Interdisciplinarity was the nature of my work this summer, looking to see if there was a correlation between social distance and geographic distance within the farmer social network. The farmer social network is a visual diagram that was created by the sociologists from last year’s LAKES team that shows which farmers talk to each other about farming practices within the Red Cedar watershed. Small dots called nodes represent people, while lines connecting them called edges represent their connection.
Surveys that were sent to farmers create the social network. These surveys ask about best management practices (BMP), as well as interest in learning about conservation agriculture, and who the farmers talks to about their land use practices.
Why does this information matter? By seeing the lines of communication between farmers, we can see who would be open to learning about BMPs for their farms, and how to effectively disseminate information about conservation agriculture.
While the sociologists were able to create a fantastic social network, they did not look at it in terms of geography. That is, seeing if there is a correlation between social distance and geographic distance. I started my work by getting familiar with the survey that created the farmer social network, as well as prepping the current data for statistical analysis in a geographic information system (GIS) software called ArcMap. I also recreated the farmer social network in terms of geography.
In AcrMap, I looked to see if there was a spatial autocorrelation between any of the questions in the survey. Spatial autocorrelation measures the clustering of values within a map.
As for the new social network, I made the sub-watersheds within the Red Cedar into nodes from which to anchor the social network. Edges were made, connecting to the farmers within their boundaries. By viewing the social network as such, I could see if there was any clustering of social connection based on sub-watershed.
From my analysis of the farmer social network, I found that there is a contrasting relationship between two sub-watersheds within the Red Cedar.
On one hand, the Lower Pine Creek-Red Cedar shows a high clustering of farmers who would be interested in learning about conservation agriculture, soil health, and economic projections for their farms, while at the same time, having a high clustering of BMP usage.
On the other hand, Hay River showed a randomization in those interested in educational programs, as well as randomization in BMP usage in general.
Where do we go from here? My findings open the door for many different avenues of research. From what I’ve found, we can start asking: what are the differences between these two sub-watersheds in terms of geography, and society? What is working for one, and why is it not working for the other?
With this information, we can better learn the factors that affect the farmer social network in the Red Cedar, and how to better disseminate information about conservation agriculture.