What shapes silvopastoralism in Mediterranean mid-mountain areas? Understanding factors, drivers, and dynamics using fuzzy cognitive mapping
Bibliographic record
Abstract
Silvopastoral systems, integrating woody vegetation and livestock farming, are increasingly recognized as a sustainable land-use supporting biodiversity and ecosystem services provision in the Mediterranean. However, this traditional practice has declined in recent decades, mainly because of land abandonment and intensification processes. We investigated the relationships between the factors involved in the dynamics of silvopastoralism in two contrasting Spanish case studies in Mediterranean mid-mountain areas: Sierra de Guara Natural Park and Lluçanès region. Combining semi-structured interviews with researchers and participatory workshops with key stakeholders, we built a Fuzzy Cognitive Map (FCM) representing the shared perception of forest grazing in each region and implemented methodological improvements in FCM post-processing and analysis leading to an improved understanding of FCM outcomes. Results revealed that the dynamics of forest grazing are highly influenced by the socioeconomic attractiveness of the farming sector because it was a central factor in both case studies, whereas the importance of other factors such as farm abandonment in Guara and access to land in Lluçanès were site-specific. Climate change and the Common Agricultural Policy (CAP) were identified as the main external drivers undermining forest grazing in both sites while the incidence of other economic activities (i.e., tourism or other livestock production systems) relied on the context of each region. In contrast, technological innovations, including GPS collars and virtual fencing, along with the existence of infrastructures such as water points and active forest management, were identified to promote beneficial feedback loops for forest grazing. Although the current policy framework is failing in fostering silvopastoralism, a policy shift from direct payments to result-based schemes for biomass reduction and wildfire prevention tailored to each region’s environmental constraints and potentials would lead to better outcomes for society as a whole.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".