1. Ahmadi, K., Hatami, H., Abdeshah, F. and Kazemian, V. 2020. Agricultural Statistics (2019–2020 Cropping Year): Crop Plants. Ministry of Agriculture-Jahad 1, 97.
2. 2019. Statistical data. www.fao.org/faostat.
3. Rabiei, B., Valizdah, M., Ghareyazie, B. and Moghaddam, M. 2004. Evaluation of selection indices for improving rice grain shape. Field Crops Res. 89: 359-367.
4. Smith, H.F. 1936. A discriminant function for plant selection. Ann. Eugenics 7: 240-250.
5. Hazel, L. 1943. The genetic basis for constructions selection indices. Genetics. 28: 476-490.
6. Zali, H., Sofalian, O., Hasanloo, T., Asghari, A. and Hoseini, S.M. 2015. Appraising of drought tolerance relying on stability analysis indices in canola genotypes simultaneously, using selection index of ideal genotype (SIIG) technique: Introduction of new method. Biol Forum Int J. 7: 2. 703-711.
7. Olivoto, T., Licio, 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. Agron J. 111: 6. 2949-2960.
8. Olivoto, T. and Nardino, M. 2020. MGIDI: A novel multi-trait index for genotype selection in plant breeding. Bioinformatics. 7: 23. 1-22.
9. Olivoto, T. and Nardino, M. 2021. MGIDI: Toward an effective multivariate selection in biological experiments. Bioinformatics. 37: 1383-1389.
10. Jahufer, M.Z.Z. and Casler, M.D. 2015. Application of the Smith-Hazel selection index for improving biomass yield and quality of switch grass. Crop Sci. 55: 1212-1222.
11. Bizari, E.H., Pedroso Val, B.H., Pereira, E.M., Di Mauro, A.O. and Uneda-Trevisoli, S. 2017. Selection indices for agronomic traits in segregating populations of soybean. Revista Ciencia Argon. 48: 110-117.
12. Olivoto, T. and Lucio, A.D. 2020. Metan: an R package for multi-environment trial analysis. Methods Ecol Evol. 11: 783-789
13. Olivoto, T. and Nardino, M. 2021. MGIDI: Toward an effective multivariate selection in biological experiments. Bioinformatics. 37: 1383-1389.
14. Pour-Aboughadareha, A. and Poczaib, P. 2021b. A dataset on multi-trait selection approaches for screening desirable wild relatives of wheat. Data in Brief. 39: 107541.
15. Pour-Aboughadareh, A., Sanjani, S., Nikkhah-Chamanabad, H., Mehrvar, M.R., Asadi, A. and Amini A. 2021. Identification of salt-tolerant barley genotypes using multi-traits index and yield performance at the early growth and maturity stage. Bull Natl Res Cent. 45: 1-16.
16. Rocha, J.R.dA.S.dC, Nunes, K.V., Carneiro, A.L.N., Marcal, T.D.S., Salvador, FV., Careiro, P.C.S. and Carneiro, J.E.S. 2019. Selection of superior inbred progenies toward the common bean ideotype. Agron J. 111: 1. 1181-1189.
17. Resende, M.D.V. 2016. Software Selegen-REML/BLUP: A useful tool for plant breeding. CBAB. 16: 330-339.
18. Silva, M.J., Careiro, P.C.S., Careiro, J.E., Damasceno, C.M.B., Parrella, N.N.L.D., Pastina, M.M., Simeone, M.L.F., Schaffert, R.E. and Parrella, R.A. 2018. Evaluation of the potential of lines and hybrids of biomass sorghum. Indust. Crops Prod. 125: 1. 379-385.
19. Oliveira, I.C.M, Marcal, T.D.S., Bernardino, K.D.C., Ribeiro, P.C.D.O., Parrella, R.A.D.C., Carneiro, P.C.S., Schaffert, R.E. and Carneiro, J.E.D.S. 2019. Combining ability of biomass sorghum lines for agroindustrial characters and multitrait selection of photosensitive hybrids for energy cogeneration. Crop Sci. 59: 1. 1554-1566.
20. Woyann, L.G., Meira, D., Zdziarski, A.D., Matei, G., Milioli, A.S., Rosa, A.C., Madella, L. A. and Benin, G. 2019. Multiple-trait selection of soybean for biodiesel production in Brazil. Indust Crops Prod. 140: e111721.
