Plant Productions

Plant Productions

Identification of superior genotypes of barley for cultivation in the south regions of Fars province using MGIDI و FAI-BLUP indices

Document Type : Research Paper - Plant Breeding

Authors
Hassan Zali 1
Alireza Pour-Aboughadareh 2
1 Researcher Assistant Professor, 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 Researcher Assistant Professor, Seed and Plant Improvement Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran
10.22055/ppd.2024.45295.2134
Abstract
Introduction
Barley (Hordeum vulgare L.) has been known as one of the most adapted cereal crops to various environmental conditions. In addition, this cereal crop ranks fourth in the world in terms of economic importance after wheat, rice, and corn. Identification of high-yielding genotypes with highly desirable growth traits is one of the most important goals in barley breeding programs. However, the present study aimed to select superior genotypes based on grain yield and some morpho-phenological traits using different selection models.
 
Materials and methods
To identify superior genotypes of barley for cultivation in the warm and dry conditions in the southern regions in the Fars province, 21 genotypes were investigated in a randomized complete block with three replications during the 2021-2023 cropping seasons. During the plant growth and development, several morpho-phenological traits were recorded. Agronomic traits measured were days to spike emergence (DHE), days to maturity (DMA), plant height (PLH), grain filling period (GFP), grain yield (GY), 1000-kernel weight (TKW), spike length (SL), spike density (SD), awn length (AL), spike weight (SPW), number of grains per spike (NGS), row type (RT) and number of spikes per m2 (NS). After collecting experimental data, statistical analyses were computed using SAS and R softwares
 
Results and Discussion
Results of the analysis of variance (ANOVA) showed that there is a significant difference among the investigated genotypes in terms of all the measured traits (except for Awn length and spike length). Based on the multi-trait genotype-ideotype distance index (MGIDI), genotypes G1 (Oxin), G7, G3, and G14 with the lowest values were identified as superior genotypes. Moreover, the ideotype design via the best linear unbiased prediction (FAI-BLUP) model identified genotypes G20, G8, G2 (Golchin) and G1 (Oxin) as the desirable genotypes compared with other genotypes. The Venn diagram revealed that genotypes G1 and G7 were selected using both selection indices, simultaneously. The MGIDI index showed a positive and significant correlation with DHE (0.58**), while it negatively and significantly correlated with GFP (-0.51*) and GY (-0.74 **). The FAI-BLUP index showed a significant positive relationship with SL (0.50 *) and SPW (0.45*), while it negatively and significantly correlated with DHE (-0.47 *). To better evaluate and group the investigated genotypes, principal components analysis (PCA) was used. Results of the PCA showed that the first and second principal components explained 30.5 and 20.6 percent, respectively. Moreover, the results of the PCA showed that the selected genotypes based on MGIDI and FAI-BLUP indices are located in the 1st and 2nd regions of the PCA diagram. In total, the results showed that there is a relative compliance between the MGIDI and FAI-BLUP indices with PCA. Additionlly, there was a high agreement between the MGIDI indices and the first principal component, and on the other hand, the second principal component showed a high agreement with the FAI-BLUP index.
 
