The strategic diagnosis of the potassium fertilizer industry in Brazil
Bibliographic record
Abstract
Fertilizers and crop nutrition play a key role in fulfilling the United Nations Sustainable Development Agenda. Brazil is the world’s fourth largest consumer of fertilizers. Between 1998 and 2018, the apparent consumption of potash fertilizers (in terms of K2O content) almost tripled, while the production of potassium chloride decreased in Brazil. Two important projects (Carnalita and Autazes) are still in the design phase, and there is considerable uncertainty surrounding them. Potassium producers located in Canada, Russia and Belarus have competitive advantages when compared to Brazil. The aim of this paper is to make a strategic diagnosis of the potassium fertilizer industry. The major variables that characterize the status of the Brazilian fertilizer industry could be identified, on the basis of data collected from annual business reports, reports from consulting firms, government websites, books, newspaper articles, sectoral studies and scientific articles. This paper concluded that, after detection of the current fragility of this sector, which is essential to the agribusiness production chain, public policies have to be formulated with a view to boosting this industry, based on the competitive advantages related to proximity to the Brazilian consumer market. However, there are disadvantages, namely, access to raw material, the need for infrastructure development and entry barriers caused by factors of scale and idle installed capacity in the major global players.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".