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1. Ahmadi, K., Ebadzadeh H.R., Hatami, F., Abdshah, H. and Kazemian, A. (2019). Agricultural Statistics of 2017-18 Growing Year. Volume One: Crop Products. Ministry of Jihad Agriculture, Deputy of Planning and Economy, Information and Communication Technology Center, IR (In Persian). 2. Akter, A., Jamil Hassan, M., Umma Kulsum, M., Islam, M.R., Hossain, K. and Mamunur Rahman, M. (2014). AMMI biplot analysis for stability of grain yield in hybrid rice (Oryza sativa L.). Journal of Rice Research, 2(2): 126-138. 3. Balestre, M., Von Pinho, R.G., Souza, J.C. and Oliveira, R.L. (2009). Genotypic stability and adaptability in tropical maize based on AMMI and GGE biplot analysis. Genetic Moleular Research, 8(4): 1311-1322. 4. Bocianowski, J., Niemann, J. and Nowosad, K. (2019). Genotype-byenvironment interaction for seed quality traits in interspecific cross-derived Brassica lines using additive main effects and multiplicative interaction model. Euphytica, 215(7): 1-13. 5. Bornhofen, E., Benin, G., Storck, L., Woyann, L.G., Duarte, T., Stoco, M.G. and Marchioro, S.V. (2017). Statistical methods to study adaptability and stability of wheat genotypes. Bragantia, 76(1): 1-10. 6. Bose, L.K., Jambhulkar, N.N., Pande, K. and Singh, O.N. (2014a). Use of AMMI and other stability statistics in the simultaneous selection of rice genotypes for yield and stability under direct-seeded conditions. Chilean Journal of Agricultural Research, 74(1): 1-7. 7. Bose, L.K., Jambhulkar, N.N. and Singh, O.N. (2014b). Additive main effects and multiplicative interaction (AMMI) analysis of grain yield stability in early duration rice. Journal of Animal and Plant Science, 24(6): 1885-1897. 8. Donoso-Ñanculao, G., Paredes, M., Becerra, V., Arrepol, C. and Balzarini, M. (2016). GGE biplot analysis of multi-environment yield trials of rice produced in a temperate climate. Chilean Journal of Agricultural Research, 76(2): 152-157. 9. Gauch, H.G. (1988). Model selection and validation for yield trials with interaction. Biometrics, 44(3): 705-715. 10. Gauch, H.G. and Zobel, R.W. (1988). Predictive and postdictive success of statistical analyses of yield trials. Theoretical and Applied Genetis, 76(1): 1-10. 11. Gauch, H.G. and Zobel, R.W. (1997). Identifying mega-environments and targeting genotypes. Crop Science, 37(1): 311-326. 12. Kempton, R.A. (1984). The use of biplots in interpreting variety by environment interactions. Journal of Agricultural Science, 103(1): 123-135. 13. Mohaddesi, A., Erfani, R., Sharifi, P., Aminpanah, H. and Abbasian, A. (2017). Studying the relationships between yield and yield components and stability of some of rice genotypes using biplot method. Cereal Research, 6(4): 411-421 (In Persian) 14. Nardino, M., Baretta, D., Carvalho, I.R., Olivoto, T., Follmann, D.N., Vincius, J.S., Ferrari, M., de Pelegrin, A.J., Konflanz, V.A. and de Souza, V.Q. (2016). Restricted maximum likelihood/best linear unbiased prediction (REML/BLUP) for analyzing the agronomic performance of corn. African Journal of Agricultural Research, 11(48): 4864-4872. 15. Olivoto, T., Lúcio, A.D.C., da Silva, J.A.G., Sari, B.G. and Diel, M.I. (2019a). Mean performance and stability in multi-environment trials II: selection based on multiple traits. Agronomy Journal, 111(6): 2961-2969. 16. Olivoto, T., Lúcio, A.D.C., da Silva, J.A.G., Marchioro, V.S., de Souza, V.Q. and Jost, E. (2019b). Mean performance and stability in multi-environment trials I: combining features of AMMI and BLUP techniques. Agronomy Journal, 111(6): 2949-2960. 17. Olivoto, T. and Lúcio, A.D.C. (2020). Metan: An R package for multi-environment trial analysis. Methods in Ecology and Evolution, 11: 783-789. 18. Olivoto, T., Nardino, M., Carvalho, I.R., Follmann, D.N., Ferrari, M., Szareski, V.J., de Pelegrin, A.J. and de Souza, V.Q. (2017). REML/BLUP and sequential path analysis in estimating genotypic values and interrelationships among simple maize grain yield-related traits. Genetics and Molecular Research, 16(1): 1-19. 19. Rahayu, S. (2020). Yield stability analysis of rice mutant lines using AMMI Method. IOP Conf. Series: Journal of Physics: Conference Series, 1436(1): 1-9. 20. Rocha, J.R.d.A.S.d.C., Machado, J.C. and Carneiro, P.C.S. (2018). Multitrait index based on factor analysis and ideotype-design: Proposal and application on elephant grass breeding for bioenergy. Global Change Biology and Bioenergy, 10(1): 52-60. 21. Sadimantara, G.R., Kadidaa B., Suaib, L. and Safuan, O. (2018). Growth performance and yield stability of selected local upland rice genotypes in Buton Utara of Southeast Sulawesi. IOP Conference Series: Earth and Environment Science, 122(1): 1-7. 22. Samonte, S.O.P., Wilson, L.T., McClung, A.M. and Medley, J.C. (2005). Targeting cultivars onto rice growing environments using AMMI and SREG GGE biplot analyses. Crop Science, 45(6): 2414-2424. 23. Sharifi, P. (2020a). Application of Multivariate Analysis Methods in Agriculural Sciences. Rasht Branch, Islamic Azad University Press, IR (In Persian). 24. Sharifi, P. (2020b). Evolution, Domesicatin, Breeding Methods and the Latest Breeding Findings in Rice. Agricultural and Natural Resources Engineering Organization of IRAN, IR (In Persian). 25. Sharifi, P., Aminpanah, H., Erfani, R., Mohaddesi, A. and Abbasian, A. (2017). Evaluation of Genotype×Environment Interaction in Rice Based on AMMI model in Iran. Rice Science, 24(3): 173-180. 26. Sharifi, P. and Aminpanah, H. (2016). Evaluation of genotype × environment interactions, stability and a number of genetic parameters in rice genotypes. Plant Genetic Researches, 3(2): 25-42 (In Persian). 27. Smith, A.B., Cullis, B.R. and Thompson, R. (2005). The analysis of crop cultivar breeding and evaluation trials: An overview of current mixed model approaches. Journal of Agriculture Science, 143(1): 449-462. 28. Ullman, J.B. (2006). Structural equation modeling: Reviewing the basics and moving forward. Journal of Personality Assessment, 87: 35-50. 29. Van Eeuwijk, F.A., Bustos-Korts, D.V. and Malosetti, M. (2016). What should students in plant breeding know about the statistical aspects of genotype × environment interactions? Crop Science, 56(5): 2119-2140. 30. Veenstra, L.D., Santantonio, N., Jannink, J.L. and Sorrells, M.E. (2019). Influence of genotype and environment on wheat grain fructan content. Crop Science, 59(5): 190-198. 31. Yan, W., Hunt, L.A., Sheng, Q. and Szlavnics, Z. (2000). Cultivar evaluation and mega-environment investigation based on the GGE biplot. Crop Science, 40: 597-605.
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