Front-Load and Free-Ride: Farmers’ Responses to Antibiotic Regulation and Peer Stewardship in U.S. Apple Production

Antibiotic resistance in plant agriculture poses a growing threat to sustainable food production. In U.S. apple orchards, fire blight disease has long been managed primarily with an antibiotic whose efficacy is eroding due to widespread resistance. Decades of antibiotic reliance have accelerated the emergence of resistant bacterial strains. Using less antibiotics, rotating between antibiotics with different modes of actions, and using non-antibiotic alternatives are three pillars of Integrated Pest Management (IPM) strategies to control fire blight while reducing the antibiotic resistance development. Previous studies show that conserving public access resources such as antibiotic efficacy can be exacerbated by neighbors’ adoption. Additionally, state and federal level regulations can be adopted to restrict farmers’ access to resources and suppress over exploitation (i.e. antibiotic resistance). Despite increasing regulatory pressure to restrict agricultural antibiotic use, little is known about how farmers respond to prospective bans or how peer behavior shapes adoption of IPM strategies. This paper fills that gap by examining U.S. apple farmers' willingness to adopt IPM practices that reduce antibiotic dependence and manage resistance, and by investigating how expectations about future regulation and neighbors' behavior affect those decisions. We develop a theoretical model of strategic antibiotic use that generates two testable predictions. First, farmers who anticipate an antibiotic ban optimally front-load antibiotic applications before restrictions become binding, depleting efficacy more rapidly than farmers operating without regulatory expectations. Second, farmers adjust their antibiotic use in response to neighbors' resistance management decisions, exhibiting either free-riding incentives or private conservation incentives. To test these predictions empirically, we design a discrete choice experiment (DCE) embedded with a randomized information treatment and administer it to commercial apple farmers across major apple-producing states in the US. The DCE elicits preferences over fire blight management bundles that vary in antibiotic rotation, biopesticide use, streptomycin application rate, short- and long-run disease control effectiveness, neighbors' resistance management adoption rates, and cost per acre. To isolate the effect of a future ban, respondents randomly received an information treatment stating that a ban on agricultural antibiotics would be implemented in ten years, while the control group received neutral information. Both groups completed two blocks of choice tasks (one before and one after the information intervention) yielding 1,008 unique choice observations from 126 complete and usable survey responses. We estimate preference heterogeneity using a mixed multinomial logit (MXL) model and identify distinct behavioral segments using a latent class model (LCM). The MXL results reveal that antibiotic rotation is positively valued on average, while biopesticides and reduced streptomycin use exhibit no significant mean effects but considerable variance, indicating the presence of distinct preference types. Critically, the positive and statistically significant interaction between streptomycin use and the ban information treatment confirms the theoretical prediction that exposure to a prospective antibiotic ban increases the marginal utility of antibiotic applications rather than triggering a shift toward alternatives. The ban signal induces a short-run intensification of antibiotic use which is an unintended behavioral consequence consistent with race-to-depletion dynamics documented in other common-pool resource settings. The interaction between biopesticide adoption and the ban is negative but insignificant, suggesting that policy signals alone are insufficient to accelerate the transition to non-antibiotic alternatives. In fact, the negative and significant interaction between antibiotic application and biopesticide implies that farmers consider biopesticides and antibiotics as substitutes. We also find that farmers increase antibiotic applications when they perceive higher levels of resistance management among neighbors, consistent with free-riding behavior, in which individual producers exploit the stewardship efforts of others. The LCM identifies three economically interpretable classes. The IPM adopter class (26%) strongly prefers rotation and biopesticides and reduces streptomycin use. The forward-looking class (28%) discounts antibiotic applications and prioritizes long-run disease control effectiveness and is more likely to anticipate future bans and operate under higher disease pressure. The myopic free-rider class (46%) favors using less streptomycin out of the three IPM strategies to manage resistance, emphasizes short-run effectiveness, discounts long-run outcomes, and is highly responsive to peer adoption, consistent with free-riding incentives. These findings carry important implications for policy design. Regulatory strategies that announce future bans without accompanying transitional incentives risk inducing a short-run surge in antibiotic use. Effective policies should pair prospective restrictions with instruments that reward early IPM adoption and leverage peer dynamics through targeted extension engagement with opinion leaders. Tailoring interventions to the distinct behavioral segments identified here, rather than targeting the average grower, can improve both the uptake and cost-effectiveness of strategies aimed at preserving antibiotic efficacy in specialty crop systems.