The economic potential of the Quebec cropping sector to sequester carbon in agricultural soils /
Bibliographic record
Abstract
This research simulates the response of the Quebec cropping sector to the introduction of carbon credit revenue which could be made available through the implementation of a greenhouse gas emissions trading and offset system in Canada. Eligible carbon sequestering practices investigated in the simulations include adoption of moderate tillage and no-till as well as the conversion to a permanent cover crop. Monetary demand for greenhouse gas emissions offsets from the cropping sector is endogenized in the objective function of the Canadian Regional Agriculture Model (CRAM) which has been modified to account for the simulations and for the disaggregation of the single crop region of Quebec into eleven sub-regions. Changes in the cropping sector induced by the introduction of seven different carbon price levels, ranging from $1/t CO2 to $100/t CO2, are compared to a baseline. Variables covered in the simulation results include: relative profitability of carbon sequestering crops/technology; adoption rates of moderate tillage and no-till; carbon sequestration levels; carbon credit revenue; cropping pattern, crop production and livestock. Results indicate that carbon sequestration in agricultural soils could only contribute a minor share of the total emission reduction in Quebec, even with very high carbon price levels. At a carbon price of $15/t CO2, it is estimated that changes in tillage practices and permanent cover would result in an additional 12,328 t CO2 per year sequestered by the cropping sector in Quebec. However, some regions display higher adoption rates of carbon sequestering practices than other regions and appear to be more responsive to the price incentive. The introduction of a monetary demand for GHGE offsets from the cropping sector induces some changes in terms of cropping pattern and crop production level, while it has almost no impact on the livestock sector.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".