1. Finding Suitable Sites for Aggregation of
Locally Grown Food Products to Distribute
to Wholesale Markets
Using Spatial Analysis and Geostatistical Methods to Identify Clusters and Trends in Geospatial Data
Mike Bularz
Geography 486 - Geostatistics
Prof. Robert Hasenstab
April 2012
1
2. Sustainable Agriculture: Locating Food Hubs in
Illinois Using GIS
Abstract
Currently, 90% of food consumed in Illinois is imported. Food traveling long distances incurs
environmental costs through transportation and reduces the quality and nutritional value of food with
the use of preservatives. Global food distribution prevents the recirculation of food dollars in Illinois,
while changes in consumer preferences are shifting towards locally grown healthy food options. Despite
the increasing demand for local food procurement, infrastructural barriers prevent potential suppliers
from entering larger markets such as wholesale and institutions. Aggregating suppliers through
optimally located processing and storage facilities will reduce barriers and encourage sustainable
agricultural practices. There is a lack of information and data to facilitate the creation of new
aggregation locations (food hubs) in Illinois. Asset mapping of food and agricultural infrastructure
(farms, CSA, markets, processing, and transportation) in Illinois requires the integration of decentralized
databases. Bringing data together using Geographic Information Systems (GIS) allows for spatial and
network analysis of the food supply chain. Site specific locations for favorable food aggregation can be
determined by using selection criteria such as; delivery cost, local skilled labor, availability of suitable
land as well as existing clusters of particular specialty crops. Analyzing site suitability for food hub
creation requires spatially enabled data and GIS tools. Advancing local food production encourages
sustainable agriculture by reducing transportation cost and pollution while increasing the availability
and nutritional value of food.
Scope of Study and Basic Definitions
Locating a generally suitable area for aggregating locally farmed food products into a packing
house or other distribution facility requires many considerations as the food supply-chain is composed
of several stages, depending on the product as well. 1The scope of this project was to look for areas
where there is generally a significant amount of local food producers, more specifically: farmers in
Illinois engaged in “direct-to-consumer” markets such as CSA (community supported agriculture, where
customers buy a share of the cost of producing a weekly, or monthly basket of food items that may be
either picked up or delivered), farmers participating in farmers markets where they sell their food
directly, wholesale farmers that are already equipped to provide some level of bulk supply, and even
other methods of sale such as on-site farm stands customers can visit. More broadly, this category of
farming is defined as “specialty crop production” as farmers engage in producing crops that are not feed
and ethanol grade corn or soybeans, which compose about 90% of what is currently grown in Illinois.
1
"Regional Food Hub Resource Guide." Agricultural Marketing Service. United States Department of Agriculture, 20 Apr. 2012.
Web. <http://www.ams.usda.gov/AMSv1.0/getfile?dDocName=STELPRDC5097957>.
2
3. Rationale and Motivation for Study
Current trends in consumer preferences are moving towards sustainable and healthier choices
in daily habits, particularly food choices.2 The majority of our food is imported, and, despite the media
popularized “1500 miles” claim that this is the average distance of food product travel, research reveals
otherwise. For instance, the Aldo Leopold Institute for Sustainability, a lead research organization on the
topic of “food miles” recently published study puts the distance at more around 2,500 miles on average
for a sample town in the Midwest (Waterloo, IA). (See Fig. 1)
Figure 1: Average Food Miles for Waterloo, IA*
*WASD (Weighted Average Standard Distance) calculated by taking the average traveled distance of major suppliers of products, weighted by
the amount of the product consumed
Besides the obvious environmental implications from transporting our food several miles (fuel
wasted, environments disturbed by shipping routes and pollution from the routes), the health quality of
our food is increasingly decreasing as foods are selectively bred, as well as chemically preserved to
increase shelf life and withstand these distant trips. Growing concern about genetically-modified
organisms exists as well.3 Outside of rational decisions, distrust for globalized companies as well as “Big
2
Organic Food and Drink Reatailing - US. Rep. Mintel, 2009. Web.
