Annicchiarico, P. 1997. Additive main effects and multiplicative interaction (AMMI) analysis of genotype-location interaction in variety trials repeated over years. Theor. Appl. Genet. 94: 1072-1077.

Borlaug, N. E., and C. R. Dowswell. 1997. The acid lands: One of agriculture’s last frontiers. Pp. 5-15. In Moniz, A. C., A. M. C. Furlani, R. E. Shaffert, N. K. Fageira, C. A. Rosolem, and H. Cantarella (eds.).  Plant-Soil Interactions at Low pH. Braz. Soil Sci. Soci., Brazil.

Burgueno, J., J. Crossa, and M. Vargas. 2001. SAS programs for graphing GE and GGE biplots. Biometrics and Statistics Unit, International Maize and Wheat Improvement Center (CIMMYT), México.

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

Finlay, K. W., and G. N. Wilkinson. 1963. The analysis of adaptation in a plant-breeding programme. Aust. J. Agric. Res. 14: 742–754.

Gabriel, K. R. 1971. The biplot graphic display of matrices with application to principal component analysis. Biometrika 58: 453–467.

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

Gauch, H. G., and R. W. Zobel. 1996. Optimal replication in selection experiments. Crop Sci. 36: 838–843.

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

Gauch, H. G., Piepho, H. P., and Annicchiarico, P. (2008). Statistical analysis of yield trials by AMMI and GGE: Further considerations. Crop Sci. 48(3): 866-889.

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

Kang, M. S. 2004. Breeding: genotype-by-environment interaction. p. 218-221. In R. M. Goodman (ed.). Encyclopedia of Plant and Crop Sci. New York Basel.

Kempton, R. A. 1984. The use of biplots in interpreting variety by environment interactions. J. Agri. Sci. 103: 123–135.

Mahmoodi, N., A. Yaghotipoor, and E. Farshadfar. 2011. AMMI stability value and simultaneous estimation of yield and yield stability in bread wheat (Triticum aestivum L.). Aust. J. Crop Sci. 5(13): 1837-1844.

Matus-Cadiz, M. A., P. Hucl, C. E. Perron, and R. T. Tyler. 2003. Genotype × environment interaction for grain color in hard white spring wheat. Crop Sci. 43: 219-226.

Mohammadi, M. 2012. Parameters of additive main effects and multiplicative interaction model for interpreting of genotype × environment interaction. J. Food, Agric. Environ. 10 (1): 777-781.

Nachit, M. M., M. E. Sorrells, R. W. Zobel, H. G. Gauch, R. A. Fischer, and W. R. Coffman. 1992. Association of environmental variables with sites’ mean grain yield and components of genotype-environment interaction in durum wheat. J. Genet. Breed. 46: 369–372.

Oikeh, S. O., A. Menki, B. Maziya-Dixon, R. M. Welch, R. P. Glahn, and G. Gauchjr. 2004. Environmental stability of iron and zinc concentrations in grain of elite early-maturing tropical maize genotypes grown under field conditions. J. Agric. Sci. 142: 543–551.

Ortiz, R, W.W. Wagoire, J. Hill, and S. Chandra. 2001. Heritability of and correlations among genotype-environment stability statistics for grain yield in bread wheat. Theor. Appl. Genet. 103: 469–474.

 

Petrovic, S., M. Dimitrijevic, M. Belic, B. Banjac, J. Boskovic, V. Zecevic, and B. Pejic. 2010. The variation of yield components in wheat (Triticum aestivum L.) in response to stressful growing conditions of alkaline soil. Genetika 4(3): 545-555.

Piepho, H. P., and J. Möhring. 2005. Best linear unbiased prediction of cultivar effects for subdivided target regions. Crop Sci. 45: 1151–1159.

Romagosa. I., S. E. Ullrich, F. Han, and P. M. Hayes. 1996. Use of the additive main effects and multiplicative interaction model in QTL mapping for adaptation in barley. Theor. Appl. Genet. 93: 30–37.

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., H. Dehghani, and S. H. Sabaghpour. 2008. Graphic analysis of genotype by environment interaction for lentil yield in Iran. Agron. J. 100: 760–764.

Sabaghnia,N, M. Mohammadi, and R. Karimizadeh. 2012. The evaluation of genotype × environment interactions of durum wheat’s yield using of the AMMI model. Agric. Forest. 55: 5-21.

Sabaghnia, N., M. Mohammadi, and R. Karimizadeh, 2013. Parameters of AMMI modelforyield stability analysis in durum wheat. Agric. Cons. Sci. 78, 119-124.

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

Van Eeuwijk, F. A., J. B. Denis, and M. S. Kang. 1996. Incorporating additional information on genotypes and environments in models for two-way genotype by environment tables. p. 15–50. In Kang, M. S., and H. G. Gauch (eds.). Genotype by environment interaction. CRC Press, Boca Raton, Florida, USA.

Yan, W., M. S. Kang, B. Ma, S. Woods, and P. L. Cornelius. 2007. GGE biplot vs. AMMI analysis of genotype-by-environment data. Crop Sci. 47: 643–655.