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.
