Quantification of N2O fluxes and EF values in a pasture using chamber and eddy-covariance technique
Bibliographic record
Abstract
In grassland ecosystems nitrogen (N) inputs are mainly attributed to fertilizer applications for increasing herbage productivity and to excreta of grazing animals. Cattle, for instance, excrete 75-95 % of the N intake. Accordingly, dung and urine patches of grazing animals form hotspots of nitrate leaching and gaseous N emissions as ammonia (NH3) or the important greenhouse gas nitrous oxide (N2O). Global default emission factor (EF) values for N2O, 2.0 % for grazing based nitrogen inputs (EF3) and 1.0 % for nitrogen inputs via fertilizer applications (EF1) have been suggested by IPCC. However, some countries like New Zealand, Canada or the Netherlands have established country-specific EFs showing considerable regional differences. In the present research study, we examine N2O emissions of a pasture field in Switzerland in relation to possible drivers. Field scale emissions by eddy covariance are measured in parallel to patch-scale N2O fluxes from controlled applications of urine, dung and fertilizer. The patch-scale fluxes are measured by a manually operated chamber ('fast-box') connected to an online gas analyzer. Besides estimating EF values on annual and seasonal basis, relevant factors that might control N2O fluxes like environmental conditions (weather parameters, soil moisture, soil temperature), vegetation characteristics (height, composition, nitrogen and carbon content) and pasture management (patch age, grazing, fertilization, cut events, interactive effects) are analyzed. We present and discuss results of the first measurement year 2020. Three artificial urine applications during summer and autumn were performed. They show peak N2O fluxes of 279-1718 μg m-2 h-1 directly after application that decrease to near-background fluxes within 19-43 days. Using a simple linear interpolation of measured N2O fluxes, EF values of artificial urine patches vary between 0.57 and 2.44 % indicating a seasonal variability of N2O fluxes.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".