A stacked gaussian process for predicting geographical incidence of aflatoxin with quantified uncertainties

The objective of this paper is to develop a methodology for generating probabilistic risk maps for unobserved quantities of interests such as aflatoxin. Aflatoxin is a naturally occurring carcinogenic and it is a serious global issue and an emerging risk for crop producers. The production of aflatoxin is highly dependent on environmental conditions such temperature and water activity, and it can contaminate grains before harvest or during storage. The focus of this paper is to develop a procedure to account for spatial dependencies and uncertainties in risk calculations, to provide various stakeholders with situational awareness to better understand, communicate, and mitigate the aflatoxin risk before harvest. The proposed probabilistic model is obtained in two stages: the production of aflatoxin with quantified uncertainties is modeled under various temperature and water activity conditions within a controlled environment (wet-lab), and then the predictive aflatoxin model is linked with environmental conditions obtained on a regular basis to generate regional probabilistic risk maps. Since both aflatoxin production and environmental data are modeled using Gaussian processes, the resulted probabilistic model is a stacked Gaussian process, where the environmental Gaussian process model governs the input space of the aflatoxin Gaussian process model. The regional prediction of aflatoxin is obtained by marginalizing over the latent space provided by the environmental variables. The methodology is applied to calculate the aflatoxin levels of corn lands in South Carolina in the drought year 2012, where few field measurements are available for an initial comparison with our aflatoxin predictions.

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