Comparison of vegetable production, resource-use efficiency and environmental performance of high-technology and conventional farming systems for urban agriculture in the tropical city of Singapore.

Urban farming can improve cities' food security and resilience, but the performance of different farming systems with respect to land and investment constraints has not been systematically investigated. Here, we compared conventional soil-based farming, vertical farming with natural lighting (Vnat), and indoor vertical farming. This study aimed to compare (1) the dynamic production of leafy vegetables over time given the same amount of investment and land constraints, (2) the associated water and energy use, and (3) the global warming potential (GWP) of the urban farming sector if each of the three farming systems was solely used in the tropical city-state of Singapore. A system dynamics (SD) model was constructed to map the potential quantity of leafy vegetables produced, together with the water and energy use of each farming system. The land and monetary investment constraints were set at an additional 0.3% of the total land area of Singapore and an annual investment of SGD 10-20 million (0.001-0.005% of Singapore's annual GDP). Vnat farming was predicted to have the highest production level (110,000 t) and self-sufficiency (76.9% of total demand) by 2050 based on the SD model. This would be >3 times the self-sufficiency level achieved by indoor and soil-based farming systems given the same investment and land constraints. Indoor farming was simulated to use <14% the land area of Vnat while soil-based farming exhausted the additional 0.3% of the land allocated. Indoor farming was also the most energy intensive system, requiring 100 times more than Vnat farming. Comparison of the GHG emission rates showed that indoor farming had the greatest GWP-at 2.51 kg CO2-eq per kg of lettuce produced. Our results suggest that Vnat farming may be the best form of urban farming system to provide large amounts of food in Singapore, considering the production level, the amount of resources used, and the environmental impacts.

Empirical food webs of 12 tropical reservoirs in Singapore.

Background: Food webs summarise trophic interactions of the biotic components within an ecosystem, which can influence nutrient dynamics and energy flows, ultimately affecting ecosystem functions and services. Food webs represent the hypothesised trophic links between predators and prey and can be presented as empirical food webs, in which the relative strength/importance of the respective links are quantified. Some common methods used in food web research include gut content analysis (GCA) and stable isotope analysis (SIA). We combine both methods to construct empirical food web models as a basis for monitoring and studying ecosystem-level outcomes of natural (e.g. species turnover in fish assemblage) and intentional environmental change (e.g. biomanipulation). New information: We present 12 food webs from tropical reservoir communities in Singapore and summarise the topology of each with widely-used network indices (e.g. connectance, link density). Each reservoir was surveyed over 4-6 sampling occasions, during which, representative animal groups (i.e. fish species and taxonomic/functional groups of zooplankton and benthic macroinvertebrates) and all likely sources of primary production (i.e. macrophytes, periphyton, phytoplankton and riparian terrestrial plants) were collected. We analysed gut content in fishes and bulk isotope (d13C and d15N) profiles of all animals (i.e. fishes and invertebrates) and plants collected. Both sets of information were used to estimate the relative strength of trophic relationships using Bayesian mixing models. We document our protocol here, alongside a script in the R programming language for executing data management/analyses/visualisation procedures used in our study. These data can be used to glean insights into trends in inter- and intra-specific or guild interactions in analogous freshwater lake habitats.