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In the present study, in addition to farmyard manure (FYM), cowpea was applied as green manure and faba bean as an intercrop in an organic greenhouse tomato crop, aiming to increase the levels of soil N. Three experiments (E1, E2, E3) were carried out, in which legumes were either noninoculated or inoculated with rhizobia alone or together with plant growth, promoting rhizobacteria. Inoculation of legumes with rhizobia considerably increased N2 fixation in E1 but had no impact on N2 fixation in E2 and E3. In E1, the application of cowpea decreased yield because it imposed a stronger nematode infection as the cowpea plants acted as a good host for Meloidogyne. However, in E2 and E3 the nematode infection was successfully controlled and the legumes significantly increased the tomato yield when inoculated in E2, irrespective of legume inoculation in E3. The total N concentration in the tomato plant tissues was significantly increased by legume application in E2 and E3, but not in E1. These results show that legumes applied as green manure can successfully complement N supply via FYM in organic greenhouse tomato, while legume inoculation with rhizobia can increase the amounts of nitrogen provided to the crop via green manure.

[Main conclusion] Both AE and SIE rarity on La Palma show a clear spatial pattern, with hotspots of endemic rarity found at high elevations and in rare climates, presumably owing to geographical and climatic constraints and possibly anthropogenic pressure (e.g., land use, introduced herbivores, fire). Areas of high rarity estimates coincide with the distribution and extent of PAs on La Palma, especially since the recent addition of the Natura 2000 sites. The hypervolume approach is a promising tool to estimate species range sizes, and can be applied on all scales where point/plot data are available. [Methods] We recorded all present endemic vascular plant species in 1,212 plots covering the entire island. We calculated endemic rarity (corrected range-rarity richness for endemics) using a rarity estimation approach based on kernel density estimations (hypervolume approach). We performed a sensitivity analysis based on multiple linear regressions and relative importance estimations of environmental drivers to estimate the performance of the hypervolume-based rarity estimation compared to standard methods (occurrence frequency, convex hulls, alpha hulls). [Aim] Rarity—an important measure for conservation biogeography—can vary over many orders of magnitude. However, it is unclear which regional-scale abiotic conditions drive processes affecting rarity of endemic species on islands. To support conservation efforts, we (1) assess the main abiotic drivers of endemic rarity, (2) determine how well existing protected areas (PAs) coincide with hotspots of endemic rarity and (3) introduce and evaluate a new hypervolume-based rarity estimator. [Results] Climate variables (mean annual temperature, climatic rarity, precipitation variability) best explained archipelago endemic (AE) and single-island endemic (SIE) rarity. Existing PAs covered the majority of AE and SIE rarity, especially national and natural parks as well as the Natura 2000 sites. In our study system, hypervolumes performed better than standard measures of range size. This work contributed to and was partially supported by the European H2020 Project 641762 ECOPOTENTIAL: Improving future ecosystem benefits through Earth Observations. [Location] La Palma (Canary Islands). © 2017 The Authors. Peer Reviewed

This poster presents the SET-Nav project model linkage and the SET-Nav case studies which will produce the SET-Nav pathways in order to support the SET-Plan implementation.

This Deliverable presents the result from the application of a policy Delphi and highlights how experts assess the potential of governance contexts that may favour the realisation of legume-supported value chains.

Urbanization rate in Central America is the second fastest worldwide and its major cities face challenges regarding urban sustainability. Urban Green Fabric (UGF) is an important material condition for the urban quality of life and, therefore, key to planning processes. We performed an analysis of the UGF of Guatemala City including the identification and classification of UGF, their spatial pattern analysis, construction of ensembles of districts (zones) and revealing citizen&rsquo recognition of green spaces, their perceptions about green space amount and availability as well as their support for UGF future interventions. Finally, we discuss the implications for planning promoted by our results and suggest three actions for UGF sustainability: Creation of new green spaces, protecting existing green spaces and enhancing the mosaic with different green spaces types. UGF is an essential decision support tool for a diversity of actors. s interactions with UGF. We used remote sensing and land use mapping techniques, spatial metrics and a questionnaire survey. Main results are the UGF map of Guatemala City and six ensembles of zones based on a set of indicators. We further revealed citizens&rsquo

This is chapter 11 of the State of Environmental Science in Svalbard (SESS) report 2020 (https://sios-svalbard.org/SESS_Issue3). Data on snow properties such as cover fraction, depth, water equivalents, and melt date are important per se, but also as input in various models, and to verify model results. Earth observation (EO) gathers information on these parameters. Different EO methods for snow have different strengths. Manual measurements and locally deployed sensors give precise data, but only at individual sites. Satellite-based methods give huge amounts of data covering vast areas, but at lower resolution, and only when the satellite passes over relevant sites. Three SIOS projects attempt to bridge the spatial and temporal gaps between remote sensing data and point measurements of snow cover. PASSES gathers information about time-lapse cameras already deployed around Svalbard for research or other purposes. Most of them show snow-cover extent on an intermediate scale (10 m2 to 10 km2), with good temporal resolution. Some have been in place for 20 years, providing a valuable historic record. SATMODSNOW finds that discrepancies between satellite data and model results arise from weaknesses in how the models handle precipitation and temperature. Since snow cover disappears in similar patterns every year, with a time shift depending on precipitation and temperature, close examination of satellite observations offers a way to refine hydrological snow models. SvalSCESIA compares satellite data on both sea ice area and snow cover against ground-based monitoring data and snow model output. They find major shifts in the duration of summer snow-free periods, especially in valleys and lowlands. Snow cover also correlates with the ice cover in adjacent seas, indicating a strong effect of energy exchange between land and sea. Integration and intercomparison of EO data obtained with different methods and on different scales will likely improve snow models.

The aim of this manual is to compile and transfer the best waste management solutions tested by the four pilots, so that these experiences can be replicated in other municipalities. Therefore, this document is presented in such a way that it can be used as an inspiring catalog.

The objective of this document is to provide a handbook and guideline to support the CLUSTERS 2.0 partners in preparing, discussing and presenting plans for the exploitation of the Project’s solutions and results. In the second part of the project, this methodology and guidelines will be used by the partners to develop the draft and final exploitation plans, to be further discussed in the Exploitation Workshops and documented in Deliverable D1.5.