Narrow and Brittle or Broad and Nimble? Comparing Adaptive Capacity in Simplifying and Diversifying Farming Systems
Bibliographic record
Abstract
Humanity faces a triple threat of climate change, biodiversity loss, and global food insecurity. In response, increasing the general adaptive capacity of farming systems is essential. We identify two divergent strategies for building adaptive capacity. Simplifying processes seek to narrowly maximize production by shifting the basis of agricultural production toward centralized control of socially and ecologically homogenized systems. Diversifying processes cultivate social-ecological complexity in order to provide multiple ecosystem services, maintain management flexibility, and promote coordinated adaptation across levels. Through five primarily United States focused cases of distinct agricultural challenges—foodborne pathogens, drought, marginal lands, labor availability, and land access and tenure—we compare simplifying and diversifying responses to assess how these pathways differentially enhance or degrade the adaptive capacity of farming systems in the context of the triple threat. These cases show that diversifying processes can weave a form of broad and nimble adaptive capacity that is fundamentally distinct from the narrow and brittle adaptive capacity produced through simplification. We find that while there are structural limitations and tradeoffs to diversifying processes, adaptive capacity can be facilitated by empowering people and enhancing ecosystem functionality to proactively distribute resources and knowledge where needed and to nimbly respond to changing circumstances. Our cases suggest that, in order to garner the most adaptive benefits from diversification, farming systems should balance the pursuit of multiple goals, which in turn requires an inclusive process for active dialogue and negotiation among diverse perspectives. Instead of locking farming systems into pernicious cycles that reproduce social and ecological externalities, diversification processes can enable nimble responses to a broad spectrum of possible stressors and shocks, while also promoting social equity and ecological sustainability.
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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) | 0.001 | 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".