<http://oxygen.mintel.com/sinatra/oxygen/display/id=393415>.
3
Saenz, Aaron. "The Battle Over Genetically Modified Food Continues – FDA Petitioned To Require GM Labels." Singularity Hub.
10 Oct. 2011. Web. 11 Mar. 2012. <http://singularityhub.com/2011/10/10/the-battle-over-genetically-modified-food-continues-
%E2%80%93-fda-petitioned-to-require-gm-labels/>.
3
4. Ag” and factory farming push consumers into conspicuous consumption, both for these reasons as well
as fresh taste.
All of these factors amount to an incredible demand for these local foods, and driving the
number of local food producers and farmers markets up. (See Fig. 2) Demand from chain stores,
institutions, and other large bulk purchasers for these items is increasing as well, which is highlighting
our incredible lack of infrastructure to supply these items at this scale. These current demand trends are
pushing local farmers and food policy interests to push for the USDA and government organizations to
assist in creating “food hubs” which would aggregate these products together, either at a packing house
where they are inspected, washed, graded, and certified before being bulked together, or a distribution
facility.4 For meat products, this is particularly important as proper slaughter, processing, and
refrigeration facilities are of utmost importance to provide marketable products. A huge need for food
hubs comes from several sectors which need bulk food products for their daily operations: grocery
stores such as Whole Foods need large quantities of these foods supplied, and government institutions
such as schools, military bases, and even prisons seek to incorporate healthier food choices into their
menus. From the government perspective, there is a huge economic loss that could be captured if we
produced more items locally, instead of importing, and therefore exporting our wealth.
Figure 2- Growth in Farmers Markets*
*Numbers obtained from Mintel food industry reports
True, some products can only grow efficiently in certain regions, but technology on the
production side is becoming more realistic and financially, as well as environmentally sustainable. From
urban agriculture initiatives such as indoor farming and aquaculture system advances, to rural advances
such as hoop houses, which are similar in function to greenhouses, but are heated with decomposing
compost, more crops are becoming a reality in various climates. The increased demand, and trend in
consumer preferences for these items continues to fuel this boom, but without aggregation in food
hubs, these products cannot compete with large distributors. The need to aggregate these products is
4
"Farmers Markets and Local Food Marketing." Agricultural Marketing Service. United States Department of Agriculture, 20 June
2011. Web. 01 May 2012. <http://www.ams.usda.gov/AMSv1.0/foodhubs>.
4
5. apparent, and currently, Illinois has no real “food hub” that is recognizes, as opposed to ones being
created and supported through policy on the East and West coasts of the U.S.. Identifying a significant
cluster of farmers, and showing some potential locations suitable for local food production, as well as
getting all of the data in the same conversation, is a start to answering this question.
Data Sources
Data for this project incorporates many sources, and the level of coverage varies; some data sets
can be taken as almost comprehensive - such as lists of meat processors in Illinois, which are regulated
by the Illinois Department of Agriculture and the U.S. Department of Agriculture (USDA), and are
therefore complete lists derived from public inspection data.5 On the other side of this continuum, some
data sets are taken as “sample” data either somewhat representative of trends, which is sometimes
used as a sample with which to extrapolate in the analysis, and sometimes used more literally as sample
data. The chief example of this scenario is our actual farms data, which was derived by combining
several online social network type website’s data into one file based on a designed template. These
websites, such as RealTime farms6, Market Maker (run by University of Illinois)7, Eat Well Guide8, among
other lists9, are websites on which farms claim their business profile, and market their products. The
information derived from these websites can be very useful and specific, as the farms claim their
business, but are also able to list exactly which product they provide, what kind of value added (on-site
processed products such as cheese, honey, bread, wine, crackers), methods of sale (CSA, farmers
market, delivery from online CSA, on-site farm stand, etc), and even product attributes such as “USDA
organic” or “Grass-fed”. Advanced queries can be made from these websites, as well as the template
that we designed, such as “All tomato producers that also sell homemade pasta” or “Lamb herders that
offer their meats as part of CSA”. Within the scope of this project, we have not comprehensively looked
at clusters of individual products, rather, we simply looked at the aggregate. Some statistical clustering
analyses were performed on certain products for the sake of curiosity, and plans to look for highly
clustered projects are being developed at the time of writing this paper.
