DEMO FARMS in Green Point Living Lab: A strategy for engaging farmers in sustainable innovation
Bibliographic record
Abstract
European agriculture is at a crossroads, balancing rising food demands with sustainability targets outlined in the EU Green Deal, Farm to Fork Strategy, and Digital Europe Programme. Despite the potential of digital technologies, adoption on farms remains limited due to barriers such as fragmented farm structures, ageing farmer population, low digital skills, and weak rural infrastructure. At the same time, enablers such as strong advisory networks, policy incentives, and traditions of farmer cooperation provide ground for innovation. The DEMO FARMS initiative, embedded in the Green Point Living Lab, offers a structured, place-based methodology for co-developing and scaling regenerative and digital agricultural practices. DEMO FARMS foster real-life experimentation, stakeholder co-creation, and open innovation. The model is built on five pillars: sustainable practice deployment with AI integration, use of digital tools, stakeholder data integration via the DIH AGRIFOOD Data Space, capacity building, and strategic support through digital maturity assessments. By directly addressing both enablers (knowledge networks, policies, cooperation) and barriers (farm fragmentation, ageing, low digital skills), DEMO FARMS ensures that solutions are adapted to the realities of agri-food systems. The outcome is a scalable, transferable innovation framework that strengthens resilience, digital readiness, and sustainability across diverse regional farming contexts.
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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".