Modelling Plant Disease Risk Areas Based on Brazilian Climate Change Scenarios  

作　　者：M. de C. Alves L.G. de Carvalho E.A. Pozza L. Sanches J.C. de S. Maia 

机构地区：[1]Department of Soil and Rural Engineering, Federal University of Mato Grosso, Brazil, Av. Fernando Correa da Costa, Boa Esperanca 2367, Cuiaba, Brazil. [2]Department of Engineering, Federal University of Lavras, Brazil, Cx. 3037. [3]Department of Phytopathology, Federal University of Lavras, Brazil, Cx. 3037. [4]Department of Sanitary and Environmental Engineering, Federal University ofMato Grosso, Av. Fernando Correa da Costa, Boa Esperanga 2367, Cuiaba, Brazil.

出　　处：《Journal of Environmental Science and Engineering》2011年第3期333-343,共11页环境科学与工程（英文版）

摘　　要：Geosciences and statistics were applied to develop predictive models to study the areas of risk to soybean rust （Phakopsora pachyrhizi Sydow） in soybean （Glycine max L.）, coffee leaf rust （Hemileia vastatrix Berk ＆ Br） in coffee and Black Sigatoka （Mycosphaerella fijiensis var. difformis） in banana, considering to Brazilian climatic characterization and distribution of soybean, coffee and banana crops in the period of observed data of 1950 to 2000 and A2 climate change scenarios of simulated data of 2020, 2050 and 2080. The technique of principal components allowed generating 1 variable based on 57 variables in order to determine an index explaining 87%, 88% and 90% of the variability of soybean, coffee and banana crops in Brazilian municipal districts. The climatic model of each disease was used to generate the zoning of the coffee rust, soybean rust and black sigatoka based on temperature and leaf wetness. Areas of favorability of the diseases were plotted inside to the main coffee, soybean and banana growing in Brazil. The applied methodology enabled to visualize the variation of favorable areas of epidemics according to the studied scenarios of climate change.

关 键 词：Plant disease geographic information systems STATISTICS AGROECOSYSTEMS Brazil. 

分 类 号：S432[农业科学—植物病理学] P467[农业科学—农业昆虫与害虫防治]