Climate versatile biological farm to flush surveillance and management of aflatoxins in Kenya

project summary

Aflatoxins (AF), natural toxins produced by fungus in maize, the main staple food in Kenya, impose a significant health, food security, and economic burden in Kenya. AF contamination has been attributed to agricultural practices and climate change among other factors. Several solutions based on microbiomes and their products offer the potential for the detection, surveillance, and reduction of aflatoxins from farm to flush. This project aims to improve food safety threat preparedness and response capacity through the development, capacity building, and dissemination of novel climate-resilient farm-to-flush AF surveillance and control tools based on biological solutions in Kenya.
An initial step will include the identification of national strain hot spots through geospatially resolved soil-to-fork quantitative exposure models for the validation and benchmarking of solutions from the project. This will be supported by the design of a national federated AF data and information platform.
New biological control agents at the farm will be developed from climatic zone-specific microbial strains that inhibit AF production, and their mechanism of action will be studied using new high-throughput methods. At the consumer level locally adapted indigenous food fermentation bacteria with high AF inhibitory potential will be examined. Citizen science initiatives will be used to conduct validation tests in fermented foods and field trials. This will also allow the isolation of biological catalysts with multiple targets for the detoxification of AFs. We will also study the fate of AFs and the products of AF breakdown once excreted. This will assist in developing new waste and wastewater-based assays to support a rapid and early detection approach to the surveillance of diverse mycotoxins in large populations. These assays will be developed into user-friendly mobile phone-based detection methods.
We will subsequently develop and validate easy-to-use, updatable online early warning tools based on predictive and prescriptive artificial intelligence and statistical models.
We will, during the project, strengthen the AF research, surveillance, and management capacity of Kenyan laboratories, students, researchers, farm-to-fork stakeholders, and policymakers. The project implementation and results dissemination will be performed interactively in public-private partnerships.