Hydrological and productive impacts of recent land‐use and land‐cover changes in the semiarid Chaco: Understanding novel water excess in water scarce farmlands
Bibliographic record
Abstract
Abstract Over the last decades, the rapid replacement of native forests by crops and pastures in the Argentinean semiarid Chaco plains has triggered unprecedented groundwater level raises resulting from deep drainage increases, leading to the first massive waterlogging event on records (~25,000 Ha flooded in 2015 near Bandera, one of the most cultivated clusters of the Chaco). In this paper, we link this episode to the ongoing deforestation and cropping scheme shifts through the combined analysis of remote sensing data, agricultural surveys, local farmer information and hydrologic modelling. From 2000 to 2015, the agricultural area of Bandera increased from 21% to 50%, mostly at the expense of dry forests. In this period, agriculture migrated from more intensive (i.e., double‐cropping) to more water‐conservative (i.e., late‐summer single crops) schemes as a general strategy to reduce drought risks. These changes reduced regional evapotranspiration and increased the intensity of deep drainage in wet years. Contrasting cropping schemes displayed significant evapotranspiration differences, but all of them experienced substantial drainage losses (~100–200 mm) during the wettest year (2014/2015), suggesting that cropping adjustments have a limited capacity to halt the generation of water excesses. Nearly 50% of the cropped area in Bandera could not be sown or harvested following the groundwater recharge event of 2014/2015. In the ongoing context of shallow and rising water tables, the introduction of novel cropping schemes that include deep‐rooted perennials, to promote transpirative groundwater discharge, seems crucial to avoid the recurrence of water excesses and their associated dryland salinity risk in the region.
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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 | 0.001 | 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".