5 Key Challenges and Solutions for Governing Complex Adaptive (Food) Systems
Bibliographic record
Abstract
There is increasing recognition in academic circles of the importance of adaptive governance for the sustainability of social-ecological systems, but little examination of specific implications for the 34% of land-use where human activities are pervasive but potentially commensurate with functioning ecosystems: agricultural production systems. In this paper, we argue for the need to view food systems and agro-ecosystems as multi-scalar complex adaptive systems and identify five key challenging characteristics of such systems: multi-causality; cumulative impacts; regime shifts; teleconnections and mismatch of scales. These characteristics are necessary features of multi-scalar adaptive systems, and apply equally to social and natural subsystems. We discuss the implications of these characteristics for agricultural production systems and consider how governance can rise to these challenges. We present five case studies that highlight these issues: pollinator declines; payments for ecosystem services; pest control and pesticide resistance; downstream aquatic systems in Tasman Bay, New Zealand; and riparian buffers in Puget Sound, USA. From these case studies we derive recommendations for managing agricultural systems, both specific and general. Ultimately, adaptive governance of agro-ecosystems will likely hinge upon three paradigm shifts: viewing farmers and ranchers not only as food producers but also as land and water managers; seeking not yield maximization but rather resilient management of food ecosystems; and critically, as it transcends the production-system literature, engaging broad audiences not only as consumers but also citizens.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".