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 (N 2 O) in the atmosphere. However, the impacts of N addition on soil organic matter (SOM) turnover, SOM availability, and the ensuing SOM‐derived N 2 O emissions remain elusive. Within this context, the net change in direction and rate of SOM‐derived N 2 O production triggered by added N is termed the N 2 O priming effect. This incubation study examined the sources and priming of N 2 O 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 N 2 O production by 2.6 times ( P < .001). Cumulative N 2 O production correlated well with nitrification rates ( r = .75; P = .03). We used 15 N‐labeled urea to trace the added urea into N 2 O. Of the N added via urea, the recovery as N 2 O–N shifted from 0.02 to 0.17% when WFPS increased from 28 to 64% ( P < .05). We also partitioned the N 2 O production into urea vs. SOM sources. More N 2 O was sourced from SOM than urea, with 59 ± 2% N 2 O originating from SOM. The magnitude of SOM‐derived N 2 O under urea was larger than that of the control, revealing that positive N 2 O priming was triggered by urea addition. Upon subtracting the controls, the primed N 2 O was a consistent 19 ± 2% of the total N 2 O 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 N 2 O–N kg −1 soil and in exponential mode ( R 2 = .98). Soil moisture, SOM, and nitrification interacted to drive the sources and priming of N 2 O.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".