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Record W4390770621 · doi:10.1016/j.agee.2023.108878

Methane uptake rates across different soil types and agricultural management practices in Denmark

2024· article· en· W4390770621 on OpenAlexaff
Line Vinther Hansen, Andreas Brændholt, Azeem Tariq, Lars Stoumann Jensen, Leanne Peixoto, Søren O. Petersen, Sander Bruun

Bibliographic record

VenueAgriculture Ecosystems & Environment · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsUniversity of Guelph
FundersNational Institute of Food and Agriculture
KeywordsEnvironmental scienceLoamSoil waterSoil textureSink (geography)AgronomyAgricultureHydrology (agriculture)Water contentSoil scienceEcologyGeographyBiologyGeology

Abstract

fetched live from OpenAlex

Upland terrestrial soils play a crucial role in the global methane (CH4) cycle, although their potential as sinks for CH4 remains inadequately quantified. This study investigated CH4 uptake rates spanning two full years at four locations representing typical soil types and crop rotations in Denmark. The sampling campaigns included a total of more than 5000 manual static chamber flux measurements. All locations were, on average, small net sinks for CH4 while displaying significant differences between locations ranging from −1.5±0.3 g CH4-C ha−1 day−1 on the loamy soils to −3.8±0.3 g CH4-C ha−1 day−1 on the sandy soil with the lowest bulk density. A significant negative effect of soil moisture on CH4 uptake was also identified across the locations. Therefore, this study highlights the importance of soil texture and moisture as key controlling variables for CH4 uptake and emphasizes the need for local estimates of sink capacity. Overall, there were no detectable effects of agricultural management practices and fertilisation events did not affect the CH4 flux except for some high emissions following cattle slurry application on coarse sandy soil, which needs some consideration.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.391
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.007
GPT teacher head0.218
Teacher spread0.212 · 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 teacher head, not a consensus.

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

Citations25
Published2024
Admission routes1
Has abstractyes

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