Balancing agriculture and environment: Andrew Sharpley's nutrient, soil, and water management legacy
Bibliographic record
Abstract
Managing agricultural phosphorus (P) to balance food security and water quality priorities is a massive challenge fraught with uncertainty and competing interests. Throughout his career, Andrew Sharpley addressed this challenge by building our understanding of the fundamental principles and processes that control P behavior in agricultural land, developing tools to assess P losses, and then evaluating and refining nutrient, soil, and water beneficial management practices (BMPs). Together with an exceptionally large and diverse group of collaborators, Sharpley developed, tested, refined, calibrated, and validated management practices and risk assessment tools to develop site-specific recommendations for the right practices, in the right places, and at the right times. This approach has resonated globally, with the strategic use of BMPs in "critical source areas" widely implemented in an effort to improve the effectiveness of BMPs while reducing implementation costs. Additional contributions to nutrient management include determining environmental thresholds for soil test P and measuring the risk of P loss from different sources of P (e.g., various manures and commercial fertilizers). Sharpley's work was also distinctly realistic, ensuring that strategies for mitigating P loss were critically evaluated so that not only were the benefits highlighted, but also that trade-offs were measured. Nowhere is this better illustrated than with trade-offs in particulate P loss and dissolved P loss with conservation tillage. This review summarizes Sharpley's enormous contributions to our knowledge of agricultural P stewardship as well as his model of collaborative, multi-disciplinary leadership, helping the world to maintain agricultural productivity and protect water quality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".