Impact of Agricultural Management Practices on Mitigating Soil Phosphorus Loss: A multi-scale study
Bibliographic record
Abstract
The widespread use of inorganic phosphorus (P) fertilizers has had a profound impact on the global P cycle, leading to increased crop yields but also contributing to P pollution in freshwater and coastal ecosystems. Canada, a world-leading agricultural producer, is facing water pollution challenges due to agricultural P losses, which not only affect water quality but also impact the economy. In addressing this challenge, many efforts have explored the effects of agricultural practices on sustainable P management. However, the outcomes have been somewhat controversial, likely due to variations in field design and experimental conditions. Furthermore, historical (residual) P from previous applications has garnered substantial attention in recent years due to its potential to sustain crop yields while mitigating P runoff. Despite this, a comprehensive national-scale assessment of its benefits for Canada remains unclear. My research endeavors to tackle the P pollution challenge in Canada by employing various methodologies. To begin, I conducted a meta-analysis to assess the efficacy of different agricultural practices in reducing soil P loss while considering their impact on crop yields. Our synthesis of field data suggests that conservation practices tend to be the most practical and effective approach for sustainable P management. Subsequently, I employed machine learning (ML) techniques to evaluate the effectiveness of conservation practices in mitigating P export from the Maumee River watershed to Lake Erie over the coming decades. The ML models indicate that additional practices may still be urgently required to address the ongoing P pollution in Lake Erie. Finally, I assessed the potential of reusing residual soil P to reduce P losses across Canadian agricultural land. Developing a P cycling model allowed me to analyze Canada’s P dynamics. Coupled with a soil P dynamics model, my findings suggest that using residual P could reduce mineral P demand in Canada. The Atlantic provinces, Quebec, Ontario, and British Columbia exhibit the highest potential for reducing P applications. Notably, the Atlantic provinces and Quebec are poised to experience the greatest reductions in runoff P loss with this strategy, while Ontario, Manitoba, and British Columbia may experience relatively lower reductions. In conclusion, my research contributes to safeguarding water ecosystems and achieving long-term P sustainability. It underscores the importance of considering residual soil P as a valuable resource and its potential role in mitigating 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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.013 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".