Disparities in nitrogen and phosphorus management across time and space: a case study of the Chesapeake Bay using the CAFE framework
Bibliographic record
Abstract
Abstract Efficient management of nitrogen (N) and phosphorus (P) is imperative for sustainable agriculture, resource conservation, and reducing environmental pollution. Despite progress in on-farm practices and urban wastewater treatment in the Chesapeake Bay (CB) watershed, limited attention has been given to nutrient transport, use, and handling between farms and urban environments. This study uses the hierarchical CAFE (Cropping system, Animal-crop system, Food system, and Ecosystem) framework to evaluate nutrient management performances within the watershed. We first develop a three-decade, county-level nutrient budget database (1985–2019), then analyze the spatiotemporal patterns of N and P budgets, as well as N and P use efficiencies, within the four CAFE hierarchies. Our results indicate a sizable increase in potential N and P losses beyond crop fields (i.e. in the Animal-crop system, Food system, and Ecosystem), surpassing losses from cropland in over 90% of counties. To address these system-wide trade-offs, we estimate the nutrient resources in waste streams beyond croplands, which, if recovered and recycled, could theoretically offset mineral fertilizer inputs in over 60% of counties. Additionally, the growing imbalance in excess N versus P across systems, which increases the N:P ratio of potential losses, could pose an emerging risk to downstream aquatic ecosystems. By utilizing a systematic approach, our novel application of the CAFE framework reveals trade-offs and synergies in nutrient management outcomes that transcend agro-environmental and political boundaries, underscores disparities in N and P management, and helps to identify unique opportunities for enhancing holistic nutrient management across systems within the CB watershed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".