DETERMINING YIELD STABILITY AND MODEL SELECTION BY AMMI METHOD IN RAIN-FED DURUM WHEAT GENOTYPES

Year 2016, , 174 - 183, 15.12.2016

Rahmatollah Karımızadeh Ali Asgharı Rahim Chınıpardaz Omid Sofalıan Abdolali Ghaffarı

https://doi.org/10.17557/tjfc.17390

Cited By: 11

Abstract

Selection of durum wheat genotypes with wide adaptability across various environments is important before
recommending them to reach a high rate of genotype adoption. Multi-environment grain yield trials of 20
durum wheat genotypes were conducted at five locations of Iran (Gachsaran, Gonbad, Moghan, Ilam and
Khorram abad) over four years (2009-2013). Combined ANOVA of yield data of the 20 environments revealed
highly significant differences among genotypes and environments as well as significant GE interaction
indicated differential performance of genotypes over test environments. Results of F Ratio indicated that only
five interaction principal components (IPCs) were significant at the 0.01 probability level. Also, the GE
interaction is comprised of 29.7% noise and 70.03% signal. According to these distinct numbers of significant
axes, fourteen AMMI stability parameters were computed. Finally according to the most of type 1 of AMMI
parameters (EV1, AMGE1, SIPC1 and D1), genotypes G8, G17 and G11; based on the type 2 of AMMI
parameters and ASV, genotypes G4, G5, G10, G11 and G17; due to type 3 of AMMI parameters and MASV,
genotypes G8, G10 and G12 were detected as the most stable genotypes. Considering all of the AMMI stability
parameters, genotypes G8, G10, G11, G12 and G17 following to genotypes G7 and G9 were the most stable
genotypes. The best recommended genotypes according to the present study are G10 with 3470 kg ha-1 grain
yield for Gachsaran and Khorramabad, G12 with 3343 kg ha-1 grain yield for Ilam and G10 and G12 for
Moghan and Gonbad regions wich had high mean yield and were most stable for related mega-environments. 

