Ecological Strategies for Legume Production on Extensive Green Roofs Under Heat and Nutrient Stress
Bibliographic record
Abstract
ABSTRACT Urban agriculture on extensive green roofs presents opportunities for sustainable food production but is challenged by shallow substrates, nutrient limitations and heat stress. Ecological strategies such as companion planting and organic amendments may help alleviate these constraints, yet their effectiveness across plant life stages remains unclear. In this study, we examined the effects of Sedum L. (Crassulaceae) companion planting and vermicompost amendments on the performance of bush bean ‘Contender’, Phaseolus vulgaris L. (Fabaceae) grown in 56 green roof modules. We measured plant and leaf traits across three phenological stages (pre‐flowering, flowering and pod filling) to assess how treatments influenced physiological responses, morphology and trait correlations under stressful rooftop conditions. Vermicompost application significantly influenced leaf level traits, enhancing water use efficiency, photosynthetic function and leaf morphology across stages, whereas Sedum planting had limited direct effects. Trait correlations revealed that stress amelioration strategies reduced coupling among physiological, morphological and chemical traits, indicating improved plant health and resilience. While nutrient amendments were most beneficial in early growth stages, Sedum companion planting appeared to support plants during later phenological stages. These findings highlight the importance of integrating ecological strategies into rooftop farming, with nutrient additions aiding crop establishment and companion planting contributing during yield formation. More broadly, this work emphasises the potential of combining ecological principles with urban design to optimise crop performance in resource limited and stressful green roof environments, thus supporting urban agriculture in sustainable food systems.
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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 | 0.001 | 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".