Using additive main effect and multiplicative interaction model for exploration of yield stability in some lentil (Lens culinaris Medik.) genotypes



Abstract

The additive main effect and multiplicative interaction (AMMI) analysis has been indicated to be effective in interpreting complex genotype by environment (GE) interactions of lentil (Lens culinaris Medik.) multi- environmental trials. Eighteen improved lentil genotypes were grown in 12 semiarid environments in Iran from 2007 to 2009. Complex GE interactions are difficult to understand with ordinary analysis of variance (ANOVA) or conventional stability methods. Combined analysis of variance indicated the genotype by loca- tion interaction (GL) and three way interactions (GYL) were highly significant. FGH1 and FGH2 tests indicated the five  significant  components; FRatio  showed  three significant  components  and F-Gollob detected  seven significant components. The RMSPD (root mean square predicted difference) values of validation procedure indicated seven significant components. Using five components in AMMI  stability parameters (EVFI, SIP- CFI, AMGEFI and DFI) indicated that genotypes G5 and G6 were the most stable genotypes while consider- ing three components in of AMMI stability parameters (EVFII, SIPCFII, AMGEFII and DFII) showed that genotypes G8 and G18 were the most stable genotypes. Also genotypes G2, G5 and G18 were the most stable genotypes according to AMMI stability parameters which calculated from seven components  (EVFIII, SIP- CFIII, AMGEFIII and DFIII). Among these stable genotypes, only genotypes G2 (1365.63 kg × ha-1), G11 (1374.13 kg × ha-1) and G12 (1334.73 kg × ha-1) had high mean yield and so could be regarded as the most favorable genotype. These genotypes are therefore recommended for release as commercial cultivars


Keywords

adaptation; AMMI stability parameters; genotype by environment (GE) interactions

Annicchiarico P. 1997. Joint regression vs AMMI analysis of genotype-environment interactions for cereals in Italy. Euphytica 94: 53–62.

Cornelius P.L. 1980. Functions approximating Mandel’s tables for the means and standard deviations of the first three roots of a wishart matrix. Technometrics 224: 613–616.

Cornelius P.L. 1993. Statistical tests and retention of terms in the additive main effects and multiplicative interaction model for cultivar trials. Crop Sci. 33: 1186–1193.

Cornelius P.L., Seyedsadr M.S., Crossa J. 1992. Using the shifted multiplicative model to search for "separability" in crop cultivar trials. Theor. Appl. Genet. 84: 161–172.

Crossa J., Vargas M., Joshi A.K. 2010. Linear, bilinear, and linear-bilinear fixed and mixed models for ana- lyzing genotype × environment interaction in plant breeding and agronomy. Can. J. Plant Sci. 90: 561– 574,

Dehghani, H., Sabaghpour, S.H., Ebadi A. 2010. Study of Genotype × Environment Interaction for Chickpea Yield in Iran. Agron. J. 102: 1–8

Ebdon J.S. Gauch H.G. 2002. Additive main effect and multiplicative interaction analysis of national turfgrass performance trials: I. Interpretation of genotype × environment interaction. Crop Sci. 42: 489–496.

Flores F., Moreno M.T., Cubero J.I. 1998. A comparison of univariate and multivariate methods to analyze environments. Field Crops Res. 56: 271–286.

Gauch H.G. 2006. Statistical analysis of yield trials by AMMI and GGE. Crop Sci. 46: 1488–1500.

Gauch H.G. 2007. MATMODEL version 3.0: Open source software for AMMI and related analyses. Avail- able at http://www.css.cornell.edu/staff/gauch (verified 6 April 2012). Crop and Soil Sciences, Cornell Univ., Ithaca, NY.

Gauch H.G., Zobel R.W. 1988. Predictive and postdictive success of statistical analyses of yield trials. Theor. Appl. Genet. 76: 1–10.

Gauch H.G., Zobel R.W. 1996. AMMI analysis of yield trials. p. 85–122. In M.S. Kang and H.G. Gauch (ed.) Genotype-by-environment interaction. CRC Press, Boca Raton, FL.

