Monday, December 17, 2012

Another benefit from premises identification

Research at Cornell University, New York, is identifying another benefit of Canada’s program to identify every farm on the Geographic Information System (GIS).

The researchers are checking fields growing produce for bacteria that cause food poisoning and combining those findings with factors such as topography, wind patterns, water flow and the weather to predict where a hot spot might develop.

The aim is to prevent foodborne illnesses, such as vegetable and fruit crops that carry E. coli 0157:H7.

They have combined geospatial algorithms, foodborne pathogen ecology and the GIS database in their research.

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 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.

"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," said co-author Martin Wiedmann, a food science professor and study co-author.

By knowing where the hot spots are, farmers may then implement such preventive practices as draining standing water, adjusting where livestock graze, or planting crops that should be consumed cooked rather than raw, for example, Strawn said.

The research team collected 588 samples of soil, water, feces and drag swabs (gauze attached to a string and dragged over a field) from four produce fields on five farms each.

Samples were collected four times a year, during each season, from 2009 to 2011.

The prevalence of Listeria monocytogenes, Salmonella and E. coli were 15.0, 4.6 and 2.7 per cent respectively across all the samples.

Listeria monocytogenes and Salmonella were detected more frequently in water samples from irrigation sources or nearby streams, while E. coli was found in equal distributions across all the sample types.

Listeria monocytogenes and Salmonella were found in higher frequencies in areas with moist soils.

For Salmonella, "if you had more precipitation before a sample was collected, you were more likely to find that pathogen," said Ms Strawn.
Also, well-drained fields had lower Salmonella prevalence.

Knowing this helps identify times and places where the risks are greater.
For Listeria, proximity to water, pastures, livestock and grazing cattle, wildlife habitation and nearby impervious surfaces, roads and ditches all predicted a higher prevalence of the pathogen. 
Once such factors have been identified, the GIS platform may be used to filter out specific areas based upon those factors (such as filtering areas that have moist soils and close proximity to water) to create a color-coded map of any farm area with predicted prevalence for a pathogen. 

"This work advances our understanding of the environmental microbiology of foodborne pathogens and permits tailored solutions to predict contamination of produce commodities during cultivation," said Peter Bergholz, a research associate in food science and the study's corresponding author.

This approach for produce could also find applications for livestock and poultry farmers trying to contain the spread of diseases such as PRRS to pigs and avian influenza to poultry.

For example, a purebred pig farmer whose barns are identified as at risk of wind spread of PRRS from neighbouring commercial pig barns might consider installing an air filtration system and stepping up biosecurity.

The GIS, topography, weather data, wildlife and rodent traffic patterns and vehicle traffic information would help the purebred hog farmer predict where PRRS might travel towards his barns.