Genetic variability information on any crop germplasm is imperative for conservation and effective utilization in the breeding program. The field experiment was conducted to estimate the extent of genetic variability of 64 soybean genotypes for grain yield and other agronomic traits at Jimma and Metu in 2017 and 2018 main cropping seasons. The trial was laid down using 8×8 simple lattice design. The data was subjected to statistical analysis using R-software. The combined analysis of variance revealed the presence of significant (P<0.01) and wide range of variation among the tested genotypes for all of the traits. Based on the mean performance, latest maturing genotype was PI567104B (145 days), while the earliest was PI615437 (105 days). Genotype, PI567104B was the tallest in plant height (149.01 cm) while the shortest was PI507004 (44.24cm). Genotype PI567090 was found moderately susceptible in soybean rust (25.52%), while genotypes PI594538A was found the most tolerant (3.78%). Maximum hundred seed weight (24.01gm) was found from genotype PI506677, whereas the minimum seed weight (8.32gm) was recorded from genotype PI567068A. Coker240 scored maximum grain yield (3.09 t/ha) followed by genotype PI567104B (3.00 t/ha), while the minimum yield was scored from PI416826A (1.33tha-1). Cluster analysis categorized 64 soybean genotypes into five clusters. The maximum inter cluster distance was found between clusters-I and V, suggesting best recombinants can be found by crossing genotypes in these clusters. Principal component analysis (PCA) revealed that, the 1st four PCA with Eigen values exceeding one were responsible for about 88.74% of the total variation. Out of the entire variations, 1stPCA and the 2ndPCA accounted for more than two third of the total variations (68.47%). Generally, the present study indicated the existence of enormous genetic variability among soybean genotypes for various important morphological traits. Therefore, information and genetic variability obtained in this finding could be used to plan conservation, effective crossing and line selection in soybean variety improvement programs.
| Published in | American Journal of Bioscience and Bioengineering (Volume 11, Issue 2) |
| DOI | 10.11648/j.bio.20231102.12 |
| Page(s) | 20-26 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2023. Published by Science Publishing Group |
Soybean, Cluster, D2, Eigen Value, PCA
| [1] | Singh, P., R., Kumar, S. N., Sabapathy, and Bawa, A. S., 2008. Functional and edible uses of soyprotein products. Reviews in Food Science and Food Safety, 7: 14–28. |
| [2] | Fekadu Gurmu, Hussein Mohammed and GetnetAlemaw, 2009. Genotype X environment interactions and stability of soybean for grain yield and nutrition quality. African Crop Science Journal 17: 87-99. |
| [3] | Clarke E. J. and Wiseman, J., 2000. Developments in plant breeding for improved nutritional quality of soybeans. Protein and amino acid content. Journal of Agricultural Science, 134: 111- 124. |
| [4] | Stevanović, P., V. Popović, V. Filipović, D. Terzić, V. Rajičić, D. Simić, M, Tatić, M. Tabaković, 2017. Proceedings of the Institute of PKB Agroeconomics. Counseling of agronomists, veterinarians, technologists and agroeconomists 23: 119-127. |
| [5] | Graham, P. H. and Vance, C. P., 2003. Legumes: importance and constraints to greater use. Plant Physiology, 131: 872–877. |
| [6] | CSA (Central Statistical Authority of Ethiopia), 2021. Area and production of major crops, Central Statistics Agency (CSA), Addis Ababa, Ethiopia. |
| [7] | Murty, B. R. and Arunachalam, V. 1966. The nature of genetic divergence in relation to breeding system in crop plants. Indian J. Genet, 26A: 188-198. |
| [8] | Das, P. K. and Gupta, T. D., 1984. Multivariate analysis in Blackgram. Indian Journal of Genetics. 44: 243-247. |
| [9] | Joshi, A. B. and Dhawan, N. L. 1966. Genetic improvement of yield with special reference to self-fertilizing crops. Ind. J. Genet. Pl. Br., 26A: 101-113. |
| [10] | Tadesse G. and Sentayehu A., 2015. Genetic Divergence Analysis on Some Soybean (Glycine max L. Merrill) Genotypes Grown in Pawe, Ethiopia. Am-Euras. J. Agric. And Environ. Sci., 15: 1927-1933. |
| [11] | Abush Tesfaye, M. Githiri, J. Derera and TolessaDebele., 2017. Genetic Variability in Soybean (Glycine max L.) for Low Soil Phosphorus Tolerance. Ethiopian Journal of Agricultural Sciences, 27 (2), pp. 1-15. |
| [12] | Yechalew S., Andargachew G., Abush T., and Hailemariam, M., 2019. Contribution of Morphological Traits to the Total Variability in Soybean (Glycine max (L.) Merr.) Genotypes in Western Parts of Ethiopia. Acad. Res. J. Agri. Sci. Res. 7 (7): 408-413. |
| [13] | Masreshaw Yirga, Yechalew Sileshi, Abush Tesfaye, and Mesfin Hailemariam, 2022. Genetic Variability and Association of Traits in Soybean (Glycine max (L.) Genotypes in Ethiopia. Ethiop. J. Crop Sci. Vol 9 No. 2. |
| [14] | Paulos Dubale, 2001. Soil and water resources and degradation factors affecting their productivity in the Ethiopian highland agro - ecosystems. Michigan State University Press, 8 (1): 1-18p. |
| [15] | Copper, M. C. and Milligan, G. W. 1988. The effect of error on determining the clusters. Proceedings on the International workshop on Data Analysis, Decision support and Expert Knowedge Representation in Marketing and Relayted Areas of Research, June 21–23, 1987, University of Karlsruhe, WestGeremany. Pp. 319-328. |
