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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 | |
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| Created: | Jan 20, 2026 at 10:22 p.m. (UTC) | |
| Last updated: | Jan 26, 2026 at 4:30 p.m. (UTC) | |
| Citation: | See how to cite this resource |
| Sharing Status: | Discoverable |
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| Views: | 43 |
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Abstract
Non-point source pollution from crop production threatens global water quality, endangering both human and ecosystem health. Best management practices (BMPs) can mitigate such loads, yet predicting farmers’ adoption remains challenging where behavioral data are scarce. This study embeds insights from economic experiments into a machine learning agent-based model (ABM) that represents heterogeneous farmer behavior under environmental policies and information nudges. Coupled with a semi-empirical phosphorus load model, the framework evaluates two phosphorus management baselines, crop pattern change and fertilizer change, alongside adoption of the BMP filter strips in Delaware’s Murderkill River watershed to assess progress toward the phosphorus reduction target. We find that ambient subsidies, especially when combined with information nudges, outperform taxes in driving filter strip uptake. Small farm fields only adopt with a subsidy-plus-nudge, whereas medium and large fields respond more readily to subsidies alone. These findings point to the potentially large value of coupling data-driven ABMs with nutrient load modeling to capture farmer-environment dynamic feedback and guide targeted, sustainable agricultural interventions.
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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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