Using weather forecasts to avoid major emission events of N2O in connection with manure application
Bibliographic record
Abstract
Choosing the appropriate timing of fertilisation is one of the primary managerial strategies to avoid emissions of nitrous oxide. To minimise odour nuisance and ammonia volatilisation, farmers are advised to apply pig slurry (PS) before light rainfall events. However, application of manure at the time of heavy rain events can pose a high risk of nitrous oxide (N 2 O) emissions, and analysing weather forecasts to avoid this could be important to mitigate emissions. A controlled field experiment was conducted to assess the effect of rainfall around the time of PS fertilisation on soil N 2 O emissions. The main findings were: 1) Rainfall treatments showed slight peaks in N 2 O emissions up to 3.9 mg N 2 O-N ha −1 day −1 after a rain event, and cumulative N 2 O emissions were numerically higher compared to the treatment without rainfall, suggesting a higher risk when manure application coincides with rainfall. 2) However, the short duration of elevated soil water content with more than 80 % water-filled pore space (WFPS) lasting only a few days after a rain event, may not have been sufficient to create anoxic conditions necessary for substantial N 2 O emissions from PS. This study's simulated rain approach can be used for future manipulative studies of field sites to further investigate mitigation options related to rainfall to inform best practices for fertiliser application in relation to weather forecasts.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".