Reducing nitrous oxide emissions from a maize‐wheat sequence by decreasing soil nitrate concentration: effects of split application of pig slurry and dicyandiamide
Why this work is in the frame
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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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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 | 0.001 | 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 | 0.001 |
| 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 it