Keywords

AMMI, Durum wheat, GE interaction, Yield stability

References

• Adugna, W. and M.T. Labuschagne. 2002. Genotype × environment interactions and phenotypic stability analyses of linseed in Ethiopia. Plant Breed. 121:66-71.
• Akbarpour, O., H. Dehghani, B. Sorkhi and G. Guach. 2014. Evaluation of Genotype × Environment Interaction in Barley (Hordeum Vulgare L.) Based on AMMI model Using Developed SAS Program. J. Agric. Sci. Tech. 16: 919-930.
• Akcura, M., Y. Kaya and S. Taner. 2009. Evaluation of durum wheat genotypes using parametric and nonparametric stability statistics. Turk. J. Field Crop. 14(2): 111-122.
• Annicchiarico, P. 1997. Joint regression vs AMMI analysis of genotype × environment interactions for cereals in Italy. Euphytica 94:53-62.
• Annicchiarico, P. 2002. Genotype × environment interaction: challenges and opportunities for plant breeding and cultivar recommendations, Food and Agriculture Organization of the United Nations.
• Becker, H.C. 1981. Correlations among some statistical measures of phenotypic stability. Euphytica 30: 835–840. Becker, H.C. and J. Leon. 1988. Stability analysis in plant breeding. Plant Breed. 101:1-23.
• Bertero H.D., A.J.G. dela Vega Correa, S.E. Jacobsen and A. Mujic. 2004. Genotype and genotype-by-environment interaction effects for grain yield seed yield and grain size of quinoa (Chenopodium quinoa Wild.) as revealed by pattern analysis of international multi-environment trials. Field Crops Res. 89: 299-318.
• Burgueño, J., J. Crossa and M. Vargas. 2000. SAS programs for graphing GE and GGE biplots. Biometrics and Statistics Unit, Centro Internacional de Mejoramiento de Maíz y Trigo (CIMMYT), México.
• Cooper, M., D.R. Woodruff, R.L. Eisemann, P.S. Brennan and I.H. DeLacy. 1995. A selection strategy to accommodate genotype-by-environment interaction for grain yield, seed yield of wheat: managed-environments for selection among genotypes. Theor. Appl. Genet. 90: 492-502.
• 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., J. Crossa and M. Seyedsadr. 1996. Statistical tests and estimators of multiplicative models for cultivar trials. In: Kang, M.S. and Gauch, H.G., Jr (eds) Genotypeby-Environment Interaction. CRC Press, Boca Raton, Florida, pp. 199–234.
• Dehghani, H., S.H. Sabaghpour and A. Ebadi. 2010. Study of genotype × environment interaction for chickpea yield in Iran. Agron. J. 102: 1-8.
• Ebdon, J.S. and H.G. Gauch. 2002. AMMI analysis of national turfgrass performance trials. II. cultivar recommendations. Crop Sci. 42: 497–506 11.
• Eberhart, S.A. and W.A. Russell. 1966. Stability parameters for comparing varieties. Crop Sci. 6: 36-40. Finlay, K.W. and G.N. Wilkinson. 1963. The analysis of adaptation in a plant-breeding programme. Aus. J. Agric. Res. 14: 742-754.
• Fisher, R.A. and W.A. MacKenzie. 1923. Studies in variation II. The manorial response in different potato varieties. J. Agric. Sci. 13: 311-320.
• Gabriel, K.R. 1978. Least squares approximation of matrices by additive and multiplicative models. J. Royal Stat. Soc. 40: 186-196.
• Gauch, H.G. 1988. Model selection and validation for yield trials with interaction. Biometrics 44:705-715.
• Gauch, H.G. and R.W. Zobel. 1996. AMMI analysis of yield trials. In Kang, M.S., Gauch, H.G. (ed.) Genotype by environment interaction. CRC Press, Boca Raton, FL.
• Gauch, H.G. and R.W. Zobel. 1997. Identifying megaenvironments and targeting genotypes. Crop Sci. 37: 311- 326.
• Gauch, H.G. 2006. Statistical analysis of yield trials by AMMI and GGE. Crop Sci. 46:1488-1500.
• Gauch, H.G., H.P. Piepho and P. Annicchiarico. 2008. Statistical analysis of yield trials by AMMI and GGE. Further considerations. Crop Sci. 48:866-889.
• Gollob, H.F. 1968. A statistical model which combines features of factor analysis and analysis of variance techniques. Psychometrika 33: 73-115.
• Ilker, E., H. Geren, R. Unsal, I. Sevim, F. Aykut Tonk, and M. Tosun. 2011. AMMI-biplot analysis of yield performances of bread wheat cultivars grown at different locations. Turk J Field Crops. 16(1): 64-68.
• Iwata H., H. Nesumi, S. Ninomiya, Y. Takano, and Y. Ukai. 2002. The evaluation of genotype × environment interactions of citrus leaf morphology using image analysis and elliptic fourier descriptors. Breeding Sci. 52: 243–251.
• Johnson, D.E. and F.A. Graybill. 1972. An analysis of a two-way model with interaction and no replication. J. Am. Stat. Assoc. 67: 862–868.
• Kang, M.S., 1998. Using genotype by environment interaction for crop cultivar development. Adv. Agron. 35: 199-240.
