N<sub>2</sub>O emissions and carbon sequestration in a nitrogen‐fertilized Douglas fir stand
Bibliographic record
Abstract
This study investigated how nitrogen (N) fertilization with 200 kg N ha−1 of a 58‐year‐old West Coast Douglas fir stand influenced its net greenhouse gas (GHG) global warming potential (GWP) in the first year after fertilization. Effects of fertilization on GHG GWP were calculated considering changes in soil N2O emissions, measured using the static chamber technique and the soil N2O gradient technique; eddy covariance (EC) measured net ecosystem productivity (NEP); and energy requirements of fertilizer production, transport, and its aerial spreading. We found significant N2O losses in fertilized plots compared to a small uptake in nonfertilized plots. Chamber‐measured N loss in the fertilized plots was about 16 kg N2O ha−1 in the first year, which is equivalent to 10 kg N ha−1 or 5% of the applied fertilizer N. Soil N2O emissions measured using the gradient technique, however, exceeded the chamber measurements by about 50%. We also compared a polymer‐coated slow‐release urea with regular urea and found that the former delayed N2O emissions but the year‐end total loss was about the same as that from regular urea. Change in NEP due to fertilization was determined by relating annual NEP for the nonfertilized stand to environmental controls using an empirical and a process‐based model. Annual NEP increased by 64%, from 326 g C m−2, calculated assuming that the stand was not fertilized, to the measured value of 535 g C m−2 with fertilization. At the end of the year, net change in GHG GWP was −2.28 t CO2 ha−1 compared to what it would have been without fertilization, thereby indicating favorable effect of fertilization even in the first year after fertilization with significant emissions 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".