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Coupling Agent-based and Nutrient Load Models to Simulate Best Management Practice Adoption for Mitigating Agricultural Non-point Source Pollution
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| Type: | Resource | |
| Storage: | The size of this resource is 269.4 KB | |
| Created: | Jan 20, 2026 at 10:22 p.m. (UTC) | |
| Last updated: | Aug 07, 2026 at 7:53 p.m. (UTC) | |
| Citation: | See how to cite this resource |
| Sharing Status: | Public |
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| Views: | 700 |
| Downloads: | 24 |
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Abstract
Nonpoint source pollution from crop production threatens global water quality and endangers both human and ecosystem health. Best management practices (BMPs) can mitigate these loads, yet predicting farmer adoption remains challenging where behavioral data are scarce. This study embeds insights from an economic experiment into a machine learning agent-based model (ABM) that learns a reduced-form decision rule rather than solving a structural optimization, representing heterogeneous farmer agents under an ambient-policy treatment and two information treatments. Coupled with a semi-empirical phosphorus load model, the framework links agent-level filter strip adoption to total phosphorus and the time required to reach reduction targets under crop pattern and fertilizer change scenarios in Delaware’s Murderkill River watershed. In an exploratory, treatment-conditional comparison, simulated filter strip uptake was higher under the ambient-policy label, particularly when paired with information-treatment labels, than under the no-policy baseline. In the base classifier, small-class agents adopted only under a policy-plus-nudge combination, whereas medium- and large-class agents adopted under the ambient policy alone; this subgroup pattern held in 43 of 50 participant-grouped refits and changed at one tested value when the omitted experimental location factor was included. The primary contribution is methodological: coupling laboratory-informed, reduced-form ABMs with nutrient load modeling offers a low-cost approach to explore agricultural interventions where field-level adoption data are lacking, although transferability to real-world settings remains to be validated.
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This resource was created using funding from the following sources:
| Agency Name | Award Title | Award Number |
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| 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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