Size and Persistence of Nitrous Oxide Hot-Spots in Grazed and Ungrazed Grassland
Bibliographic record
Abstract
Nitrous oxide (N2O) emissions from agriculture contributed an estimated 60% of the global total in 2005. In the UK, grassland soils account for 30% of total emissions, 22% of which are estimated to come from urine and dung patches. These patches are possible sources of ‘hot-spots’ (area ca. 1 m2) of N2O fluxes. Spatial and temporal heterogeneity of N2O hot-spot fluxes were investigated in three grassland fields (grazed with dairy cows (DG), grazed with young stock (YG) or cut for silage (SC)) using gas sampling chambers surrounding historic hot-spots to establish their size. Fluxes from old dung and urine patches were measured, as well as freshly applied dung and urine to simulate the creation of hot-spots. Potential chemical and physical drivers were also measured. Large spatial variability of N2O fluxes was seen in all three grassland fields. Mean N2O fluxes for the historic hot-spots in the grazed fields (DG and YG) were significantly greater than (SC). The mean N2O fluxes in DG and YG (117.9 and 243.5 ng N m-2 s-1) were 15 to 30% greater than for SC. Soil temperature (15 - 20 °C) was the most significant driver of N2O production with a 1°C rise in soil temperature increasing emissions under DG and YG. N2O fluxes were enhanced by the fresh dung but not by urine. However, in the urine treatment, the nutrient input increased the microbial respiration response for the CO2 flux. Hot-spot N2O emissions from old urine and dung patches were persistent several months after application.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".