Using AnnAGNPS to Predict the Effects of Tile Drainage Control on Nutrient and Sediment Loads for a River Basin
Bibliographic record
Abstract
Controlled tile drainage (CTD) can reduce pollutant loading. The Annualized Agricultural Nonpoint Source model (AnnAGNPS version 5.2) was used to examine changes in growing season discharge, sediment, nitrogen, and phosphorus loads due to CTD for a ∼3900-km agriculturally dominated river basin in Ontario, Canada. Two tile drain depth scenarios were examined in detail to mimic tile drainage control for flat cropland: 600 mm depth (CTD) and 200 mm (CTD) depth below surface. Summed for five growing seasons (CTD), direct runoff, total N, and dissolved N were reduced by 6.6, 3.5, and 13.7%, respectively. However, five seasons of summed total P, dissolved P, and total suspended solid loads increased as a result of CTD by 0.96, 1.6, and 0.23%. The AnnAGNPS results were compared with mass fluxes observed from paired experimental watersheds (250, 470 ha) in the river basin. The "test" experimental watershed was dominated by CTD and the "reference" watershed by free drainage. Notwithstanding environmental/land use differences between the watersheds and basin, comparisons of seasonal observed and predicted discharge reductions were comparable in 100% of respective cases. Nutrient load comparisons were more consistent for dissolved, relative to particulate water quality endpoints. For one season under corn crop production, AnnAGNPS predicted a 55% decrease (CTD) in dissolved N from the basin. AnnAGNPS v. 5.2 treats P transport from a surface pool perspective, which is appropriate for many systems. However, for assessment of tile drainage management practices for relatively flat tile-dominated systems, AnnAGNPS may benefit from consideration of P and particulate transport in the subsurface.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".