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In the U.S., the fifth largest ‘‘crop’’ is vegetation dedicated to environmental conservation. Over 22 million acres of perennial covers are planted on environmentally sensitive land enrolled in U.S. Department of Agriculture’s Conservation Reserve Program (CRP), one of the largest agricultural conservation programs in the world. About half of CRP lands are enrolled through a complex reverse auction called the General Signup. The communication of program options to participants could have an important behavioral impact. Psychologists have found that information presentation in complex decision environments can interact with the bounded rationality and cognitive biases of decision makers. We tested two changes in the status quo CRP decision environment using an incentivized, lab-in-the-field experiment with 701 prior General Signup participants. First, program participants typically make an active choice over which cover practice to plant and how much of a discount to offer, where the discount is a reduction in their annual program payment. Changing that default to an opt-out, high-scoring offer resulted in a 13 percentage point increase in selection of the best practice and a 48 percent increase in the average discount. In the actual CRP, that increase in discounting would reduce total program costs by about $30 million per signup. Second, shifting to real-time updating of offer scores modestly reduced the frequency with which participants revised their offers, suggesting a reduction in transaction costs. From a policy perspective, these results suggest that small changes in conservation auctions could both improve the quality of conservation practices and reduce program costs. 

Abstract Managed aquifer recharge (MAR) can provide long‐term storage of excess surface water for later use. While decades of research have focused on the physical processes of MAR and identifying suitable MAR locations, very little research has been done on how to consider competing factors and tradeoffs in siting MAR facilities. This study proposes the use of a simulation‐optimization (SO) framework to map out a cost‐effectiveness frontier for MAR by combining an evolutionary algorithm with two objective functions that seek to maximize groundwater storage gains while minimizing MAR cost. We present the theoretical framework along with a real‐world application to California's Central Valley. The result of the SO framework is a Pareto front that allows identifying suitable MAR locations for different levels of groundwater storage gain and associated MAR project costs, so stakeholders can evaluate different choices based on cost, benefits, and tradeoffs of MAR sites. Application of the SO framework to the Central Valley shows groundwater can be recharged from high‐magnitude (95th percentile) flows at a marginal cost of $57 to $110 million per km³. If the 10 percent largest flows are recharged the total groundwater storage gain would double and the marginal costs would drop to between $30 and $50 million per km³. If recharge water is sourced from outside local basins (e.g., the Sacramento‐San Joaquin Delta), groundwater storage gain is approximately 25%–80% greater than can be achieved by recharging local flows, but the total cost is about 10%–15% higher because of additional lift cost.