Carbon debt of Conservation Reserve Program (CRP) grasslands converted to bioenergy production
Bibliographic record
Abstract
Over 13 million ha of former cropland are enrolled in the US Conservation Reserve Program (CRP), providing well-recognized biodiversity, water quality, and carbon (C) sequestration benefits that could be lost on conversion back to agricultural production. Here we provide measurements of the greenhouse gas consequences of converting CRP land to continuous corn, corn-soybean, or perennial grass for biofuel production. No-till soybeans preceded the annual crops and created an initial carbon debt of 10.6 Mg CO(2) equivalents (CO(2)e)·ha(-1) that included agronomic inputs, changes in C stocks, altered N(2)O and CH(4) fluxes, and foregone C sequestration less a fossil fuel offset credit. Total debt, which includes future debt created by additional changes in soil C stocks and the loss of substantial future soil C sequestration, can be constrained to 68 Mg CO(2)e·ha(-1) if subsequent crops are under permanent no-till management. If tilled, however, total debt triples to 222 Mg CO(2)e·ha(-1) on account of further soil C loss. Projected C debt repayment periods under no-till management range from 29 to 40 y for corn-soybean and continuous corn, respectively. Under conventional tillage repayment periods are three times longer, from 89 to 123 y, respectively. Alternatively, the direct use of existing CRP grasslands for cellulosic feedstock production would avoid C debt entirely and provide modest climate change mitigation immediately. Incentives for permanent no till and especially permission to harvest CRP biomass for cellulosic biofuel would help to blunt the climate impact of future CRP conversion.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".