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Selection of Superior Soybean Genotypes Using some Statistical Multivariate Methods in Moghan Climate Conditions
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Nasrin Razmi *   , Ebrahim Hezarjaribi , Abbasali Andarkhor |
| Field and Horticultural Crops Sciences Research Department, Ardabil Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Parsabad, Iran , n.razmi@areeo.ac.ir |
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Abstract: (3637 Views) |
Soybean is the promising oilseed in the face of protein and oil shortage. In this study 16 advanced soybean genotypes, in terms of seed yield and yield components were evaluated using multivariate statistical methods. This experiment was carried out in the form of randomized complete block design (RCBD) in the research farm of Ardabil Agricultural and Natural Resources Research Center (Moghan) for two consecutive years (2017-2018). Combined analysis of variance emphasized the statistically significant differences for seed yield, yield components and growth period among these soybean genotypes. Based on the mean comparison results, G1, G5 and G11 genotypes had the highest grain yield, longest growth period was observed in G1, G16 and G6 genotypes and highest number of seeds per m2 was belonged to G1, G16 and G9 genotypes. The broad sense heritability for plant height, seed yield and number seed in m2 were 0.92.07, 75.31 and 79.25 percentage, respectively. Also, the results showed that there was a positive and significant correlation between seed yield and leaf area of per plant, growth period, number of seeds per m2 and number of pods per plant. Genotypes were classified into four distinct groups in cluster analysis and the Ward method. The results of principal component analysis and biplot confirmed by the clustering results, too.G1, G2, G5 and G11 genotypes belong to the first group from cluster analysis with higher seed yield and number of seed per m2, and these genotypes are recommended in future breeding programs. |
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| Keywords: Number of seed, Leaf area index, Growth period, Seed yield |
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Full-Text [PDF 687 kb]
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Type of Study: Research |
Subject:
Plant improvement
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1. Astaraki, H., Sharifi, P. and Sheikh, F. (2020). Estimation of genotypic correlation and heritability of some of traits in faba bean genotypes using restricted maximum likelihood (REML). Plant Genetic Researches, 6(2): 111-128 (In Persian). [ DOI:10.29252/pgr.6.2.111] 2. Azevedo, C.V.G., Val, B.H.P., Araujo, L.C.A., Juhasz, A.C.P., Di Mauro, A.O. and Uneda Trevisoli, S.H. (2021). Genetic parameters of soybean populations obtained from crosses between grain and food genotypes. Acta Scientiarum Agronomy, 43: e46968. [ DOI:10.4025/actasciagron.v43i1.46968] 3. Babaei, H.R., Razmi, N. and Sabzi, H. (2021). Study on grain yield stability of soybean genotypes [Glycine max (L.) Merril] through GGE biplot analysis. Applied Research in Feilf Crops, 34(1): 39-54 (In Pershian). 4. Benjamin, B., Stewart-Brown, B.B., Song, Q., Vaughn, J.N. and Li, Z. (2019). Genomic selection for yield and seed composition traits within an applied soybean breeding program. G3 (Bethesda), 9(7): 2253-2265. [ DOI:10.1534/g3.118.200917] 5. Dubey, N., Avinashe, H.A. and Shrivastava, A.N. (2018). Principal component analysis in advanced genotypes of soybean [Glycine max (L.) Merrill] over seasons. Plant Archives, 18(1): 501-506. 