Modeling the Impacts of Manure on Phosphorus Loss in Surface Runoff and Subsurface Drainage
Bibliographic record
Abstract
Simulation of phosphorus (P) transfer from manured agricultural lands to water bodies via surface runoff and subsurface drainage is potentially of great help in evaluating the risks and effects of eutrophication under a range of best management practice scenarios. However, it remains a challenge since few models are capable of providing a reasonably accurate prediction of P losses under manure treatment. The Environmental Policy Integrated Climate (EPIC) model was applied to simulate the impacts on dissolved reactive P (DRP) losses through surface runoff and subsurface drainage from a solid cattle manure-amended corn ( L.)-soybean [ (L.) Merr.] rotation on a clay loam soil (Vertisol) located in the Lake Erie region. Simulations of DRP loss in surface runoff and tile drainage were satisfactory; however, EPIC did not consider DRP loss directly from manure, weakening its accuracy in the prediction of DRP loss in surface runoff. Having previously drawn on EPIC-predicted surface runoff to initiate SurPhos (Surface Phosphorus and Runoff Model) predictions of DRP losses strictly in surface runoff, no comparison had been made of differences in manure application impacts on EPIC- or SurPhos-predicted DRP losses-accordingly, this was assessed. The SurPhos improved the estimation of DRP loss in surface runoff (Nash-Sutcliffe coefficient, 0.53), especially when large rain events occurred immediately after or within 6 wk of manure application. Generally, EPIC can capture the impacts of manure application on DRP loss in surface runoff and subsurface drainage; however, coupling of the EPIC and SurPhos models increased the accuracy of simulation of runoff DRP losses.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".