Identification of key agronomical traits contributing to the selection of superior lines of soybean in Moghan Plain

Document Type : scientific research article

Authors

1 Assistant Prof., Dept. of Field and Horticultural Crops Sciences Research, Ardabil Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Parsabad, Iran.

2 Corresponding Author, Assistant Prof., Dept. of Seed and Plant Improvement Research, Seed and Plant Improvement Institute, Agricultural Research Education and Extension Organization, Karaj, Iran.

3 Assistant Prof., Dept. of Field and Horticultural Crops Sciences Research, Golestan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Gorgan, Iran.

Abstract

Background and Objectives: Soybean is a valuable industrial plant worldwide because of its high protein content, high quality unsaturated oil, and many direct and indirect uses. Seed yield is determined by many vegetative and reproductive traits in different amounts. In some cases, the improvement of a specific trait is accompanied by simultaneous changes in one or more other traits, which provides the possibility of using indirect selection. This study aimed to investigate the correlation between phenotypic traits and yield components, propose cause-and-effect relationships for grain yield components, and determine the direct and indirect effects of traits on soybean seed yield.

Materials and Methods: In this study, 13 pure lines obtained from breeding programs with two control varieties, Amir and Saba, were cultivated during the agricultural year of 2019/2020 at the Agricultural and Natural Resources Research Center of Ardabil (Moghan). The experimental arrangement was a randomized complete block design with three repetitions. Correlation coefficients were used to understand the relationships between traits, path analysis was used to estimate the direct and indirect effects of traits on seed yield, and factor analysis was used to explain the correlation between variables and selection of the desired lines was done using factor analysis and cluster analysis.

Results: Based on the results of variance analysis for testing differences in composite variables, the genotypes had significant differences in terms of vegetative traits, yield components, and seed yield. Correlation between phenotypic traits showed that the number of nodes per plant was positively and significantly correlated with the number of pods per plant and the number of seeds per plant. In addition, the correlation of the number of seeds per plant with seed yield was positive and significant (r=0.76**). The results of path analysis showed that traits related to the number of seeds per plant, such as the number of pods per plant, showed a high direct and indirect correlation with seed yield. The direct effect of seed number on yield was positive (0.66), whereas the weight of one hundred seeds had a high negative direct effect (-0.44) on seed yield and a positive indirect effect through the number of days to flowering (0.24). Factor analysis showed that the first factor justified 41.2 of the total variation, and the highest positive factor coefficients belonged to plant height, number of nods, total pods and number of seeds per plant. The second factor explained 21.78% of the total variation, day to of flowering, days to maturity, number of branches per plant and one hundred seed weight had the highest coefficients. Factor analysis was able to identify high yielding and earliness lines, which were placed in group 2 of cluster analysis.

Conclusion: Factor analysis showed that four factors explained 62.98% of the total variance. According to path analysis, among the yield components, the number of seeds per plant has the largest contribution in determining the yield, and the trait of the number of pods per plant, which causes the production of more seeds in soybean lines, should be prioritized in breeding programs and selection of superior lines. Lines G14, G5, G2, G10 and G8 were placed in the group of lines with high performance and early maturity groups.

