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Record W7161803772 · doi:10.82308/24149

Impact of Agricultural Management Practices on Mitigating Soil Phosphorus Loss: A multi-scale study

2024· dissertation· en· W7161803772 on OpenAlexaboutno aff
Jiaxin Wang

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsAgricultureWater qualitySustainabilityWatershedNonpoint source pollutionPollutionSoil conservationSurface runoffAgricultural pollution

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.017
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.013
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.012
GPT teacher head0.296
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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