Global mineral fertilizer market analysis.
Bibliographic record
Abstract
The article presents an analysis of trends in the dynamics and structure of demand, supply, foreign trade, development factors of the world mineral fertilizer market: nitrogen, phosphate and potash segments. There have been identified the market trend of growing demand for mineral fertilizers, which has increased 6 times since 1961, the fact being connected with the population growth. A model of the correlation between the fertilizers demand growth and arable land scale shows the following correlation: when arable land scale increases by 1%, fertilizer consumption grows by 0.7%, with the determination of 50%. The calculation of changes in using fertilizers in terms of cutting down the arable land area under crops in the Russian Federation compared to the USSR using this model has revealed the fertilizer underutilization which is equal to the half of the amount that could be applied on average in Russia today. Production approached the regions of consumption and was relocated from the developed countries to developing ones. The largest dealers in the world market in 2017 were China, Russia, India, the USA, Canada, Brazil, and market concentration is quite high, especially in the potash segment. The dependence on foreign trade of both exporting and importing countries is high. Analysis of the specific market condition factors showed that the countries with the highest cereal yields in the world do not coincide with the largest fertilizer consumers. Since the cereal yields and the level of using fertilizers per hectare of arable land in the largest mineral fertilizers consuming countries are not directly correlated, the countries aiming to increase yields are less likely to achieve it by increasing their aggregate fertilizer consumption, but using other yields rising methods
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".