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Acta Prataculturae Sinica ›› 2014, Vol. 23 ›› Issue (4): 79-86.DOI: 10.11686/cyxb20140409

• Orginal Article • Previous Articles     Next Articles

Analysis of variation in physio-biochemical characteristics and cold resistance in winter rapeseed F2 populations

KONG De-jing1,WANG Yue1,SUN Wan-cang1,ZENG Xiu-cun1,2,FANG Yan3,LU Mei-hong1,YANG Ning-ning1   

  1. 1.Gansu Provincial Key Laboratory of Arid Land Crop Sciences,Lanzhou 730070,China;
    2.Hexi University,Zhangye 734000,China;
    3.Research and Testing Center of Gansu Agricultural University,Lanzhou 730070,China
  • Received:2013-07-05 Online:2014-08-20 Published:2014-08-20

Abstract: Overwintering rates,seedling habits,SOD activity,and five other physio-biochemical indicators in 103 F2 populations of a winter rapeseed (Brassica rapa) cross between Longyou 7 and Longyou 9 were studied using variance analysis,correlation analysis,cluster analysis and path analysis. The frequency of all indicators (except SOD) activity in the F2 populations was approximately normally distributed but with a slight skew. A tremendous transgressive segregation for all indicators was observed in the populations. At a cluster distance of 3.58,F2 populations were divided into eight groups,with great genetic differences. Correlation analysis showed that all indicators had significant differences in overwintering rates. The path coefficient and correlation coefficient between all indicators and overwintering rate were consistent,but the explanation of their relationships were better explained by the path coefficient. Through path analysis,the relative importance in descending order of B. rapa winter survival rate was: Seedling habits,SOD activity,MDA content,free proline content,POD activity,CAT activity,and soluble protein content. Seedling habits directly impacted winter survival rates,and the influence reached 79.8%,but CAT activity was mainly through indirect effects and the indirect affect rate was 68.7%,with the rest of the indexes co-acting through both direct and indirect effects. These results provide useful information for quantitative trait loci (QTL) mapping.

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