Phosphorus Sorption Capacity and Its Relationships With Soil Properties Under Podzolic Soils of Atlantic Canada
Bibliographic record
Abstract
Repetitive long-term fertilizer application leads to phosphorus (P) accumulation in agricultural soils. This can pose environmental risks if the soil’s phosphorus storage capacity is not well understood and considered when planning nutrient management. We investigated the P sorption capacity (PSC) in the surface (0-20 cm, n = 23) and subsurface (20-40 cm, n = 23) of long-term managed podzolic soils in Newfoundland (Nfld), Canada, through batch adsorption using two P concentrations of 150 and 500 mg P L-1, and developed pedotransfer functions to estimate PSC using selected soil properties. Also, the correlation between actual PSC, soil properties, and estimated Phosphorus saturation index (PSI) and soil P sorption capacity (SPSC) both from standard soil test were evaluated. The surface and subsurface soils provided similar median PSC (1.34 and 1.32 mg g-1, respectively, p = 0.16) when examined with the 150 mg P L-1 solution. With 500 mg P L-1 solution, the subsurface soils had significantly higher median PSC than the surface soils of the same fields (2.74 and 2.27 mg g-1, respectively, p = 0.02); and had a better linear relationship (R2 >0.40, p <0.05) with SPSC than at the lower P concentration. The surface soils had significantly higher extractable median P in water, citric acid, and Mehlich-3, higher soil organic matter (SOM), moisture content, Mehlich-3-Fe, -Ca, and -K, PSI, electrical conductivity, silt, and clay contents, while Mehlich-3-Al, Mehlich-3-Al : Fe ratio, SPSC, and sand were lower than those in the subsurface soils. All soils had comparable pH (~6.3). Pedotransfer function revealed that the PSC could be predicted using SOM, Mehlich-3-Al, and Mehlich-3-PICP and thus may be employed for developing testable hypotheses relevant to environmentally and economically viable P management strategies for acidic soils in boreal regions.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".