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  • Energy Research
  • 2016-2025
  • 6. Clean water
  • DE
  • NL

  • Authors: Reinsch, S.; Koller, E.; Sowerby, A.; De Dato, G.; +17 Authors

    The data consists of annual measurements of standing aboveground plant biomass, annual aboveground net primary productivity and annual soil respiration between 1998 and 2012. Data were collected from seven European shrublands that were subject to the climate manipulations drought and warming. Sites were located in the United Kingdom (UK), the Netherlands (NL), Denmark ( two sites, DK-B and DK-M), Hungary (HU), Spain (SP) and Italy (IT). All field sites consisted of untreated control plots, plots where the plant canopy air is artificially warmed during night time hours, and plots where rainfall is excluded from the plots at least during the plants growing season. Standing aboveground plant biomass (grams biomass per square metre) was measured in two undisturbed areas within the plots using the pin-point method (UK, DK-M, DK-B), or along a transect (IT, SP, HU, NL). Aboveground net primary productivity was calculated from measurements of standing aboveground plant biomass estimates and litterfall measurements. Soil respiration was measured in pre-installed opaque soil collars bi-weekly, monthly, or in measurement campaigns (SP only). The datasets provided are the basis for the data analysis presented in Reinsch et al. (2017) Shrubland primary production and soil respiration diverge along European climate gradient. Scientific Reports 7:43952 https://doi.org/10.1038/srep43952 Standing biomass was measured using the non-destructive pin-point method to assess aboveground biomass. Measurements were conducted at the state of peak biomass specific for each site. Litterfall was measured annually using litterfall traps. Litter collected in the traps was dried and the weight was measured. Aboveground biomass productivity was estimated as the difference between the measured standing biomass in year x minus the standing biomass measured the previous year. Soil respiration was measured bi-weekly or monthly, or in campaigns (Spain only). It was measured on permanently installed soil collars in treatment plots. The Gaussen Index of Aridity (an index that combines information on rainfall and temperature) was calculated using mean annual precipitation, mean annual temperature. The reduction in precipitation and increase in temperature for each site was used to calculate the Gaussen Index for the climate treatments for each site. Data of standing biomass and soil respiration was provided by the site responsible. Data from all sites were collated into one data file for data analysis. A summary data set was combined with information on the Gaussen Index of Aridity Data were then exported from these Excel spreadsheet to .csv files for ingestion into the EIDC.

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    Authors: Arts, Gertie; van Smeden, J.; Wolters, M.F.; Belgers, J.D.M.; +6 Authors

    The dataset covers biotic and abiotic data from the aquatic habitat of a population of the sediment-rooted macrophyte Myriophyllum spicatum in the temperate climate region (The Netherlands). The growth of M. spicatum was monitored in 0.2025 m2 plant baskets installed in an experimental ditch. Parameters monitored included biomass (fresh and dry weight), shoot length, seasonal short-term growth rates of shoots, relevant environmental parameters and weather data. This dataset includes the 2-year experimental biotic (macrophyte biomass and growth data) and environmental data (water quality data, sediment data). A second file includes the statistical data. A third file includes the weather data.

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    4TU.ResearchData | science.engineering.design
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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    4TU.ResearchData | science.engineering.design
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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    Research@WUR
    Dataset . 2021
    Data sources: Research@WUR
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    Smithsonian figshare
    Dataset . 2021
    License: CC BY
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      4TU.ResearchData | science.engineering.design
      Dataset . 2021
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      4TU.ResearchData | science.engineering.design
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
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      Research@WUR
      Dataset . 2021
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      Smithsonian figshare
      Dataset . 2021
      License: CC BY
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    Authors: Sriyana, Ignatius (author); de Gijt, J.G. (author); Parahyangsari, Sri Kumala (author); Niyomukiza, John Bosco (author);

