Cornell researchers have created a method that uses geospatial algorithms, foodborne pathogen ecology and Geographic Information System (GIS) tools to predict hot spots where these pathogens may be present and spread on farms prior to harvest. Many of the recent outbreaks of foodborne pathogens have been linked to contamination on the farm.
The method, which can be applied to any farm, uses classification tree tools with remotely sensed data, such as topography, soil type, weather trends, proximity to various sources (water, forests) and more, to predict areas where pathogens are likely to be present.
"We wanted to see if we could identify factors that gave us a higher or lower prevalence of finding these pathogens," said Laura Strawn, a graduate student in the field of food science and lead author of a study published online Nov. 9 in the journal Applied and Environmental Microbiology. "We can look at a farm and use this data analysis tool to tell the farmer where these hotspots may be for foodborne pathogens," she said.
