Using conservation auctions informed by environmental performance models to reduce agricultural nutrient flows into Lake Erie
Bibliographic record
Abstract
Cost-effectively mitigating agricultural nutrient export requires an understanding of the biophysical characteristics of cropland as well as the behavioral and economic factors that drive land management decisions. Conservation auctions informed by models that simulate environmental outcomes have the potential to allocate conservation payments cost-effectively by funding practices that provide high predicted environmental benefits per dollar spent. This research tested two forms of conservation auctions. First, experimental auctions were used to analyze farmer preferences for different types of financial incentives for voluntary conservation, including direct payments, insurance, tax credits, and stewardship certification benefits. Second, conservation auctions were conducted in two Ohio counties to evaluate performance under real-world conditions. Supporting both types of auctions, the Soil and Water Assessment Tool (SWAT) predicted reductions in phosphorus exported as a function of the type of conservation practice and farm location. Results of the experimental auctions showed direct payments and tax credits to be the most cost-effective incentives to mitigate phosphorus export. The real auctions yielded two important lessons: 1) participation was very low, due to perceived transaction costs of participation—especially on rented fields and for group bids, and 2) the cost-effectiveness ranking of bids was highly sensitive to the parameters for soluble reactive phosphorus concentrations in the SWAT model. Future socio-economic research into payment for environmental services programs should seek cost-effective mechanisms with lower transaction costs for participants. Future biophysical research should strengthen our understanding of the factors governing soluble reactive phosphorus movement, so that models like SWAT can be more reliably parameterized.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".