Modeling the Effectiveness of Sustainable Agricultural Practices in Reducing Sediments and Nutrient Export from a River Basin
Bibliographic record
Abstract
Water pollution from unsustainable agricultural practices is a global problem that undermines human health and economic development. Sustainable agricultural practices have been considered to maintain global food production without compromising water quality and ecosystem health. However, the effectiveness of sustainable agricultural practices in reducing sediments and nutrient export and the combination of practices that will best achieve water quality objectives is still under-explored. In this study, we assess the effectiveness of sustainable agricultural practices in reducing sediments and nutrients export to rivers and determine the combination of practices that would allow the highest reductions of sediments and nutrients, using the Soil and Water Assessment Tool (SWAT) in a Portuguese river basin highly affected by agricultural pollution. SWAT was calibrated and validated for river discharge, sediments, phosphorous, and nitrate loads at the outlet of the basin, with a good agreement between simulated and observed values. The effects of filter strips, fertilizer incorporation, and conservation tillage were analyzed considering both individual and combined effects. Our study shows that sustainable agricultural practices can substantially reduce sediments and nutrients export from a river basin, with the highest average combined depletion of sediments, phosphorus, and nitrate export (25%) achieved when fertilizer incorporation, conservation tillage, and filter strips were implemented simultaneously. Additional studies exploring the effect of sustainable agricultural practices across a range of climate and watershed characteristics, as well as their capacity to deal with challenges related to climate change, will further improve our understanding of the effectiveness of sustainable agricultural practices.
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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 | 0.001 |
| 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".