Evapotranspiration and Precipitation over Pasture and Soybean Areas in the Xingu River Basin, an Expanding Amazonian Agricultural Frontier
Bibliographic record
Abstract
The conversion from primary forest to agriculture drives widespread changes that have the potential to modify the hydroclimatology of the Xingu River Basin. Moreover, climate impacts over eastern Amazonia have been strongly related to pasture and soybean expansion. This study carries out a remote-sensing, spatial-temporal approach to analyze inter- and intra-annual patterns in evapotranspiration (ET) and precipitation (PPT) over pasture and soybean areas in the Xingu River Basin during a 13-year period. We used ET estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS) and PPT estimates from the Tropical Rainfall Measurement Mission (TRMM) satellite. Our results showed that the annual average ET in the pasture was ~20% lower than the annual average in soybean areas. We show that PPT is notably higher in the northern part of the Xingu River Basin than the drier southern part. ET, on the other hand, appears to be strongly linked to land-use and land-cover (LULC) patterns in the Xingu River Basin. Lower annual ET averages occur in southern areas where dominant LULC is savanna, pasture, and soybean, while more intense ET is observed over primary forests (northern portion of the basin). The primary finding of our study is related to the fact that the seasonality patterns of ET can be strongly linked to LULC in the Xingu River Basin. Further studies should focus on the relationship between ET, gross primary productivity, and water-use efficiency in order to better understand the coupling between water and carbon cycling over this expanding Amazonian agricultural frontier.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".