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哈姆林甜橙果实油斑病砧穗特异性调控农艺因子筛选
引用本文:郑永强,邓烈,何绍兰,周志钦,易时来,赵旭阳,王亮. 哈姆林甜橙果实油斑病砧穗特异性调控农艺因子筛选[J]. 中国农业科学, 2010, 43(23): 4877-4885. DOI: 10.3864/j.issn.0578-1752.2010.23.013
作者姓名:郑永强  邓烈  何绍兰  周志钦  易时来  赵旭阳  王亮
作者单位:(西南大学-中国农业科学院柑橘研究所国家柑橘工程技术研究中心) ;
基金项目:国家自然科学基金项目(31000893); 重庆市自然科学基金项目(CSTC,2010BB1151); 中央高校科研基本业务费专项(XDJK2009B025); 农业部公益性行业科研专项(nyhyzx07-23); 科技部科技人员服务企业行动专项(2009GJF10004)
摘    要:【目的】探讨哈姆林果实油斑病砧穗特异性调控的主要农艺相关因子,为提出果实油斑病综合防治措施,有效降低柑橘果实油斑病发生提供理论基础。【方法】利用主成分分析(principal component analysis,PCA)筛选对哈姆林果实油斑病发生率及发生程度有显著影响的主要农艺因子,并用模糊系统聚类分析(fuzzy cluster analysis,FC)对哈姆林甜橙砧穗组合植株果实油斑病抗性进行分类鉴定及成因分析。【结果】主成分分析结果表明,砧木通过调控果实成熟度调控因子和油斑病敏感度因子(穗砧比和叶片Ca含量)调控油斑病发生率,通过调控繁茂决定因子(植物学性状)、油斑病程度营养调控因子(叶片N、Mg和Cu含量)调控油斑病发生程度,而且油斑病植株营养生理决定因子(叶片P、Zn和S含量)参与油斑病发生率和发生程度的调控。利用果实油斑病各因子度量值进行模糊聚类,可将11种砧木的哈姆林甜橙植株果实油斑病抗性划分为4类,其中以兰普莱檬、玻美、路比和沃尔卡姆为砧木的哈姆林甜橙植株综合指标值最低,果实油斑病抗性最强,但抗性获得的成因不同。【结论】利用主成分分析结合模糊聚类的方法较好地揭示哈姆林甜橙果实油斑病发生率调控因子(果实成熟度和油斑病敏感度)和发生程度调控因子(繁茂决定因子和油斑病营养调控因子),有利于进一步揭示柑橘果实油斑病抗性的调控机制,为柑橘果实生长期间油斑病综合防治提供理论依据。

关 键 词:哈姆林甜橙  砧木  油斑病  主成分分析  模糊聚类
收稿时间:2010-07-02;

Screening for the Agronomic Traits Regulating Fruit Oleocellosis with the Specificity Between Rootstocks and Scions of 'Hamlin' Sweet Orange
ZHENG Yong-qiang,DENG Lie,HE Shao-lan,ZHOU Zhi-qin,YI Shi-lai,ZHAO Xu-yang,WANG Liang. Screening for the Agronomic Traits Regulating Fruit Oleocellosis with the Specificity Between Rootstocks and Scions of 'Hamlin' Sweet Orange[J]. Scientia Agricultura Sinica, 2010, 43(23): 4877-4885. DOI: 10.3864/j.issn.0578-1752.2010.23.013
Authors:ZHENG Yong-qiang  DENG Lie  HE Shao-lan  ZHOU Zhi-qin  YI Shi-lai  ZHAO Xu-yang  WANG Liang
Affiliation:ZHENG Yong-qiang 1,2,DENG Lie 1,HE Shao-lan 1,ZHOU Zhi-qin2,YI Shi-lai 1,ZHAO Xu-yang 2,WANG Liang 2(1National Engineering Research Center for Citrus Technology,Citrus Research Institute,Southwest University-Chinese Academy of Agricultural Sciences,Chongqing 400712,2College of Horticulture and Landscape Architecture,Southwest University/Key Laboratory of Horticulture Science for Southern Mountainous Regions,Ministry of Education,Chongqing 400715)
Abstract:【Objective】 The main purpose of this paper was to screen agronomic traits of the specificity to fruit oleocellosis-tolerance between rootstocks and scions of ‘Hamlin’ sweet orange (Citrus sinensis L. cv. Hamlin), and to discuss the mechanism of oleocellosis-tolerance. 【Method】 Principal component analysis (PCA) and fuzzy clustering (FC) were used to evaluate the fruit oleocellosis-tolerance of ‘Hamlin’ sweet orange on 11 rootstocks based on their oleocellosis performance under the field conditions. 【Result】 The result of PCA indicated that the rates of oleocellosis (RO) were regulated by fruit maturity factor and oleocellsis sensitivity factor (S/R ratio and leaf Ca content), and the degree of oleocellosis (DO) were regulated by the flourishing decision factors (botanical characters) and the nutritional factors controlling DO (leaf N, Mg and Cu content). Whilst, plant nutrient physiology factors (leaf P, Zn and S content) participate in the RO and DO controlling. The ‘Hamlin’ sweet orange on 11 rootstocks used in this study could be divided into 4 clusters by FC based on their Oleocellosis Value of each PC (Fi). Among those ‘Hamlin’ sweet orange on 11 rootstocks, the oleocellosis tolerance of ‘Hamlin’ sweet orange on Rangpur Lime (Citrus limonia Osbeck, RL), Pomeroy trifoliate [P. trifoliate (L.), PM], Rubidoux trifoliate [P. trifoliate (L.), RB] and Volkamer lemon (C. volkameriana Pasqual, VL) were better, however, the causes of oleocellosis-tolerance were different; 【Conclusion】 It is an effective way by PCA and FC to screen for the agronomic traits regulating RO (fruit maturity factor and deocellsis sensitivity foctor) and DO (the flourishing decision factors and plant mutritional factors) and comprehensively evaluate the oleocellosis tolerance mechanism of Hamlin’ sweet orange by PCA and FC,which help to provide a theoretical basis for comprehensive control of citrus fruit oleocellosis.
Keywords:'Hamlin' sweet orange(Citrus sinensis L.cv.Hamlin)  rootstocks  oleocellosis  principal component analysis  fuzzy clustering  
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