Bayesian Hierarchical Models to Study a Spatial Distribution about Broca Infestation of Local Coffee Plantations

Ramiro Ruíz, Clarice Demetrio, Renato Assuncao & Roseli Leandro

 

Abstract                  

Studying the spatial distribution of agricultural pests can provide important information about the species dispersion mechanisms and its interaction with environmental factors. It also helps the development of sampling plans, the integrated pest management and planning of experiments. This work compared several models for studying the spatial variation of the coffee berry borer infestation in order to produce risk maps and identify areas of low/high levels of infestation. Firstly spatial analysis was carried out using different combinations of random effects representing spatially structured and unstructured variability. Also different neighborhood schemes were used to represent the spatial correlation of the data. Mixture models allowing for the excess of zeros in the first months were also considered. The model fitting was done using MCMC methods. The results are presented as a sequence of risk maps.

 

Key words: Markov chain Monte Carlo, Risk maps, Mixture models, Zero inflated models.

 

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