Gauch H.G., Zobel R.W. 1997. Identifying mega-environments and targeting genotypes. Crop Sci. 37: 311– 326.

Gauch H.G., Piepho H.P., Annicchiarico P. 2008. Statistical Analysis of Yield Trials by AMMI and GGE: Further Considerations Crop Sci. 48: 866–889.

Genstat 2010. Genstat for Windows. 12th ed. VSN Int., Hemel Hempstead, UK.

Gollob H.F. 1968. A statistical model which combines features of factor ana-lytic and analysis of variance techniques. Psychometrika 33: 73–115.

Goyal A., Beres B.L., Randhawa H.S., Navabi A., Salmon D.F., Eudes F. 2011. Yield stability analysis of broadly adaptive triticale germplasm in southern and central Alberta, Canada, for industrial end-use suitability Can. J. Plant Sci. 91: 125–135.

Lin C.S., Binns M.R., Lefkovitch L.P. 1986. Stability analysis: Where do we stand?. Crop Sci. 26: 894–900. Mohebodini M., Dehghani H., Sabaghpour S.H. 2006. Stability of performance in lentil (Lens culinaris Medik) genotypes in Iran. Euphytica 149: 343–352.

Purchase J.L. 1997. Parametric analysis to describe G × E interaction and yield stability in winter wheat.Ph.D. thesis. Dep. of Agronomy, Faculty of Agriculture, Univ. of the Orange Free State, Bloemfontein, South Africa.

Sabaghnia N., Dehghani H., Sabaghpour S.H. 2008a. Graphic Analysis of Genotype by Environment Interac- tion for Lentil Yield in Iran. Agron. J. 100: 760–764.

Sabaghnia N., Dehghani H., Sabaghpour S.H. 2008b. The use of an AMMI model and its parameters to ana- lyze yield stability in multi-envi-ronment trials. J. Agric. Sci. 146: 571–581.

SAS Institute 2004. SAS/STAT user’s guide. v. 9.1. SAS Inst., Cary, NC.

Sneller C.H., Cilgore-Norquest L., Dombek D. 1997. Repeatability of yield stability in soybean. Crop Sci. 37: 383–390.

Williams E.J. 1952. The interpretation of interactions in factorial experiments. Biometrika 39: 65–81

Yan W., Tinker N.A. 2006. Biplot analysis of multi-environment trial data: Principles and applications. Can. J. Plant Sci. 86: 623–645.

Yan W., Pageau D., Frégeau-Reid J., Lajeunesse J., Goulet J., Durand J., Marois D. 2011. Oat mega- environments and test-locations in Quebec. Can. J. Plant Sci. 91: 643–649.

Zobel R.W. 1994. Stress resistance and root systems. p. 80–99. In Proc. of the Workshop on Adaptation of Plants to Soil Stress. 1–4 Aug. 1993. INTSORMIL Publ. 94–2. Inst. of Agriculture and Natural Re- sources, Univ. of Nebraska, Lincoln.

Zobel R.W., Gauch H.G. 1988. Statistical analysis of a yield trial. Agron. J. 80: 388–393.

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

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Published : 2012-12-20


Sabaghnia, N., Karimizadeh, R., & Mohammadi, M. (2012). Using additive main effect and multiplicative interaction model for exploration of yield stability in some lentil (Lens culinaris Medik.) genotypes. Plant Breeding and Seed Science, 67, 45-60. Retrieved from http://ojs.ihar.edu.pl/index.php/pbss/article/view/304

Naser Sabaghnia  sabaghnia@yahoo.com
Department of Agronomy and Plant Breeding, Faculty of Agriculture, University of Maragheh, Maragheh, Iran  Iran, Islamic Republic of
Rahmatollah Karimizadeh 
Dryland Agricultural Research Institute (DARI), Gachsaran, Iran  Iran, Islamic Republic of
Mohtasham Mohammadi 
Dryland Agricultural Research Institute (DARI), Gachsaran, Iran  Iran, Islamic Republic of