Global Wheat Market Dynamics: What Is the Role of the EU and the Black Sea Wheat Exporters?
Bibliographic record
Abstract
Over the last two decades, three countries in the Black Sea Region—Russia, Ukraine, and Kazakhstan—became global leaders in grain production and trade, and replaced the USA and France as the most previous largest wheat exporting countries. In this study we investigate world wheat price linkages and identify the current “price leaders” of the global wheat market. This empirical analysis is focused on the price relationships between eight of the largest wheat exporting countries and uses a cointegration framework and a vector error-correction model. The results show that, regarding price formation on the world wheat market, the French price is more important for transmitting price signals to other wheat export markets compared to the USA. Furthermore, our results indicate that, despite being leaders in wheat export volumes, the Black Sea wheat prices in Russia and Ukraine adjust to price changes in France, the USA, and Canada. Albeit unrealistic in the short run, the creation of the futures market in the Black Sea region might significantly improve the participation of Black Sea markets in price formation of the global wheat market.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".