Nitrogen fertilization increases N2O emission but does not offset the reduced radiative forcing caused by the increased carbon uptake in boreal forests
Bibliographic record
Abstract
Net primary production in boreal coniferous forests is generally severely limited by N deficiency. Nitrogen fertilization has thus the potential to strongly increase forest tree biomass production in the boreal region and consequently increase the biosphere uptake of atmospheric CO2. Increased N availability may though increase the production and emission of soil N2O, counteracting the climate mitigation potential from increased forest biomass production. Studies in the boreal region on the net effect on the climate mitigation potential from N fertilization are scarcer than in other biomes. Therefore, we explored how N affected soil GHG fluxes in two boreal field N-loading experiments, of which one is a long-term experiment (40 years), and the other established 6 years before investigation. We also estimated whether the increased soil N2O emission could offset the N-driven increased C sequestration by the trees. Nitrogen additions affected the soil GHG fluxes in both stands. Soil N2O emission was enhanced by N addition at every fertilization rate, though marginally compared to the reduced soil CO2 emission and the increased atmospheric CO2 uptake and biomass production. The estimated annual uptake of CH4 by soil under long-term N addition increased. The magnitude of soil CH4 uptake was on the same order of magnitude as the increase in soil N2O emissions caused by N addition, when compared as CO2 equivalents. In conclusion, forest N fertilization in boreal areas increased the GHG net uptake and, thus, provides a means to mitigate increasing atmospheric concentrations of GHG.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".