Other sources of data include several data sets listing various meat processors, slaughter
facilities, fat rendering plants, grain warehouses, cold storage facilities which are derived from Illinois
Department of Agriculture listings. Demographic and labor data, such as the U.S. Decennial Census
201010, Illinois Department of Labor – Local Employment Dynamics11 data by county of Agricultural
Employment filtered for the Agricultural trades NAICS (National Industry Classification System) code12,
and Data products from the USDA-NASS (National Agricultural Statistical Service) Census of Agriculture
5
Illinois Meat and Slaughter Facilities. 2012. Raw data. Illinois Department of Agriculture, Springfield, IL.
<http://www.agr.state.il.us/regulation/licenses.php>.
6
RealTime Farms Farm Listings. 2012. Raw data. Real Time Farms Servers, California.
<http://www.realtimefarms.com/farms>.
7
Farmers, Fisheries, and Businesses. 2012. Raw data. University of Illinois. Urbana, IL.
<http://www.marketmaker.uiuc.edu/>.
8
All Listings for Illinois. 2012. Raw data. Eat Well Guide servers, Illinois.
< http://www.eatwellguide.org/search/results/?device=iframe&iframe=11>.
9
2012 Buy Fresh, Buy Local Central Illinois Directory. Raw data. Illinois Stewardship Alliance. Rochester,
IL.<http://sfc.smallfarmcentral.com/dynamic_content/uploadfiles/101/2011_BFBL_Directory_Print%20Ready.pdf>
10
United States Decenial Census 2010. Raw data. 2010. United States Census Servers. Washington, D.C.
12
"North American Industry Classification System." NAICS Main Page. Web. 01 May 2012.
<http://www.census.gov/eos/www/naics/>.
5
6. 200713 were used to look at factors such as availability of skilled agricultural labor when considering
locations for distribution and packing facilities.
Data Processing and Structure
The most intensive phase of data acquisition included the entry of several of these data
products into compatible formats such as .xls (Microsoft Excel) files, and also designing a template to
enter data into which could then be queried. Data from the Univeristy of Illinois - Urbana Champaign
Market Maker website was provided through generous help from database administrators at the
university. The data template includes fields for farm name, contact phone number, address, latitude,
longitude, website and the following product fields: a vegetable, grains, and herbs field, a protein field
(poultry, red meat, eggs, fish), a value added product field, methods of sale field, and certifications field.
Each of the product fields is entered as a comma separated value, which can then be separated and
queried in excel with a “Text to columns...” and “Filter unique” commands. Exports can be queried
within excel, ArcGIS, or other software that reads tabular or spatial data.
Other data sets, such as Illinois Labor data14, were coded, or “joined” to U.S. Census TIGER data
representing geographies such as zip code boundaries or county boundaries. These data sets were used
primarily to look at spatial trends in agricultural employment. A remote-sensing derived data set, the
NASS (National Agricultural Statistical Service) Cropland Data layer provides raster image data that uses
spectral characteristics of Landsat multispectral images which detail the location of over 100 varieties of
crop types. These NASS derivatives were used to estimate total agricultural land in the scope of our
project, but looking at clusters of specific products are in the plans for future work.
