Long‐term Changes in Grassland Soil Phosphorus with Fertilizer Application and Withdrawal
Bibliographic record
Abstract
Long‐term phosphorus (P) applications can increase soil P concentrations in excess of agronomic optima, posing a risk to water quality. Once fertilization stops, however, it may take time for soil P concentrations to decline. Whereas P fertilization adds orthophosphate, little is known about changes in other soil P forms during P buildup and drawdown. This study examined changes in P pools (total P, Olsen P, Mehlich P, and water‐extractable P) and P forms determined by 31P‐nuclear magnetic resonance spectroscopy (P‐NMR) in grazed grassland plots from Northern Ireland. Between 1994 and 1999, all plots received 8.3 kg P ha−1 yr−1 with variable rates of nitrogen (100–500 kg N ha−1 yr−1). From 2000 to 2005, plots received 0, 20, 40, or 80 kg P ha−1 yr−1 and 250 kg N ha−1 yr−1; from 2005 to 2010, no P fertilizer was applied to any plots. In 2005, soil P pool concentrations at the highest P fertilization rates were significantly elevated compared with those in 2000 but had decreased to 2000 concentrations by 2010. In soils receiving no P, soil P pool concentrations were significantly lower than those in 1994 only in 2010. There were few changes in P forms determined by P‐NMR. Orthophosphate followed the same trend observed for the soil P pools; total organic P, total inositol phosphates, and total orthophosphate monoesters and diesters were highest in 2010 in the soil receiving no P fertilizer for 10 yr. For these soils, fertilizer application and cessation influenced inorganic P more than organic P. Core Ideas Investigated soil P forms with P fertilization and then without P fertilization. Adding inorganic P in fertilizer increased inorganic P in soil. Inorganic P concentrations decreased in soil after fertilizer cessation. There were few changes in organic P during fertilization and after fertilizer cessation.
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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 | 0.001 | 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".