Designing resilient farming systems for a turbulent world: learning from communities at the frontline
Bibliographic record
Abstract
Abstract In a rapidly changing world, designing resilient farming systems is critical. Recent socio-ecological research hypothesized that the general resilience of farming system to disturbances is related to the interplay between four key resilience attributes—Agencies, Buffers, Connectivity, and Diversity (ABCD). However, the relative importance of these attributes in coping with multiple concurrent disturbances remains unclear. This study leverages longitudinal socio-ecological data, including biotic, abiotic and socio-political shocks and community responses, to explore how the ABCD attributes mediate farming system resilience. Using satellite-derived soil moisture content, green soil cover, and aboveground biomass data, complemented by focus group discussions in twelve communities, we analyzed the land restoration outcomes in the face of multiple disturbance and the contributions of ABCD attributes to resilience. The findings revealed that “bright spot” communities that had already been improving their natural resource management were consistently more resilient to multiple shocks. Our results also show that attributes A and B are essential to cope with multiple disturbances, while the contributions of C and D were more nuanced and depended on the type of disturbance.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".