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INVESTIGATOR: Terejanu, G.

PERFORMING INSTITUTION:
UNIVERSITY OF SOUTH CAROLINA
COLUMBIA, SOUTH CAROLINA 29208

TOXIMAP: COMPUTATIONAL FRAMEWORK FOR PREDICTION OF GEOGRAPHICAL AND TEMPORAL INCIDENCE OF MYCOTOXINS IN US CROP FIELDS

NON-TECHNICAL SUMMARY: Aflatoxin is a carcinogenic toxin produced by Aspergillus-family fungi occurring in soil and decaying vegetation, which can contaminate corn along with other relevant US crops like peanuts, treenuts and cotton before harvest and/or during storage. Aflatoxin causes liver cancer in humans and in a variety of animal species, and it has been associated with childhood stunting; an important public health issue that increases vulnerability to infectious diseases and cognitive impairments. It is estimated that 5 billion people worldwide are at risk of aflatoxin exposure. Prediction and control of aflatoxin contamination is a fundamental challenge for US grain industry, poultry producers, and makers of dairy products. The production of aflatoxin is highly dependent on environmental conditions and it is projected that environmental perturbations due to climate change will result in a significant increase in aflatoxin contamination incidents, further aggravating its economic impact. The lack of a systematic approach to determine the distribution of aflatoxin occurrence before harvest adversely affects the grain industry.The goal of this proposal is to develop a general predictive modeling framework for calculating aflatoxin occurrence in US crop fields before harvest, and package this knowledge in a user-friendly predictive web/mobile interface for generating nation-wide and real-time aflatoxin hazard maps. This has the potential to change certain behaviors in crop management to improve food safety (lower levels of aflatoxin) such as providing valuable information of when and where to test for aflatoxin before harvest, reducing the amount of chemicals used to grow the crops, determining the best time to harvest, as well as indicating the cornfield areas with the highest contamination levels for grain segregation. The mathematical foundation and the proposed software tool are generally applicable to any type of mycotoxin and crop. While hazard maps for aflatoxin and other mycotoxin occurrence are important to crop producers, they are also of interest to consumers, regulators, and insurers to enhance their decision-making process. Furthermore, the presence of mycotoxins in agricultural run-offs is a critical issue in environmental sustainability and ecological physiology of various land and aquatic ecosystems. Hence, the approach to be developed will be of relevance to a broader community beyond the agricultural community.

OBJECTIVES: The major goal of this proposal is to develop a general predictive modeling framework for calculating mycotoxin occurrence in US crop fields before harvest. This mathematical framework will be accompanied by a web/mobile interface to explore temporal and geographical mycotoxin hazard maps for the entire United States.Objective #1. Build an inventory of favorable environmental conditions for aflatoxin production in corn using extensive field measurements. The PIs have already acquired a network of meteorological stations (soil moisture and temperature, atmospheric temperature, relative humidity, wind speed and direction) using a recent USDA/NRCS data collection grant. The sensors will be used to instrument a cornfield in partnership with a local farmer. In this project the PIs propose to leverage this existing infrastructure and supplement the data collection obtained from the sensor network with a plethora of remote sensing data at various resolutions and frequencies obtained using Google Earth Engine (imagery, geophysical, climate and weather). In addition, weekly corn samples will be collected to measure the levels of aflatoxin concentration during various developmental stages of corn. This data will be used to develop advanced aflatoxin predictive models with quantified uncertainties.Objective #2. Develop novel probabilistic data-driven models for general mycotoxin prediction with quantified uncertainties. A novel approach is proposed to obtain analytical expectations of mycotoxin concentrations as a function of environmental factors. These external variables (temperature, humidity etc.) are determined at a location of interest using spatial interpolations, which is subject to interpolation errors. The proposed approach accounts for both model uncertainties and interpolation errors in generating predictions for mycotoxin concentrations. Separate Gaussian processes are trained using data collected in Objective #1 for both physical models and forcing models, and then they are stacked to obtain prediction of mycotoxin production. Analytical expressions will be derived for first and second-order moments of the proposed stacked Gaussian process. While in this project only the aflatoxin will be modeled, the proposed nonparametric models can be generally applied across the mycotoxin spectrum and in other environmental science projects.Objective #3. Web/mobile interface to explore temporal and geographical aflatoxin hazard maps for the entire Continental United States (CONUS). Predictions generated in Objective #2 will be made broadly available to the community via a dedicated web/mobile interface that will be built and hosted as part of this project. The PIs propose to leverage the data collected in Objective #1 and computational framework derived in Objective #2 to provide both a realtime monitoring aflatoxin tool for CONUS and a forecasting tool to study the impact of climate change on aflatoxin production. The proposed software platform will be based on a plug-and-play framework, where new mycotoxin models, crops, geographical regions, and environmental data can be easily integrated.