Assessing the hydrologic impacts of soil conservation practices using a field-scale experimental setup and physically based modelling
Bibliographic record
Abstract
An agricultural experimental setup has been constructed with the aim of assessing the impact of soil conservation practices on surface runoff and water quality. The site is located at Saint-Lambert-de-Lauzon (near Québec City, Canada) and is composed by twelve 624 m 2 catchments for which surface and tillage runoffs, water quality (suspended matter, phosphorus, nitrate-nitrite, dissolved metals), soil physical and chemical properties, and crop yields are monitored. The experimental design allows the comparison of four agricultural treatments: two compaction treatments (with and without soil compaction) and two conservation regimes (conventional and soil conservation agricultural practices), each regime being duplicated three times. Generalized Additive Mixed Model (GAMM) highlighted significant relations between the conservation regimes and suspended matter charges, and surface runoff. In other words, conservation practices allow a significant short-term reduction of suspended matter at the field scale. On the other hand, they appear to favour an increase of surface runoff in the springtime. Since only one three-year rotation cycle has been conducted, no effect was observed on soil properties, and crop yields. Long term impacts of soil conservation practices were estimated by implementing a physically based hydrologic model SWAT over each catchment. Restored soil properties were scenarized using measurements conducted over surrounding unperturbed sites. Modelling results suggest that a restoration of soil physical properties would translate into a moderate decrease surface runoff (-5%) at the field scale. The study brings an advanced and multidimensional understanding of the field-scale processes driving soil health, quantitative hydrology, and water quality. It also quantifies potential long-term benefits of implementing soil conservation practices.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".