Cornell University researchers have turned to computers to try to predict where foodborne pathogens may be present and how they may spread on farms before harvest.
The method uses geospatial algorithms, foodborne pathogen ecology and geographic information systems, according to a news release.
Predictions are based on remotely sensed data, such as topography, soil type, weather trends, proximity to forests and water, to name a few.
The results can be used to help predict possible hot spots on farms.
"These tools are likely to provide a completely new science-based approach for guidance on how to reduce the likelihood of contamination with these bacteria," study co-author Martin Wiedmann, a food science professor, said in the release.
By knowing where the hot spots are, growers could adopt preventive practices, such as adjusting livestock grazing locations or switching to crops that are cooked before being consumed.