Wet‐dry cycles control the emissions and sources of greenhouse gases in agricultural soil: An incubation study
Bibliographic record
Abstract
Abstract This study examines the impact of wet‐drying cycles and nitrogen (N) fertilization on soil greenhouse gas fluxes, specifically nitrous oxide (N 2 O) and carbon dioxide (CO 2 ). Nine treatments were tested, combining three soil moisture regimes (55% constant, 55%–30% cycle, and 80%–55% cycle) with three N addition rates (0, 100, and 150 kg N ha −1 ) using 15 N‐labeled urea. Soil samples from a potato ( Solanum tuberosum ) field in Lethbridge, Alberta, were incubated for 28 days under controlled conditions. Wet‐drying cycles involved initially wetting the soil to the upper threshold (55% or 80% WFPS) and allowing it to dry to the lower threshold (30% or 55% WFPS), followed by rewetting to restore upper moisture levels. N 2 O and CO 2 fluxes were measured regularly using a recirculation chamber system to quantify gas emissions and determine N 2 O sources. Soil moisture significantly increased N 2 O and CO 2 production ( p < 0.001), with the highest emissions under wet conditions (80%–55% WFPS cycle), moderate production at 55% WFPS, and the lowest under dry conditions (30%–55% WFPS cycle). Compared to constant 55% WFPS, N 2 O and CO 2 production were 33% and 403% higher, respectively, under wet conditions and 28% and 3% lower under dry conditions. Rewetting events triggered temporary increases in gas emissions due to enhanced microbial activity. Urea addition caused a stronger priming effect on N 2 O production under wet conditions, with urea‐derived N 2 O more prominent in wetter soils.
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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".