Mitigating Nitrous Oxide Emissions from Corn Cropping Systems in the Midwestern U.S.: Potential and Data Gaps
Bibliographic record
Abstract
One of the unintended nitrogen (N)-loss pathways from cropland is the emission of nitrous oxide (N2O), a potent greenhouse gas and ozone depleting substance. This study explores the potential of alternative agronomic management practices to mitigate N2O emissions from corn cropping systems in major corn producing regions in the U.S. and Canada, using meta-analysis. The use of the urease inhibitor N-(n-butyl) thiophosphoric triamide (NBPT) in combination with the nitrification inhibitor Dicyandiamide (DCD) was the only management strategy that consistently reduced N2O emissions, but the number of observations underlying this effect was relatively low. Manure application caused higher N2O emissions compared to the use of synthetic fertilizer N. This warrants further investigation in appropriate manure N-management, particularly in the Lake States where manure application is common. The N2O response to increasing N-rate varied by region, indicating the importance of region-specific approaches for quantifying N2O emissions and mitigation potential. In general, more data collection on side-by-side comparisons of common and alternative management practices, especially those pertaining to N-placement, N-timing, and N-source, in combination with biogeochemical model simulations, will be needed to further develop and improve N2O mitigation strategies for corn cropping systems in the major corn producing regions in the U.S.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".