Mitigating\nNitrous Oxide Emissions from Corn Cropping\nSystems in the Midwestern U.S.: Potential and Data Gaps
Bibliographic record
Abstract
One\nof the unintended nitrogen (N)-loss pathways from cropland\nis the emission of nitrous oxide (N<sub>2</sub>O), a potent greenhouse\ngas and ozone depleting substance. This study explores the potential\nof alternative agronomic management practices to mitigate N<sub>2</sub>O emissions from corn cropping systems in major corn producing regions\nin the U.S. and Canada, using meta-analysis. The use of the urease\ninhibitor N-(n-butyl) thiophosphoric triamide (NBPT) in combination\nwith the nitrification inhibitor Dicyandiamide (DCD) was the only\nmanagement strategy that consistently reduced N<sub>2</sub>O emissions,\nbut the number of observations underlying this effect was relatively\nlow. Manure application caused higher N<sub>2</sub>O emissions compared\nto the use of synthetic fertilizer N. This warrants further investigation\nin appropriate manure N-management, particularly in the Lake States\nwhere manure application is common. The N<sub>2</sub>O response to\nincreasing N-rate varied by region, indicating the importance of region-specific\napproaches for quantifying N<sub>2</sub>O emissions and mitigation\npotential. In general, more data collection on side-by-side comparisons\nof common and alternative management practices, especially those pertaining\nto N-placement, N-timing, and N-source, in combination with biogeochemical\nmodel simulations, will be needed to further develop and improve N<sub>2</sub>O mitigation strategies for corn cropping systems in the major\ncorn 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.045 | 0.001 |
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; both teacher heads agree on what is shown here.
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".