Adoption of Agri‐environmental Programmes in Swiss Crop Production L’adoption des programmes agroenvironnementaux dans les grandes cultures suisses Die Adoption von Agrarumweltprogrammen in der Schweizer Getreideproduktion
Bibliographic record
Abstract
summary Adoption of Agri‐environmental Programmes in Swiss Crop Production We analyse the adoption of agri‐environmental programmes, i.e. extensive and organic crop production, in Switzerland for the years 2008 and 2009. While extensive crop production is adopted by about 60 per cent of all eligible farms, the adoption of organic crop production is very limited. Using logistic regressions we find that farms located in adverse production conditions (e.g. at higher altitudes) are more likely to be adopters of extensive crop production. Moreover, our results show that the probability of adopting organic production decreases with increasing specialisation in crop production. We find that organic producers tend to be slightly younger and better educated than other farmers. However, no differences in farmers’ age and education are found between non‐adopters and extensive crop producers. This shows that, in general, the non‐adoption of agri‐environmental programmes in Switzerland is not primarily an educational problem. Moreover, we find that land tenure has no influence on the adoption of agri‐environmental crop production schemes. Thus, large shares of rented land do not limit the adoption of agri‐environmental programmes. In order to increase the adoption rates of extensive and organic production, particularly in non‐adverse production conditions, site‐ and region‐specific levels of ecological direct payments could be used.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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".