Conclusion
In conclusion, the genotype G7 with the highest grain performance and the relative superiority in terms of MGIDI and FAI-BLUP indices was identified as a superior genotype compared to all reference genotypes. Hence, this genotype can be recommended for further comprehensive evaluation before commercial release in the southern regions in Fars province.
Keywords
Biplot
Heat map
Multivariate selection
Multi-trait index
Venn plot
Subjects
Bengtsson, B. O. (1992). Barley genetics-not only here for the beer. Trends in Genetics, 8(1), 3–5.
Benakanahalli, N. K., Sridhara, S., Ramesh, N., Olivoto, T., Sreekantappa, G., Tamam, N., Abdelbacki, A. M. M., Elansary, H. O. & Abdelmohsen, S. A. M. (2021). A framework for identification of stable genotypes based on MTSI and MGDII Indexes: an example in guar (Cymopsis tetragonoloba L.). Agronomy, 11, 1221.
Bhering, L. L., Laviola, B. G., Salgado, C. C., Sanchez, C. F. B., Rosado, T. T. & Alves, A. A. (2012). Genetic gains in physic nut using selection indexes. Pesquisa Agropecuária Brasileira, 47, 402–408.
Bizari, E. H., Pedroso Val, B. H., Pereira, E. M., Di Mauro, A. O. & Uneda-Trevisoli, S. (2017). Selection indices for agronomic traits in segregating populations of soybean. Revista Ciencia Agronomy, 48, 110-117.
Cerón-Rojas, J. J., Crossa, J., Sahagún-Castellanos, J., Castillo-González, F., & Santacruz-Varela, A.  (2006). A selection index method based on eigen analysis. Crop Science, 46, 1711-1721.
Hazel, L. N.  (1943). The genetic basis for constructing selection indexes. Genetics, 28, 476-490.
Hazel, L. H., Dickerson, G. E. & Freeman, A. E. (1994). The Selection index-then, now, and for the future. Journal of Dairy Science, 77(10), 3236-3251.
Kassambara, A. & Mundt, F. (2020). Factoextra: Extract and visualize the results of multivariate data analyses. R package version 1.0.7.  https://CRAN.R-project.org/package=factoextra.
Olivoto, T. & Lúcio, A.D. (2020). Metan: an R package for multi-environment trial analysis. Methods in Ecology and Evolution, 11(6), 783-789.
Olivoto, T. & Nardino, M. (2020). MGIDI: A novel multi-trait index for genotype selection in plant breeding. Bioinformatics, 1-22.
Pour-Aboughadareha, A. & 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.
Pour-Aboughadareh, A., Barati, A., Koohkan, S. A., Jabari, M., Marzoghian, A., Gholipoor, A., Shahbazi-Homonloom, K., Zali, H., Poodineh, O. & Kheirgo, M. (2022). Dissection of genotype-by-environment interaction and yield stability analysis in barley using AMMI model and stability statistics. Bulletin of the National Research Centre, 46(19), 4-12.
Pour-Aboughadareh, A., Barati, A., Gholipoor, A. Zali, H., Marzoghian, A., Koohkan, S. A. Shahbazi-Homonloo, K. & Houseinpour, A. (2023a). Deciphering genotype-by-environment interaction in barley genotypes using different adaptability and stability methods. Crop Breeding and Applied Biotechnology, 26, 547-562.
Pour-Aboughadareh, A., Koohkan, S., Zali, H., Marzooghian, A., Gholipour, A., Kheirgo, M., Barati, A., Bocianowski, J. & Askari-Kelestani, A. (2023b). Identification of high-yielding genotypes of barley in the warm regions of Iran. Plants. 12(22): 1-13.
Pureisa, M., Nabipour , M. & Meskarbashee, M. (2015). Investigating of grain growth and contribution of stem reserves in grain yield of barley (Hordeum vulgare L.) cultivars under terminal drought conditions. Plant Productions, 38(1), 41-53. [In Persian]
Resende, M.D.V. (2016). Software Selegen-REML/BLUP: A useful tool for plant breeding. Crop Breeding and Applied Biotechnology, 16, 330-339.
Rocha J.R.dA.S.dC., Machado, J.C. & 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.
Shayan. S., Vahed, M. M., Mohammadi, S. A., Ghassemi-Golezani, K., Sadeghpour, F., & Yousefi, A. (2020). Genetic diversity and grouping of winter barley genotypes for root characteristics and ISSR markers. Plant Productions, 43(3), 323-336. [In Persian]
Shirzad, A., Asghari, A., Zali, H., Sofalian, O., & Mohammaddoust Chamanabad, H. (2022a). Application of the multi-trait genotype-ideotype distance index in the selection of top barley genotypes in the warm and dry region of Darab. Journal of Crop Breeding, 14(44), 65-76.
Smith, H. F. (1936). A discriminant function for plant selection. Annals Eugenics, 7, 240-250.
Stephens, M. J., Alspach, P. A., Beatson, R. A., Winefield, C., & Buck, E. J.  (2012). Genetic parameters and development of a selection index for breeding red raspberries for processing. Journal of the American Society of Horticultural Science, 137, 236-242.
Volpato, L., Rocha, J. R. D. A. S. D. C., Alves, R. S., Ludke, W. H., Borém, A. & Silva, F. L. D. (2021). Inference of population effect and progeny selection via a multi-trait index in soybean breeding. Acta Scientiarum Agronomy, 43, 1-10.
Zali, H., Barati, A. & Pour-Aboughadareha, A. (2023a). Screening of barley elite genotypes using different selection indices based on multi-traits. Crop Production Journal, 15 (4), 159-182.  
Zali, H., Barati, A., Pour-Aboughadareh, A., Gholipour, A., Koohkan, S., Marzoghiyan, A., Bocianowski, J., Bujak, H. & Nowosad, K. (2023b). Identification of superior barley genotypes using selection index of ideal genotype (SIIG). Plants, 12(9), 1843.
Plant Productions
Volume 46, Issue 3
Autumn 2023
Pages 335-351