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Record W3172483983 · doi:10.5194/egusphere-egu21-14398

Quantification of N2O fluxes and EF values in a pasture using chamber and eddy-covariance technique

2021· article· en· W3172483983 on OpenAlexaboutno aff
Lena Barczyk, Kate Kuntu-Blankson, Pierluigi Calanca, Johan Six, Christof Ammann

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsNitrous oxideAnimal scienceGrazingNitrogenAmmoniaEddy covarianceChemistryPastureFertilizerNitrateAgronomyBiologyEcologyEcosystemBiochemistry

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.019
GPT teacher head0.252
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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