Tackling policy leakage and targeting hotspots could be key to addressing the ‘Wicked’ challenge of nutrient pollution from corn production in the U.S.
Bibliographic record
Abstract
Abstract Reducing nutrient loss from agriculture to improve water quality requires a combination of management practices. However, it has been unclear what pattern of mitigation is likely to emerge from different policies, individually and combined, and the consequences for local and national land use and farm returns. We address this research gap by constructing an integrated multi-scale framework for evaluating alternative nitrogen loss management policies for corn production in the US. This approach combines site- and practice-specific agro-ecosystem processes with a grid-resolving economic model to identify locations that can be prioritized to increase the economic efficiency of the policies. We find that regional measures, albeit effective in reducing local nitrogen loss, can displace corn production to the area where nitrogen fertilizer productivity is low and nutrient loss rate is high, thereby offsetting the overall effectiveness of the nutrient management strategy. This spatial spillover effect can be suppressed by implementing the partial measures in tandem with nationwide policies. Wetland restoration combined with split fertilizer application, along with a nitrogen loss tax could reduce nitrate nitrogen loss to the Mississippi River by 30% while only increasing corn prices by less than 2%.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".