A case-control study of the health and wellbeing benefits of allotment gardening

Background - Allotments in the UK are popular and waiting lists long. There is, however, little evidence on the health benefits of allotment gardening. The aims of this study were to determine the impacts of a session of allotment gardening on self-esteem and mood and to compare the mental well-being of allotment gardeners with non-gardeners. Methods - Self-esteem, mood and general health were measured in 136 allotment gardeners pre- and post- an allotment session, and 133 non-gardener controls. Allotment gardeners also detailed the time spent on their allotment in the current session and previous 7 days, and their length of tenure. Results - Paired t-tests revealed a significant improvement in self-esteem (P < 0.05) and mood (P < 0.001) as a result of one allotment session. Linear regression revealed that neither the time spent on the allotment in the current session, the previous 7 days or the length of tenure affected the impacts on self-esteem and mood (P > 0.05). One-way ANCOVA revealed that allotment gardeners had a significantly better self-esteem, total mood disturbance and general health (P < 0.001), experiencing less depression and fatigue and more vigour (P < 0.0083). Conclusions - Allotment gardening can play a key role in promoting mental well-being and could be used as a preventive health measure

Wind resource mapping using landscape roughness and spatial interpolation methods

Energy saving, reduction of greenhouse gasses and increased use of renewables are key policies to achieve the European 2020 targets. In particular, distributed renewable energy sources, integrated with spatial planning, require novel methods to optimise supply and demand. In contrast with large scale wind turbines, small and medium wind turbines (SMWTs) have a less extensive impact on the use of space and the power system, nevertheless, a significant spatial footprint is still present and the need for good spatial planning is a necessity. To optimise the location of SMWTs, detailed knowledge of the spatial distribution of the average wind speed is essential, hence, in this article, wind measurements and roughness maps were used to create a reliable annual mean wind speed map of Flanders at 10 m above the Earth’s surface. Via roughness transformation, the surface wind speed measurements were converted into meso- and macroscale wind data. The data were further processed by using seven different spatial interpolation methods in order to develop regional wind resource maps. Based on statistical analysis, it was found that the transformation into mesoscale wind, in combination with Simple Kriging, was the most adequate method to create reliable maps for decision-making on optimal production sites for SMWTs in Flanders.