6. Girgel, U. (2021). Principle component analysis (PCA) in bean genotypes (Phaseolus vulgaris L.) for agronomic, morphological and biochemical characteristics. Applied Ecology and Environmental Research, 19(3): 1999-2011. [ DOI:10.15666/aeer/1903_19992011] 7. Hezarjaribi, E., Andarkhor, A. and Razmi, N. (2018). Evaluation of Adaptability of Early Matured Pure-Lines Soybean (2017-18). Final Report. Seed and Plant Improvement Institute.Karaj, IR (In Persian). 8. Kahlon, C.S., Li, B., Board, J., Dia, M., Sharma, P. and Jat, P. (2018). Cluster and principle component analysis of soybean grown at various row spacings, planting dates and plant populations. De Gruyter, 3: 110-122. [ DOI:10.1515/opag-2018-0011] 9. Kuswantoro, H., Artari, R., Iswanto, R. and Iman, H. (2020). Family structure of F5 soybeans lines derived from soybean varieties with the main differences on seed size and maturity traits. Biodiversitas, 21(6): 2576-2585. [ DOI:10.13057/biodiv/d210630] 10. Machado, B.Q.V., Nogueira, A.P.O., Hamawaki, O.T., Rezende, G.F., Jorge, G.L., Silveira, I.C., Medeiros, L.A., Hamawaki, R.L. and Hamawak, C.D.L. (2017). Phenotypic and genotypic correlations between soybean agronomic traits and path analysis. Genetics and Molecular Research, 16(2). doi http://dx.doi.org/10.4238/gmr16029696. [ DOI:10.4238/gmr16029696] 11. Milioli, A.S., Zdziarski, A.D., Woyann, L.G., Santos, R., Rosa, A., Madureira, A. and Benin, G. (2018). Yield stability and relationships among stability parameters in soybean genotypes across years. Chilean Journal of Agricultural Research, 78(2): 299-310. [ DOI:10.4067/S0718-58392018000200299] 12. Razmi, N., Rameeh, V., Hezarjeribi, E. and Kalantar Ahmadi, A. (2021). Investigation of grain yield, number of pods and plant height of new soybean lines in Sari, Gorgan, Moghan and Dezful regions. Journal of Crop Breeding, 12: 36-42 (In Persian). [ DOI:10.52547/jcb.12.36.21] 13. Rodrigues, B., Serafim, F., Nogueira, A.P., Hamawaki, O.T., Sousa, L.B. and Hamawaki, R.L. (2015). Correlations between traits in soybean (Glycine max L.) naturally infected with Asian rust (Phakopsora pachyrhizi). Genetics and Molecular Research, 14(4): 17718-29. [ DOI:10.4238/2015.December.21.45] [ PMID] 14. Sousa, L.B., Hamawaki, O.T., Santos Júnior, C.D. and Oliveira, V.M. (2015). Correlation between yield components in F6 soybean progenies derived from seven biparental crosses. Journal of Biosciences, 31: 1692-1699. [ DOI:10.14393/BJ-v31n6a2015-26217] 15. Tahmasebi, A.K., Darvishzadeh, R., Moghaddam, A.F., Gholinezhad, E. and Abdi, H. (2022). Use of selection indices for improving grain yield in sesame local populations. Plant Genetic Researches, 8(2): 117-130 (In Persian). [ DOI:10.52547/pgr.8.2.9] 16. Teodoro, P.E., Ribeiro, L.P., Corrêa, C.C.G. and Luz Júnior, R.A.A. (2015). Path analysis in soybean genotypes as function of growth habit. Journal of Biosciences, 31: 794-799. [ DOI:10.14393/BJ-v31n1a2015-26094] 17. Yoosefzadeh-Najafabadi, M., Tulpan, D. and Eskandari, M. (2021). Application of machine learning and genetic optimization algorithms for modeling and optimizing soybean yield using its component traits. PlosOne, 16(4): e0250665. [ DOI:10.1371/journal.pone.0250665] [ PMID] [ PMCID] 18. You, M.K., Song, Q., Jia, G., Cheng, Y., Duguid, S., Booker, H. and Cloutier, S. (2016). Estimation of genetic parameters and their sampling variances for quantitative traits in the type 2 modified augmented design. The Crop Journal, 4: 107-118. [ DOI:10.1016/j.cj.2016.01.003] |
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Razmi N, Hezarjaribi E, Andarkhor A. Selection of Superior Soybean Genotypes Using some Statistical Multivariate Methods in Moghan Climate Conditions. pgr 2023; 9 (2) :41-54 URL: http://pgr.lu.ac.ir/article-1-237-en.html
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