Cattle, Clean Water, and Climate Change: Policy Choices for the Brazilian Agricultural Frontier
Bibliographic record
Abstract
In the Amazonian agricultural frontier, pasture for cattle ranching is an important and potentially hazardous form of land use because of sediment erosion as pastures degrade. This relationship between ranching, sediment load, and water quality is likely to further exacerbate environmental impacts, particularly in the context of climate change. We examine the role that river basin councils (RBCs) - a water governance option of Brazil’s 1997 National Water Act - might play in managing this nonpoint-source pollution in the Amazônian state of Rondônia. We implement a simple coupled rancher-water system model to compare two potential governance options: a bulk water cleanup charge (BWC) implemented by RBCs and a land-use fine (LUF) for failing to maintain riparian buffers. We find no significant advantage of BWC over LUF in reducing sediment loading while keeping ranching profitable, under a changing climate. We also fail to find in Rondônia the important stake in water issues that has driven water reform elsewhere in Brazil. Moreover, the comparative success of reforestation programs suggests these programs may, in fact, have the potential to manage nonpoint-source agricultural pollution in the region.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".