title: Use of a climatic rule and fuzzy sets to model geographic distribution of climatic risk for European canker (Neonectria galligena) of apple.
abstract: A rule-based model was developed to assess climatic risk of European canker (Neonectria galligena), which is a major disease of apple in some temperate zones. A descriptive rule was derived from published observations on climatic conditions favorable for European canker development. Fuzzy set theory was used to evaluate the descriptive rule quantitatively. The amount and frequency of rainfall and the average number of hours between 11 and 16°C/day were used as input variables whose values were matched with terms in the rule, e.g., 'high' or 'low'. The degree of a term, e.g., the state of being high or low, to a given input value was determined using a membership function that converts an input value to a number between 0 and 1. The rule was evaluated by combining the degree of the terms associated with monthly climate data. Monthly risk index values derived using the rule were combined for pairs of consecutive months over 12 months. The annual risk of European canker development was represented by the maximum risk index value for 2 months combined. The membership function parameters were adjusted iteratively to achieve a specified level of risk at Talca (Chile), Loughgall (Northern Ireland), East Malling (UK), and Sebastopol (USA), where European canker risk was known. The rule-based model was validated with data collected from Canada, Ecuador, Denmark, Germany, Norway, Poland, Sweden, the Netherlands, New Zealand, and the Pacific Northwest (USA), where European canker has been reported to occur. In these validation areas, the model's risk prediction agreed with reports of disease occurrence. The rule-based model also predicted high risk areas more reliably than the climate matching model, CLIMEX, which relies on correlations between the spatial distribution of a species and climatic conditions. The combination of a climatic rule and fuzzy sets could be used for other applications where prediction of the geographic distribution of organisms is required for climatic risk assessment.
authors.affiliation: Department of Plant Science and Research Institute for Agriculture and Life Sciences, Seoul National University, Seoul, Korea. luxkwang@snu.ac.kr
authors.forename: Kwang Soo
authors.initials: KS
authors.lastname: Kim
authors.forename: Robert M
authors.initials: RM
authors.lastname: Beresford
mesh_terms: D002138:Calibration; D002170:Canada; D002677:Chile; D002980:Climate; D004484:Ecuador; D005060:Europe; D017143:Fuzzy Logic; D006999:Hypocreales / Q000502:physiology*; D027845:Malus / Q000382:microbiology*; D008954:Models, Biological; D009520:New Zealand; D010935:Plant Diseases / Q000382:microbiology*; D011891:Rain; D012306:Risk; D013648:Tasmania; D013997:Time Factors; D014481:United States