A Market Analysis of a Set-aside Program by the Five Major Grain and Oilseed Exporting Countries
Bibliographic record
Abstract
Nominal grain and oilseed prices have declined dramatically from the high levels observed in the mid-1990s. As a result, it has been suggested by some industry stakeholders that measures should be taken to limit world crop supplies to raise prices. One such measure is a set-aside program involving a number of participants that would significantly reduce the quantity of land in production over a certain period of time. In fact, the European Union and the United States currently have set-aside programs that, together, already have had a positive effect on crop prices. Set-aside programs could also become relevant if the use of the World Trade Organization blue box is extended to more countries after the next round of negotiations. This analysis focuses on the quantitative aspects of a substantial multi-country, multi-commodity, set-aside program and its impact on major agricultural commodity prices. Three main conclusions can be drawn from the analysis: · A permanent set-aside program would be necessary to keep prices from returning to relatively low levels · Even with a permanent set-aside program, the results show a declining effect over time mostly because of additional production by non-participating countries and because of crop substitution in favour of cereals and oilseeds and higher yield in participating countries. · For a set-aside program to have any significant impact on prices, the program must include many commodities in many countries. A program involving Canada alone is doomed to fail. And given that the United States and the European Union-two large producers-already have their respective set-aside programs, it might be difficult to convince them to participate in an international effort.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".