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Record W4392601582 · doi:10.5194/egusphere-egu24-6373

Carbon Emissions from Active Horticulture Peat Extraction Sites in Canada: Five Years of Field-based Measurements

2024· preprint· en· W4392601582 on OpenAlexaffabout
M. L. Hunter, Laura Clark, Rebecca J. Frei, Ian B. Strachan, Nigel T. Roulet, Maria Strack

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsQueen's UniversityMcGill UniversityUniversity of Waterloo
Fundersnot available
KeywordsPeatExtraction (chemistry)Carbon fibersEnvironmental scienceField (mathematics)HorticultureEnvironmental chemistryEnvironmental protectionGeographyForestryChemistryArchaeologyMaterials scienceBiologyMathematicsOrganic chemistry

Abstract

fetched live from OpenAlex

Peat extraction substantially alters the carbon dynamics, peat structure, and hydrology of peatland sites. In Canada, companies install drainage ditches every ~30 m, dividing the sites into fields of peat bounded by ditches, and remove the surface vegetation and upper acrotelm. Peat is then vacuum harvested, processed, and sold for horticulture use. Despite this disturbance covering only a small percentage of Canadian peatlands, the shift from being a net sink to a net source of carbon during the 15-35 years of extraction makes them an important system to study.We conducted research at eight actively extracted peatland study sites in Quebec (Eastern Canada) and Alberta (Western Canada), ranging from 3–28 years post the start of extraction. Our objectives were to i) assess spatial distribution of CO2 and CH4 emissions; 2) assess seasonal and interannual variability of these emissions; and 3) understand their environmental drivers. To do this, we employed measurement techniques at the plot and ecosystem scale. Plot scale chamber-based measurements of CO2 and CH4 were conducted weekly to biweekly from May to September at eight sites from 2018 to 2022, with each site being measured in at least one study year. The drainage ditches were hotspots of carbon emissions with around double and at least seven times the CO2 and CH4 emissions respectively, of the fields. Time since the start of extraction was a useful metric to estimate current CO2 emissions when sites were within one bog complex. More research will be required to extrapolate emissions to other locations however, as peat substrate quality differences between locations also contributed to variation in carbon loss.Ecosystem scale measurements of daytime March to October CO2 and CH4 emissions were conducted at a subset of the study sites for two to three years using the eddy covariance technique. We observed comparable March and April CO2 emissions to those in July, highlighting the importance of thaw dynamics on the yearly carbon budget. Interannually, CO2 emissions were lowest during a dry summer, suggesting a moisture limitation for decomposition at the surface under severe drainage. We found weak dependence of CO2 emissions on soil temperature, though it was strongest when the water table was within the top 40 cm of the peat.This research will aid in validating Canada’s emission factor values for peat extraction, which are currently based on a few measurements in Quebec at post extracted, unrestored peatlands. Using several different assumptions for wintertime emissions, we estimated annual CO2 budget of 256 – 385 g C m-2 yr-1, which agrees with Canada’s current Tier 2 emission factor value of 310 g C m-2 yr-1. Methane emissions accounted for < 1 g C m-2 yr-1. This research will also support process-based models looking at the effect of site management, and the changing climate, on carbon emissions from these sites.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.091

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.000
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 head0.017
GPT teacher head0.241
Teacher spread0.224 · 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

Citations2
Published2024
Admission routes2
Has abstractyes

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