The Extent of Soil Drying and Rewetting Affects Nitrous Oxide Emissions, Denitrification, and Nitrogen Mineralization
Bibliographic record
Abstract
Soil drying and subsequent rewetting induces N mineralization and denitrification, but the effects of the “extent” or “degree” of drying and rewetting remains poorly understood. The impacts of different degrees of soil drying (drying to 45, 30, 20, or 10% water-filled pore space, WFPS) and subsequent rewetting (rewetting to 75 or 90% WFPS) on N2O emissions, denitrification, and net N mineralization were investigated. The highest N2O emissions (201 µg N2O-N kg-1) occurred when the soils were dried to 10% WFPS followed by rewetting to 90% WFPS, whereas the lowest emissions (4.72 µg N2O-N kg-1) occurred when the soil was dried to 45% WFPS followed by rewetting to 75% WFPS. When soil was rewetted from 10 to 90% WFPS, cumulative N2O emissions over 120 h were 7.4 times greater than when the soil was rewetted from 10 to 75% WFPS. The proportion of N2O evolved [N2O/(N2O+N2)] generally increased as the soil dried. Soil rewetting to 75% WFPS generally produced greater N2O/(N2O+N2) ratios than rewetting to 90% WFPS. Net N mineralization rates in soils rewetted to 75% WFPS significantly increased from 0.78 mg N kg-1 d-1 for the soils dried to 45% WFPS to 1.69 mg N kg-1 d-1 for the soils dried to 10% WFPS. More extensive soil drying and more extensive rewetting stimulated N2O emissions and total denitrification losses, whereas net N mineralization rates were stimulated only by more extensive drying. Management practices which reduce extreme fluctuations in soil water content may consequently reduce N2O and total denitrification losses.
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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) | 0.000 | 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".