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 31 P‐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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".