Seasonal and event‐based drivers of runoff and phosphorus export through agricultural tile drains under sandy loam soil in a cool temperate region
Bibliographic record
Abstract
Abstract Frequent algal blooms in surface water bodies caused by nutrient loading from agricultural lands are an ongoing problem in many regions globally. Tile drains beneath poorly and imperfectly drained agricultural soils have been identified as key pathways for phosphorus (P) transport. Two tile drains in an agricultural field with sandy loam soil in southern Ontario, Canada were monitored over a 28‐month period to quantify discharge and the concentrations and loads of dissolved reactive P (DRP) and total P (TP) in their effluent. This paper characterizes seasonal differences in runoff generation and P export in tile drain effluent and relates hydrologic and biogeochemical responses to precipitation inputs and antecedent soil moisture conditions. The generation of runoff in tile drains was only observed above a clear threshold soil moisture content (~0.49 m3·m−3 in the top 10 cm of the soil; above field capacity and close to saturation), indicating that tile discharge responses to precipitation inputs were governed by the available soil‐water storage capacity of the soil. Soil moisture content approached this threshold throughout the non‐growing season (October – April), leading to runoff responses to most events. Concentrations of P in effluent were variable throughout the study but were not correlated with discharge (p > 0.05). However, there were significant relationships between discharge volume (mm) and DRP and TP loads (kg ha−1) for events occurring over the study period (R2 ≥ 0.49, p ≤ 0.001). This research has shown that the hydrologic and biogeochemical responses of tile drains in a sandy loam soil can be predicted to within an order of magnitude from simple hydrometric data such as precipitation and soil moisture once baseline conditions at a site have been determined. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.000 | 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".