Biochar–Compost blends modulate trace element and nutrient dynamics in rooftop farming systems under Mediterranean conditions
Bibliographic record
Abstract
The rising interest in Rooftop Agriculture (RA) has stemmed a demand for sustainable, lightweight alternatives to peat as plant growing media. Co-composting organic waste with biochar could represent a solution with reduced environmental impact. However, knowledge gaps remain regarding the food safety and environmental performance of these materials. This study examined trace element and nutrient dynamics in six substrates derived from three feedstocks: spent coffee grounds, coffee silverskin, and seaweeds, composted with and without biochar. Over three years, tomato (Solanum lycopersicum L., cv. Moruno de Aranjuez) and a mix of lettuce (Lactuca sativa L., cv. Romana) and Swiss chard (Beta vulgaris var. Cicla) were cultivated on a rooftop in central Madrid (Spain). To unveil the bigger picture behind element uptake by plants and leaching into drainage water, specific indices were calculated by grouping elements by risk-based categories. Results showed feedstock-dependent trace element and nutrient dynamics, with biochar reducing their plant uptake and leaching. Despite seaweed-based compost showing the highest arsenic levels, biochar lowered plant uptake by up to 40 %. Cadmium and lead in edible parts varied by year and substrate, but they remained within EU safety limits. Atmospheric deposition minimally affected lettuce trace element content, while washing reduced hazardous elements. Biochar improved nutrient retention, reducing phosphorus and nitrogen losses by 40 % and 25 %, respectively, over three years. These findings underline the potential of biochar-amended composts as sustainable, safe peat alternatives for RA, supporting crop production while mitigating environmental and health risks.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".