Field-scale nutrient transport monitoring and modeling of subsurface and naturally drained agricultural lands
Bibliographic record
Abstract
Eutrophication impacts the quality of many surface waters worldwide. Algal blooms threaten lake water quality and in order to control their growth, understanding of nutrient transport at the field-scale is essential. In order to accomplish this, a combination of field monitoring and computer modeling with the SWAT model was undertaken. Four sites located in the Pike River watershed of southern Quebec were instrumented to monitor nutrient losses from both clay loam and sandy loam soils under both subsurface and naturally drained conditions. Results illustrate how the presence of subsurface drainage influences phosphorus loss depending on soil texture and structure. Total phosphorus loss from the clay loam subsurface drained site was 4.0 kg ha-1, 55% greater than the naturally drained clay loam site. Total phosphorus loss from the sandy loam subsurface drained site was 1.2 kg ha-1, 14% less than the naturally drained sandy loam site. Total phosphorus losses from the subsurface drainage systems in the clay loam field and the sandy loam field were 2.3 and 0.4 kg ha-1, respectively. Particulate phosphorus was the dominant (78%) form of phosphorus loss from the subsurface drainage system at the clay loam site. This indicates that bypass flow through the soil profile in the clay loam field led to excessive total phosphorus loss. SWAT, a watershed-scale model was calibrated with over 6 site years of data, in an attempt to simulate hydrology and pollutant transport at the field-scale. After calibration, the monthly Nash-Sutcliffe Efficiency varied from 0.09 to 0.74 for total drainage; 0.04 to 0.71 for sediment loading; 0.29 to 0.48 for nitrate loads and 0.28 to 0.64 for total phosphorus loads. Overall, SWAT has shown that it has the ability to simulate long-term sediment and nutrient transport at the field-scale. This makes SWAT a valuable tool for the development and evaluation of various beneficial management practices which control sediment and nutrient loss from agricultural fields.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".