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| Created: | Jan 20, 2026 at 10:22 p.m. (UTC) | |
| Last updated: | Sep 18, 2026 at 12:22 p.m. (UTC) | |
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Abstract
Non-point source pollution from crop production threatens water quality, yet linking heterogeneous practice adoption to watershed outcomes remains challenging where farmer decision data are limited. We couple a laboratory-informed agent-based model (ABM) with a semi-empirical phosphorus-load model for Delaware’s Murderkill River watershed. A reduced-form XGBoost classifier is fitted to incentivized binary abatement choices from a context-free experiment with undergraduate participants. These choices and experimental production capacities are mapped conceptually to filter-strip adoption and corn-area classes for 165 simulated farmer agents. Six fixed policy and information-treatment combinations are evaluated under prescribed crop pattern change and fertilizer change scenarios. In the base simulations, the ambient policy without information or with group-level information produced full predicted adoption and reached the 50% total phosphorus reduction target soonest among the six combinations within each scenario. Despite identical adoption levels, the scenario-specific 50% reduction thresholds were reached after 35 years under crop conversion and 9 years under fertilizer reduction. Under the ambient policy, agents with medium and large corn areas were predicted to adopt with every information treatment, whereas agents with small corn areas were predicted not to adopt when individual-level information was provided; this pattern varied across refits and a location-inclusion check. These results illustrate how the environmental consequences of adoption depend on prescribed management and load assumptions. The conditional predictions require independent behavioral and environmental evaluation. By explicitly linking experimental choices, heterogeneous adoption predictions, and phosphorus trajectories, the framework provides a foundation for integrating behavioral evidence into watershed scenario analysis.
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Funding Agencies
This resource was created using funding from the following sources:
| Agency Name | Award Title | Award Number |
|---|---|---|
| U.S. National Science Foundation | EPSCoR | 1757353 |
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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