Best Management Practices: Farmers and their Social Networks

Best Management Practices are farming practices that help increase soil health, decrease soil erosion and stop excessive amounts of farm runoff. Less phosphorous and solids enter the streams, rivers, and lakes, thus lowering the amounts of blue green algae blooms that are causing the toxicity, cloudiness, and smell of the lake. There are many predictors that have a significant input on increasing or decreasing BMP implementation but, for the sake of this summer’s project, I looked into social network analysis to find interesting predictors for higher uses of BMPs. The statistics that we ran were closeness centrality and some basic descriptive statistics about networks.  The results stated that farmers with a higher closeness centrality would have a higher usage of BMPs, also that farmers within the network with four or more connections had a significantly higher amount of BMP Index than those without any connections. My research suggests that to increase BMP Index farmers need to become part of a social network, most effectively through Farmer-Led Councils, which would help deal with other problems that hinder the adoption of BMPs.

The LAKES REU program was a great experience because in an 8 week program I learned so much of professional knowledge on survey creation, data input, and statistics. The farmer survey that we created consisted of about 15 minutes worth of questions that ranged from personal values to fairness to education on farming practices and implementation of best management practices. Finally, there were question about willingness to work with local and state agencies to implement BMPs. All of the questions were shaped to get a feel about what stands in the way and what is helpful for the adoption of best management practices. Data input was done through the qualtrics survey engine that uses online surveys for data collection and storage. We ran the data through Gephi, a social network analysis software, and then STATA, a statistics software, to get the final results for this project thus far. Regression models and correlations were the two most used statistics as well as standard descriptive statistics.

The results for my project were two different regression models with an interaction and a social network graph. The first regression model was created with best management practices as a dependent variable, mean the variable that is dependent upon the actions of another variable called the independent variable. The independent variables for the model were soil test frequency, closeness centrality, ecological impact, unfairly targeting farmers for problems of water quality, the value of organic matter, and farm size. All of these variables came together as valuable predictors for the best management practice index, with soil test frequency, closeness centrality, unfairly targeting farmers, and value of organic matter all being statistically significant. This brings in the second regression model and where it gets really interesting.

In the second regression model we used closeness centrality as the dependent variable and age, social connection info, capital cost, farm size and an interaction between farm size and capital cost as independent variables. The really interesting part of these results were the interaction. This interaction states that while all farmers worry about capital costs, smaller farmers say that it has much more of an impact on them, than on the larger farms, and because of that they might not be able to implement the best management practices. Smaller farmers need financial help if these actions are going to be done and implemented.

Social network analysis looked at degree which is numbers of connections a single person has and closeness centrality that looks more at their placement within the network. Once again this is very important because of the implication that it has on implementing BMPs into the fields and improve water quality. Closeness centrality regressions said that the higher the closeness centrality is the more farmers are using BMPs. This suggests that farmers even at the outskirts of the social networks are learning the information they need from the network to put these practices in place. Degree looked at direct connections and found that people with 4 or higher direct connections had more BMPs used than those with 1 connection. People outside of the network at the lowest mean BMP Index.

What does this mean? All of this together means that farmers need help with the finances behind implementing best management practices. It also suggests that farmers need to be brought into the networks, because word alone within a network has impact on the farmers BMP index. Farmer Led Councils would tackle both of these problems up to a point by getting more farmers into larger social networks, also Farmer Led Councils are creating incentive programs to help ease the cost of BMP implementation. Ultimately, relatively easy changes can be made if farmers work together to create the infrastructure they need to implement best management practices and remove phosphorous from our waterways.