Quantifying CO2 emissions from Quebec's agricultural peatland and identifying key parameters for guiding soil conservation strategies
Bibliographic record
Abstract
In Quebec, Canada, field vegetable production largely occurs on cultivated organic soils of Montérégie. These soils become arable following extensive drainage of peatlands, which are highly fertile but vulnerable to subsidence, erosion, and organic matter mineralization. The latter causes carbon losses to the atmosphere through CO₂ emissions and can also lead to dissolved organic carbon leaching. This study quantified CO₂ emissions and identified governing edaphic and meteorological parameters to support the development of carbon compensation strategies for peatland managers. Easily measurable soil parameters were selected to provide farmers with potential proxies for routine soil analysis. Five commercial sites were selected based on their organic matter (OM) content: F1 (52.2 %), F2 (56.7 %), F3 (74.0 %), F4 (77.4 %), and F5 (91.3 %). All sites, except F3, were devoid of vegetation. Soil CO₂ emissions were measured using manual static chambers over one year (September 2021–September 2022) at bimonthly intervals. Gross annual carbon losses were 4.94 Mg C-CO₂ ha −1 yr −1 for F1, 5.47 for F2, 15.30 for F3, 7.62 for F4, and 3.20 for F5. Soil temperature, total microbiological activity (fluorescein diacetate hydrolysis), total nitrogen, and pH significantly and positively influenced CO₂ fluxes, while soil water content showed a negative correlation. Annual carbon losses were highly and exponentially correlated with total microbiological activity, underscoring its relevance as a biological indicator and promising proxy for CO₂ emissions. This study advances understanding of CO₂ emissions from cultivated organic soils and highlights the importance of targeted strategies to mitigate carbon losses and conserve these valuable peatland resources.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".