Summary
The objectives of this project are to develop a risk assessment model that will take knowledge from multiple sources and research studies into account to determine where and when the risk of a contamination event is increased. Input will be sought from the scientific literature as well as experts in the field. Data will be distilled into online risk maps that point out where and when to concentrate efforts to mitigate known risks. The tool will help growers to be more pro-active in managing risk from foodborne pathogen intrusion into their fields and may lead to an even safer supply of the nutritious products grown in California.
Technical Abstract
We are proposing to develop a quantitative microbial risk assessment model that will enable produce growers to more easily assess their specific risk so that appropriate intervention measures may be taken when they are likely most effective and necessary. Such a new approach would distill the current knowledge into a spatio-temporal risk assessment tool supplemented with expert opinion where data may be missing. We will start with focus group meetings of researchers, extension specialists, representatives of industry, and governmental agencies who will provide feedback on data sources to include into the model, and who will point out which information may still be unavailable but desirable to include. The goal of the focus group meetings is to verify that appropriate pathways, model assumptions and input parameters are used for the risk model and to identify any additional information sources that may have been missed by the research team. Further, current knowledge gaps will be discussed to ensure acknowledgement of risk model limitations. The focus group will be updated on project progress twice yearly and further feedback sought for the duration of the project. The envisioned spatial-explicit quantitative microbiological risk assessment model will integrate qualitative and quantitative data that are translated into probability distributions that will be used to estimate the risk of contamination under various scenarios. We combine scenario tree modeling with high-resolution spatial information, which will enable quantification of risks at a fine spatial scale allowing producers to identify their specific risk. The final step in the process will be to develop a user-friendly web-based decision support tool based on the programming language R and R package Shiny that enables us to design interactive web apps. Together, the project will create a framework for the implementation of best practices for risk mitigation for fresh produce fields under different agricultural, climatic and environmental system scenarios. We envision this tool to contribute to safer produce production while allowing for co-existence with livestock commodities in a safe and sustainable fashion.
Research Objectives
The project aims to develop a risk assessment framework for the implementation of best practices for risk mitigation for fresh produce contamination under different agricultural scenarios. 4
Objective 1: Focus-groups -listening sessions
• Focus groups with researchers, extension specialists, representatives of industry, and governmental agencies will be conducted for collection and discussion of model inputs.
Objective 2: Quantitative risk assessment to evaluate the risk of fresh produce contamination under various agricultural, climatic and environmental system scenarios
• A quantitative microbial risk assessment model will be developed to estimate the probability of the presence/absence of pathogenic E. coli under diverse epidemiological scenarios The risk assessment models will predict the risk of produce contamination in relation to: 1) proximity to grazing cattle (different distances and stocking density), 2) watershed microbial quality, 3) weather events (wet, floods, drought, wind, etc.), 4) adjacent land use (CAFOs, pastures, rangeland, natural barriers) and 5) presence of various types of wildlife.
Objective 3: Decision support tool: Online user-friendly platform
• A web-based platform based of the findings of Objectives 1 and 2 will be developed, using R and shiny, to help produce growers and stakeholders better understand the pathogen dynamics under different agricultural scenarios. This user-friendly tool will support decision-making in order to prevent the contamination of fresh produce.
Findings & Recommendations
This project is ongoing. A final report will be provided when the project is finished.