Managing mineral phosphorus application with soil residual phosphorus reuse in Canada
Bibliographic record
Abstract
Abstract With limited phosphorus (P) supplies, increasing P demand, and issues with P runoff and pollution, developing an ability to reuse the large amounts of residual P stored in agricultural soils is increasingly important. In this study, we investigated the potential for residual soil P to maintain crop yields while reducing P applications and losses in Canada. Using a P cycling model coupled with a soil P dynamics model, we analyzed soil P dynamics over 110 years across Canada's provinces. We found that using soil residual P may reduce mineral P demand as large as 132 Gg P year −1 (29%) in Canada, with the highest potential for reducing P applications in the Atlantic provinces, Quebec, Ontario, and British Columbia. Using residual soil P would result in a 21% increase in Canada's cropland P use efficiency. We expected that the Atlantic provinces and Quebec would have the greatest runoff P loss reduction with use of residual soil P, with the average P loss rate decreasing from 4.24 and 1.69 kg ha −1 to 3.45 and 1.38 kg ha −1 , respectively. Ontario, Manitoba, and British Columbia would experience relatively lower reductions in P loss through use of residual soil P, with the average runoff P loss rate decreasing from 0.44, 0.36, and 4.33 kg ha −1 to 0.19, 0.26, and 4.14 kg ha −1 , respectively. Our study highlights the importance of considering residual soil P as a valuable resource and its potential for reducing P pollution.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".