Economic constraints to the adoption of carbon farming
Bibliographic record
Abstract
The potential for sustainable agricultural practices to sequester C is substantial. The economic feasibility and competitiveness of soil C sequestration depends on the opportunity cost per tonne of C stored. The key issue is whether the cost is competitive with alternative methods of reducing greenhouse gas emissions. The high spatial variability in land productivity means that the soil characteristics are important when designing public policies to address this issue. Empirical evidence suggests that the opportunity cost per tonne of C stored can be as low as US$10 to US$25 t-1 (Can$12–30), but that for the majority of temperate agriculture it exceeds US$50 t-1 (Can$60). The final monetary value placed on a tonne reduction of C will emerge either from the establishment of a fully functioning market or from government payment schemes. Estimates of the value of stored C have ranged from US$100 t-1 (Can$120) to a low of less than US$5 t-1 (Can$6). Current evidence suggests a likely price in the lower region of this range. Key words: Carbon farming, carbon sequestration, soil organic carbon, soil sustainability
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 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 | 0.003 | 0.013 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 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".