Research on the optimization of supply chain decisions for green agricultural products based on farmers' risk preferences and disaster year subsidies
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
This study focuses on optimizing supply chain decisions under two scenarios: government subsidies during disaster years and farmers with varying risk preferences. An order-agriculture supply chain model is constructed, involving three parties: farmers, distributors, and insurance companies. Farmers cultivate agricultural products with varying levels of greenness. A three-stage game model is employed to derive the optimal planting scale for farmers, the optimal wholesale price for distributors, and the optimal premium rate for insurance companies. The results indicate that government disaster year subsidies directly increase the Conditional Value-at-Risk (CVaR) of farmers, although a maximum subsidy rate exists to prevent inequity. Enhancing the greenness of agricultural products has a positive impact on agricultural production. As the probability of disaster years increases, loan guarantee insurance becomes more effective in expanding farmers' planting scales, while yield guarantee insurance demonstrates superior performance in improving farmers' CVaR. The practical value of this study lies in providing farmers with optimal decision-making frameworks and profit calculations for loan guarantee insurance and yield guarantee insurance under varying disaster-year probability scenarios. Additionally, it explores the impact of government subsidies during disaster years, the greenness level of agricultural products, and the risk of crop failure on changes in farmers' value. These findings contribute to the optimization of farmers' decision-making processes, enhancement of their economic welfare, and the promotion of sustainable agricultural development, ultimately improving the livelihoods of farmers.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it