Implications of Seasonal Climate Forecasts on World Wheat Trade: A Stochastic, Dynamic Analysis
Bibliographic record
Abstract
Improvements in seasonal climate forecasts have potential economic implications for international agriculture. A stochastic, dynamic simulation model of the international wheat economy is developed to estimate the potential effects of seasonal climate forecasts for various countries' wheat production, exports and world trade. Previous studies have generally ignored the stochastic and dynamic aspects of the effects associated with the use of climate forecasts. This study shows the importance of these aspects. In particular with free trade, the use of seasonal forecasts results in increased producer surplus across all exporting countries. In fact, producers appear to capture a large share of the economic surplus created by using the forecasts. Further, the stochastic dimensions suggest that while the expected long‐run benefits of seasonal forecasts are positive, considerable year‐to‐year variation in the distribution of benefits between producers and consumers should be expected. The possibility exists for an economic measure to increase or decrease over a 20‐year horizon, depending on the particular sequence of years. Le progrès des prévisions saisonnières du climat a une portée économique pour l'agriculture internationale. Un modèle stochastique et dynamique de l'économie internationale du blé est développé afin d'estimer les effets potentiels des prévisions saisonnières du climat sur la production de blé de divers pays, leurs exportations et le commerce mondial. Les études précédentes ont généralement ignoré les aspects stochastiques et dynamiques des effets liés à l'utilisation des prévisions climatiques. Cette étude montre l'importance de ces aspects. En particulier avec le libre échange l'utilisation de ces prévisions aboutit à l'augmentation des excédents dans tous les pays exportateurs. En fait, il apparaît que les producteurs accaparent une grande part de l'excédent économique créé par l'utilisation de ces prévisions. De plus, les dimensions stochastiques suggèrent que bien que les bénéfices à long terme des prévisions climatiques puissent être substantiels, on s'attend à des variations considérables d'une année à l'autre dans la distribution des bénéfices entre les producteurs et les consommateurs. Il est possible qu'un indicateur économique varie à la baisse sur 20 ans eus fonction de la séquence de variation climatique.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".