Climate change and N2O emissions from South West England grasslands: A modelling approach
Bibliographic record
Abstract
Unravelling the impacts of climate change on agriculture becomes increasingly important, as the rates and magnitude of its effects are accelerating. Current estimates of the consequences of climate change on nitrous oxide (N2O) emissions remain largely uncertain; there is a need for more consistent and comprehensive assessments of this impact. In this study we explored the implications of two IPCC climate change projections (high and medium emissions scenarios) on N2O emissions from South West England grasslands for the time slices of a baseline, the 2020s, the 2050s and the 2080s, employing a process-based model (SPACSYS). The model was initially calibrated and validated using datasets collected from three grassland sites of the region. Statistical analysis showed that simulated results had no significant total error or bias compared to measured values. We found a consistent increase in N2O emissions of up to 94% under future climate change scenarios compared to those under the baseline, and warming rather than precipitation variability was the overriding factor controlling the N2O rise. Modelling fertilizer forms showed that replacing ammonium-nitrate fertilizers with urea or slurry significantly reduced N2O emissions (c. 30%). Our study highlights the urgent necessity to adopt viable N2O mitigation measures now in order to avoid higher emissions in the future.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".