Screening of barley elite genotypes using different selection indices based on multi-traits

Document Type : Research Paper

Authors

1 Assistant professor, Seed and Plant Improvement Department, Fars Agricultural and Natural Resources Research Center, Agricultural Research, Education and Extension Organization (AREEO), Darab, iran

2 Seed and Plant Improvement Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran

3 Seed and Plant Improvement Department, Agricultural Research, Education and Extension Organization (AREEO), Karaj, iran

Abstract

Background and objectives: Identifying genotypes that combine high performance across many traits has been a challenger task. Classical linear multi-trait selection indexes are available, but the presence of multicollinearity and the arbitrary choosing of weighting coefficients may erode the genetic gains. Therefore it is necessary use novel approach for elite genotype selection based on multiple traits that overcome the fragility of classical linear indexes. The aim of this study was initially to select superior genotypes based on grain yield and a number of morpho-phenological traits and finally to compare different indicators for selecting ideal genotypes.

Materials and methods: In order to evaluate a number of pure barley lines using multi-trait selection indicators in the Darab Agricultural Research Station, two one-year experiments in the crop years (2017-19) and one two-year experiment (2019-2021) were done. The first year experiment consisted of 108 pure barley lines which were executed as an Augment design and in the second year 34 lines were selected for Darab region. The selected lines were performed using two separate 1 and 2 experiments in a randomized complete block design with three replications. Then, 11 promising lines were selected and along with 6 other lines that were top in Zabul and Moghan regions, were examined in a two-year experiment. The two-year experiment was performed using a randomized complete block design with three replications.


Results: The results of variance analysis of two-year experiments showed that the effect of year was significant for all traits except plant height. The effect of genotype was significant for all traits except grain yield and on the other hand, the genotype × year interaction was significant only for grain yield at 5% probability level. The Smith-Hazel index selected three genotypes, G7, G2 and G15, as the superior genotypes. In the multi-trait genotype-ideotype distance index (MGIDI), genotypes G15, G2, G16 and G20 were selected as the best genotypes. G14, G8 and G18 genotypes were among the top genotypes using factor analysis and ideotype design via best linear unbiased prediction index (FAI-BLUP). According to the results of selection index of ideal genotype (SIIG), G20, G17, G19 and G15 genotypes were the top genotypes with the highest SIIG values, respectively. The results of correlation between the studied traits and MGIDI, FAI-BLUP and SIIG indices showed that all indices except FAI-BLUP had a significant correlation with grain yield. Among the indicators, FAI-BLUP did not show a significant correlation with any of the indicators.

Conclusion: In general, the results of different indices showed that in the conditions of this study, none of the indices showed superiority over each other, and therefore finally the G15 genotype, which was the ideal genotype based on most indices, and the genotype G14 (selected genotype by FAI-BLUP index) due to its earlier maturity and high grain yield as the best genotypes in this study for cultivation and introduction in the southern regions of the country that have a similar climate to Darab, is recommended.

Keywords

Main Subjects

References

  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.

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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
  7. 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)
  8. 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)
  9. 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.
Journal of Crop Production
Volume 15, Issue 4
January 2023
Pages 159-182

Files

Share

How to cite

Statistics