ABSTRACTMIDWEST FARMERS’ DECISION-MAKING IN CONSERVATION AGRICULTURE ADOPTIONByQi TianConservation Agriculture (CA) adoption can alleviate the environmental consequences of conventional agricultural production while maintaining yields. A better understanding of farmers’ decision-making in CA adoption is needed to inform policy design that encourages adoption. In the absence of the CA adoption market, experimental methods provide an essential alternative to investigate decision-makers’ preference. Therefore, this dissertation leverages a Discrete Choice Experiment (DCE) to analyze farmers’ decision-making to shed light on policy design as well as to inform methodological issues associated with DCE approach.The first chapter evaluates farmers’ Willingness-to-Accept (WTA) CA practices and assesses the factors affecting the WTA. In addition to the payment to compensate the expenses or efforts of taking a CA practice, a substantial payment is needed to incentivize farmers leaving the status quo and committing to a CA program. Internal factors, such as farmers’ characteristics and experience with CA practices, as well as external factors, i.e., policy design in terms of information framing and the decision time window, both have impacts on the WTA. These findings provide a practical guide for cost-efficient policy design.The traditional DCE approach for stated preference evaluation builds on an essential assumption that decision-making is reference independent, i.e., independent of irrelevant alternatives. The second chapter develops a new framework to relax and test this assumption by incorporating behavioral realism into modeling. I found that decision-makers use behavioral strategies, i.e., reference dependence, in decision making, and that different sources of information are evaluated differently as reference points. These findings, on the one hand, set caveats for modeling DCE data based on independence of irrelevance assumption, and on the other hand, indicate a more cost-efficient policy design tool that nudges desired behaviors through shaping the reference point.Three decision-making strategies could describe the decision making in a DCE: reference independence, reference dependence, and attributes non-attendance. This last chapter explicitly discusses which strategy is adopted and how such strategies evolve in repeated choice tasks. I found that decision-makers use behavioral strategies to make decisions. As decision-makers collect information over the repeated choice scenarios, they are shifting from the current choice set to the path as the reference point. Failing to account for the reference dependence behavior in choice modeling could misidentify the attended attributes as non-attended. This finding suggests that the reference dependence model can be a guiding choice for DCE modeling. Again, this chapter implies that discrete choice modeling without accounting for behavioral realism will fail to reveal the true preference.
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