摘要
【目的】通过对不同磷处理下小麦代换系多项生理指标研究,筛选和鉴定耐低磷基因型。【方法】以小麦CS-Synthetic 6x整套染色体代换系及其亲本为材料,对其在不同磷处理条件下叶片可溶性糖含量、可溶性蛋白质含量、SOD、POD活性、丙二醛含量、叶绿素、类胡萝卜素含量、光合速率、蒸腾速率,气孔导度、胞间二氧化碳浓度、初始荧光、最大荧光,原初光能转化效率(Fv/Fm)等14个生理指标进行测定,以各项指标的耐低磷系数作为耐低磷指标,通过主成分分析、聚类分析对其耐低磷特性进行综合评价。【结果】将14个单项生理指标转换成为7个相互独立的综合指标;并通过聚类分析将23个基因型划分为3类:父本Synthetic 6x与2A代换系耐低磷特性较强,母本中国春与4A、7A、2B、1D、3D代换系耐低磷特性较弱,其余15个代换系(1A、3A、5A、6A、1B、3B、4B、5B、6B、7B、2D、4D、5D、6D、7D)耐低磷特性中等。【结论】Synthetic 6x的2A染色体上具有耐低磷胁迫特性的有关基因。
【Objective】The physiological indices of substitution lines in different phosphorus treatments were studied to select species of tolerance to low phosphorus stress.【Method】 The comprehensive tolerance to low phosphorus stress of low phosphorus tolerance coefficient(LPTC) of soluble sugar,soluble protein content,SOD activity,POD activity,MDA content,leaf chlorophyll content,corticoid content,Pn,Tr,Gs,Ci,Fv/Fm of Chinese Spring(CS)-Synthetic 6x substitution lines as well as their parents were evaluated by principal components analysis and cluster analysis.【Result】 The results showed that the 14 single indexes could be classified into 7 independent comprehensive components.The cluster analysis was used to divide the 21 substitution lines and their parents into three types of tolerance to low phosphorus.2A substitution line and Synthetic 6x belonged to high tolerance types to low phosphorus stress;Chinese Spring and 4A,7A,2B,1D and 3D substitution lines belonged to poor tolerance types to low-phosphorus stress.The others including 1A,3A,5A,6A,1B,3B,4B,5B,6B,7B,2D,4D,5D,6D,and 7D substitution lines belonged to middle tolerance types to low-phosphorus stress.【Conclusion】 The comprehensive evaluation and principal component analysis provide a useful way for selecting species of tolerance to low phosphorus stress in wheat.
出处
《中国农业科学》
CAS
CSCD
北大核心
2013年第10期1984-1993,共10页
Scientia Agricultura Sinica
基金
科技部“973”计划前期研究专项(2007CB116209)
河北省自然基金项目(C2008000341,C2011204016)
关键词
小麦代换系
耐低磷特性
生理性状
综合评价
主成分分析
wheat substitution line
tolerant character to low phosphorus stress
physiological indices
comprehensive evaluation
principal component analysis