    In the current study, we examine the Indonesian government's watershed management program, which was established in 2001. In 2005, the Coordination Team for Rescue of Water Resources (CTRWR) was established to execute the program on a national level. However, at the time, field implementation was a sectoral interest due to the lack of program integration. To this end, the Indonesian government promoted integrated watershed management in 2009, which since then has been implemented by all stakeholders (in Top–Down management form), with application limited to preparing and planning documents. This is mainly driven by the stakeholders’ lack of understanding with regard to watershed systems as integrated management units. Field implementation results have not yet been realized, including the promotion of community-based watershed management (through Bottom–Up management). The purpose of our research was to determine the index numbers by measuring the level of cooperation between watershed management workers based on the Village Watershed Model (VWM) specifically surface water which includes six variables: planning, participation, institutional, fund sharing, gender, and management systems. The method used was an ordinal measure with the Likert scale. Our data showed successful watershed management, in which five of the six VWM variables—planning, participation, institutional, fund sharing, and management systems—were in the “good” category with indices ranging from 73.08 to 78.27. The gender variable index (69.12) was in the “medium” category.

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    International Soil and Water Conservation Research
    Article . 2020 . Peer-reviewed
    License: CC BY NC ND
    Data sources: Crossref
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      International Soil and Water Conservation Research
      Article . 2020 . Peer-reviewed
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    Authors: Luís Resende; Juan Flores; Cláudia Moreira; Diana Pacheco; +3 Authors

    Integrated multitrophic aquaculture (IMTA) is a versatile technology emerging as an ecological and sustainable solution for traditional monoculture aquacultures in terms of effluent treatment. Nevertheless, IMTA is still poorly applied in aquaculture industry due to, among other reasons, the lack of effective, low-investment and low-maintenance solutions. In this study, one has developed a practical and low maintenance IMTA-pilot system, settled in a semi-intensive coastal aquaculture. The optimisation and performance of the system was validated using Ulva spp., a macroalgae that naturally grows in the fishponds of the local aquaculture. Several cultivation experiments were performed at lab-scale and in the IMTA-pilot system, in static mode. The specific growth rate (SGR), yield, nutrient removal, N and C enrichment, protein and pigment content were monitored. Ulva spp. successfully thrived in effluent from the fish species sea bream (Sparus aurata) and sea bass (Dicentrarchus labrax) production tanks and significantly reduced inorganic nutrient load in the effluent, particularly, NH4+, PO43− and NO3−. The enrichment of nitrogen in Ulva spp.’s tissues indicated nitrogen assimilation by the algae, though, the cultivated Ulva spp. showed lower amounts of protein and pigments in comparison to the wild type. This study indicates that the designed IMTA-pilot system is an efficient solution for fish effluent treatment and Ulva spp., a suitable effluent remediator.

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    Applied Sciences
    Article . 2021 . Peer-reviewed
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    Applied Sciences
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    Estudo Geral
    Article . 2021
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      Estudo Geral
      Article . 2021
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    Authors: Mohamed Samer; Omar Hijazi; Badr A. Mohamed; Essam M. Abdelsalam; +4 Authors

    Bioplastics are alternatives of conventional petroleum-based plastics. Bioplastics are polymers processed from renewable sources and are biodegradable. This study aims at conducting an environmental impact assessment of the bioprocessing of agricultural wastes into bioplastics compared to petro-plastics using an LCA approach. Bioplastics were produced from potato peels in laboratory. In a biochemical reaction under heating, starch was extracted from peels and glycerin, vinegar and water were added with a range of different ratios, which resulted in producing different samples of bio-based plastics. Nevertheless, the environmental impact of the bioplastics production process was evaluated and compared to petro-plastics. A life cycle analysis of bioplastics produced in laboratory and petro-plastics was conducted. The results are presented in the form of global warming potential, and other environmental impacts including acidification potential, eutrophication potential, freshwater ecotoxicity potential, human toxicity potential, and ozone layer depletion of producing bioplastics are compared to petro-plastics. The results show that the greenhouse gases (GHG) emissions, through the different experiments to produce bioplastics, range between 0.354 and 0.623 kg CO2 eq. per kg bioplastic compared to 2.37 kg CO2 eq. per kg polypropylene as a petro-plastic. The results also showed that there are no significant potential effects for the bioplastics produced from potato peels on different environmental impacts in comparison with poly-β-hydroxybutyric acid and polypropylene. Thus, the bioplastics produced from agricultural wastes can be manufactured in industrial scale to reduce the dependence on petroleum-based plastics. This in turn will mitigate GHG emissions and reduce the negative environmental impacts on climate change.

