We utilize a machine learning model built around 1,200 completed farmer surveys. We asked questions about their views on different behaviors, which point to the various personas and then ask them to self-identify. If there is agreement between their answers and self-identifying, we create a training data set of these growers which added in over 300 data points. We then built and refined a model that we used to score our entire universe of growers.