Agroecosystem modeling of reactive nitrogen emissions from U.S. agricultural soils with carbon amendments
Bibliographic record
Abstract
Abstract Fertilizer-intensive agriculture is a leading source of reactive nitrogen (Nr) emissions that damage climate, air quality, and human health. Biochar has long been studied as a soil amendment, but its influence on Nr emissions remains insufficiently characterized. More recently, the pyrolysis of light hydrocarbons has been suggested as a source of hydrogen fuel, resulting in a solid zero-valent carbon (ZVC) byproduct whose impact on soil emissions has yet to be tested. We incorporate carbon amendment algorithms into an agroecosystem model to simulate emission changes in the year following the application of biochar or ZVC to the US. fertilized soils. Our simulations predicted that the impacts of biochar amendments on Nr emissions would vary widely (− 17% to + 27% under 5 ton ha−1 applications, − 38% to + 18% under 20 ton ha−1 applications) and depend mostly on how nitrification is affected. Low-dose biochar application (5 ton ha−1) stimulated emissions of all three nitrogen species in 75% of simulated agricultural areas, while high-dose applications (20 ton ha−1) mitigated emissions in 76% of simulated areas. Applying zero-valent carbon at 20 ton ha−1 exhibited similar effects on nitrogen emissions as biochar applications at 5 ton ha−1. Biochar amendments are most likely to mitigate emissions if applied at high rates in acidic soils (pH < 5.84) with low organic carbon (< 55.9 kg C ha−1) and inorganic nitrogen (< 101.5 kg N ha−1) content. Our simulations could inform where the application of carbon amendments would most likely mitigate Nr emissions and their associated adverse impacts. Graphical Abstract
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".