Potential Phosphorus Export in Snowmelt as Influenced by Fertilizer Placement Method in the Canadian Prairies
Bibliographic record
Abstract
Placement strategies for P fertilizer can affect P availability to crops and influence the amounts and forms of P removed from soil in runoff, contributing to eutrophication. On the Canadian prairies, most runoff occurs during snowmelt. Two adjacent farm fields in Saskatchewan, Canada, were used to assess the effects of spring P fertilizer placement on crop P uptake, residual soil P, and potential P export in simulated snowmelt. One was in conventional tillage (CT) with no history of P fertilization, and the other was in a no‐till (NT) system with multiyear P fertilization at recommended rates. Fertilization (monoammonium phosphate) treatments were no P fertilizer (control); seed placed, deep banded, and broadcast and incorporated at 20 kg P2O5 ha−1; and broadcast treatments at 20, 40, and 80 kg P2O5 ha−1. Yield and P uptake were not affected by placement method. Water‐extractable P at the soil surface after harvest was unaffected by placement or rate at either site but increased below the 5‐cm depth at the NT site in 2016. Broadcast treatments increased P in runoff relative to in‐soil P placement for the 20‐ and 80‐kg P2O5 ha−1 treatments at the CT site and for the 80‐kg P2O5 ha−1 treatment at the NT site. Thus, in‐soil application of P fertilizer appears to be an effective strategy to reduce the risk of P export in snowmelt runoff. Core Ideas In‐soil fertilizer placement reduced P loss in snowmelt runoff from cropland. Broadcasting P fertilizer increased P loss in snowmelt runoff. Crop yield and P uptake were not affected by fertilizer placement method. Soil P was largely unaffected by placement over the 1 or 2 yr of the study.
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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 | 0.001 | 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".