Population growth increases the demand for food and thus leads to expansion of cultivated land and intensification of agricultural production. There is a definite limit to both of these options for food security and their multiple negative effects on the environment undermine the aim for sustainability. Presently the impact of the Green Revolution on crop production is levelling off at high yields attained and even the potential of large scale irrigation programmes and transgenic crops seem to be limited in view of the expected increase in demand for food. Moreover, climate change threatens to affect agricultural production across the globe. Skyfarming represents a promising approach for food production that is largely environment independent and therefore immune to climate change. Optimal growing conditions, shielded from weather extremes and pests are aimed at raising plant production towards the physiological potential. Selecting rice as a pioneer crop for Skyfarming will not only provide a staple for a large part of the global population, but also significantly reduce the greenhouse gas emission caused by paddy cultivation. Multiplication of the benefits could be achieved by stacking production floors vertically. In Skyfarming the crop, with its requirements for optimal growth, development and production, determines the system's design. Accordingly, the initial development must focus on the growing environment, lighting, temperature, humidity regulation and plant protection strategies as well as on the overall energy supply. For each of these areas potentially suitable technologies are presented and discussed
Littoral macroinvertebrates are increasingly used for assessing the ecological status of lakes according to the EU Water Framework Directive. This requires harmonised sampling methods, but information on the appropriate spatial scale of the sampling as well as on the adequate sample sizes are mostly lacking. In this study, we compared the spatial variability of littoral (\1.2 m water depth) macroinvertebrate community composition within habitats and within sites to test whether habitat-specific sampling can reduce their spatial variability. Furthermore, we determined the sample size necessary to obtain maximum species richness for a given habitat type. Spatial variability of macroinvertebrate community composition was significantly lower within habitats than within sampling sites, except for communities of coarse woody debris. Species-area curves revealed that a sample size of 1 m 2 per habitat was not sufficient to obtain the maximum species richness due to the dominance of rare species, which suggests that compilation of taxon inventories may require more exhaustive sampling with sampling sizes substantially larger than 1 m 2 . Separate analysis for species assigned to incidence classes showed that a mean area of 0.63 m 2 per habitat is sufficient to record all species with frequent and medium incidences, and 76% of the rare species. We conclude that habitat-specific sampling is an effective way to reduce the inherent spatial variability of littoral macroinvertebrate communities and that a sample size of 0.63 m 2 per habitat is sufficient to represent their dominant and subdominant elements. The application of this adequate sample size to other lake types than large oligotrophic lakes has to be exercised with caution, in particular if community composition and richness patterns differ. However, our results are based on data from lakes that represent the typical lake type found throughout the Central Baltic ecoregion ensuring its wider applicability in this ecoregion.
No abstract
No abstract
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.