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This special issue aims to extend the active discourse on applying behavioral science-based tools to policymaking in the fields of food, agriculture, and agri-environmental issues. Papers in this special issue evaluate the impact of behavioral science-based tools to understand their effectiveness and limitations. Additionally, for this introductory paper, we collected and analyzed data from the 91 submissions we received for this special issue to identify knowledge gaps and priorities for future policy research. Our findings show that behavioral interventions have small effect sizes but, when coupled with other policy tools, can have larger effects. We highlight that future research in these areas must aim to overcome the current shortcomings of the literature in terms of the use of hypothetical or low stakes in experiments, the focus on only measuring short-term behavior change, and the general lack of discussion on cost-effectiveness and mechanisms. Furthermore, we found that most behavioral science interventions in these submitted papers focused either on consumers or producers and thus offered little insight into other actors in the supply chain. We argue that a focus on better research practices is needed to improve policy-oriented behavioral science-based research in the future and note that accepted papers in this special issue were more likely to employ these practices. Finally, we offer six insights and recommendations for researchers and practitioners that arise from this special issue 

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. 

Scientists seek to understand the causal processes that generate sustainability problems and determine effective solutions. Yet, causal inquiry in nature–society systems is hampered by conceptual and methodological challenges that arise from nature–society interdependencies and the complex dynamics they create. Here, we demonstrate how sustainability scientists can address these challenges and make more robust causal claims through better integration between empirical analyses and process- or agent-based modeling. To illustrate how these different epistemological traditions can be integrated, we present four studies of air pollution regulation, natural resource management, and the spread of COVID-19. The studies show how integration can improve empirical estimates of causal effects, inform future research designs and data collection, enhance understanding of the complex dynamics that underlie observed temporal patterns, and elucidate causal mechanisms and the contexts in which they operate. These advances in causal understanding can help sustainability scientists develop better theories of phenomena where social and ecological processes are dynamically intertwined and prior causal knowledge and data are limited. The improved causal understanding also enhances governance by helping scientists and practitioners choose among potential interventions, decide when and how the timing of an intervention matters, and anticipate unexpected outcomes. Methodological integration, however, requires skills and efforts of all involved to learn how members of the respective other tradition think and analyze nature–society systems. 

Abstract Experimental research in behavioral economics focuses on consumer behaviors. Similar experimental research on profit-maximizing producers is rare. In three field experiments involving commercial agricultural producers in the US, we detect evidence of anchoring in competitive auctions for conservation contracts related to nutrient and pest management that were worth, on average, nearly nine thousand dollars. In these auctions, the value of the starting cost-share bid was randomized to be either 0% or 100%. When the starting value was 100%, final bids were 46% higher, on average. We find weak evidence that experience with conservation contracts may modestly attenuate the anchoring effect. 

Economic experiments have emerged as a powerful tool for agricultural policy evaluations. In this perspective, we argue that involving stakeholders in the design of economic experiments is critical to satisfy mandates for evidence-based policies and encourage policymakers' usage of experimental results. To identify advantages and disadvantages of involving stakeholders when designing experiments, we synthesize observations from six experiments in Europe and North America. In these experiments, the primary advantage was the ability to learn within realistic decision environments and thus make relevant policy recommendations. Disadvantages include complicated implementation and constraints on treatment design. We compile 12 recommendations for researchers. 

A popular strategy for mitigating climate change is to persuade or incentivize individuals to limit behaviors associated with high greenhouse gas emissions. In this study, adults in the mid-Atlantic United States bid in an auction to receive compensation for eliminating beef consumption or limiting vehicle use. The auction incentivized participants to reveal their true costs of accepting these limits for periods ranging from one week to one year. Compliance with the conditions of the auction was confirmed via a random field audit of the behavioral changes. The estimated median abatement costs were greater than $600 per tCO2e for beef consumption and $1,300 per tCO2e for vehicle use, values much higher than the price of carbon offsets and most estimates of the social cost of carbon. Although these values may decline over time with experience or broader social adoption, they imply that policies that encourage innovations to reduce the costs of behavior change, such as meat alternatives or emission-free vehicles, may be a more fruitful than those that limit beef consumption or vehicle use. 

Abstract Causal effects of biodiversity on ecosystem functions can be estimated using experimental or observational designs — designs that pose a tradeoff between drawing credible causal inferences from correlations and drawing generalizable inferences. Here, we develop a design that reduces this tradeoff and revisits the question of how plant species diversity affects productivity. Our design leverages longitudinal data from 43 grasslands in 11 countries and approaches borrowed from fields outside of ecology to draw causal inferences from observational data. Contrary to many prior studies, we estimate that increases in plot-level species richness caused productivity to decline: a 10% increase in richness decreased productivity by 2.4%, 95% CI [−4.1, −0.74]. This contradiction stems from two sources. First, prior observational studies incompletely control for confounding factors. Second, most experiments plant fewer rare and non-native species than exist in nature. Although increases in native, dominant species increased productivity, increases in rare and non-native species decreased productivity, making the average effect negative in our study. By reducing the tradeoff between experimental and observational designs, our study demonstrates how observational studies can complement prior ecological experiments and inform future ones.