Tile Drainage Phosphorus Loss with Long-Term Consistent Cropping Systems and Fertilization
Bibliographic record
Abstract
Phosphorus (P) loss in tile drainage water may vary with agricultural practices, and the impacts are often hard to detect with short-term studies. We evaluated the effects of long-term (≥43 yr) cropping systems (continuous corn [CC], corn-oats-alfalfa-alfalfa rotation [CR], and continuous grass [CS]) and fertilization (fertilization [F] vs. no-fertilization [NF]) on P loss in tile drainage water from a clay loam soil over a 4-yr period. Compared with NF, long-term fertilization increased concentrations and losses of dissolved reactive P (DRP), dissolved unreactive P (DURP), and total P (TP) in tile drainage water, with the increments following the order: CS > CR > CC. Dissolved P (dissolved reactive P [DRP] and dissolved unreactive P [DURP]) was the dominant P form in drainage outflow, accounting for 72% of TP loss under F-CS, whereas particulate P (PP) was the major form of TP loss under F-CC (72%), F-CR (62%), NF-CS (66%), NF-CC (74%), and NF-CR (72%). Dissolved unreactive P played nearly equal roles as DRP in P losses in tile drainage water. Stepwise regression analysis showed that the concentration of P (DRP, DURP, and PP) in tile drainage flow, rather than event flow volume, was the most important factor contributing to P loss in tile drainage water, although event flow volume was more important in PP loss than in dissolved P loss. Continuous grass significantly increased P loss by increasing P concentration and flow volume of tile drainage water, especially under the fertilization treatment. Long-term grasslands may become a significant P source in tile-drained systems when they receive regular P addition.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".