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

Published online by Cambridge University Press:  08 May 2008

N. SABAGHNIA ,
S. H. SABAGHPOUR  and
H. DEHGHANI
N. SABAGHNIA
Affiliation:
Department of Plant Breeding, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran
S. H. SABAGHPOUR
Affiliation:
Dry Land Agricultural Research Institute, Kermanshah, Iran
H. DEHGHANI*
Affiliation:
Department of Plant Breeding, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran
*
*To whom all correspondence should be addressed. Email: dehghanr@modares.ac.ir
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Summary

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.

Information

Type
Crops and Soils
Information
The Journal of Agricultural Science , Volume 146 , Issue 5 , October 2008 , pp. 571 - 581
Copyright
Copyright © 2008 Cambridge University Press

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References

REFERENCES

Adugna, W. & Labuschagne, M. T. (2002). Genotype×environment interactions and phenotypic stability analyses of linseed in Ethiopia. Plant Breeding 121, 66–71.CrossRefGoogle Scholar
Becker, H. C. (1981). Correlations among some statistical measures of phenotypic stability. Euphytica 30, 835–840.CrossRefGoogle Scholar
Becker, H. C. & Leon, J. (1988). Stability analysis in plant breeding. Plant Breeding 101, 1–23.CrossRefGoogle 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
Flores, F. (1993). Interaccion Genotipo–Ambiente en Vicia faba L. Ph.D. Thesis, University of Cordoba, Spain.Google Scholar
Flores, F., Moreno, M. T. & Cubero, J. I. (1998). A comparison of univariate and multivariate methods to analyze environments. Field Crops Research 56, 271–286.CrossRefGoogle Scholar
Gauch, H. G. (1992). Statistical Analysis of Regional Yield Trials: AMMI Analysis of Factorial Designs. Amsterdam: Elsevier.Google Scholar
Gauch, H. G. & Zobel, R. W. (1988). Predictive and postdictive success of statistical analysis of yield trials. Theoretical and Applied Genetics 76, 1–10.CrossRefGoogle Scholar
GENSTAT Committee (2004). GENSTAT 7 Release 1 Reference Manual. Oxford, UK: Clarendon Press.Google Scholar
Gollob, H. F. (1968). A statistical model which combines features of factor analytic and analysis of variance techniques. Psychometrika 33, 73–115.CrossRefGoogle ScholarPubMed
Kang, M. S. (1988). A rank–sum method for selecting high-yielding, stable corn genotypes. Cereal Research Communications 16, 113–115.Google Scholar
Kang, M. S. & Pham, H. N. (1991). Simultaneous selection for high yielding and stable crop genotypes. Agronomy Journal 83, 161–165.CrossRefGoogle Scholar
Lin, C. S. & Binns, M. R. (1988). A superiority measure of cultivar performance for cultivar×location data. Canadian Journal of Plant Science 68, 193–198.CrossRefGoogle Scholar
Lin, C.S., Binns, M. R. & Lefkovitch, L. P. (1986). Stability analysis: where do we stand? Crop Science 26, 894–900.Google Scholar
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, 597–601.CrossRefGoogle ScholarPubMed
Nassar, R. & Huhn, M. (1987). Studies on estimation of phenotypic stability: tests of significance for nonparametric measures of phenotypic stability. Biometrics 43, 45–53.CrossRefGoogle Scholar
Nielsen, D. C. (2001). Production functions for chickpea, field pea, and lentil in the central great plains. Agronomy Journal 93, 563–569.CrossRefGoogle Scholar
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.Google Scholar
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.Google Scholar
Sarker, A., Erskine, W. & Singh, M. (2003). Regression models for lentil seed and straw yields in Near East. Agricultural and Forest Meteorology 116, 61–72.CrossRefGoogle Scholar
SAS Institute (1996). SAS User's Guide, Version 6.12. Cary, NC: SAS Institute Inc.Google Scholar
Sneller, C. H., Cilgore-Norquest, L. & Dombek, D. (1997). Repeatability of yield stability statistics in soybean. Crop Science 37, 383–390.CrossRefGoogle Scholar
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, 1–6.CrossRefGoogle Scholar
Wright, A. J. (1971). The analysis and prediction of some two factor interactions in grass breeding. Journal of Agricultural Science, Cambridge 76, 301–306.CrossRefGoogle Scholar
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.Google Scholar
Zobel, R. W., Wright, M. J. & Gauch, H. G. (1988). Statistical analysis of a yield trial. Agronomy Journal 80, 388–393.CrossRefGoogle Scholar