Increased N2O Production from Soil Organic Matter Following a Simulated Fall-Freeze-Thaw Cycle: Effects of Fall Urea Addition, Soil Moisture, and History of Manure Applications
Bibliographic record
Abstract
Abstract Adding nitrogen substrates to soils can induce short-term changes in soil organic matter (SOM) transformations – a response termed the ‘priming effect’. However, it is unknown how priming effects on nitrous oxide (N2O) emissions can be altered following a strong freeze-thaw cycle. A mesocosm experiment evaluated two soil managements: with and without history of manure applications. These soils were subjected to three moisture regimes: Low, Medium and High. Apart from the controls, which received no N, we banded 15N-labelled urea into these soils representing a typical fall fertilization, and subsequently simulated a wide fall-freeze-thaw cycle, with temperatures from + 2, to -18, and finally + 23°C, respectively. The overall highest N2O production was observed 1 day after thawing. At that time, measurements of N2O site preference indicated that denitrification produced 83% of the N2O flux. Relative to the unamended controls (baseline), adding urea consistently triggered a 24% greater cumulative N2O production specifically originated from SOM following thawing (245 vs. 305 µg N2O-N kg− 1 soil, P = 0.022). This substantiates a positive priming of SOM that manifested shortly after the rapid, wet thawing of the soils. Soils having a manure history or higher moisture also exhibited an augmented production of N2O from SOM (Ps < 0.01). Although the overall priming of SOM was positive, two weeks after thawing, negative priming of daily N2O fluxes also occurred, but only in soils under High moisture. Besides urea additions, the propensity for primed N2O emissions from SOM after thawing was influenced by increasing moisture and earlier manure applications.
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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".