| [16] | Sharma, J. R. 1998. Statistical and biometrical techniques in plant breeding. New Age Interna-tional (P) limited publishers. New Delhi. 432p. |
| [17] | Mahalanobis, P. C. 1936. On the generalized distance in statistics. National institute of science., 2: 49-55. |
| [18] | Singh, RK. and BD. Chaudhary, 1987. Biometrical methods in quantitative genetic analysis. Kalyani publishers, New Delhi-Ludhiana, India. |
| [19] | Hair, M. L., 1995. Colloids and Surfaces A: Physicochemical and …, 1995 – Elsevier. Colloids and Surfaces A: Physicochemical and Engineering Aspects. Elsevier. Volume 105, Issue 1, 1 December 1995, Pages 95-103. |
| [20] | Jagadev, P. N., Samal, K. M. and Lenka, D., 1991. Genetic divergence in rape mustard. The Indian Journal of Genetics and Plant Breeding, 51: 465-467. |
| [21] | Chahal, G. S. and Gosal, S. S., 2002. Principles and Procedures of Plant Breeding: Biotechnology and Conventional Approaches. Alpha Science International, United Kingdom, ISBN: 9781842650363: 604. |
APA Style
Masreshaw Yirga, Afework Legesse. (2023). Multivariate Analysis Among Soybean (Glycine max L.) Genotypes in Southwest Ethiopia. American Journal of Bioscience and Bioengineering, 11(2), 20-26. https://doi.org/10.11648/j.bio.20231102.12
ACS Style
Masreshaw Yirga; Afework Legesse. Multivariate Analysis Among Soybean (Glycine max L.) Genotypes in Southwest Ethiopia. Am. J. BioSci. Bioeng. 2023, 11(2), 20-26. doi: 10.11648/j.bio.20231102.12
AMA Style
Masreshaw Yirga, Afework Legesse. Multivariate Analysis Among Soybean (Glycine max L.) Genotypes in Southwest Ethiopia. Am J BioSci Bioeng. 2023;11(2):20-26. doi: 10.11648/j.bio.20231102.12
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Download@article{10.11648/j.bio.20231102.12,
author = {Masreshaw Yirga and Afework Legesse},
title = {Multivariate Analysis Among Soybean (Glycine max L.) Genotypes in Southwest Ethiopia},
journal = {American Journal of Bioscience and Bioengineering},
volume = {11},
number = {2},
pages = {20-26},
doi = {10.11648/j.bio.20231102.12},
url = {https://doi.org/10.11648/j.bio.20231102.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.bio.20231102.12},
abstract = {Genetic variability information on any crop germplasm is imperative for conservation and effective utilization in the breeding program. The field experiment was conducted to estimate the extent of genetic variability of 64 soybean genotypes for grain yield and other agronomic traits at Jimma and Metu in 2017 and 2018 main cropping seasons. The trial was laid down using 8×8 simple lattice design. The data was subjected to statistical analysis using R-software. The combined analysis of variance revealed the presence of significant (P-1). Cluster analysis categorized 64 soybean genotypes into five clusters. The maximum inter cluster distance was found between clusters-I and V, suggesting best recombinants can be found by crossing genotypes in these clusters. Principal component analysis (PCA) revealed that, the 1st four PCA with Eigen values exceeding one were responsible for about 88.74% of the total variation. Out of the entire variations, 1stPCA and the 2ndPCA accounted for more than two third of the total variations (68.47%). Generally, the present study indicated the existence of enormous genetic variability among soybean genotypes for various important morphological traits. Therefore, information and genetic variability obtained in this finding could be used to plan conservation, effective crossing and line selection in soybean variety improvement programs.},
year = {2023}
}
TY - JOUR T1 - Multivariate Analysis Among Soybean (Glycine max L.) Genotypes in Southwest Ethiopia AU - Masreshaw Yirga AU - Afework Legesse Y1 - 2023/08/22 PY - 2023 N1 - https://doi.org/10.11648/j.bio.20231102.12 DO - 10.11648/j.bio.20231102.12 T2 - American Journal of Bioscience and Bioengineering JF - American Journal of Bioscience and Bioengineering JO - American Journal of Bioscience and Bioengineering SP - 20 EP - 26 PB - Science Publishing Group SN - 2328-5893 UR - https://doi.org/10.11648/j.bio.20231102.12 AB - Genetic variability information on any crop germplasm is imperative for conservation and effective utilization in the breeding program. The field experiment was conducted to estimate the extent of genetic variability of 64 soybean genotypes for grain yield and other agronomic traits at Jimma and Metu in 2017 and 2018 main cropping seasons. The trial was laid down using 8×8 simple lattice design. The data was subjected to statistical analysis using R-software. The combined analysis of variance revealed the presence of significant (P-1). Cluster analysis categorized 64 soybean genotypes into five clusters. The maximum inter cluster distance was found between clusters-I and V, suggesting best recombinants can be found by crossing genotypes in these clusters. Principal component analysis (PCA) revealed that, the 1st four PCA with Eigen values exceeding one were responsible for about 88.74% of the total variation. Out of the entire variations, 1stPCA and the 2ndPCA accounted for more than two third of the total variations (68.47%). Generally, the present study indicated the existence of enormous genetic variability among soybean genotypes for various important morphological traits. Therefore, information and genetic variability obtained in this finding could be used to plan conservation, effective crossing and line selection in soybean variety improvement programs. VL - 11 IS - 2 ER -
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