Networking supports crop diversity decisions: insights from the Gaillac wine-growing region (France)
Bibliographic record
Abstract
The diversity of crop species and varieties is a key lever for promoting sustainable and resilient farming systems. Adopting new species and varieties requires knowledge tailored to specific crops. Farmers rely on diverse sources of knowledge to make cropping decisions, which depend on both the characteristics of the crops and the farmers themselves. Understanding the various pathways of knowledge transmission provides critical insights into the dynamics that either facilitate or constrain the local diffusion of crop diversity. This study examines how farmers engage with different social networks when selecting the three key components of grapevine cultivation: variety, clone, and rootstock. We combined social network analysis with ethnographic data to compare the composition (i.e., the sources involved), structure (i.e., the interactions between sources), and content of knowledge regarding grapevine selection in the Gaillac wine-growing region (France). Our results show that knowledge is predominantly obtained through social interactions with nearby farmers rather than through independent consultation of written sources, such as books and websites. Across all three networks, the most frequently cited knowledge providers were local experts with extensive experience managing diverse grapevine varieties. We found that grapevine varieties hold greater biocultural value for farmers than clones and rootstocks, shaping knowledge circulation networks. While varieties are central to farmers' concerns, clones and rootstocks are perceived as adjustment variables used to optimize yield and adapt to environmental conditions. As a result, farmer-to-farmer knowledge exchange primarily revolves around grapevine varieties, whereas knowledge about clones and rootstocks is largely disseminated by vine nurseries. Our findings highlight the role of social relationships in sustaining agronomic knowledge about numerous rare and uncultivated varieties within the locality, which may bolster farmers' capacity to adapt to rapid social-ecological changes.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".