Phosphorus Mobility in Heavily Manured and Waterlogged Soil Cultivated with Ryegrass (Lolium multiflorum)
Bibliographic record
Abstract
Extended waterlogging (WL) conditions in heavily manured soils can change soil phosphorus (P) dynamics. We assessed the effects of soil moisture regimes (field capacity (FC) and WL) and P rates on (i) dry matter (DM) yield and P offtake of ryegrass, (ii) changes in soil Fe3+, Fe2+, and soil P, and (iii) risk of P leaching. The treatments were tested in a four-month greenhouse experiment using intact soil columns and annual ryegrass (Lolium multiflorum). The DM yield and P offtake were lower under WL compared with FC. The concentration of Fe3+ was 1984 mg kg−1 (0–30 cm) under FC, but 1213 mg kg−1 at 0–5 cm and 2024 mg kg−1 at 25–30 cm depth under WL. The concentration of Fe2+ was 244 mg kg−1 (0–30 cm) under FC, but 2897 at 0–5 cm and 687 mg kg−1 at 25–30 cm under WL. The water extractable P (Pw) was 12.7 mg kg−1 at 0–5 cm and 9.5 mg kg−1 at 25–30 cm under FC, but 8.6 mg kg−1 at 0–5 cm and 10.5 mg kg−1 at 25–30 cm under WL. The P saturation index (PSI) was 27.2% at 0–5 cm and 17.4% at 25–30 cm under FC, but averaged 11.9% at 0–30 cm under WL. We can conclude that extended WL associated with flooding creates reducing conditions in the soil, thus decreasing the concentration of Fe3+, but increasing the concentrations of Fe2+ and the solubility of P which can exacerbate the risk of P loss with runoff and leaching.
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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 | 0.000 |
| 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".