Farmers’ Preferences for Future Agricultural Land Use Under the Consideration of Climate Change
Bibliographic record
Abstract
Cultural landscapes in Austria are multifunctional through their simultaneous support of productive, habitat, regulatory, social, and economic functions. This study investigates, if changing climatic conditions in Austria will lead to landscape change. Based on the assumption that farmers are the crucial decision makers when it comes to the implementation of agricultural climate change policies, this study analyzes farmers' decision-making under the consideration of potential future climate change scenarios and risk, varying economic conditions, and different policy regimes through a discrete choice experiment. Results show that if a warming climate will offer new opportunities to increase income, either through expansion of cash crop cultivation or new land use options such as short-term rotation forestry, these opportunities will almost always be seized. Even if high environmental premiums were offered to maintain current cultural landscapes, only 43 % of farmers would prefer the existing grassland cultivation. Therefore, the continuity of characteristic Austrian landscape patterns seems unlikely. In conclusion, despite governmental regulations of and incentives for agriculture, climate change will have significant effects on traditional landscapes. Any opportunities for crop intensification will be embraced, which will ultimately impact ecosystem services, tourism opportunities, and biodiversity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".