Impacts of Soil Conditioners and Water Table Management on Phosphorus Loss in Tile Drainage from a Clay Loam Soil
Bibliographic record
Abstract
Adoption of waste-derived soil conditioners and refined water management can improve soil physical quality and crop productivity of fine-textured soils. However, the impacts of these practices on water quality must be assessed to ensure environmental sustainability. We conducted a study to determine phosphorus (P) loss in tile drainage as affected by two types of soil conditioners (yard waste compost and swine manure compost) and water table management (free drainage and controlled drainage with subirrigation) in a clay loam soil under corn-soybean rotation in a 4-yr period from 1999 to 2003. Tile drainage flows were monitored and sampled on a year-round continuous basis using on-site auto-sampling systems. Water samples were analyzed for dissolved reactive P (DRP), particulate P (PP), and total P (TP). Substantially greater concentrations and losses of DRP, PP, and TP occurred with swine manure compost than with control and yard waste compost regardless of water table management. Compared with free drainage, controlled drainage with subirrigation was an effective way to reduce annual and cumulative losses of DRP, PP, and TP in tile drainage through reductions in flow volume and P concentration with control and yard waste compost but not with swine manure compost. Both DRP and TP concentrations in tile drainage were well above the water quality guideline for P, affirming that subsurface loss of P from fine-textured soils can be one critical source for freshwater eutrophication. Swine manure compost applied as a soil conditioner must be optimized by taking water quality impacts into consideration.
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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 | 0.000 | 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 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".