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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

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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>.

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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/>.

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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>.

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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/>.

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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

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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>.

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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)

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"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/>.

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Fig 3a

Fig 3b

Fig 3c

Fig 3d

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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

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"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>.

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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

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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)

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"RoadMap Technologies." Forecasting Software, Quantitative Analysis,. Web. 01 May 2012. <http://www.roadmap-tech.com/EEU.html>.

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Figure 6 – Results (suitability scores <5 omitted)

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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)

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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.

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