Agrobiodiversity dynamics in a French wine-growing region
Bibliographic record
Abstract
Agrobiodiversity is a promising nature-based solution in the pursuit of sustainable agriculture. In wine-growing systems, commercial pressure and varietal regulations have narrowed agrobiodiversity in vineyards despite higher diversity being an important buffer against the effects of climate change. If drivers of grape diversity change are well-understood at national to global scales, little is known about the local, past or anticipated trajectories that drive agrobiodiversity dynamics depending on growers’ cultural values, practices and choices. We combined quantitative agricultural census data and qualitative ethnographic approaches to characterise changes in the diversity of grape varieties from 1960 to 2020 at the communal and vineyard levels in a French wine-growing region, and to decipher the drivers of change. We highlight that vineyards have drastically changed in 60 years, with a decline in planted area and in farm number. We outline that despite a loss of varietal richness across both vineyard and communal scales, varietal richness remains high and evenness have increased across geographic scales in 2020. Ethnographic field observations emphasize that growers account for external drivers (e.g., market changes, regulation and policy, technology, environmental), but also cultural values when they choose which grape varieties to plant. Grape diversity was maintained despite market integration as an insurance to spread production risk, mitigate market volatility and address environmental uncertainties. Securing livelihoods in the midst of market changes has been a major concern for growers over the last six decades and remains so. Despite a pessimistic future vision of the vineyard shared by most growers, the Gaillac region has a cultural heritage that values diversity and that thereby supports adaptation to climate change. We expect that environmental factors may play a more important role in grape selection and planting sites in the future under the influence of climate change and pesticide reduction policies. In order to expand individual initiatives resulting in diversified grape selection, growers need to be better connected with stakeholders at a variety of institutional levels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".