Mapping Farm to Table:

“Buy local” and “know your farmer, know your food” are two slogans that have gained traction over the last 25 years with the rise of the local food movement. This movement is grounded in principles of food justice, health consciousness, fair labor, strengthening local economies, and primarily, environmental sustainability. Commercial monoculture farming systems heavily pollute, over fertilize, degrade the soil, and over use water resources. In contrast small-scale local farms are known promote crop diversity, use little to no pesticides, and work to build healthy, biodiverse soils. These slogans, along with a rise of local food co-ops, farmers market, farm stands, and Community Supported Agriculture (CSA) demonstrate growing consumer concern in the U.S. over citizens' lack of connectivity to their food in how and where it is grown.

While consumer consciousness surrounding locality of food through reducing "food miles" — referring to the "average" distance of 1,500 miles food travels from farm to table — has grown, both of these concepts have been found to be arbitrary and undefined. Since the concept of food miles has been repeatedly been proven to be a poor statistic, the true average transportation network of our food remains unknown and hard to quantify. This leads to the question: if we cannot quantify the distance of locally grown food as anything that has traveled less than 1,500 miles, what then counts as local? My study aimed to visually represent the meaning of “local food” through mapping the network between Central Coast CSA, SLO Veg, growers and customers, ultimately allowing CSA consumers to be one step closer to knowing both their farmer and their food.

This study was inspired by combining research on CSA's and food miles with the analysis used for modeling transportation networks in the Kuonen (2015) study: Estimating greenhouse gas emissions from travel — a GIS-based study. The background information from this study was primarily gained from Schnell (2013) "Food miles, local eating, and community supported agriculture: Putting local food in its place" and Libby et al. (2018) "Life-cycle greenhouse gas assessment of Community Supported Agriculture in California's Central Valley." Schnell (2013) reviews and critiques the derivation of the food miles figure and aims to bring complexity back to the value of eating local through interviewing CSA members and farmers. Libby et al. (2018) was used to gain data on CSAs and the complexity of quantifying environmental impacts using singular statistics.

1. First I gathered data on SLO Veg's grower and customer ZIP codes. In terms of farmer data, the spreadsheet detailed ZIP codes of SLO Veg’s 29 seasonal growers, frequency they receive boxes from that grower, should they use that certain farmer’s produce in a given week, and items received from each farmer throughout the year. 
2. This data was consolidated the following spreadsheets: Farms per ZIP Code, five customer delivery ZIP Code sheets by day of the week, and five spreadsheets categorizing farm ZIP Codes by the frequency of their deliveries to SLO Veg.
3. This data was imported into ArcGIS Pro, using the geocode addresses tool to make it spatially referenced.
4. Five customer delivery route maps were created using the Network Analysis tool.
5. The farm network was mapped using the Orgin-Desitnation (OD) Cost Matrix route analysis tool to map the linear distance of farms from SLO Veg in kilometers. Five OD Cost Matrices were created allowing the network to show the frequency in which these farms deliver to SLO Veg.
6. Summary statistics were gathered from all the maps and imported into excel.

The above map shows shows a clear negative correlation between frequency of farm deliveries to SLO Veg and distance from SLO Veg. As distance increases, farms deliver less frequently. This means that farms that are more "local" to the San Luis Obispo area are delivering to SLO Veg with a higher frequency.

This interactive map quickly summarizes where SLO Veg's growers are located (by ZIP), what produce they grow, their distance from SLO Veg, and how frequently they deliver to SLO Veg.

American's lack of connection to their food is a spatial issue. If an individual's mental map of their food network is only between their home and grocery store, it neglects the complicated networks that make up growing and transporting food. A singular food mile statistic does not accurately capture the complexity of these networks, and neither does a map of one CSA's network. However, given the lack of research in local food transportation networks, there is a lot of potential in using GIS to do more substantial research within this area. GIS could be used to increase the quantity of CSA transportation networks mapped to further quantify what local food sourcing looks like. This could serve beyond the academic world to help CSAs further connect their customers to their food, especially with the use of story maps to summarize data.