Skip to main content Accessibility help

The use of an AMMI model and its parameters to analyse yield stability in multi-environment trials

  • N. SABAGHNIA (a1), S. H. SABAGHPOUR (a2) and H. DEHGHANI (a1)


Genotype by environment (G×E) interaction effects are of special interest for breeding programmes to identify adaptation targets and test locations. Their assessment by additive main effect and multiplicative interaction (AMMI) model analysis is currently defined for this situation. A combined analysis of two former parametric measures and seven AMMI stability statistics was undertaken to assess G×E interactions and stability analysis to identify stable genotypes of 11 lentil genotypes across 20 environments. G×E interaction introduces inconsistency in the relative rating of genotypes across environments and plays a key role in formulating strategies for crop improvement. The combined analysis of variance for environments (E), genotypes (G) and G×E interaction was highly significant (P<0·01), suggesting differential responses of the genotypes and the need for stability analysis. The parametric stability measures of environmental variance showed that genotype ILL 6037 was the most stable genotype, whereas the priority index measure indicated genotype FLIP 82-1L to be the most stable genotype. The first seven principal component (PC) axes (PC1–PC7) were significant (P<0·01), but the first two PC axes cumulatively accounted for 71% of the total G×E interaction. In contrast, the AMMI stability statistics suggested different genotypes to be the most stable. Most of the AMMI stability statistics showed biological stability, but the SIPCF statistics of AMMI model had agronomical concept stability. The AMMI stability value (ASV) identified genotype FLIP 92-12L as a more stable genotype, which also had high mean performance. Such an outcome could be regularly employed in the future to delineate predictive, more rigorous recommendation strategies as well as to help define stability concepts for recommendations for lentil and other crops in the Middle East and other areas of the world.


Corresponding author

*To whom all correspondence should be addressed. Email:


Hide All
Adugna, W. & Labuschagne, M. T. (2002). Genotype×environment interactions and phenotypic stability analyses of linseed in Ethiopia. Plant Breeding 121, 6671.
Becker, H. C. (1981). Correlations among some statistical measures of phenotypic stability. Euphytica 30, 835840.
Becker, H. C. & Leon, J. (1988). Stability analysis in plant breeding. Plant Breeding 101, 123.
Finlay, K. W. & Wilkinson, G. N. (1963). The analysis of adaptation in a plant breeding programme. Australian Journal of Agricultural Research 14, 742754.
Flores, F. (1993). Interaccion Genotipo–Ambiente en Vicia faba L. Ph.D. Thesis, University of Cordoba, Spain.
Flores, F., Moreno, M. T. & Cubero, J. I. (1998). A comparison of univariate and multivariate methods to analyze environments. Field Crops Research 56, 271286.
Gauch, H. G. (1992). Statistical Analysis of Regional Yield Trials: AMMI Analysis of Factorial Designs. Amsterdam: Elsevier.
Gauch, H. G. & Zobel, R. W. (1988). Predictive and postdictive success of statistical analysis of yield trials. Theoretical and Applied Genetics 76, 110.
GENSTAT Committee (2004). GENSTAT 7 Release 1 Reference Manual. Oxford, UK: Clarendon Press.
Gollob, H. F. (1968). A statistical model which combines features of factor analytic and analysis of variance techniques. Psychometrika 33, 73115.
Kang, M. S. (1988). A rank–sum method for selecting high-yielding, stable corn genotypes. Cereal Research Communications 16, 113115.
Kang, M. S. & Pham, H. N. (1991). Simultaneous selection for high yielding and stable crop genotypes. Agronomy Journal 83, 161165.
Lin, C. S. & Binns, M. R. (1988). A superiority measure of cultivar performance for cultivar×location data. Canadian Journal of Plant Science 68, 193198.
Lin, C.S., Binns, M. R. & Lefkovitch, L. P. (1986). Stability analysis: where do we stand? Crop Science 26, 894900.
Nachit, M. M., Nachit, G., Ketata, H., Gauch, H. G. & Zobel, R. W. (1992). Use of AMMI and linear regression models to analyse genotype×environment interaction in durum wheat. Theoretical and Applied Genetics 83, 597601.
Nassar, R. & Huhn, M. (1987). Studies on estimation of phenotypic stability: tests of significance for nonparametric measures of phenotypic stability. Biometrics 43, 4553.
Nielsen, D. C. (2001). Production functions for chickpea, field pea, and lentil in the central great plains. Agronomy Journal 93, 563569.
Purchase, J. L. (1997). Parametric analysis to describe genotype×environment interaction and yield stability in winter wheat. Ph.D. Thesis, Department of Agronomy, Faculty of Agriculture of the University of the Free State, Bloemfontein, South Africa.
Sabaghpour, S. H., Safikhni, M., Sarker, A., Ghaffri, A. & Ketata, H. (2004). Present status and future prospects of lentil cultivation in Iran. In Proceedings of the 5th European Conference on Grain Legumes, Dijon, France, 7–11 June 2004. p. 23. Paris, France: AEP Publishers.
Sarker, A., Erskine, W. & Singh, M. (2003). Regression models for lentil seed and straw yields in Near East. Agricultural and Forest Meteorology 116, 6172.
SAS Institute (1996). SAS User's Guide, Version 6.12. Cary, NC: SAS Institute Inc.
Sneller, C. H., Cilgore-Norquest, L. & Dombek, D. (1997). Repeatability of yield stability statistics in soybean. Crop Science 37, 383390.
Turk, M. A., Tawaha, A. M. & El-Shatnawi, M. K. J. (2003). Response of lentil (Lens culinaris Medik) to plant density, sowing date, phosphorus fertilization and ethephon application in the absence of moisture stress. Journal of Agronomy and Crop Science 189, 16.
Wright, A. J. (1971). The analysis and prediction of some two factor interactions in grass breeding. Journal of Agricultural Science, Cambridge 76, 301306.
Zobel, R. (1994). Stress resistance and root systems. In Proceedings of the Workshop on Adaptation of Plants to Serious Stresses. 1–4 August. INTSORMIL Publication 94-2, Institute of Agriculture and Natural Recourses. Lincoln, USA: University of Nebraska.
Zobel, R. W., Wright, M. J. & Gauch, H. G. (1988). Statistical analysis of a yield trial. Agronomy Journal 80, 388393.

The use of an AMMI model and its parameters to analyse yield stability in multi-environment trials

  • N. SABAGHNIA (a1), S. H. SABAGHPOUR (a2) and H. DEHGHANI (a1)


Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed