Welcome to Chinese Agricultural Science Bulletin,

Chinese Agricultural Science Bulletin ›› 2021, Vol. 37 ›› Issue (28): 9-13.doi: 10.11924/j.issn.1000-6850.casb2020-0808

Special Issue: 油料作物

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Principal Component Analysis of Main Agronomic Traits in Brassica napus Population

Shang Liping(), Zhao Weiguo, Guo Kaihong, Zhang Lijian, Luo Bin, Zhao Yajun, Wang Hao()   

  1. Hybrid Rapeseed Research Center of Shaanxi Province, Yangling Shaanxi 712100
  • Received:2020-12-18 Revised:2021-05-06 Online:2021-10-05 Published:2021-10-28
  • Contact: Wang Hao E-mail:shang_liping@163.com;wangzy846@sohu.com

Abstract:

To study the relationship among agronomic traits of Brassica napus and improve the breeding efficiency of new varieties, the correlation analysis and principal component analysis of agronomic traits were carried out for 229 DH materials, those traits included plant height, height of branch, number of first effective branches, height of main inflorescence, number of main inflorescence pods, economic yield per plant and biological yield per plant. The results showed that: plant height, height of branch, height of main inflorescence, economic yield per plant and biological yield per plant were partial to male parent, but the number of first effective branches and the number of main inflorescence pods were partial to female parent. The correlation analysis indicated that plant height, height of branch, number of main inflorescence pods, economic yield per plant and biological yield per plant were significantly and positively correlated, and the number of first effective branches was negatively correlated with plant height and branch height. Through the principal component analysis, the rapeseed quality traits could be integrated into plant height, number of first effective branches, and number of main inflorescence pods and branch height, with the total cumulative contribution rate of 90.0%, which basically covered the full information of the agronomic traits of Brassica napus.

Key words: Brassica napus, agronomic traits, correlation analysis, principal component analysis, rapeseed breeding

CLC Number: