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Record W3013559259

Distribution spatiale des communautés de vers de terre et leur effet sur les gaz à effet de serre en champs agricoles et en bandes riveraine forestières

2020· article· fr· W3013559259 on OpenAlexfundaboutno aff
Ashley Cameron

Bibliographic record

VenueKnowledge UdeS (Institutional Deposit of the University of Sherbrooke) · 2020
Typearticle
Languagefr
FieldEngineering
TopicForest Biomass Utilization and Management
Canadian institutionsnot available
FundersAgriculture and Agri-Food CanadaCentre SèveUniversité de Sherbrooke
KeywordsGeographyForestry
DOInot available

Abstract

fetched live from OpenAlex

This thesis reports the findings from a research project that aimed to determine the effect of earthworms on greenhouse gas (GHG) emissions in forested riparian buffer strips (FRBS). This project had two research questions. Firstly, we wanted to determine how earthworms are distributed in agricultural ecosystems and whether they had a preference for FRBS over adjacent agricultural fields. Secondly, we wanted to determine the effect of earthworms on the emission of the three most prominent GHG (CO2, N2O and CH4) and how the effect of earthworms is affected by soil characteristics, namely, soil origin and soil texture. We expected earthworms to have a preference for FRBS and for them to have a positive effect on GHG emissions.
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\nFor the first research question, we conducted a field survey on agricultural fields with adjacent FRBS in Southern Quebec and Ontario as well as in the Czech Republic. At each site, we quantified earthworm numbers from each functional group (anecic, endogeic and epigeic) and characterized the site by noting the percentage coverage of the different types of vegetation and analysing soil’s physicochemical properties. We found that for Eastern Canada, earthworm numbers, organic matter and soil moisture were all higher in FRBS than in adjacent agricultural fields. However, in Czech Republic, earthworm numbers were higher in agricultural fields than FRBS and there was no significant difference in moisture between agricultural fields and FRBS. This indicated that moisture is an important variable in predicting the distribution of earthworms. Furthermore, we found that earthworm numbers are positively associated with organic matter, pH, clay content and the percent coverage of deciduous trees and negatively associated with sand content and the percent coverage of coniferous trees. Following these results, the next step was to determine what effect earthworms have on GHG emissions.
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\nIn order to determine the effect of earthworms on GHG emissions we conducted controlled microcosm experiments. These experiments were conducted using a replicated factorial design comprising of 3 soil origins (deciduous FRBS, coniferous FRBS, agricultural field) x 2 soil textures (field conditions, high clay) x 3 earthworm life habits (anecic, endogeic, no earthworm). Soils originating from FRBS emitted more CO2 than soils from agricultural fields with soils from deciduous stands having higher emissions than soils from coniferous stands. Soils with a higher clay content emitted less CO2 than soils with a lower clay content. Soils with earthworms emitted more CO2 than soils without earthworms, however, this effect diminished with time and was no longer significant after ten weeks. Additionally, soils with earthworms emitted more N2O than soils without earthworms. For CH4, the transformation rates were higher for soils from FRBS than soils from agricultural fields under both anaerobic and aerobic conditions.
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\nWith earthworms having an overall positive effect on GHG emissions, FRBS should be designed such that they prevent the establishment of earthworms. Therefore, coniferous trees would be preferable over deciduous trees. Firstly, earthworm numbers were shown to be negatively associated with coniferous tree coverage, and, in the event that earthworms do become established, GHG emissions were shown to be lower from coniferous soils than deciduous stands.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.530
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.013
GPT teacher head0.205
Teacher spread0.191 · 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 designSimulation or modeling
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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