Beyond soil and yields: a systematic review of social capital’s role in regenerative agriculture
Bibliographic record
Abstract
Despite growing global interest in regenerative agriculture (RA), the majority of the literature still focuses on biophysical benefits, overlooking the social and relational dynamics that play a role in its adoption and persistence. In order to close this critical gap, this review synthesizes recent research on the function of social capital in RA systems. From an initial list of approximately 400 peer-reviewed articles published within the last decade, 43 studies met predefined inclusion criteria for detailed analysis. These studies reveal important conceptual connections, thematic patterns, and case-based insights into how trust, networks, knowledge exchange, and collective action support regenerative practices. We also explored how RA intersects with other associated paradigms such as agroecology, organic farming, and sustainable agriculture. Although social capital is increasingly acknowledged, its integration into RA policy and research remains inconsistent. To advance the field, this review proposes a structured framework that organizes the role of social capital into five interconnected dimensions: knowledge exchange and learning systems, community building and collective identity, trust and relationship quality, participation, and organizational networks. This framework highlights the multidimensional ways in which social relations shape regenerative transitions and provides a transferable basis for guiding future research, practice, and policy design.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".