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    Clean Technologies and Environmental Policy
    Article . 2021 . Peer-reviewed
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      Clean Technologies and Environmental Policy
      Article . 2021 . Peer-reviewed
      License: Springer TDM
      Data sources: Crossref
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    Energy Climate dataset consistent with ENTSO-E Pan-European Climatic Database (PECD 2021.3) in CSV and netCDF format TL;DR: this is a nationally aggregated hourly dataset for the capacity factors per unit installed capacity for storage hydropower plants and run-of-river hydropower plants in the European region. All the data is provided for 30 climatic years (1981-2010). Method Description The hydro inflow data is based on historical river runoff reanalysis data simulated by the E-HYPE model. E-HYPE is a pan-European model developed by The Swedish Meteorological and Hydrological Institute (SMHI), which describes hydrological processes including flow paths at the subbasin level. E-hype only provides the time series of daily river runoff entering the inlet of each European subbasin over 1981-2010. To match the operational resolution of the dispatch model, we linearly downscale these time series to hourly. By summing up runoff associated with the inlet subbasins of each country, we also obtain the country-level river runoff. The hydro inflow time series per country is defined as the normalized energy inflows (per unit installed capacity of hydropower) embodied in the country-level river runoff. A dispatch model can be used to decides whether the energy inflows are actually used for electricity generation, stored, or spilled (in case the storage reservoir is already full). Data coverage This dataset considers two types of hydropower plants, namely storage hydropower plant (STO) and run-of-river hydropower plant (ROR). Not all countries have both types of hydropower plants installed (see table). The countries and their acronyms for both technologies included in this dataset are: Country Run-of-River Storage Austria AT_ROR AT_STO Belgium BE_ROR BE_STO Bulgaria BG_ROR BG_STO Switzerland CH_ROR CH_STO Cyprus CZ_ROR CZ_STO Germany DE_ROR DE_STO Denmark DK_ROR Estonia EE_ROR Greece EL_ROR EL_STO Spain ES_ROR ES_STO Finland FI_ROR FI_STO France FR_ROR FR_STO Great Britain GB_ROR GB_STO Croatia HR_ROR HR_STO Hungary HU_ROR HU_STO Ireland IE_ROR IE_STO Italy IT_ROR IT_STO Luxembourg LU_ROR Latvia LV_ROR the Netherlands NL_ROR Norway NO_ROR NO_STO Poland PL_ROR PL_STO Portugal PT_ROR PT_STO Romania RO_ROR RO_STO Sweden SE_ROR SE_STO Slovenia SI_ROR SI_STO Slovakia SK_ROR SK_STO Data structure description The files is provided in CSV (.csv) format with a comma (,) as separator and double-quote mark (") as text indicator. The first row stores the column labels. The columns contain the following: first column (or A) contains the row number Label: unlabeled Contents: interger range [1,262968] second column (or B) contains the valid-time Label: T1h Contents represent time with text as [DD/MM/YYYY HH:MM]) column 3-52 (or C-AY) each contain the capacity factor for each valid combination of a country and hydropower plant type Label: XX_YYY the two letter country code (XX) and the hydropower plant type (YYY) acronym for storage hydropower plant (STO) and run-of-river hydropower plant (ROR) Contents represent the capacity factor as a floating value in the range [0,1], the decimal separator is a point (.). DISCLAIMER: the content of this dataset has been created with the greatest possible care. However, we invite to use the original data for critical applications and studies. The raw hydro data was generated as part of 'Evaluating sediment Delivery Impacts on Reservoirs in changing climaTe and society across scales and sectors (DIRT-X)', this project and therefor, Jing hu, received funding from the European Research Area Network (ERA-NET) under grant number 438.19.902. Laurens P. Stoop received funding from the Netherlands Organization for Scientific Research (NWO) under Grant No. 647.003.005.

