Summary of Awards to Date

Developing a user-friendly risk assessment tool to assess the food safety risks of fresh produce production and landscape use


Jan. 1, 2024 - Dec. 31, 2025

Award Number


Funding Agency

University of California, Davis

Amount Awarded



Alda Pires, Ph.D.
University of California, Davis


Beatriz Martinez-Lopez, Ph.D., Gabriele Maier, Ph.D., Erin DiCaprio, Ph.D.


Fresh leafy greens and other produce are a billion-dollar industry in California and are concentrated in the Salinas and Imperial Valley growing regions. Outbreaks of foodborne bacterial infections due to E. coli O157:H7 in people have been traced back to produce grown in these regions, which have led to investigations on the origin of the bacterial contamination. Despite much scientific effort, it is difficult to determine exact causes of E. coli O157:H7 contamination in fresh produce. The objectives of this project are therefore 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:

Contamination of fresh produce through E. coli O157:H7 and other pathogenic E. coli continues to threaten public health and puts a burden on those who farm and distribute these food products. Research into the issue has revealed various pathways that enable the translocation of these bacteria onto produce including wildlife intrusion, contaminated agricultural water, adjacent livestock, flying insects, and certain climatic or weather patterns. What is still lacking are models that take into account how all the factors previously identified may act together and how each of them may contribute to the risk in a specific location at a specific time. We are therefore 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.