Highlighting the Roles of Producers and Consumers in Land and Water use for Agricultural Production in Southern Amazonia
Bibliographic record
Abstract
For decades, agricultural expansion in Southern Amazonia has relied on deforestation to increase the national and global supplies of cattle and soybean products. Research on the use of land and water for increasing the region’s pasture and cropland outputs has provided important insight into the role of producers in managing these resources. However, the roles of other actors on cattle and soybean production systems (e.g. traders) have been less apparent. For instance, some private initiatives, such as the Cattle Agreement or the Soybean Moratorium, have been proposed to curb deforestation through the cattle and soybean supply chains as a means to influence the production process. Here, we highlight the role of producers and consumers in the use of land and water resources for agricultural production in the state of Mato Grosso, Brazil, by combining field measurements and modelling with trade mapping. We use the Transparency for sustainable economies platform (Trase, https://trase.earth) to highlight the role of trade actors by combining high resolution trade information (e.g. custom declarations) with up-to-date deforestation and water scarcity maps. In 2015, up to five traders with zero deforestation commitments exported over 40% of soybean produced in the state of Mato Grosso. Within this context, producers can increase agricultural output by irrigating cropland, and/or concentrating cattle production on current pastureland, but this combined system has the potential to increase water scarcity in the dry season. Our analysis provides additional information on the drivers shaping the Brazilian agricultural frontier and attempts to bring producers and consumers closer together in agricultural supply chains.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".