Size and Persistence of Nitrous Oxide Hot-Spots in Grazed and Ungrazed Grassland
Bibliographic record
Abstract
<p class="1Body">Nitrous oxide (N<sub>2</sub>O) 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 <em>ca.</em> 1 m<sup>2</sup>) of N<sub>2</sub>O fluxes. Spatial and temporal heterogeneity of N<sub>2</sub>O 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 N<sub>2</sub>O fluxes was seen in all three grassland fields. Mean N<sub>2</sub>O fluxes for the historic hot-spots in the grazed fields (DG and YG) were significantly greater than (SC). The mean N<sub>2</sub>O fluxes in DG and YG (117.9 and 243.5 ng N m<sup>-2</sup> s<sup>-1</sup>) were 15 to 30% greater than for SC. Soil temperature (15 - 20 °C) was the most significant driver of N<sub>2</sub>O production with a 1°C rise in soil temperature increasing emissions under DG and YG. N<sub>2</sub>O 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 CO<sub>2</sub> flux. Hot-spot N<sub>2</sub>O emissions from old urine and dung patches were persistent several months after application.</p>
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 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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".