Summary of Geostatistical Methods and Spatial Analyses Used
SITE SUITABILITY WITH WEIGHTED OVERLAY OF VARIABLES
A primary form of geographic analysis in this project involved spatial analysis with a weighted
overlay. The weighted overlay method overlays raster images representing desired phenomenon and
averages each pixel of input variables, with the ability to weigh the significance of each variable. To
produce suitable input variable surfaces, each variable had to have data first represented spatially as
polygons or points, and then get converted into a “heat map” raster representing a continuous surface
of our desired phenomenon. These heat maps had to be binned in a histogram into values of 1 to 9, 1
representing least desirable, and 9 representing the most desirable value in a variable. The values had to
be binned so they could be averaged based on a common denominator in the weighted overlay tool.
This is a basic methodology used for site selection in GIS.
The variables considered to represent the Supply were the location of the farms from the social
network and online listings data, which we take as a sample representing direct to consumer farms.
Although it was very wanted, there is no way to check the completeness of the data against the Census
of Agriculture, even though the agricultural census details by county the amount of X product, there is
no way to tell if the producer participates in the direct to consumer market. For example, a hypothetical
county with 14 asparagus growers, which is a specialty crop, in which we have data for 4 farmers does
13
Census of Agriculture. 2007. Raw data. United Stated Department of Agriculture - National Agricultural Statistical Service.
Washington, D.C.
14
Illinois Department of Employment Security - Local Employment Dynamics. 2011 Quarterly Average. Raw Data. Illinois Deparment
of Employment Security. Springfield, IL. <http://lehd.did.census.gov/led/datatools/qwiapp.html>.
6
7. not mean that our data is incomplete, as the other 10 may only grow for global markets. It is because of
this that we are only able to take the data as a sample representing those farmers interested in
supplying direct to consumer, who volunteered their information on these websites. In the GIS analysis,
a Kernel Density15 was performed, which not only accounts distance but the amount of farms in an area,
creating a sufficient heat map. This is then reclassified, or binned into groups in the histogram using the
Jenks Natural Breaks method into 9 groups (1 to 9), which gives enough significance to each value bin,
while preserving trends. (See Fig. 3a)
A similar methodology was performed on the Demand side by taking farmers markets, which are
a sample that is hard to determine completeness of. The idea is that these farmers markets represent
areas that are in demand of local food, or at least have an interest in it. A kernel density of these was
performed, and Natural Jenks re-classify of 1 to 9 as well. This factor was questionable as significant as a
variable, and further analysis would probably employ it differently. In the weighted overlay, this factor
was only given a 10% weight. (See Fig. 3b)
For our polygon data, we combined the 2010 U.S. population census data and local Illinois labor
data about agricultural employment by joining these two numbers to their respective county features.
The local Illinois labor data had to be used, as the federal ACS (American Community Survey) which is
usually a good source of demographic and employment data only had information for metropolitan
statistical areas. The Illinois labor data had to be manually entered into a table and then joined to the
county shapefile. A derivative of these two numbers, “Percent of the Population in Agricultural
Employment” was produced by doing the basic calculation (Number of People Employed in Agriculture
divided by Total Population, times 100) and the shapefile was converted to a raster surface representing
this variable, which was re-classified with Jenks Natural Breaks, 1 to 9. (See Fig. 3c)
One last variable was considered, which was the available arable (growable) land. We derived
this from the Cropland Data layer, which details each crop type in a derivative of remotely sensed
satellite data.16 All crops were considered, including corn and soybeans, as well as more than 80 other
crops. Excluded classifications were urban, wetland, barren, and fallow soil. These were all combined in
one surface representing arable land as either 0 (none) or 9 (yes). (See Fig. 3d)
These three surfaces were pulled into the Weighted Overlay tool, and then averaged. Weights
were chosen based loosely on a recent survey of farmers which indicated which factors were the highest
barrier for them to enter into the direct to consumer and specialty grower market. The density of
existing farms was given a higher priority, as the actual location of the farms is the most important in
locating our aggregation facility. Farms were given a 50% weight, availability of labor 20%, arable land
20%, and farmers markets 10%. (See Fig. 4)
15
"How Kernel Density Works." ArcGIS Desktop 9.3. ESRI, 07 Sept. 2011. Web. 01 May 2012.
