Lessons learned from existing soil carbon removals methodologies in agriculture to drive European Union policies
Bibliographic record
Abstract
Soil plays a central role in the global carbon cycle and in the fight against climate change and the protection of soil organic carbon (SOC) is fundamental. However, more than 33% of global soils are subject to moderate to severe degradation caused and the stocks of SOC in farmland and the extent of wetlands and peatlands are steadily decreasing. To maintain and increase C stocks in agricultural soils, carbon farming (CF) practices can be supported by carbon credits, tradable credits corresponding to 1 ton of CO2eq that are issued upon the demonstration of increased SOC stocks over time by C accounting methodologies for each agroecosystem and farming practice. In this study, an analysis of carbon credits methodologies focusing on agricultural soil C in extra-EU countries (Australia, Alberta in Canada, United States) is offered. Based on this review, we recommend to strengthening the European Commission proposal of regulation on Carbon removals (COM(2022) 672 final) by i) expanding the list of eligible agricultural practices ii) setting a permanence time frame for each agricultural practice, iii) setting the GAECs of the CAP as regulatory baseline, iv) including GHG emissions in the calculation of carbon removals, v) prioritizing CF projects on low-SOC lands, vi) clarifying the interaction with the CAP and the Soil Monitoring Law, vii) basing Carbon removals calculation on SOC maps, land use information and modelling, viii) setting a base price for carbon credits. These recommendations and many more are proposed to guarantee effective environmental protection, feasibility and economic affordability for farmers.
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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 | 0.019 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".