摘要
在自然条件下测定52个茶树品种叶片的光合与生理参数并对其进行数值分类以及主成分、聚类和判别分析。结果表明,叶片净光合速率(Pn)、蒸腾速率(Tr)、气孔导度(Gs)、胞间CO2浓度(Ci)及水分利用效率(WUE)的品种间差异达极显著水平(P<0.01);选出3个主成分分析,方差累计贡献率达到96.98%,Pn、Tr、Gs为第一主成分的主导因子,WUE、Ci均为第二主成分和第三主成分的主导因子;根据Pn、Tr、Gs和WUE4个指标聚类,52个茶树品种聚为5类,其中I类的15个品种为高光合速率、中等蒸腾速率、低气孔阻力、高水分利用效率类型,可用于茶树的品种改良;在聚类分析基础上用判别分析选出对茶树光合与生理性状数值分类有显著影响的4个参数,建立了5个判别能力较高的判别模型。
The importance of carbon input in tea productivity can be appreciated from the fact that 95% of the dry weight of the plant is derived from photosynthesis. Therefore, an increase in net photosynthesis and more efficient partitioning are logical steps for increasing productivity, which was subject to the tea genotypes. The photosynthetic parameters in leaves of 52 tea cultivars were determined under field conditions, and studied by using phenetic classification, principal component, cluster, discrimination analyses. The results showed that there were significant differences (P〈0.01) in net photosynthesis rate (Pn), transpiration rate (Tr) stomatal conductance (Gs), intercellular CO2 concentration (Ci), water use efficiency (WUE) among the tested cultivars. The first three principal components were analyzed and their cumulative contribution of variance accounted for 96.98%. Pn, Tr, and Gs were the main factors in the first principal component. WUE and Ci were the main factors in the second and third principal components respectively. Fifty two tea cultivars were clustered into five groups based on Pn, Tr, Gs, and WUE. The group I including 15 tea cultivars with high photosynthesis rate, medium transpiration rate, lower stomatal resistance and high water use efficiency could be used for improvement of tea cultivars. Then, with discrimination analysis, the parameters of,.Pn, Tr, Gs, and WUE greatly affecting the classification were selected, and five discrimination models with better distinguishing ability were set up.
出处
《作物学报》
CAS
CSCD
北大核心
2008年第10期1797-1804,共8页
Acta Agronomica Sinica
基金
福建省自然科学基金项目(B0510025)
福建省教育厅科技计划项目(JA05333)
关键词
茶树
光合特性
数值分类
聚类分析
Tea (Camellia sinensis)
Photosynthetic characters
Phenetic classification
Cluster analysis