• Karimizadeh, R. and M. Mohammadi, M. 2010. AMMI adjustment for rainfed lentil yield trials in Iran. Bul. J. Agric. Sci. 16: 66-73.
• Karimizadeh, R., M. Mohammadi, M. Armion, M.K. Shefazadeh and, H. Chalajour. 2012a. Determining heritability, reliability and stability of grain yield and yield-related components in durum wheat (Triticum durum L.). Bul. J. Agric. Sci. 18(4): 595-607.
• Karimizadeh, R., M. Mohammadi, M.K. Shefazadeh, A.A. Mahmoodi, B. Rostami, and F. Karimpour. 2012b. Relationship among and repeatability of ten stability indices for grain yield of food lentil genotypes in Iran. Turk. J. Field Crops. 17(1): 51-61.
• Mandel, J. 1961. Non-additivity in two-way analysis of variance. J. Am. Stat. Assoc. 56: 878-888.
• Mandel, J. 1971. New analysis of variance model for nonadditive data. Technometrics 13:1-18.
• Mladenov, V., B. Banjac, and M. Milosevic. 2012. Evaluation of Yield and Seed Requirements Stability of Bread Wheat (Triticum aestivum L.) Via AMMI Model. Turk. J. Field Crops. 17(2): 203-207.
• Mohammadi, M., P. Sharifi, R. Karimizadeh, J.A. Jafarby, H. Khanzadeh, T. Hosseinpour, M.M. Poursiabidi, M. Roustaii, M. Hassanpour Hosni, and P. Mohammadi. 2015. Stability of grain yield of durum wheat genotypes by AMMI model. Agric. For. 61(3): 181-193.
• Mohammadi, M., R. Karimizadeh, N. Sabaghnia, and M.K. Shefazadeh. 2012. Genotype × Environment Interaction and Yield Stability Analysis of New Improved Bread Wheat Genotypes. Turk. J. Field Crop. 17(1): 67-73.
• Moreno-Gonzalez, J., J. Crossa and P.L. Cornelius. 2003. Additive Main Effects and Multiplicative Interaction Model. I. Theory on Variance Components for Predicting Cell Means. Crop Sci. 43: 1967-1975.
• Nachit, M.M. 1992. Use of AMMI and linear regression models to analyze genotype environment interaction in durum wheat. Theor. Appl. Genet. 83: 597-601.
• Payne, R.W., D.A. Murray, S.A. Harding, D.B. Baird, and D.M. Soutar. 2009. GenStat for Windows (12th Edition) Introduction. VSN International, Hemel Hempstead.
• 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., H. Dehghani and S.H. Sabaghpour. 2006. Nonparametric methods for interpreting genotype × environment interaction of lentil genotypes. Crop Sci 46: 1100-1106.
• Sabaghnia, N., S.H. Sabaghpour and H. Dehghani, 2008. The use of an AMMI model and its parameters to analyze yield stability in multi-environment trials. J. Agric. Sci. 146:571- 581.
• Sabaghnia, N., M. Mohammadi and R. Karimizadeh, 2012a. The evaluation of genotype × environment interactions of durum wheat’s yield using of the AMMI model. Agric. For. 55(9): 5-21.
• Sabaghnia, N., R. Karimizadeh and M. Mohammadi. 2012b. Model selection in additive main effect and multiplicative interaction model in durum wheat. Genetika 44(2): 325-339.
• Sabaghnia, N., M. Mohammadi and R. Karimizadeh. 2013. Parameters of AMMI model for yield stability analysis in durum wheat. Agric. Con. Sci. 78(2): 119-124.
• Sneller, C.H., L. Kilgore-Norquest and D. Dombek. 1997. Repeatability of yield stability statistics in soybean. Crop Sci. 37: 383–390.
• Solomon, K.F., H.A. Smit, E. Malan and W.J. Du Toit. 2008. Parametric model based assessment of genotype × environment interactions for grain yield in durum wheat under irrigation. Int. J. Plant Pro. 2(1): 23-26.
• Tukey, J.W. 1949. One degree of freedom for non-additivity. Biometrics 5: 232–242.
• Williams, E.J. 1952. The interpretation of interactions in factorial experiments. Biometrika 39:65-81.
• Wold, S. 1978. Cross-validatory Estimation of the Number of Components in Factor and Principal Component Models. Technometrics 20: 397-405.
• Yan, W., L.A., Hunt, Q. Sheng, and Z. Szlavnics. 2000. Cultivar evaluation and mega–environment investigations based on the GGE biplot. Crop Sci. 40: 597-605.
• Yates, F. and W.G. Cochran. 1938. The analysis of groups of experiments. J. Agric. Sci. 28: 556–580.
• Yau, S.K. 1995. Regression and AMMI analyses of genotype × environment interactions: An empirical comparison. Agron. J. 87: 121–126.
• Yue, G., K.L. Roozeboom, W.T. Schapaughjr and G.H. Liang. 1997. Evaluation of soybean cultivars using parametric and nonparametric stability estimates. Plant Breed. 116:271-275.
• Zali, H., E. Farshadfar, S.H. Sabaghpour and R. Karimizadeh. 2012. Evaluation of genotype× environment interaction in chickpea using measures of stability from AMMI model. Annal. Bio. Res. 3(7): 3126-3136.
• 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 Resources, Univ. of Nebraska, Lincoln.
• Zobel, R.W., M.J. Wright and H.G. Gauch. 1988. Statistical analysis of a yield trial. Agron. J. 80:388-393.