Evaluating Water Use for Agricultural Intensification in Southern Amazonia Using the Water Footprint Sustainability Assessment
Bibliographic record
Abstract
We performed a Water Footprint Sustainability Assessment (WFSA) in the Xingu Basin of Mato Grosso (XBMT), Brazil, with the objectives of (1) tracking blue (as surface water) and green water (as soil moisture regenerated by precipitation) consumption in recent years (2000, 2014); and (2) evaluating agricultural intensification options for future years (2030, 2050) considering the effects of deforestation and climate change on water availability in the basin. The agricultural sector was the largest consumer of water in the basin despite there being almost no irrigation of cropland or pastures. In addition to water use by crops and pasture grass, water consumption attributed to cattle production included evaporation from roughly 9463 ha of small farm reservoirs used to provide drinking water for cattle in 2014. The WFSA showed that while blue and green water consumptive uses were within sustainable limits in 2014, deforestation, cattle confinement, and the use of irrigation to increase cropping frequency could drive water use to unsustainable levels in the future. While land management policies and practices should strive for protection of the remaining natural vegetation, increased agricultural production will require reservoir and irrigation water management to reduce the potential threat of blue water scarcity in the dry season. In addition to providing general guidance for future water allocation decisions in the basin, our study offers an interpretation of blue and green water scarcities with changes in land use and climate in a rapidly evolving agricultural frontier.
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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 | 0.000 | 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) | 0.001 | 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".