Reducing nitrous oxide emissions from a maize‐wheat sequence by decreasing soil nitrate concentration: effects of split application of pig slurry and dicyandiamide
Bibliographic record
Abstract
Summary Pig slurry ( PS ) is a valuable nitrogen ( N ) source for agricultural crops but the simultaneous supply of readily decomposable carbon and mineral N can result in large soil nitrous oxide ( N 2 O ) emissions. Our objective was to determine the individual and combined effects of split PS application and addition of a nitrification inhibitor (dicyandiamide, DCD ) on N 2 O emissions and soil mineral N concentration in southern B razil. Soil N 2 O fluxes were measured from N ovember 2010 to N ovember 2011 from a maize ( Zea mays L. )‐wheat ( Triticum aestivum L. ) sequence under various fertilizer treatments: no‐ N control, PS applied in a single pre‐plant dose with or without DCD , PS split‐applied with or without DCD , and urea split‐applied. Cumulative N 2 O emissions increased linearly ( R 2 = 0.73) with increasing soil nitrate ( NO 3 − ) exposure, indicating that management practices aimed at reducing soil NO 3 − concentrations can decrease soil N 2 O emissions. In total for the two crops, splitting PS reduced N 2 O emission factors ( EF ) by 33%, whereas the addition of DCD reduced EF by 60 and 41% when PS was applied in single and split doses, respectively. However, splitting PS or adding DCD failed to reduce N 2 O losses more than a single pre‐plant PS application in maize where background soil NO 3 − concentrations were large. The addition of DCD to PS applied as a single pre‐plant dose resulted in the largest reduction in soil N 2 O emissions, whereas splitting PS with and without DCD resulted in significantly smaller abatements. Consequently, we concluded that adding DCD to PS in a single pre‐plant application is a better option than splitting PS applications for reducing soil N 2 O emissions in no‐till cereal cropping systems in southern B razil.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".