Lime and cow slurry application temporarily increases organic phosphorus mobility in an acid soil
Bibliographic record
Abstract
Summary Phosphorus loss from agricultural soils to water is recognized as a major contributor to eutrophication of surface water bodies. There is much evidence to suggest that liming, a common agricultural practice, may decrease the risk of P loss by decreasing P solubility. An unsaturated leaching column experiment, with treatments of control and two lime rates, was carried out to investigate the effects of liming on P mobility in a low‐P acid Irish soil, which was sieved and then packed in columns. Phosphorus was applied at the soil surface in the form of KH 2 PO 4 in solution or as cow slurry. Soil solution was sampled at time intervals over depth and analysed for P fractions. Organic P (OP) was the dominant form of P mobile in soil solution. Liming increased OP mobility, probably through increased dispersion of OP with increased pH. Slurry application also increased OP mobility. Results indicated the potential for OP loss following heavy (100 m –3 ha –1 ) cow slurry application, even from low‐P soils, and suggested that liming may increase this risk. Reactive P (RP) was sorbed strongly and rapidly by the soil and did not move substantially below 5 cm depth. As a result, Olsen‐P values in the top 2 cm were greatly increased, which indicates an increased risk of RP loss in overland flow. Lime showed little potential as a soil amendment to reduce the risk of P loss.
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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 | 0.000 | 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) | 0.001 | 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".