Obj. 1: Develop an agent-based model (ABM) to model the dispersal and population dynamics of bacterial pathogens and to predict appropriate risk-based set-back distances.
Obj. 2: Perform mesocosm studies using different soil types and moisture levels to collect dispersion rates from flooded soil to unflooded soil for the ABM developed in Obj. 1.
Obj. 3: Validate the ABM predictions through a simulated flooding event in a commercial scale field as well as published and un-published data collected from natural and simulated flooding events
Abstract: Flooding of produce fields presents a possible food safety risk, as flooding can (i) transport and introduce hazards as well as (ii) facilitate growth of microbial hazards. While dispersal, growth, die-off, and persistence of pathogens after flooding events is dependent on many factors, current guidelines suggest a standard setback distances of 30 ft. from the leading edge of flooding with a recently proposed change to a 100 ft. setback (with a statement that setback distances may be longer based on risk assessments). Regardless of distance, there are limited data supporting setback distances and there is a need to generate data and develop tools that can be used to define setback distances that allow for appropriate risk management, while accounting for field and flood specific factors (e.g., water flow in the soil subsurface) and how they impact pathogen and indicator organism dispersal, growth, die-off, and persistence in the context of varying soil, landscape and climate properties (e.g., soil type, weather, temperature). The research proposed here will develop an agent-based model (ABM) that simulates surface and subsurface pathogen dispersal, growth, die-off, and persistence after flooding events to identify evidence-based setback distances that are predicted to assure that food safety risks are not elevated over non-flooded fields with similar characteristics. More specifically, we will initially develop an ABM based on existing data obtained from published and unpublished research. Development of this ABM will identify data gaps that will be addressed through mesocosm studies with soils representing different soil properties; data generated from these studies will be used to define and/or refine parameter distributions for the ABM. The revised ABM will then be validated through (i) data collected during a simulated flooding event in an experimental plot as well as (ii) published and unpublished data on pathogen detection and dispersal during natural flooding events. Key project outcomes will include (i) a validated ABM that can be used to predict location and situation appropriate setback distances after flooding using site specific parameters (e.g., soil type, weather) as inputs and (ii) a publicly available datasets on pathogen dynamics after flooding events, which can broadly be used for post-flood risk management decisions. These outcomes will help industry to apply improved risk-based approaches to manage food safety hazards after flooding events, which in the long term should replace one-size fits all setback distances and wait-periods with location specific risk management approaches.