Exploring the barriers to farmer participation in soil carbon projects under the Australian Carbon Credit Unit Scheme
Bibliographic record
Abstract
The Australian Carbon Credit Unit Scheme was established to incentivise reductions in emissions or carbon storage. However, there has been low participation by farmers in soil carbon projects since the first soil carbon method was established for agriculture in 2014, even though carbon prices are high and management changes to sequester carbon on farms should be complementary to other business outcomes. This study explores the barriers that might limit participation in soil carbon projects. A novel approach is that instead of interviewing landholders, we worked with agents, service providers and agencies in Australia to gain their insights about the participation challenges for farmers. The main barriers identified are information gaps, risk and uncertainty about returns, high upfront costs, poor knowledge, limited business cases and program complexity. Potential opportunities to overcome barriers include increasing awareness of and access to factual and science-based information, reducing risk and uncertainties, reducing measurement and practice change costs, increasing financial support and incentives, quantifying environmental benefits and complementary benefits of the practices, and simplifying the methods and program systems. This study also suggests better business models for carbon projects need to be developed, with adjustable scenarios, so that farmers can tailor them to their enterprise.
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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 | 0.018 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".