Selection of barley superior promising lines using selection indexes of multi-trait

Document Type : Research Paper

Authors

1 7. Crop and Horticultural Science Research Department, Fars Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Darab, iran

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

3 Crop and Horticultural Science Research Department, Golestan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Gonbad, iran

4 7. Crop and Horticultural Science Research Department, Sistan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Zabol, iran

5 Faculty member of department of cereal research of seed and plant improvement institute, Karaj

6 Crop and Horticultural Science Research Department, Ahvaz Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Ahvaz, iran

Abstract

Background and objectives: The aim of this study was to select of the barley superior lines based on some of morpho-phenological traits using various indices based on REML/BLUP model and as well as the SIIG index and comparison of these indices.

Materials and methods: A set of 17 promising lines along two checks were evaluated in a RCBD with three replications at three research stations including Zabol, Darab, and Gonbad Kavous during the 2020-2021 cropping year.

Results: The results showed that there was a significant difference between the test regions and genotypes in terms of all measured traits. The results obtained by REML model indicated that the highest and lowest heritability values were related to 1000-grain weight and grain filling period, respectively. The MTSI index identified lines G9, G2 and G3 as superior lines, while the FAI-BLUP index identified G13, G11 and G3 as the best lines based on their traits and grain yield. Based on the SIIG index, lines G7, G2, G8 and G17 were identified as the most superior lines.

Conclusion: In general, by comparing the results obtained from different indices, selected lines with SIIG index due to high grain yield and high 1000-grain weight were chosen as selected genotypes for complementary tests. In addition, the selected genotypes using FAI-BLUP and MGIDI indices were early maturity and had a high 1000-grain weight, but their grain yield were lower than both checks of experiment.

Keywords

References

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. Journal of Crop Breeding, 12(34): 93-104. (In Persian)..
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. Journal of Crop Production, 14 (1): 199-218. (In Persian)..
Benakanahalli NK, Sridhara S, Ramesh N, Olivoto T, Sreekantappa G, Tamam N, Abdelbacki AMM, Elansary HO and Abdelmohsen SAM. 2021. A framework for identification of stable genotypes based on MTSI and MGDII Indexes: an example in guar (Cymopsis tetragonoloba L.). Agronomy, 11: 1221.
Bhandari H, Bhanu A, Srivastava K, Singh M and Shreya HA. 2017. Assessment of genetic diversity in crop plants-an overview. Advances in Plants & Agriculture Research, 7(3):279-286.
Bizari EH, Pedroso Val BH, Pereira EM, Di Mauro AO and Uneda-Trevisoli S. 2017. Selection indices for agronomic traits in segregating populations of soybean. Revista Ciencia Agronomica, 48:110–117
Emami S, Asghari A, Mohammaddoust Chamanabad H, Rasoulzadeh A and Ramzi E. 2019. Evaluation of osmotic stress tolerance in durum wheat (Triticum durum L.) advanced lines. Environmental Stresses in Crop Sciences, 12 (3): 697-707. (In Persian).
FAO. 2019. Statistical data. www. Fao. Org/faostat.
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. Journal of Crop Breeding, 13 (38): 116-123. (In Persian)..
Holland JB. 2006. Estimating genotypic correlations and their standard errors using multivariate restricted maximum likelihood estimation with SAS Proc MIXED. Crop Science, 46: 642-654.
Hussain T, Akram Z, Shabbir G, Manaf A and Ahmed M. 2021. Identification of drought tolerant chickpea genotypes through multi trait stability index. Saudi Journal of Biological Sciences, doi:10.1016/j.sjbs.2021.07.056
Haghighatnia H and Alhani F. 2020.  Evaluation of irrigation water salinity tolerance indices in new cultivars and lines of safflower. Iranian Journal of Soil and Water Research, 51(7): 1181-1821. (In Persian)..
Jahufer MZZ and Casler MD. 2015. Application of the Smith-Hazel selection index for improving biomass yield and quality of switch grass. Crop Science, 55:1212–1222.
Meier C, Marchioro VS, Daniela Meira D, Olivoto T, Klein LA. 2021. Genetic parameters and multiple-trait selection in wheat genotypes. Pesquisa Agropecuária Tropical, 51: e67996.
Mirzaei MR and Hemayati, SS. 2021. The effect of environment and maternal plant on germination traits of sugar beet seeds and an approach to select the superior genotype. Agricultural Research, https://doi.org/10.1007/s40003-021-00607-2
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. Journal of Crop Production and Processing, 8 (2): 79-96. (In Persian).
Oliveira ICM, Marcal TDS, Bernardino KDC, Ribeiro PCDO, Parrella RADC, Carneiro PCS, Schaffert RE and Carneiro JEDS. 2019. Combining ability of biomass sorghum lines for agroindustrial characters and multitrait selection of photosensitive hybrids for energy cogeneration. Crop Science, 59(1): 1554-1566.
Olivoto T, Licio ADC, da Silva JAG, Sari BG and Diel MI. 2019a. Mean performance and stability in multi-environment trials II: selection based on multiple traits. Agronomy Journal, 111(6): 2961-2969.
Olivoto T, Licio ADC, da Silva JAG, Marchioro VS, de Souza VQ 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.
Olivoto T and Lucio AD. 2020. Metan: an R package for multi-environment trial analysis. Methods in Ecology and Evolution, 11: 783-789
Olivoto T and Nardino M. 2020. MGIDI: A novel multi-trait index for genotype selection in plant breeding. Bioinformatics, 1–22.
Olivoto T and Nardino M. 2021. MGIDI: Toward an effective multivariate selection in biological experiments. Bioinformatics, 37: 1383–1389.

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

Pour-Aboughadareh A. and Poczaib P. 2021. A dataset on multi-trait selection approaches for screening desirable wild relatives of wheat. Data in Brief, 39: 107541.
Pour-Aboughadareh A, Sanjani S, Nikkhah-Chamanabad H, Mehrvar MR, Asadi A, Amini A. 2021. Identification of salt-tolerant barley genotypes using multi-traits index and yield performance at the early growth and maturity stage. Bulletin of the National Research Centre, 45:1-16.
JOURNAL OF AGRICULTURAL SCIENCE AND SUSTAINABLE PRODUCTION
Volume 33, Issue 3
October 2023
Pages 1-16

Files

Share

How to cite

Statistics