<http://webhelp.esri.com/arcgisdesktop/9.3/index.cfm?TopicName=How Kernel Density works>.
16
Cropland Data layer for Illinois. 2011. Raw data. United States Department of Agriculture - National Agricultural Statistical Service.
Washington, D.C.
<http://nassgeodata.gmu.edu/CropScape/>.
7
9. Figure 4 – Model for Comparison and Overlay of Indicator Variables
STATISTICAL ANALYSIS FOR CLUSTERS
Geostatistical tools were employed to look for “statistically significant” clusters to an extent.
Some clusters were vaguely identified with various methods, and these clusters seemed to correspond
to the results of our weighted overlay (see “Results” and Fig. 6 below). A primary method used to define
clusters was the Nearest Neighbor Heirarchical clustering tools in the Crimestat17 statistical software.
Nearest Neighbor Heirarchical not only defines the center of point clusters by looking at the distances
between each point and the average distance over the data set, but it identifies multiple heirarchies of
clusters (clusters of clusters). The tool also outputs ellipses roughly delineating these clusters. (See Fig. 5
clusters) Several parameters for what a cluster is defined by can be set, for our case, a cluster of 3, and
then 5 farms was experimented as significant enough to be considered a cluster worth some aggregating
of products nearby. With these parameters the tool output 7 - 8 clusters, and one 2nd order of clusters
(a cluster of clusters). These outputs, and their implications are discussed in the Results section below.
A few other statistical tools were used, which included performing a Morans I, which is a global
statistic similar to the Nearest Neighbor methodology, although it takes into account intensity of the
values of sample points or polygons. The points were joined to their nearest point cluster using the
“Integrate” tool, to provide a sample intensity value for the tool. The Morans I tool was used to analyze
for clusters of a few particular farm or product selections: tomato only, any vegetables, poultry, game
17
"CrimeStat III User Workbook and Data." CrimeStat III User Workbook. Ned Levine, 02 Nov. 2008. Web. 01 May 2012.
<http://www.icpsr.umich.edu/CrimeStat/workbook.html>.
9
10. birds, farmers selling at farmers markets, and farmers selling organic products. Significant implications
can be made just from these cursory surveys, but a complete comparison of every attribute in the future
would show if certain items were significantly more clustered and worth aggregating than others. (See
Fig. 4)
Figure 4: Selected Moran’s I (Spatial Autocorrelation)
PLANNED NETWORK - BASED ANALYSES FOR FUTURE WORK
Although attempts were made at modeling trips on the highway network, the current
computing power available to my disposal needs to be accounted for when doing this, and a new
network has to be built that is potentially thinned of lower-level roads. Further, Train and Ferries along
the Mississippi River would have to be modeled to be accurate. Transportation data about volumes of
travel exists, and suggests that this mode is a significant part of the picture. Because of these issues, the
network analysis was suspended until a more clearer definition of what type of network we are trying to
set up to make analyses on would be sufficient.
There are also limitations in both, ArcGIS extensions and the data available. Besides Network
Analyst, the extension used to create networks of nodes and edges (lines) representing roads, turns, rail,
and intermodal nodes, the Schematics extension needs further development, as it could model the more
complicated portions of the food supply chain. ArcGIS Schematics extension allows the user to use a
crude interface to diagram a “schematic” representing a topology of rules in a supply chain, and perform
queries on this topology. The extension has not seen significant development since its deployment on
10
11. earlier versions of the software, and provides no substantial platform to do analysis of the network like
Network Analyst.
