Do organic farming policies need to be more target-oriented to achieve sustainability?
Bibliographic record
Abstract
Organic farming is a key element of the EU Green Deal's Farm-to-Fork strategy, which aims to achieve 25 % of agricultural land being organic by 2030. Within the context of the current organic conversion policy (CAP Strategic Plans for 2023-2027), the aim of this paper is to assess whether a more complex and targeted organic support mechanism delivers greater GHG emissions reduction compared to a simpler but less targeted option. Using the IFM-CAP model, three contrasting organic conversion policy strategies for the EU are assessed: an action-oriented approach, a result-oriented approach focused on the GHG abatement potential, and a combined approach emphasizing cost-effectiveness. The findings reveal significant trade-offs: while the result-oriented strategy is more costly and complex due to higher monitoring requirements, it achieves greater emission reductions per euro spent, mainly by converting high-emitting livestock farms. However, it results in a larger gap to the 25 % organic area target. Conversely, the action-oriented strategy is less costly, focuses on arable farm conversion, comes closer to the 25 % organic area target, but achieves lower emission reductions. Therefore, to achieve environmental benefits from organic farming, it is necessary to focus on farms with higher environmental improvement potential rather than just on the amount of land converted.
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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 | 0.006 | 0.008 |
| 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 | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".