Effect of Poultry Diet on Phosphorus in Runoff from Soils Amended with Poultry Manure and Compost
Bibliographic record
Abstract
Phosphorus in runoff from fields where poultry litter is surface-applied is an environmental concern. We investigated the effect of adding phytase and reducing supplemental P in poultry diets and composting poultry manures, with and without Fe and Al amendments, on P in manures, composts, and runoff. We used four diets: normal (no phytase) with 0.4% supplemental P, normal + phytase, phytase + 0.3% P, and phytase + 0.2% P. Adding phytase and decreasing supplemental P in diets reduced total P but increased water-extractable P in manure. Compared with manures, composting reduced both total P, due to dilution of manure with woodchips and straw, and water-extractable P, but beyond a dilution effect so that the ratio of water-extractable P to total P was less in compost than manure. Adding Fe and Al during composting did not consistently change total P or water-extractable P. Manures and composts were surface-applied to soil boxes at a rate of 50 kg total P ha(-1) and subjected to simulated rainfall, with runoff collected for 30 min. For manures, phytase and decreased P in diets had no significant effect on total P or molybdate-reactive P loads (kg ha(-1)) in runoff. Composting reduced total P and molybdate-reactive P loads in runoff, and adding Fe and Al to compost reduced total P but not molybdate-reactive P loads in runoff. Molybdate-reactive P in runoff (mg box(-1)) was well correlated to water-extractable P applied to boxes (mg box(-1)) in manures and composts. Therefore, the final environmental impact of dietary phytase will depend on the management of poultry diets, manure, and farm-scale P balances.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".