GeoComputational Approaches to Evaluate the Impacts of Communication on Decision-making in Agriculture

Abstract

This dissertation proposes a new geocomputational approach to evaluate how communication-based interventions impact outcomes in agriculture. The decisions that people make in agriculture over the next ten to fifteen years will have long-term global consequences because agriculture is going to need to broadly change in order to meet the needs of the future. Many of the technical requirements and economic demands needed to enhance agriculture’s sustainability have been articulated with relative clarity. What remains opaque are the details of who should change, when, where, and how. A growing number of organizations are turning to communication-based interventions to answer these questions with people who will be impacted by changes. Evaluating these interventions is difficult because they are qualitative, affective, meaning-oriented, and discursive. This dissertation builds on existing trends in geocomputation around qualitative geographic information systems and incorporates new methods from machine learning into spatial agent-based modeling. Doing so allows for largely automated creation of agents from natural language text. The dissertation expands on these new tools in each chapter and applies them to the challenge of evaluating communication- based interventions focused on Midwest agriculture. Results suggest that novel insights can be gained into the inner workings of communication-based interventions for improving decision-making using the approaches described in this dissertation.