Mathematical Modeling of Phosphorus Losses from Land Application of Hog and Cattle Manure
Bibliographic record
Abstract
Mathematical models may provide a means to estimate phosphorus (P) losses from land application of manure. Phosphorus losses typically occur during brief episodes of runoff and erosion. Models must be able to simulate P losses during these episodes by representing the basic chemical, physical, and biological processes by which these losses occur. The mathematical model ecosys combines dynamic distributed flow of solutes and nonsolutes through runoff and erosion with convective-dispersive transport of solutes, and both biologically and thermodynamically driven transformations between solutes and nonsolutes. This model was tested against P lost in runoff, erosion, and leachate measured during 90 min of controlled rainfall at 65 mm h(-1) on soils from six sites at which different rates of manure had been applied over the previous 3 to 6 yr. Transport and transformation kinetics in the model enabled it to simulate changes of dissolved inorganic phosphorus (DIP) in runoff from >1.0 to <0.05 mg L(-1) and changes of total phosphorus (TP) in sediment from 15 to 3 mg L(-1) measured during controlled rainfall on soils with diverse P contents. Results from 60-yr model runs using these kinetics with different application rates of cattle manure indicated that (i) a positive interaction exists between annual rainfall and application rate on P losses and (ii) rates greater than 30 Mg ha(-1) yr(-1) would cause TP concentrations in water leaving the site to rise above acceptable limits. The interaction between rainfall and rate suggests that P losses from manure application at any site should be assessed under the upper range of likely rainfall intensities.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".