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    ZENODO
    Dataset . 2023
    License: CC BY SA
    Data sources: Datacite
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    ZENODO
    Dataset . 2023
    License: CC BY SA
    Data sources: Datacite
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    ZENODO
    Dataset . 2023
    License: CC BY SA
    Data sources: ZENODO
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      ZENODO
      Dataset . 2023
      License: CC BY SA
      Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY SA
      Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY SA
      Data sources: ZENODO
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    Authors: Song, Bingnan; Weijma, Jan; van der Weijden, Renata; Buisman, Cees; +1 Authors

    Results belonging to paper "High-rate biological selenate reduction in a sequencing batch reactor for recovery of hexagonal selenium".Recovery of selenium (Se) from wastewater provides a solution for both securing Se supply and preventing Se pollution. Here, we developed a high-rate process for biological selenate reduction to elemental selenium. Distinctive from other studies, we aimed for a process with selenate as the main biological electron sink, with minimal formation of methane or sulfide. A sequencing batch reactor, fed with an influent containing 120 mgSe L-1 selenate and ethanol as electron donor and carbon source, was operated for 495 days. The high rates (419 �� 17 mgSe L-1 day-1) were recorded between day 446 and day 495 for a hydraulic retention time of 6h. The maximum conversion efficiency of selenate amounted to 96% with a volumetric conversion rate of 444 mgSe L-1 day-1, which is 6 times higher than the rates reported in the literature thus far. At the end of the experiment, a highly enriched selenate reducing biomass had developed, with a specific activity of 856��26 mgSe-1day-1gbiomass-1, which was nearly 1000-fold higher than that of the inoculum. No evidence was found for the formation of methane, sulfide, or volatile reduced selenium compounds like dimethyl-selenide or H2Se, revealing a high selectivity. Ethanol was incompletely oxidized to acetate. The produced elemental selenium partially accumulated in the reactor as pure (���80% Se of the total mixture of biomass sludge flocs and flaky aggregates, and ~100% of the specific flaky aggregates) selenium black hexagonal needles, with cluster sizes between 20-200 ��m. The new process may serve as the basis for a high-rate technology to remove and recover pure selenium from wastewater or process streams with high selectivity.

