Solid Cattle Manure Less Prone to Phosphorus Loss in Tile Drainage Water
Bibliographic record
Abstract
Forms (e.g., liquid and solid) of manure influence the risk of P loss after land application. The objective of this study was to investigate the effects of P‐based application of various forms of cattle manure (liquid, LCM; or solid, SCM) or inorganic P as triple superphosphate (IP) on soil P losses in tile drainage water. A 4‐yr field experiment was conducted in a clay loam soil with a corn (Zea mays L.)–soybean [Glycine max (L.) Merr.] rotation in the Lake Erie basin. Over the 4 yr, the dissolved reactive P (DRP) flow‐weighted mean concentration (FWMC) in tile drainage water was greater under SCM fertilization than under either IP or LCM fertilization. Despite its lower value on an annual basis, DRP FWMC rose dramatically immediately after LCM application. However, the differences in DRP FWMC did not result in detectable differences in DRP loads. Regarding particulate P and total P losses during the 4 yr, they were 68 and 47%, respectively, lower in the soils amended with SCM than in those with IP, whereas both values were similar between IP and LCM treatments. Overall, the P contained in solid cattle manure was less prone to P loss after land application. Accordingly, the present results can provide a basis for manure storage and application of best management practices designed to reduce P losses and improve crop growth. Core Ideas Phosphorus‐based addition of liquid and solid manure was evaluated on P loss. Liquid manure increased dissolved P loss immediately after application. Solid and liquid manures had long‐term dissolved P loss similar to fertilizer P. Solid manure reduced particulate P and total P loss over the long term.
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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 | 0.000 |
| 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".