Spatial Targeting of Conservation Tillage to Improve Water Quality and Carbon Retention Benefits
Bibliographic record
Abstract
This paper develops a GIS‐based modeling framework that integrates a farm model, a hydrologic model, and a soil organic matter model to examine spatial targeting of conservation tillage to jointly improve water quality and carbon retention benefits in agricultural watersheds. Previous studies have examined the targeting of conservation tillage, land retirement, and riparian buffers at watershed scale to achieve water quality benefits but not considered carbon retention benefits. An empirical application of the framework in the Fairchild Creek watershed in Ontario shows that targeting conservation tillage based on sediment abatement goal can also achieve comparable carbon retention benefits in terms of percentage reduction of its base carbon losses. The targeted subcatchments for conservation tillage vary across the watershed based on benefit to cost ratios. The pattern of conservation tillage targeted based on carbon retention goal is similar to that with a sediment abatement goal but slight differences are found because of different carbon content in the soils. The modeling results have important policy implications for the design of conservation stewardship programs such as setting sediment abatement goal as an indicator to achieve joint environmental benefits and direct public fund to locations that can achieve environmental goals at least costs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".