Emphasizing the economic and environmental benefits of conservation is business-as-usual for environmental organizations seeking to influence conservation behavior, but these message frames are rarely tested. We embedded a large message framing experiment into the recruitment for a conservation agriculture program targeting farmland owners in the Mississippi River Basin. We found that framed messages do not increase enrollment in the agricultural program-the desired conservation behavior-compared to an informational message (control) and may decrease enrollment among farmland owners not already using conservation practices (i.e., cover crops).

To develop evidence-based agricultural policies, researchers increasingly use insights from economic field experiments. These insights are often limited by the challenges of recruiting large and representative samples of farmers. To improve the effectiveness and cost efficiency of farmer recruitment, researchers should apply the same experimental methods to the recruitment process that they apply to their main research questions. Here we experimentally evaluate ten recruiting strategies in two large-scale, high stakes experiments. We find that monetary incentives and reminders are effective, but costly. Costless strategies, such as prominently citing a well-known institution as the sponsor, had positive but small, effects on recruitment.

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Economic experiments have been used to inform evidence-based policymaking in a variety of fields but have rarely been used to address agricultural policy topics in the United States. Several barriers exist in designing and funding experimental studies with farmer participants, which limits the usefulness of this approach for informing agricultural policymaking. We review three such barriers: heterogenous treatment effects, access to participants, and aligning funding agencies' priorities. We document the extent of these barriers using original analyses of the literature. We then suggest potential methods of mitigating these barriers through changes in how experiments are designed, reviewed, and funded.