Consequences of sanctions restrictions on the global fertilizer market
Bibliographic record
Abstract
The purpose of the article is to analyze the changes in the global mineral fertilizer market due to the imposed restrictions, as well as to determine quantitative indicators indicating the transformation of this market. The study allows us to conclude that the structure of global production and consumption of mineral fertilizers is generally stable, which is due to macroeconomic factors in the functioning of this market, including a high concentration of production in a limited number of countries and consumer dependence. International trade in mineral fertilizers retains its traditional destinations, the largest exporters are the Russian Federation, China, the USA, Belarus and Canada, the main importers are India, the USA and Brazil. The greatest transformation is observed in European countries, associated with a sharp reduction in the production of nitrogen fertilizers and a reduction in imports of potash and phosphate fertilizers. The production of nitrogen fertilizers in Europe was suspended in mid-2022 due to rising natural gas prices. An assessment of the transformation of international exchange showed the active use of import diversification strategies by countries that consume mineral fertilizers: in particular, a significant part of large importers from Latin America are reorienting supplies to the United States and Canada, and the intermediary activity of Central Asian states is increasing.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".