D2.8 – Analysis of Innovative Approaches to Market Monitoring – Draft 1 : Work Package 2 - Uptake of Digital Agriculture & Forestry Technologies
Bibliographic record
Abstract
In this analysis, we introduce a digital ecosystem approach to better understand the uptake of digital and data-driven innovations in agriculture and forestry. Traditional market-based models often focus solely on buyer-seller interactions and monetary value. In contrast, the digital ecosystem framework considers a broader network of actors, data flows, and relationships that shape how innovation spreads and creates value. The analysis identifies five key actor types: digiproducers, collaborators, digiproduct users, data intermediaries, and peripheral data users. These actors interact across overlapping digiproduct and data ecosystems, and span both upstream (e.g., producers, policymakers) and downstream (e.g., farmers, foresters) segments. Innovations generate social data externalities, whereby data from one actor benefits others, further extending ecosystem impact. To analyse how actors position themselves and interact, the study introduces the concept of ecosystem space, where strategic activities—termed ecosystem scoping—determine engagement, needs, collaboration, and role transitions. The PARATA principle (Potential, Relevant, and Targetable Actors) helps map these dynamics and inform engagement strategies. Innovation diffusion is explored through cascading behaviour, social influence, and network effects. The Bass Diffusion Model is discussed to understand the roles of initial adopters and imitators in driving uptake. Adoption barriers and enablers are analysed using behavioural models that account for capability, opportunity, and motivation. The study also considers innovative monitoring techniques, such as API-based data extraction and Large Language Models (LLMs), which enable real-time, scalable analysis. Combining top-down and bottom-up monitoring offers a comprehensive view of adoption dynamics. Finally, it is shown that forecasting adoption before usage data becomes available is made possible through intention surveys, analogical reasoning, and cross-ecosystem insights. Together, these tools and concepts form a new framework for policymakers and stakeholders to monitor adaptive innovation in digital agri-forestry ecosystems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".