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Record W7049243429

Non-growing season nitrous oxide fluxes from agricultural soils

2020· dissertation· en· W7049243429 on OpenAlexfundaboutno aff

Bibliographic record

VenueThe Atrium (University of Guelph) · 2020
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicLightning and Electromagnetic Phenomena
Canadian institutionsnot available
FundersMinistry of Agriculture, Food and Rural AffairsOntario Ministry of Agriculture, Food and Rural Affairs
KeywordsNitrous oxideManureSoil waterGreenhouse gasGrowing seasonDenitrificationFlux (metallurgy)NitrogenSpring (device)
DOInot available

Abstract

fetched live from OpenAlex

A two-year field experiment was conducted at the Arkell Research Station, Ontario, Canada to evaluate composting as a mitigation strategy for greenhouse gases (GHGs). The objectives were to quantify and compare non-growing season nitrous oxide (N2O) fluxes from agricultural soils after fall manure application of composted and untreated liquid swine manure. Nitrous oxide fluxes were measured using a micrometeorological method. Compared to untreated liquid swine manure (LSM), composted swine manure (CSM) resulted in 57% reduction of soil N2O emissions during February to April in 2005, but emissions during the same period in 2006 were not affected by treatments. This effect was related to fall and winter weather conditions with the significant reduction occurring in the year when soil freezing was more pronounced. The DNDC (DeNitrification-DeComposition) model was tested against data measured during the non-growing seasons from 2000 to 2004, for farming with conventional management at the Elora Research Station, Ontario, Canada. The objective was to assess the ability of the DNDC model to simulate non-growing season N2O fluxes from soils in southwestern Ontario. Comparison between model-simulated and measured data indicated that background fluxes were relatively well predicted. The spring thaw N2O flux event was correctly timed by the DNDC model, but was smaller than the measured spring thaw event. Though there was no N2O emission event measured in early May, the DNDC model predicted a large event, simultaneous with the physical release of predicted ice-trapped N2O. Removing the large and late predicted emission peak and increasing the contribution of newly produced N2O due to denitrification to the early spring thaw event were proposed. Three data sets from studies conducted in Ontario, Canada were used to estimate and compare the overall GHG (N2O and methane) emissions from LSM and CSM. Compared to LSM storage, the composting process reduced GHG emissions by 35% (CO2-eq), mainly due to decreased methane fluxes. Land application of CSM showed a 38% reduction of total GHGs (CO 2-eq), compared to fall application of LSM. In comparison to liquid swine manure management systems, aerobic composting reduced the overall GHG emissions on a CO2-equivalent basis by 35%.

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 score0.656
Threshold uncertainty score0.692

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.006
GPT teacher head0.186
Teacher spread0.179 · 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
Published2020
Admission routes2
Has abstractyes

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