The role of pasture and soybean in deforestation of the Brazilian Amazon
Bibliographic record
Abstract
The dynamics of deforestation in the Brazilian Amazon are complex. A growing debate considers the extent to which deforestation is a result of the expansion of the Brazilian soy industry. Most recent analyses suggest that deforestation is driven by the expansion of cattle ranching, rather than soy. Soy seems to be replacing previously deforested land and/or land previously under pasture. In this study, we use municipality-level statistics on agricultural and deforested areas across the Legal Amazon from 2000 to 2006 to examine the spatial patterns and statistical relationships between deforestation and changes in pasture and soybean areas. Our results support previous studies that showed that deforestation is predominantly a result of pasture expansion. However, we also find support for the hypothesis that an increase of soy in Mato Grosso has displaced pasture further north, leading to deforestation elsewhere. Although not conclusive, our findings suggest that the debate surrounding the drivers of Amazon deforestation is not over, and that indirect causal links between soy and deforestation may exist that need further exploration. Future research should examine more closely how interlinkages between land area, prices, and policies influence the relationship between soy and deforestation, in order to make a conclusive case for 'displacement deforestation'.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".