Huanglongbing (HLB) is one of the most important diseases for the citriculture in the world. Knowledge of climatic factors linked to HLB risk at the large spatial scale is limited. We gathered HLB presence/absence data from official surveys conducted in the state of Minas Gerais, Brazil, for 13 years. The total count of orange and mandarin orchards, and mean orchard area, normalized to a spatial grid of 60 cells (55 x 55 km), were derived from the same database. The monthly climate normal (1984 to 2013) on rainfall, mean temperature, and wind speed were split into rainy (September to April) and dry (May to August) seasons (annual summary was retained) were also obtained for each grid cell. Two hierarchical Bayesian modeling approaches were evaluated both based on the integrated nested Laplace approximation methodology. The first, the climate covariates model (CC model), used orchard, climate, and the spatial effect as covariates. The second, principal components (PC model), used the first three components from a PCA of all variables and the spatial effect as covariates. Both models showed an inverse relationship between posterior prevalence and mean temperature during dry season across the grid cells. Annual wind speed, as well as annual and rainy season rainfall, contributed significantly towards HLB risk, in the CC and PC models, respectively. A partial influence of neighboring regions on HLB risk was observed. These results should assist policymakers in defining regions at HLB risk and monitoring strategies to avoid further spread in the target region.
Quantifying and predicting the benefits of environmental flows: Combining large-scale monitoring data and expert knowledge within hierarchical Bayesian models
