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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. 

The aim of the study was to analyse the quality of soil in urban allotment gardens in the context of the production of home-grown vegetables. The study was conducted on six allotment gardens (31 individual plots) in Gorzów Wielkopolski, a medium-sized Polish city with an average level of industrialisation. The following soil characteristics were analysed: pH, electric conductivity, organic matter, organic carbon, humus, total nitrogen, C:N ratio, NH4+-N, NO3-N−, P, K, Ca, Mg, SO4−-S, Cl, Na, Fe, Cu, Zn, Mn, Ni, Cr, Cd, Pb. The analyses showed that the soils were abundant in necessary nutrients for vegetable growing. They had high content of calcium, magnesium, and phosphorus. However, the soil pH in areas of vegetable cropping was too high. The content of toxic heavy metals—cadmium (0.22–0.59 mg∙kg−1 d.m.) and lead (3.46–16.89 mg∙kg−1 d.m.)—was within the acceptable limits. Nevertheless, the chemical analysis of carrots used as test vegetables showed that the permissible limits of cadmium and lead content in their roots were exceeded. The excessive uptake of these toxic metals can be reduced by lowering the soil pH and applying organic carbon to the soil. 

Summary The paper is concerned with testing normality in samples of curves and error curves estimated from functional regression models. We propose a general paradigm based on the application of multivariate normality tests to vectors of functional principal components scores. We examine finite sample performance of a number of such tests and select the best performing tests. We apply them to several extensively used functional data sets and determine which can be treated as normal, possibly after a suitable transformation. We also offer practical guidance on software implementations of all tests we study and develop large sample justification for tests based on sample skewness and kurtosis of functional principal component scores.