysis


Abdul, R., Ghulam, R.H., Nasir, J., Malik, S.N. and Ali, G.M. 2002. Genotype x Environment and stability analysis in Mustard. Asian Journal of Plant Sciences, 1(5): 591–592.

Aremu, C.O., Ariyo, O.J. and Ojo, D.K. 2006. Genetic variability studies of some traits in Soybean (Glycine max L.) in savanna and humid environments. ASSET Journal, 6(1): 356–359.

Ariyo, O.J. 1990. Effectiveness and relative discriminatory abilities of techniques measuring genotype X environment interaction and stability in okra (Abelmoshus esculentus (L.) Monech). Euphytica, 47: 99–105.

Ariyo, O.J. 1998. Use of additive main effect and multiplicative interaction model to analyse multilocation soybean varietal trials. Journal of Genetics & Breeding, 53: 129–134.

Ariyo, O.J. and Ayo-Vaughan, M.A. 2000. Analysis of genotype x environment interaction of okra (Abelmoschus esculentus (L) Moench). Journal of Genetics & Breeding, 54: 35–40.

Baker, R.J. 1969. Genotype – environment interaction in yield of wheat. Canadian Journal of Plant Science, 49: 743–751.

Bradu, D. 1984. Response model diagnosis in two- way tables. Communal Statistical Theoretical Methods, 13: 3059–3106.

Bradu, D. and Gabriel, K.R. 1978. The biplot as a diagnostic tool for models of two-way tables. Technometrics, 20: 47–68.

Breese, E.L. 1969. The measurement and significance of genotype- environment interactions in grasses. Heredity, 24: 27–44.

Crossa, J., Fox, P.V., Pfeiffer, N.H., Rajaram, S. and Gauch, H.G. 1991. AMMI adjustment for statistical analysis of an international wheat yield trial. Theoretical Applied Genetics, 81: 27–37.

Crossa, J., Gauch, H.G. and Zobel, R.W. 1990. Additive main effect and multiplicative interaction analysis of two international maize cultivar trials. Crop Science, 30: 493–500.

Dongara, J.J., Nunch, B.R., Moler, J.R. and Stewart, G.W. 1979. The singular value decomposition, LIMPACK Users Guide. (SIAM. Philadelphia).

Easton, A.S. and Clement, R.J. 1973. The Interaction of wheat genotypes with a specific factor of the environment. Journal of Agricultural Sciences, 80: 43–52.

Eberhart, S.A. and Russell, W.A. 1966. Stability parameters for comparing varieties. Crop Science, 6: 36–40.

Finlay, K.W. and Wilkinson, G.N. 1963. The analysis of adaptation in plant breeding programme. Australian Journal of Agricultural Research, 14: 742–754.

Freeman, G.M. and Perkins, J.M. 1971. Environmental and genotype-environmental components of variability. VIII Relations between genotypes grown in different environments and measure of these environments. Heredity, 27: 15–23.

Freeman, G.H. 1985. The analysis and interpretation of interaction. Journal of Applied Statistic, 12: 3–10.

Funnah, S.M. and Mak, C. 1980. Yield stability studies in soyabeans. Experimental Agriculture, 16: 387–390.

Gabriel, K.R. 1978. The biplot as a diagnostic tool for models of two-ways tables. Tech- nometrics, 20: 47–68.

Gauch, H.G. 1986. MATMODEL: a FORTRAN 77. Programme for AMMI analysis. Microcumputer Power. (Ithaca. N.Y).

Gauch, H.G. 1990. Full and reduced models for yield trials. Theoretical Applied Genetics, 80: 153– 160.

Gauch, H.G. and Furnas, R.E. 1991. Statistical analysis of yield trial with MATMODEL. Agronomy Journal, 83: 916–920.

Gauch, H.G. and Zobel, R.W. 1988. Predictive and Postdective success of statistical analyses of yield trials. Theoretical Applied Genetics, 76: 1–10.

Gollob, H.F. 1968. A statistical model which contains features of factors of factor analytic and analysis of variance techniques. Psychometrika, 33: 73–115.

Hasan, A.M. 1978. Stability analysis of rosselle varieties (Hibiscus sabdarifa). Indonesian Pemberitaan, 28: 76–83.

Kempton, R.A. 1984. The use of biplots in interpreting variety by environment interaction. Journal of Agricultural Science, 103: 123–135.

Makinde, S.C.O. and Ariyo, O.J. 2010. Multivariate analysis of genetic divergence in twenty two genotypes of groundnut (Arachis hypogaea L.). Journal of Plant Breeding and Crop Science, 2(7): 192–204.

Marsh, N.N.A. 1990. Fitting of two-way tables by means for rows, columns and cross term. Applied Statistics JRSS ©. 39: 283–294.

Mclaren, C.G. and Chaudary, R.C. 1994. Use of additive main effects and multiplicative interaction MODELS to analyse multilocation rice variety trials. Paper presented at the 1994 FCSSP Conference, Princesa, Palawan. Philippines.

Nassir, A.L. and Ariyo, O.J. 2007. Multivariate analysis of variation of field-planted upland rice (Oryza sativa L.) in a tropical habitat. Malaysian Applied Biology, 36(1): 47–57.

Perkins, J.M. and Jinks, L.L. 1968. Environmental and genotype-environmental components of variability. III Multiple lines and crosses. Heredity, 23: 339–356.

Sardana, S., Ghosh, A.K. and Borthakur, D.N. 1984. Adaptability of promising roselle varieties to the uplands of Tripura. Indian Journal of Agricultural Science, 54: 642–645.

Van Euwijk, F.A. and Elgersma, A. 1993. Incorporating environmental information in an analysis of GxE interaction for seed yield in perennial ryegrass. Heredity, 70: 447–457.

Yau, S.K. 1995. Regression and AMMI analysis of genotype x environment interactions. An empirical comparism. Agronomy Journal, 87: 121–126.

Zobel, R.W., Wright, M.J. and Gauch, H.G. 1988. Statistical analysis of a yield trial. Agronomy Journal, 80: 388–393.

Main Menu