The impacts of improving Brazil's transportation infrastructure on the world soybean market
Bibliographic record
Abstract
The lack of adequate transportation infrastructure in Brazil has been a bottleneck for the\nsoybean producers for many years. Moreover, the costly inland transportation incurred\nfrom this bottleneck has resulted in a loss in competitiveness for Brazil compared to\nother exporting countries, especially the United States. If transportation costs are\nreduced by introducing improved infrastructure, Brazil is expected to increase its\ncompetitiveness in the world soybean market by increasing its exports and producer\nrevenues. On the other hand, the United States and other significant soybean competing\nexporting countries are expected to lose market share as well as producer revenues.\nThis study uses a spatial equilibrium model to analyze transportation\ninfrastructure improvements proposed by the Brazilian government vis-à-vis enhance the\nnation’s soybean transportation network. The analyzed transportation improvements are:\n(i) the development of the Tapajós-Teles Pires waterway; (ii) the completion of the BR-\n163 highway; (iii) the construction of the Mortes-Araguaia waterway; (iv) the Ferronorte\nrailroad expansion to Rondonópolis and the linkage between the city of Rio Verde to\nUberlândia; and (v) the Ferropar railroad expansion to the city of Dourados. The model\nspecifies the Brazilian inland transportation network and the international ocean shipments. The model divides Brazil into 18 excess supply regions and 8 excess demand\nregions. The competing exporting countries are the United States, Argentina, Rest of\nSouth America (Bolivia, Paraguay, and Uruguay), Canada, and India. The importing\ncountries are composed of China, European Union, Southeast Asia, Mexico, and the\nRest of the World.\nResults suggest these proposed transportation improvements yield potential\nnoteworthy gains to Brazil with producer revenues increasing more than $500 million\nand exports increasing by 177 thousand metric tons. Consequently, the world soybean\nprice declines by $1.16 per metric ton and producer revenues and exports in the United\nStates fall by 63 thousand metric tons and $104.89 million, respectively. Although the\nabsolute gains in price, revenues, and exports for Brazil are considerable, they only\nrepresent in relative changes 1.48, 2.35, and 0.32 percent, respectively. Similarly, the\nloss in price, revenue, and export value for the United States is also low, declining by\n0.23, 0.23, and 0.12 percent, respectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".