Sources and priming of nitrous oxide production across a range of moisture contents in a soil with high organic matter
Bibliographic record
Abstract
Abstract Adding nitrogen fertilizers to agricultural soils contributes to increasing concentrations of nitrous oxide (N2O) in the atmosphere. However, the impacts of N addition on soil organic matter (SOM) turnover, SOM availability, and the ensuing SOM‐derived N2O emissions remain elusive. Within this context, the net change in direction and rate of SOM‐derived N2O production triggered by added N is termed the N2O priming effect. This incubation study examined the sources and priming of N2O production as a function of urea addition and multiple moisture contents in a soil with high SOM (55 g organic C kg−1). We assessed four water‐filled pore space (WFPS) conditions: 28, 40, 52, and 64%. Relative to controls receiving no N, urea addition increased N2O production by 2.6 times (P < .001). Cumulative N2O production correlated well with nitrification rates (r = .75; P = .03). We used 15N‐labeled urea to trace the added urea into N2O. Of the N added via urea, the recovery as N2O–N shifted from 0.02 to 0.17% when WFPS increased from 28 to 64% (P < .05). We also partitioned the N2O production into urea vs. SOM sources. More N2O was sourced from SOM than urea, with 59 ± 2% N2O originating from SOM. The magnitude of SOM‐derived N2O under urea was larger than that of the control, revealing that positive N2O priming was triggered by urea addition. Upon subtracting the controls, the primed N2O was a consistent 19 ± 2% of the total N2O produced by urea‐amended soils. Nevertheless, the priming magnitude rose sharply with increasing moisture by more than one order of magnitude from 4 to 48 μg N2O–N kg−1 soil and in exponential mode (R2 = .98). Soil moisture, SOM, and nitrification interacted to drive the sources and priming of N2O.
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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) | 0.000 | 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 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".