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.

Key words: ‘Hamlin’ sweet orange (Citrus sinensis L. cv. Hamlin), rootstocks, oleocellosis, principal component analysis, fuzzy clustering