For example, if we had information (data) on all cold storage facilities, the maximum capacity of
these facilities, and what type of products they are equipped to handle (ex. frozen strawberries vs. raw
meat) and all of the connections that could be made further down the line to a distributor or
wholesaler, we could perform a more reliable analysis. Current proprietary software geared around
operations management exists in databases such as that of Sysco, or Testa Produce, which use software
that is ripe with data, from truck temperatures, to truck idle time. Their software even gives them the
ability to remotely control the temperatures or shut down vehicles.18 Data integration at this deep of a
level would create a clear picture of the food supply network, but may be impossible, in code and in life
as the companies may not want to share this detail of information. Further, this depth of information is
outside of the scope of the project, unless we are to try to model more complex food-supply chains.
Figure 5 – Example of a Cost Matrix representing travel time by road for various destinations
(Image from ArcGIS.com)
18
"RoadMap Technologies." Forecasting Software, Quantitative Analysis,. Web. 01 May 2012. <http://www.roadmap-
tech.com/EEU.html>.
11
13. Results
WEIGHTED OVERLAY
The result showed suitable areas in clusters around the periphery of urban areas. This may be
because the input data is sourced from farmers who are “on the grid” and able to find out about
websites for marketing their items, as well as opportunities to sell at farmers markets or wholesale. The
analysis performed here does have caveats which need to be refined to produce a more reliable and
satisfactory output in the future: the use of farmers markets to show demand is a questionable
approach - are we trying to put a facility where there are farmers markets, or somewhere where there is
a lack of them? As far as arable land, the Cropland Data layer derivative is somewhat questionable as a
variable too, as it indicates 1) land available for farming, which we may not necessarily care about when
siting a facility, although this may depend on the case, especially with ranches used for meat. 2) the data
used includes corn and soybeans producers, which is a large majority of the state, and may not be
suitable for local specialty crops, at least not until regulations such as the farm bill are updated at the
federal level. Currently, farms producing under subsidy from the government cannot grow anything but
corn and soybeans on their property, or they lose the subsidy and have to make up the loss by selling
the specialty crops expensively. (See Fig. 6)
NEAREST NEIGHBOR HEIRARCHICAL CLUSTERING
The results of the Nearest Neighbor Heirarchical Clustering tool somewhat confirmed the results
of the weighted overlays, farms were clustered around urban areas including peripheries of Chicago,
Rockford, Kankakee, Peoria, Urbana-Champaign, St. Louis, Carbondale, and Springfield. A macro second-
order cluster exists on the northwest portion of Chicago around the area of Cook and McHenry counties,
where there is a significant amount of agricultural production as well as on-farm education programs.
It could be that these farms would show up more in the data because of proximity to cities, and
therefore more access to information about how to market their products, and general potential to
grow more than just corn and soybeans. If this is the case, it might not be that much of a
misrepresentation in terms of good areas to put these facilities, because it might be that farmers already
engaged in these types of markets are more suitable to supply a facility in general. A comparison of this
should be made in the future to a general map of the intensity of all agricultural production in Illinois,
particularly specialty crops. (See Fig. 6)
MORAN’S I
The Moran’s I tool, although not employed to its fullest capacity in the scope of this project,
shows that this research is worth continuing. There are significant differences in the statistical clustering
of meat products versus vegetable products. Vegetable products are clustered, and Farmers supplying
farmers markets are even more highly clustered with a higher z-score (derivative representing
significance and clustering). Poultry products are unclustered, and so are Game Birds (quail, cornish hen,
wild turkey, etc.) which may indicate that meat products may be much more difficult to aggregate.
Further statistical analysis of the level of clustering of multiple products, and multiple categories will be
done in the future. (See Fig. 6)
13
14. Future Goals and Plans
Although the caveats and shortfalls of some of the processes are hinted throughout this
narrative, it is worth outlining some concrete plans for the near future, and some ideas for more
advanced research possibilities that may deserve consideration for the far future.
