Fuzzy modeling of the risk of cacao moniliasis occurrence in Bahia state, Brazil

ABSTRACT This work aimed to determine potential areas for the establishment of cocoa moniliasis in Bahia state, Brazil, by means of fuzzy logic, based on historical datasets of temperature and air relative humidity, available for 519 measurement points distributed throughout the state of Bahia. The data were initially submitted to a descriptive statistical analysis. The spatial variability was determined through geostatistical analysis, followed by interpolation to map the spatial-temporal structure dependence of the phenomenon. Simulations of continuous pixel-to-pixel classification of variables were performed using fuzzy mapping to model the climatic risk of disease establishment. The exponential fuzzy model was applied to temperature data, while the linear model was used for air relative humidity data. The potential areas were defined for each month, using data of temperature and air relative humidity. The fuzzy models used allowed for modeling of the climatic risk of cocoa moniliasis establishment. A large area of the state is at high risk of disease, thus requiring mitigating measures to avoid the pathogen’s introduction and dissemination.

摘要 本研究旨在借助模糊逻辑方法，依托巴西巴伊亚州境内519个全域分布监测点的气温与空气相对湿度历史数据集，明确该州可可念珠菌病（cocoa moniliasis）的潜在发生区域。研究初始对数据集开展描述性统计分析，通过地统计分析确定空间变异性，随后进行插值以刻画该现象的时空结构依赖性。采用模糊映射对变量开展逐像元连续分类模拟，以构建病害发生的气候风险模型：气温数据采用指数模糊模型，空气相对湿度数据则采用线性模型。结合气温与空气相对湿度数据，逐月划定潜在发生区域。所采用的模糊模型可有效构建可可念珠菌病发生的气候风险模型。该州大片区域面临较高的病害发生风险，因此需采取防控措施以规避病原菌的传入与扩散。