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Chinese Agricultural Science Bulletin ›› 2021, Vol. 37 ›› Issue (30): 39-46.doi: 10.11924/j.issn.1000-6850.casb2021-0269

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The Adaptability Evaluation of Sugar Beet Varieties Based on Grey Relational Analysis and Principal Component Analysis

Su Xinxin1(), Xiao Yang1(), Hu Xiaohang1,2(), Ma Yahuai1,2, Li Yanli1,2   

  1. 1Acaademy of Modern Agriculture and Ecology Environment, Heilongjiang University, Harbin 150080
    2National Sugar Improvement Center, Harbin 150080
  • Received:2021-03-17 Revised:2021-05-13 Online:2021-10-25 Published:2021-12-08
  • Contact: Xiao Yang,Hu Xiaohang E-mail:s1359212463@163.com;xiaoyang@hlju.edu.cn;hxhlmz@163.com

Abstract:

The aim is to screen out the most suitable sugar beet variety with high production quality and resistance to root rot in Harbin, Heilongjiang Province. In 2020, the test was carried out at the test base of Hulan Campus of Heilongjiang University, with 21 introduced KWS series and 1 BTS2730 sugar beet (KWS1197 as control) as the test materials, 4 indexes of root yield, sugar content, sugar yield and root rot were comprehensively evaluated by principal component analysis (PCA) and grey relation analysis (GRA). The sorts of the sugar beet varieties obtained by the two methods were roughly the same. The contribution rate of the first primary component (root yield) was 69.704%. The contribution rate of the second primary component (sugar content) was 26.283%, and the accumulated contribution rate was 95.987%, which could fully reflect the production quality of sugar beet. 6 varieties suitable for local planting selected by comprehensive evaluation value were better than the control: KWS0023 (0.8231)>KWS0015(0.7685)> KWS6661(0.7511)> KWS9921(0.7103)> KWS0860(0.7097) >BTS2730(0.7065) >CK(0.6823). The comprehensive evaluation value of other varieties was lower than the control. In conclusion, the principal component analysis and gray relational analysis could analyze sugar beet varieties more comprehensively, and the results obtained are reliable.

Key words: sugar beet, variety, principal component analysis, grey relational analysis, adaptability

CLC Number: