Soil Nutrients and Other Major Properties in Grassland Fertilized with Nitrogen and Phosphorus
Bibliographic record
Abstract
Understanding how nutrient distribution relates to soil depth is essential for improving fertilization practices for grasslands. This study evaluated the distribution of P, other elements, and selected soil properties at 0- to 5-cm and 5- to 15-cm layers in a grass sward following several years of N and P fertilization. Soil samples were collected in the spring of 2010 from a timothy grass sward (Phleum pratense L.) established in 1998 on a gravely-sandy loam soil. Sixteen combinations of P (0, 15, 30, and 45 kg ha−1) as triple superphosphate and N (0, 60, 120, and 180 kg ha−1) as calcic ammonium nitrate were broadcast annually from 1999 to 2006 in a split-plot design with P as main plots and N as subplots. Concentration of three soil P indicators [water (CP), Mehlich-3 (PM3), and acid oxalate (POx) extractable P] and total phosphorus (PT) were greater in the 0- to 5-cm than in the 5- to 15-cm layer. Differences in concentration of CP, PM3, POx, and PT between the two soil layers increased with increasing P applications. The NH4OAc extractable Ca and Mg concentrations were significantly greater in the 0- to 5-cm soil layer compared with the 5- to 15-cm soil layer. The NH4OAc extractable K was significantly decreased by N application being greater in the 5- to 15-cm soil layer than in the 0- to 5-cm soil layer. Soil pH decreased and the concentration of Mehlich-3 Al increased with increasing N applications only in the 0- to 5-cm layer. This study demonstrated that several years of P fertilizer in grasslands could increase soil available P in the 0- to 5-cm layer, possibly due to lack of mixing and the low mobility of P. Our results also suggest that changes in soil chemical properties induced by N fertilizer in grasslands could be heightened at the 0- to 5-cm layer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 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".