Crop diversity and plant–plant interactions in urban allotment gardens
Bibliographic record
Abstract
Abstract Allotment food gardens represent important sources of food security for urban residents. Since urban gardeners rarely receive formal agricultural education and have extremely limited space, they may be relying on readily available gardening advice (e.g., seed packet instructions), inventing cultural strategies that consider inter-specific competitive dynamics, or making poor planting decisions. Knowledge of garden crop diversity and planting arrangements can aid in designing strategies for productive urban gardens and food systems. We surveyed 96 individual plots in 10 allotment gardens in the Toronto region, assessed crop diversity within gardens and recorded planting practices used by urban gardeners by measuring the proximity of individual plants relative to similar or different crop species. We also compared planting densities used by urban gardeners with those recommended by major seed distributers. Collectively, Toronto urban agriculture contributes substantially to urban plant diversity (108 crops), but each plot tends to be relatively depauperate. Carrots and lettuce were three to five times more likely to be planted in clusters than intermingled with other crops ( P < 0.05); whereas gardeners did not appear to use consistent planting arrangements for tomatoes or zucchini. Gardeners tended to plant tomatoes and zucchini 56–62.5% more densely than recommended by seed distributers ( P < 0.001), whereas they planted 147 times fewer carrots in a given area than recommended ( P < 0.05). Furthermore, neither crop planting density nor crop diversity changed with plot size. The planting arrangements we have documented suggest gardeners using allotment plots attempt plant densely in extremely limited space, and are employing cultural strategies that intensify competitive dynamics within gardens. Future research should assess the absolute and relative effect of altered cultural practices on yield, such that any modifications can be prioritized by their impact on yield.
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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 | 0.001 | 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".