Smart-Akis Policy Briefs For Mainstreaming Smart Farming In The New Cap
Bibliographic record
Abstract
Smart-AKIS has conducted a thorough review of current EU policies impacting on Smart Farming adoption, including the current and future Common Agricultural policy (CAP). This Policy Review, together with the outcomes of the regional innovation workshops of Smart-AKIS, has been used to identify several Policy Gaps which should be addressed by the future Common Agricultural Policy, such as: - Cutting red tape; - Stimulating innovation; - Meeting the sustainability goals (emission limits); - Sustainable production (producing more and better with less); - Improving social health and vitality in rural areas; - Adapting smart farming schemes to the farm scale. Smart-AKIS also proposes a number of Policy Solutions to overcome these gaps by providing examples of good practices already available at European level. They are extracted from Policy Cases assessed by Smart-AKIS. The Policy solutions include the following: - Supporting farmers investment in SFTs through the CAP Second Pillar; - Within SFTs, support Precision Agriculture tailored to farm size; - Improve farmer’s digital capabilities through lifelong learning, education and training together with demonstrations; - Research and innovation as support strategies for boosting agricultural innovation, emphasizing the importance of advisers. The Smart-AKIS vision for the new CAP after 2020 should be to turn the policy (EAFRD - European Agricultural Fund for Rural Development and EAGF - European Agricultural Guarantee Fund) into an opportunity to make EU agriculture smarter and greener, thereby contributing to a more sustainable and competitive EU agriculture. In this sense, EU policy makers are called to promote and realize a holistic approach aiming at: - Promoting solutions that are farmers-centred and that reward farmers; - Rewarding farmers also means rewarding their environmental performance and supporting demand-side policies with stricter environmental and food safety regulations; - Simplifying and improving the aid programmes management. The Policy Review, Policy Gaps, Policy Solutions and Policy Cases are described in a publication available at the Smart-AKIS website. With 7 Policy Briefs, this documents sums up the main challenges and recommendations.
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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 | 0.026 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.020 | 0.010 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".