Fertilizer source and soil properties affect soil test phosphorus and relationships with phosphorus losses in snowmelt runoff
Bibliographic record
Abstract
Plant-available soil extractable P is used for making P fertilizer recommendations and can be helpful in assessing P loss potential in surface runoff water. Current efforts to relate soil test P (STP) to soluble P concentration in snowmelt surface runoff in the Canadian Prairie Pothole Region do not account for the relative solubility of the P source used nor crop productivity as influenced by the properties of the receiving soil. These factors also potentially influence the utility of STP as an indicator of runoff P losses. In this study, the relationship between STP (modified Kelowna extraction) and P concentration in simulated snowmelt runoff was evaluated across eight inorganic P fertilizer sources applied for 3 years at three sites of variable topography and soil properties in south-central Saskatchewan, Canada. Over 3 years, lower solubility P fertilizer sources had lower P concentrations in runoff that were predicted by assessment of soil residual P level in the fall. Phosphorus fertilizer applications, landscape positions, and crops grown that gave rise to higher crop P uptake and removal over time tended to lead to lower P losses in snowmelt runoff. In contrast, soil conditions that limited crop P uptake, including elevated salinity and low soil moisture, promoted P losses in surface runoff. These influences were also predicted by STP content, demonstrating the utility of the STP measure.
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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 | 0.000 | 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) | 0.001 | 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".