Overcoming collapse of farming systems: shifting from vicious to virtuous circles in the extensive sheep farming system in Huesca (Spain)
Bibliographic record
Abstract
Farming systems in Europe are perceived by stakeholders as having a low level of resilience and sustainability when confronted with shocks and ongoing stresses. Specifically, European extensive livestock systems are faced with challenges such as low farm income, uncertain policies, and changing meat consumption patterns, which are causing these systems to decline. Focused on the extensive sheep farming system in Huesca (Spain), our aim with this paper is to analyze the dynamics that have driven this system close to collapse and propose strategies that can reverse its dynamics to make the system both resilient and sustainable. This assessment is framed under an integrated resilience and sustainability approach, in which resilience variables (challenges, functions, and resilient attributes) and sustainability dimensions (economic, social, environmental, and institutional) are considered. Drawing on qualitative system dynamics and participatory causal loop diagram mapping, we show that the extensive sheep production system is threatened not only by challenges (e.g., increasing feeding costs), but also by weakly expressed resilience attributes (e.g., lack of diverse policies) and diminished functions (e.g., declining number of farms). There are delayed vicious circles in the system’s dynamics that are interrelated across sustainability dimensions, implying that time will be required to reverse the system’s dynamics. Hence, to foster the resilience of farming systems, specifically extensive livestock systems, a coordinated range of long-term strategies and policies (agricultural, environmental, sanitary, urban, labor, consumption, and innovation) are needed, aimed at responding to challenges, weak attributes, and diminished functions across different sustainability domains.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".