21. Barati, A., Zali, H., Marzoqian, A., Koohkan, Sh. and Gholipour, A. 2021. Selection of barley pure lines with high yield and desirable agronomic characteristics in warm areas of Iran. J Crop Prod. 14: 1. 199-218. (In Persian)
22. Zali, H. and Barati, A. 2020. Evaluation of selection index of ideal genotype (SIIG) in other to selection of barley promising lines with high yield and desirable agronomy traits. J Crop Breed. 12: 34. 93-104. (In Persian)
23. Haghighatnia, H. and Alhani, F. 2020. Evaluation of irrigation water salinity tolerance indices in new cultivars and lines of safflower. Ir J Soil Water Res. 51: 7. 1181-1821. (In Persian)
24. Emami, S., Asghari, A., Mohammaddoust Chamanabad, H., Rasoulzadeh, A. and Ramzi, E. 2019. Evaluation of osmotic stress tolerance in durum wheat (Triticum durum) advanced lines. Environ. Stresses Crop Sci. 12: 3. 697-707. (In Persian)
25. Najafi Mirak, T., Dastfal, M., Andarzian, B., Farzadi, H., Bahari, M. and Zali, H. 2018. Stability analysis of grain yield of durum wheat promising lines in warm and dry areas using parametric and non-parametric methods. J Crop Prod Process. 8: 2. 79-96. (In Persian)
26. Zali, H., Sofalian, O., Hasanloo, T., Asghari, A. and Zeinalabedini, M. 2017. Appropriate strategies for selection of drought tolerant genotypes in canola. J Crop Breed. 78: 20. 77-90. (In Persian)
27. Holland, J.B. 2006. Estimating genotypic correlations and their standard errors using multivariate restricted maximum likelihood estimation with SAS Proc MIXED. Crop Sci. 46: 642-654.

28.              Pour-Aboughadareha, A. and Poczaib, P. 2021a. Dataset on the use of MGIDI index in screening drought-tolerant wild wheat accessions at the early growth stage. Data in Brief. 36: 107596.

29. Meier, C, Marchioro, V.S., Daniela Meira, D., Olivoto, T. and Klein, L.A. 2021. Genetic parameters and multiple-traitselection in wheat genotypes. Pesqu Agropec Tropic. 51: e67996.
30. Pimental, A.J.B., Guimaraes, J.F.R., Souza, M.A.D., Resense, M.D.V.D., Moura, L.M., Rocha, J.R.D.A.S.D.C. and Ribeiro, G. 2014. Estimação deparâmetros genéticos e predição de valor genético aditivode trigo utilizando modelos mistos. Pesqu Agropec Brasil. 49: 882-890.
31. Benakanahalli, N.K., Sridhara, S., Ramesh, N., Olivoto, T., Sreekantappa, G., Tamam, N., Abdelbacki, A.M.M., Elansary, H.O. and Abdelmohsen, S.A.M. 2021. A framework for identification of stable genotypes based on MTSI and MGDII Indexes: an example in guar (Cymopsis tetragonoloba). Agron. 11: 1221.
32. Olivoto, T., Licio, 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. Agron J. 111: 6. 2961-2969.
33. Volpato, L., Rocha, J.R.D.A.S.D.C., Alves, R.S., Ludke, W.H., Borém, A. and Silva, F.L.D. 2021. Inference of population effect and progeny selection via a multi-trait index in soybean breeding. Acta Sci Agron. 43: 1-10.
34. Mirzaei, M.R. and Hemayati, S.S. 2021. The effect of environment and maternal plant on germination traits of sugar beet seeds and an approach to select the superior genotype. Agric. Res. https://doi.org/10.1007/s40003-021-00607-2
35. Gholizadeh, A., Ghaffari, M. and Shariati, F .2021. Use of selection index of ideal genotype (SIIG) in order to select new high yielding sunflower hybrids with desirable agronomic characteristics. J Crop Breed. 13: 38. 116-123. (In Persian)
36. Abdollahi Hesar, A., Sofalian, O., Alizadeh, B., Asghari, A. and Zali, H. 2020. Evaluation of some autumn canola genotypes based on agronomy traits and SIIG index. J Crop Breed. 12: 34. 93-104. (In Persian)
37. Bhandari, H., Bhanu, A., Srivastava, K., Singh, M. and Shreya, H.A. 2017. Assessment of genetic diversity in crop plants-an overview. Adv Plants Agric. Res. 7: 3. 279-286.