Modelagem espaço-temporal para dados de incidência de doenças em plantas. / Spatiotemporal modelling of plant disease incidence.

AUTOR(ES)
DATA DE PUBLICAÇÃO

2005

RESUMO

The information about the spatial-temporal dynamics is of fundamental importance in epidemiological studies for describing and understanding the development of diseases, for developing efficient sampling plans, for planning controlled experiments, for evaluating the effect of different treatments, and for determining crop losses. The Citriculture is the major economic activity of more than 400 municipalities in Minas Gerais and São Paulo States. This is the largest citrus area in Brazil, and the largest sweet orange production area in the world. Therefore, it is very important to study and to characterize spatial patterns of plant diseases, such as citrus canker and citrus sudden death. In the spatial dynamics study, many different methods have been used to characterize the spatial aggregation. These include the fitting of distributions, such as the beta-binomial distribution, the study of variance-mean relationships, the calculation of intraclass correlation, the use of spatial autocorrelation techniques, distance class methods and, the fitting of continuous time spatiotemporal stochastic models. In this work, an improved technique for fitting models to the spatial incidence data by using MCMC methods is proposed. This improved technique, which is used to investigate the spatial patterns of plant disease incidence, is considerably faster than Gibson’s methodology, in terms of computational time, without any loss of accuracy.

ASSUNTO(S)

doença de planta métodos mcmc citrus cancker análise espacial citrus controle fitossanitário cancro-cítrico processo estocástico plant disease control mathematics modelling plant disease spatial analysis citricultura stochastic process mcmc methods modelagem matemática

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