NEAR FUTURE
For the overlay, the overlay needs to acquire more variables, that which are known from farmer
surveys, such as maximum travel distances, or just how much labor would be needed. The use of the
farmers market data is probably inappropriate as a variable, but there are other uses for these points as
well, which I outline in the next paragraph. The arable land variable needs some consideration, and
refinement of definition. It is also not out of the question to try to develop custom remote-sensing
derivatives from the satellite images as well. Overall, the actual methods for defining “heat maps” of the
points have to be refined - Kernel Density was chosen as the tool to use, but there is methodology that
suggests using defined buffers with Euclidean Distance based on performing a Moran’s I at various
distances to determine what buffer distance would provide the highest z-score. This is one of many ways
to provide somewhat of a “prediction” surface as opposed to general Kernel Density, reclassified into 9
categories. More variables may want to be considered, such as the actual number of specialty crop
growers per county (from the Census of Agriculture) as an indicator of available product which might be
more suitable than the arable land derivative from the Cropland Data layer.
For the Statistical methods and Network Analysis, statistical centers of clusters should be
refined to require a certain number of farms, further, maybe only statistical centers of certain products
should be used, products that are significantly clustered according to Moran’s I. These statistical centers
could be plugged into a network analysis, for instance: Giving the average network distance along
highways and major roads from the statistical center of tomatoes to the statistical center of farmers
markets, or maybe to the statistical center of a canning facility where pastas are created. The network
analysis would benefit from these points as rough representations of the locations of these items, and
step based analyses (traveling salesmen) could be performed as well, such as the travel time from the
statistical center of tomato growers to canning facilities to distributors. Derivatives, such as gas saved,
and therefore CO2 emissions mitigated could be compared to the national averages to estimate the
benefits in terms of mitigated environmental impact.
FAR FUTURE
Further, the data is worth exploring, and the tools expanding on to create more complicated
supply-chain models in the future for the entire food industry. Although there are some current efforts
at such feats, a global food supply model could act as a platform for modeling complicated effects on
the global food supply such as modeling the spread of a food-borne bacteria such as salmonella, or how
a drought would affect the supply of a certain crop, and how the commodity prices might go up, or how
the food products in the system could be rerouted to compensate for shortages in emergencies. GIS
applications in agriculture and food-supply systems have high potential to develop sophisticated tools in
the future, but the amount of variables, and the lack of data on certain parts of these issues make for
high barriers to do so, but getting a start in outlining how some of these items might be measured, or
linked together starts that conversation.
14
15. Bibliography
Data Sources
2012 Buy Fresh, Buy Local Central Illinois Directory. Raw data. Illinois Stewardship Alliance. Rochester,
IL.http://sfc.smallfarmcentral.com/dynamic_content/uploadfiles/101/2011_BFBL_Directory_Print%20Read
y.pdf.
Cropland Data layer for Illinois. 2011. Raw data. United States Department of Agriculture - National
Agricultural Statistical Service. Washington, D.C.
<http://nassgeodata.gmu.edu/CropScape/>.
Farmers, Fisheries, and Businesses. 2012. Raw data. University of Illinois. Urbana, IL.
<http://www.marketmaker.uiuc.edu/>.
Illinois Department of Employment Security - Local Employment Dynamics. 2011 Quarterly Average. Raw
Data. Illinois Deparment of Employment Security. Springfield, IL.
<http://lehd.did.census.gov/led/datatools/qwiapp.html>.
Illinois Meat and Slaughter Facilities. 2012. Raw data. Illinois Department of Agriculture, Springfield, IL.
<http://www.agr.state.il.us/regulation/licenses.php
Organic Food and Drink Retailing - US. Rep. Mintel, 2009. Web.
<http://oxygen.mintel.com/sinatra/oxygen/display/id=393415>.
RealTime Farms Farm Listings. 2012. Raw data. Real Time Farms Servers, California.
<http://www.realtimefarms.com/farms>.
United States Decenial Census 2010. Raw data. 2010. United States Census Servers. Washington, D.C.
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