A Comparative Study of Risk Management in Agriculture under Climate Change
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Climate change affects the mean and variability of weather conditions and the frequency of extreme events, which to a great extent determines the variability of production and yields. This paper reviews the scientific literature on the impacts of climate change on yield variance and investigates their implications for the demand of crop insurance and effectiveness of different farm strategies and policy measures using crop farm data in Australia, Canada and Spain. A microeconomic farm level model is calibrated to different types of farms and used to simulate the responses and impacts of four policy measures: ex post disaster payments and three types of crop insurance (individual yields, area-based yield and weather index). The strong uncertainties about climate change are captured in a set of seven scenarios covering different assumptions about the scope of climate change (no change, marginal change, and high occurrence of extreme events), and farmers' adaptation response (no adaptation, diversification, and structural adaptation). Policy decision making under these uncertainties is analysed using a standard Bayesian probabilistic approach, but also using other criteria that look for robust second best choices (MaxiMin and Satisficing criteria).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it