Modeling Impacts of Tile Drain Spacing and Depth on Nitrate‐Nitrogen Losses
Why this work is in the frame
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Bibliographic record
Abstract
Subsurface tile drainage is a major contributor of NO 3 –N from cropland in the Upper Midwest to the hypoxic zone in the Gulf of Mexico. Strategies to reduce NO 3 –N loadings to the Gulf of Mexico require better understanding of the effects of tile spacing and depth on subsurface tile drainage and NO 3 –N losses from subsurface tile drained fields. This study evaluated the sensitivity of NO 3 –N losses to changes in the spacing and depth of subsurface tile drainage systems. For this purpose, the Agricultural Drainage and Pesticide Transport (ADAPT) model was calibrated and validated using monthly subsurface tile drainage and NO 3 –N losses measured in tile drains during 1999 to 2003 from two commercial fields (west and east) in south‐central Minnesota. For the calibration period, there was good agreement between observed and predicted subsurface tile drainage and NO 3 –N losses, with Nash–Sutcliffe modeling efficiencies of 0.75 and 0.56, respectively. Better agreements were observed for the validation periods. The calibrated model was used to evaluate the effects of tile drain spacing and depth with a 50‐yr record (1954–2003) of daily precipitation. Simulation results indicated that reductions in NO 3 –N losses are possible by decreasing the depth or increasing the spacing of tile drains. For instance, for a tile drain spacing of 40 m, reducing the drain depth from 1.5 to 0.9 m reduced NO 3 –N losses by 31% (but reduced crop yield by 60%), while for a tile drain depth of 1.5 m, increasing the tile drain spacing from 27 to 40 m reduced NO 3 –N losses by 50% (while reducing crop yield by 7%). Increased tile drain spacing or decreased tile drain depth could be a potential remedy for excess NO 3 –N loadings entering the Gulf of Mexico.
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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 | 0.000 | 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 it