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A Predictive Model for Survival of Escherichia coli O157:H7 and Generic E. coli in Soil Amended with Untreated Animal Manure |
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| Authors: | Hao Pang Amir Mokhtari Yuhuan Chen David Oryang David T Ingram Manan Sharma Patricia D Millner Jane M Van Doren |
| Institution: | 1. Center for Food Safety and Applied Nutrition, Food and Drug Administration, Office of Analytics and Outreach, College Park, MD, USA;2. Center for Food Safety and Applied Nutrition, Food and Drug Administration, Office of Analytics and Outreach, College Park, MD, USA
Booz Allen Hamilton, 4747 Bethesda Ave, Bethesda, MD, 20814 USA These authors contributed equally to this work.;3. Center for Food Safety and Applied Nutrition, Food and Drug Administration, Office of Food Safety, College Park, MD, USA;4. U.S. Department of Agriculture, Agricultural Research Service, Northeast Area, Beltsville Agricultural Research Center, Environmental Microbial and Food Safety Laboratory, Beltsville, MD, USA |
| Abstract: | This study aimed at developing a predictive model that captures the influences of a variety of agricultural and environmental variables and is able to predict the concentrations of enteric bacteria in soil amended with untreated Biological Soil Amendments of Animal Origin (BSAAO) under dynamic conditions. We developed and validated a Random Forest model using data from a longitudinal field study conducted in mid-Atlantic United States investigating the survival of Escherichia coli O157:H7 and generic E. coli in soils amended with untreated dairy manure, horse manure, or poultry litter. Amendment type, days of rain since the previous sampling day, and soil moisture content were identified as the most influential agricultural and environmental variables impacting concentrations of viable E. coli O157:H7 and generic E. coli recovered from amended soils. Our model results also indicated that E. coli O157:H7 and generic E. coli declined at similar rates in amended soils under dynamic field conditions.The Random Forest model accurately predicted changes in viable E. coli concentrations over time under different agricultural and environmental conditions. Our model also accurately characterized the variability of E. coli concentration in amended soil over time by providing upper and lower prediction bound estimates. Cross-validation results indicated that our model can be potentially generalized to other geographic regions and incorporated into a risk assessment for evaluating the risks associated with application of untreated BSAAO. Our model can be validated for other regions and predictive performance also can be enhanced when data sets from additional geographic regions become available. |
| Keywords: | Biological soil amendments of animal origin enteric bacteria predictive model random forest |