Prediction of aflatoxin contamination outbreaks in Texas corn using mechanistic and machine learning models

Lina Castano-Duque ^1* Angela Avila ² Brian Mack ¹ Hans Edwin Winzeler ² Joshua M Blackstock ³ Matthew Lebar ¹ Geromy G Moore ¹ Phillip Ray Owens ³ Hillary L Mehl ⁴ Jianzhong Su ¹ James Lindsay ⁵ Kanniah Rajasekaran ¹

• ¹ USDA-ARS, Southern Regional Research Center, New Orleans, United States
• ² University of Texas at Arlington, Arlington, Virginia, United States
• ³ Dale Bumpers Small Farms Research Center, Agricultural Research Service (USDA), Booneville, Arizona, United States
• ⁴ U.S. Arid Land Agricultural Research Center, Agricultural Research Service (USDA), Maricopa, Arizona, United States
• ⁵ USDA-ARS Office of National Programs, Beltsville, United States

Aflatoxins are carcinogenic and mutagenic mycotoxins that contaminate food and feed. The objective of our research is to predict aflatoxin outbreaks in Texas-grown maize using dynamic geospatial data from remote sensing satellites, soil properties data, and meteorological data by an ensemble of models. We developed three model pipelines: two included mechanistic models that use weekly aflatoxin risk indexes (ARIs) as inputs, and one included a weather-centric model; all three models incorporated soil properties as inputs. For the mechanistic-dependent models, ARIs were weighted based on a maize phenological model that used satellite-acquired normalized difference vegetation index (NDVI) data to predict maize planting dates for each growing season on a county basis. For aflatoxin outbreak predictions, we trained, tested and validated gradient boosting and neural network models using inputs of ARIs or weather, soil properties, and county geodynamic latitude and longitude references. Our findings indicated that between the two ARI-mechanistic models evaluated (AFLA-MAIZE or Ratkowsky), the best performing was the Ratkowsky-ARI neural network (nnet) model, with an accuracy of 73%, sensitivity of 71% and specificity of 74%. Texas has significant geographical variability in ARI and ARI-hotspot responses due to the diversity of agroecological zones (hot-dry, hot-humid, mixed-dry and mixed-humid) that result in a wide variation of maize growth and development. Our Ratkowsky-ARI nnet model identified a positive correlation between aflatoxin outbreaks and prevalence of ARI hot-spots in the hot-humid areas of Texas. In these areas, temperature, precipitation and relative humidity in March and October were positively correlated with high aflatoxin contamination events. We found a positive correlation between aflatoxin outbreaks and soil pH in hotdry and hot-humid regions and minimum saturated hydraulic conductivity in mixed-dry regions. Conversely, there was a negative relationship between aflatoxin outbreaks and maximum soil organic matter (hot-dry region), and calcium carbonate (hot-dry, and mixed-dry). It is likely soil fungal communities are more diverse, and plants are healthier in soils with high organic matter content, thereby reducing the risk of aflatoxin outbreaks. Our results demonstrate that intricate relationships between soil hydrological parameters, fungal communities and plant health should be carefully considered by Texas corn growers for aflatoxin mitigation strategies.