Keywords

Main Subjects

References

1.Food and Agriculture Data FAOSTAT. (2021). Food and Agriculture Organization of the United Nations (FAO).http://www.fao.org/faostat/en/#data/QCL.
2.Anda, A., Soos, G., Menyhart, L., Kucserka, T., & Simon, B. (2020). Yield features of two soybean varieties under different water supplies and field conditions. Field crops research, 245, 107673.
3.Dubey, N., Avinashe, H. A., & Shrivastava, A. N. (2018). Principal component analysis in advanced genotypes of soybean [Glycine max (L.) Merrill] over seasons. Plant Archives, 18 (1), 501-506.
4.Vogel, J. T., Liu, W., Olhoft, P., Crafts-Brandner, S. J., Pennycooke, J. C., & Christiansen, N. (2021). Soybean yield formation physiology–a foundation for precision breeding based improvement. Frontiers in plant science, 12, 719706.
5.Egli, D. B. (2019). Crop growth rate and the establishment of sink size: a comparison of maize and soybean. Journal of Crop Improvement, 33 (3), 346-362.
6.Kahlon, C. S., Li, B., Board, J., Dia, M., Sharma, P., & Jat, P. (2018). Cluster and principle component analysis of soybean grown at various row spacings, planting dates and plant populations. Open Agriculture, 3 (1), 110-121.
7.Carvalho, I. C., Souza, V. Q., Nardino, M., Follmann, D. N., Silva, A. D. B., Szareski, V. J., & Olivoto, T. (2015). Associations phenotypic between physiological traits of soybean contrasting growth habits. Global Science and Technology, 8 (3), 30-40.
8.Bisinotto, F. F., Hamawaki, O. T., Nogueira, A. P. O., Hamawaki, R. L., Glansenapp, J. S., & Hamawaki, C. L. (2017). Path analysis and traits correlation in soybean. Communications in Plant Sciences, 7 (1/2), 27-33.
9.Viotto Del Conte, M., Carneiro, P. C. S., Vilela de Resende, M. D., Lopes da Silva, F., & Peternelli, L. A. (2020). Overcoming collinearity in path analysis of soybean [Glycine max (L.) Merr.] grain oil content. Plos One, 15 (5), e0233290.
10.Sulistyo, A., & Sari, K. P. (2018). Correlation, path analysis and heritability estimation for agronomic traits contribute to yield on soybean. In IOP Conference Series: Earth and Environmental Science, 102 (1), 012034.
11.Dalla Lana, F., Ziegelmann, P. K., Maia, A. D. H., Godoy, C. V., & Del Ponte, E. M. (2015). Meta-analysis of the relationship between crop yield and soybean rust severity. Phytopathology, 105 (3), 307-315.
12.Ferrari, M., Carvalho, I. R., de Pelegrin, A. J., Nardino, M., Szareski, V. J., Olivoto, T., ... & da Rosa, T. C. (2018). Path analysis and phenotypic correlation among yield components of soybean using environmental stratification methods. Australian Journal of Crop Science, 12 (2), 193-202.
13.Wei, M. C. F., & Molin, J. P. (2020). Soybean yield estimation and its components: A linear regression approach. Agriculture, 10 (8), 348.
14.Księżak, J., & Bojarszczuk, J. (2022). The seed yield of soybean cultivars and their quantity depending on sowing term. Agronomy, 12 (5), 1066.
15.Corassa, G. M., Santi, A. L., Amado, T. J. C., Reimche, G. B., Gaviraghi, R., Bisognin, M. B., & Pires, J. L. F. (2019). Performance of soybean varieties differs according to yield class: a case study from Southern Brazil. Precision Agriculture, 20, 520-540.
16.Gao, M., & Li, S. (2017, May). Relationship between soybean yield/ quality and soil quality in a major soybean-producing area based on a 2D-QSAR model. In AIP Conference Proceedings (Vol. 1839, No. 1). AIP Publishing.
17.Lu, S., Zhao, X., Hu, Y., Liu, S., Nan, H., Li, X., ... & Kong, F. (2017). Natural variation at the soybean J locus improves adaptation to the tropics and enhances yield. Nature genetics, 49 (5), 773-779.
18.Berhanu, H., Tesso, B., & Lule, D. (2021) Correlation and Path Coefficient Analysis for Seed Yield and Yield Related Traits in Soybean (Glycine max (L.)) Genotypes. Plant, 9 (4), 106-110.
19.Jarquin, D., Howard, R., Xavier, A., & Das Choudhury, S. (2018). Increasing predictive ability by modeling interactions between environments, genotype and canopy coverage image data for soybeans. Agronomy, 8 (4), 51.
20.Farhang-Asa, K., Khalili, A., Karami, A., & Bagheri, A. (2022). Comparison of the Yield and Yield Components of Four Imported Soybean Cultivars from Brazil with Domestic Cultivars in Khuzestan. Agrotechniques in Industrial Crops, 2 (4), 198-206.
21.Ghanbari, S., Nooshkam, A., Fakheri, B. A., & Mahdinezhad, N. (2018). Assessment of yield and yield component of soybean genotypes (Glycine max L.) in north of Khuzestan. Journal of Crop Science and Biotechnology, 21, 435-441.
Journal of Plant Production Research
Volume 31, Issue 3
October 2024
Pages 127-145

Files

Share

How to cite

Statistics