Plant facilitation in rooftop agriculture: disentangling plant interactions for sustainable crop production on extensive green roofs
Bibliographic record
Abstract
Abstract Urban agriculture on extensive green roofs (EGRs), which are lightweight, shallow-profile systems with low maintenance, presents a viable opportunity to enhance food production and ecosystem services in cities. However, the harsh environmental conditions typical of EGRs pose challenges for crop cultivation, especially regarding water availability. This study investigates whether facilitative interactions with a common green roof cover species, Sedum , with bush beans ( Phaseolus vulgaris ) as our model crop, can improve crop performance under water stress. We tested 48 EGR modules with three planting designs (no Sedum , artificial Sedum , and live Sedum ) under two watering regimes (high and low) at a green roof testing facility in Toronto, Canada, and measured above- and belowground functional traits of beans to assess productivity and trait plasticity. Results revealed that water availability had a greater influence on bean performance than planting design, where beans grown with artificial Sedum under high watering showed the highest shoot biomass and yield, while performance declined significantly under low water conditions, particularly with live Sedum. Leaf physiological traits were largely unaffected by treatment, likely due to the variable rooftop conditions, and root traits were significantly influenced by both water availability and planting design. Hypervolume analyses showed that beans exhibited greater root trait plasticity than Sedum , suggesting a stronger capacity to adapt to water stress. Contrary to the hypotheses, live Sedum did not consistently facilitate bean performance, and potential competitive interactions may have outweighed any stress-ameliorating effects. These findings show water management as the main management variable for crops on EGRs. To transition EGRs into viable agricultural spaces, future strategies should explore alternative facilitative species, nutrient amendments, and microbial inoculations to support crop resilience.
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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 | 0.000 | 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) | 0.000 | 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".