Influence of climate, topography, and soil type on soil extractable phosphorus in croplands of northern glacial‐derived landscapes
Bibliographic record
Abstract
Delineating the relative solubility of soil phosphorus (P) in agricultural landscapes is essential to predicting potential P mobilization in the landscape and can improve nutrient management strategies. This study describes spatial patterns of soil extractable P (easily, moderately, and poorly soluble P) in agricultural landscapes of the Red River basin and the southern Great Lakes region. Surface soils (0-30 cm) and select deeper cores (0-90 cm) were collected from 10 cropped fields ranging in terrain (near-level to hummocky), soil texture (clay to loam), composition (calcareous to noncalcareous), and climate across these differing glacial landscapes. Poorly soluble P dominated (up to 91%) total extractable P in the surface soils at eight sites. No differences in the relative solubilities of soil extractable P with microtopography were apparent in landscapes without defined surface depressions. In contrast, in landscapes with pronounced surface depressions, increased easily soluble P (Sol-P), and decreased soil P sorption capacity were found in soil in wetter, low-slope zones relative to drier upslope locations. The Sol-P pool was most important to soil P retention (up to 28%) within the surface depressions of the Red River basin and at sites with low-carbonate soils in the southern Lake Erie watershed (up to 28%), representing areas at elevated risk of soil P remobilization. This study demonstrates interrelationships among soil extractable P pools, soil development, and soil moisture regimes in agricultural glacial landscapes and provides insight into identifying potential areas for soil P remobilization and associated P availability to crops and runoff.
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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".