Correlations between Phosphorus Fractions and Total Leachate Phosphorus from Cattle Manure– and Swine Manure–Amended Soil
Bibliographic record
Abstract
Although many studies have examined the effect of different application rates of cattle manure, swine manure, and urea fertilizer on the distribution of phosphorus (P) fractions in soil, few studies have correlated P fractions in soil with inorganic P (Pi) and organic P (Po) in leachates. As part of a long-term field study, cattle and swine manures were applied to a loamy soil based on a nitrogen (N) content equivalent of 100 (low) and 400 (high) kg total N ha−1 yr−1 and were compared to urea fertilizer at 100 kg N ha−1 yr−1 and an unamended control soil. Readily available Pi [resin and sodium bicarbonate (NaHCO3)] was significantly greater in cattle manure– and swine manure–amended soil at a high application rate than in the control. With some exceptions, urea did not significantly affect P fractions in sequentially extracted P pools. Leaching of Pi and Po was at levels of environmental concern when cattle and swine manures were applied at the high application rate but not at the low application rate. Cattle manure had significantly greater concentrations of Pi and Po removed by leaching compared to swine manure, most likely because of its narrow N/P ratio and greater amount of P added. Positive correlations were observed between resin Pi and total leachate Pi and between NaHCO3-Pi and total leachate Pi, indicating the value of these measurements in predicting P mobility. The results suggest that a threshold (40 μg P g−1 of soil) must be exceeded before a positive correlation occurs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".