Principal Component Analysis and Cluster Analysis of 39 Spring Soybean Germplasm Resources

LIN Wen-lei; LÜ Mei-qin; LI Ming-song; KANG Rong-rong; ZENG Hong-ying; YAO Wen; CAI Jin-ling

doi:10.19303/j.issn.1008-0384.2018.10.002

Volume 33 Issue 10

Mar. 2019

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Article Navigation > Fujian Journal of Agricultural Sciences > 2018 > 33(10): 1016-1022

LIN Wen-lei, LÜ Mei-qin, LI Ming-song, KANG Rong-rong, ZENG Hong-ying, YAO Wen, CAI Jin-ling. Principal Component Analysis and Cluster Analysis of 39 Spring Soybean Germplasm Resources[J]. Fujian Journal of Agricultural Sciences, 2018, 33(10): 1016-1022. doi: 10.19303/j.issn.1008-0384.2018.10.002

Citation:

LIN Wen-lei, LÜ Mei-qin, LI Ming-song, KANG Rong-rong, ZENG Hong-ying, YAO Wen, CAI Jin-ling. Principal Component Analysis and Cluster Analysis of 39 Spring Soybean Germplasm Resources[J]. Fujian Journal of Agricultural Sciences, 2018, 33(10): 1016-1022. doi: 10.19303/j.issn.1008-0384.2018.10.002

Citation:

LIN Wen-lei, LÜ Mei-qin, LI Ming-song, KANG Rong-rong, ZENG Hong-ying, YAO Wen, CAI Jin-ling. Principal Component Analysis and Cluster Analysis of 39 Spring Soybean Germplasm Resources[J]. Fujian Journal of Agricultural Sciences, 2018, 33(10): 1016-1022. doi: 10.19303/j.issn.1008-0384.2018.10.002

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Principal Component Analysis and Cluster Analysis of 39 Spring Soybean Germplasm Resources

doi: 10.19303/j.issn.1008-0384.2018.10.002

Quanzhou Institute of Agricultural Sciences, Quanzhou, Fujian 362212, China

Received Date: 2018-04-12
Rev Recd Date: 2018-09-10
Publish Date: 2018-10-28

Abstract

Abstract

7 major agronomic traits of 39 soybean germplasm resources were analyzed with the method of principal component analysis(PCA), and two principal component factors were obtained. The first principal component(MF₁) was positively correlated with plant height, bottom pod height and the number of main stem segments. The second principal component(MF₂) was positively correlated with the number of grains per plant and the grain weight per plant, and negatively correlated with the number of 100-grain weight and effective branches. The cluster analysis were studied through the integrated principal component values and the agronomic trait normalization data. 39 germplasms were divided into 3 major categories and 2 sub-categories through the former method, and divided into 3 major categories and 4 sub-categories through the latter method. By comparison, the analysis results of the two clustering methods were generally consistent.
- spring soybean,
- principal component analysis,
- cluster analysis

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References(16)

References

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