Environmental footprints show China and Europe’s evolving resource appropriation for soybean production in Mato Grosso, Brazil
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Bibliographic record
Abstract
Mato Grosso has become the center of Brazil's soybean industry, with production located across an agricultural frontier expanding into savanna and rainforest biomes. We present environmental footprints of soybean production in Mato Grosso and resource flows accompanying exports to China and Europe for the 2000s using five indicators: deforestation, land footprint (LF), carbon footprint (CF), water footprint (WF), and nutrient footprints. Soybean production was associated with 65% of the state's deforestation, and 14–17% of total Brazilian land use change carbon emissions. The decade showed two distinct production systems illustrated by resources used in the first and second half of the decade. Deforestation and carbon footprint declined 70% while land, water, and nutrient footprints increased almost 30% between the two periods. These differences coincided with a shift in Mato Grosso's export destination. Between 2006 and 2010, China surpassed Europe in soybean imports when production was associated with 97 m2 deforestation yr−1 ton−1 of soybean, a LF of 0.34 ha yr−1 ton−1, a carbon footprint of 4.6 ton CO2-eq yr−1 ton−1, a WF of 1908 m3 yr−1 ton−1, and virtual phosphorous and potassium of 5.0 kg P yr−1 ton−1 and 0.0042 g K yr−1 ton−1. Mato Grosso constructs soil fertility via phosphorous and potassium fertilizer sourced from third party countries and imported into the region. Through the soybean produced, Mato Grosso then exports both water derived from its abundant, seasonal precipitation and nutrients obtained from fertilizer. In 2010, virtual water flows were 10.3 km3 yr−1 to China and 4.1 km3 yr−1 to Europe. The total embedded nutrient flows to China were 2.12 Mtons yr−1 and 2.85 Mtons yr−1 to Europe. As soybean production grows with global demand, the role of Mato Grosso's resource use and production vulnerabilities highlight the challenges with meeting future international food security needs.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it