Phosphorus recycling and loss from compost‐amended urban gardens: Results from a 7‐year study
Bibliographic record
Abstract
Abstract Urban vegetable gardens provide an opportunity to recycle nutrients from food waste back into the human food system through the application of compost. However, a reliance on compost for soil fertility can lead to excess phosphorus (P) inputs that can build up in garden soil and potentially be exported via leachate or runoff. We report the results of a 7‐year experiment in a campus research garden in which replicated raised‐bed garden plots received manure‐based compost or municipal compost that was applied at a higher rate targeted to meet crop nitrogen demand or a lower rate targeted to meet crop P demand. Control plots received either no soil inputs or targeted synthetic fertilizer. Higher input treatments for both types of composts showed steadily increasing concentrations of soil plant‐available P, with a corresponding increase in leachate phosphate concentration. For both higher input compost treatments, approximately 30% of P added as compost was recovered in harvested crops over the 7‐year period, compared to >88% in the lower input compost treatments. In both high‐ and low‐input manure compost treatments, export of P as leachate accounted for approximately 10% of total P input, compared to 4% for the municipal compost. Over the 7‐year study period, P exported as leachate ranged from 0.8 g P/m 2 in the no‐input treatments to 6.5 g P/m 2 in the higher input manure compost treatments. These results show that tradeoffs are not inevitable as targeted compost applications can lead to high yield and low leachate export.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".