Food production and resource use of urban farms and gardens: a five-country study
Bibliographic record
Abstract
Abstract There is a lack of data on resources used and food produced at urban farms. This hampers attempts to quantify the environmental impacts of urban agriculture or craft policies for sustainable food production in cities. To address this gap, we used a citizen science approach to collect data from 72 urban agriculture sites, representing three types of spaces (urban farms, collective gardens, individual gardens), in five countries (France, Germany, Poland, United Kingdom, and United States). We answered three key questions about urban agriculture with this unprecedented dataset: (1) What are its land, water, nutrient, and energy demands? (2) How productive is it relative to conventional agriculture and across types of farms? and (3) What are its contributions to local biodiversity? We found that participant farms used dozens of inputs, most of which were organic (e.g., manure for fertilizers). Farms required on average 71.6 L of irrigation water, 5.5 L of compost, and 0.53 m2 of land per kilogram of harvested food. Irrigation was lower in individual gardens and higher in sites using drip irrigation. While extremely variable, yields at well-managed urban farms can exceed those of conventional counterparts. Although farm type did not predict yield, our cluster analysis demonstrated that individually managed leisure gardens had lower yields than other farms and gardens. Farms in our sample contributed significantly to local biodiversity, with an average of 20 different crops per farm not including ornamental plants. Aside from clarifying important trends in resource use at urban farms using a robust and open dataset, this study also raises numerous questions about how crop selection and growing practices influence the environmental impacts of growing food in cities. We conclude with a research agenda to tackle these and other pressing questions on resource use at urban farms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".