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    4TU.ResearchData | science.engineering.design
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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    4TU.ResearchData | science.engineering.design
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    4TU.ResearchData | science.engineering.design
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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    Research@WUR
    Dataset . 2021
    Data sources: Research@WUR
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    Smithsonian figshare
    Dataset . 2021
    License: CC BY
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      4TU.ResearchData | science.engineering.design
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
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      4TU.ResearchData | science.engineering.design
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
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      4TU.ResearchData | science.engineering.design
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
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      Research@WUR
      Dataset . 2021
      Data sources: Research@WUR
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      Smithsonian figshare
      Dataset . 2021
      License: CC BY
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    Natural potentials for future cropland expansion The potential for the expansion of cropland is restricted by the availability of land resources and given local natural conditions. As a result, area that is highly suitable for agriculture according to the prevailing local biophysical conditions but is not under cultivation today has a high natural potential for expansion. Policy regulations can further restrict the availability of land for expansion by designating protected areas, although they may be suitable for agriculture. Conversely, by applying e.g. irrigation practices, land can be brought under cultivation, although it may naturally not be suitable. Here, we investigate the potentials for agricultural expansion for near future climate scenario conditions to identify the suitability of non-cropland areas for expansion according to their local natural conditions. We determine the available energy, water and nutrient supply for agricultural suitability from climate, soil and topography data, by using a fuzzy logic approach according to Zabel et al. (2014). It considers the 16 globally most important staple and energy crops. These are: barley, cassava, groundnut, maize, millet, oil palm, potato, rapeseed, rice, rye, sorghum, soy, sugarcane, sunflower, summer wheat, winter wheat. The parameterization of the membership functions that describe each of the crops’ specific natural requirements is taken from Sys et al. (1993). The considered natural conditions are: climate (temperature, precipitation, solar radiation), soil properties (texture, proportion of coarse fragments and gypsum, base saturation, pH content, organic carbon content, salinity, sodicity), and topography (elevation, slope). As a result of the fuzzy logic approach, values in a range between 0 and 1 describe the suitability of a crop for each of the prevailing natural conditions at a certain location. The smallest suitability value over all parameters finally determines the suitability of a crop. The daily climate data is provided by simulation results from the global climate model ECHAM5 (Jungclaus et al. 2006) for near future (2011-2040) SRES A1B climate scenario conditions. Soil data is taken from the Harmonized World Soil Database (HWSD) (FAO et al. 2012), and topography data is applied from the Shuttle Radar Topography Mission (SRTM) (Farr et al. 2007). In order to gather a general crop suitability, which does not refer to one specific crop, the most suitable crop with the highest suitability value is chosen at each pixel. In addition the natural biophysical conditions, we consider today’s irrigated areas according to (Siebert et al. 2013). We assume that irrigated areas globally remain constant until 2040, since adequate data on the development of irrigated areas do not exist, although it is likely that freshwater availability for irrigation could be limited in some regions, while in other regions surplus water supply could be used to expand irrigation practices (Elliott et al. 2014). However, it is difficult to project where irrigation practices will evolve, since it is driven by economic investment costs that are required to establish irrigation infrastructure. In principle, all agriculturally suitable land that is not used as cropland today has the natural potential to be converted into cropland. We assume that only urban and built-up areas are not available for conversion, although more than 80% of global urban areas are agriculturally suitable (Avellan et al. 2012). However, it seems unlikely that urban areas will be cleared at the large scale due to high investment costs, growing cities and growing demand for settlements. Concepts of urban and vertical farming usually are discussed under the aspects of cultivating fresh vegetables and salads for urban population. They are not designed to extensively grow staple crops such as wheat or maize for feeding the world in the near future. Urban farming would require one third of the total global urban area to meet only the global vegetable consumption of urban dwellers (Martellozzo et al. 2015). Thus, urban agriculture cannot substantially contribute to global agricultural production of staple crops. Protected areas or dense forested areas are not excluded from the calculation, in order not to lose any information in the further combination with the biodiversity patterns (see chapter 2.3). We use data on current cropland distribution by Ramankutty et al. (2008) and urban and built-up area according to the ESA-CCI land use/cover dataset (ESA 2014). From this data, we calculate the ‘natural expansion potential index’ (Iexp) that expresses the natural potential for an area to be converted into cropland as follows: Iexp = S * Aav The index is determined by the quality of agricultural suitability (S) (values between 0 and 1) multiplied with the amount of available area (Aav) for conversion (in percentage of pixel area). The available area includes all suitable area that is not cultivated today, and not classified as urban or artificial area. The index ranges between 0 and 100 and indicates where the conditions for cropland expansion are more or less favorable, when taking only natural conditions into account, disregarding socio-economic factors, policies and regulations that drive or inhibit cropland expansion. The index is a helpful indicator for identifying areas where cropland expansion could take place in the near future. Further information Detailled information are available in the following publication: Delzeit, R., F. Zabel, C. Meyer and T. Václavík (2017). Addressing future trade-offs between biodiversity and cropland expansion to improve food security. Regional Environmental Change 17(5): 1429-1441. DOI: 10.1007/s10113-016-0927-1 Contact Please contact: Dr. Florian Zabel, f.zabel@lmu.de, Department für Geographie, LMU München (www.geografie.uni-muenchen.de) This research was carried out within the framework of the GLUES (Global Assessment of Land Use Dynamics, Greenhouse Gas Emissions and Ecosystem Services) Project, which has been supported by the German Ministry of Education and Research (BMBF) program on sustainable land management (grant number: 01LL0901E).

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    ZENODO
    Dataset . 2016
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2016
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2016
    License: CC BY
    Data sources: ZENODO
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      ZENODO
      Dataset . 2016
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2016
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2016
      License: CC BY
      Data sources: ZENODO
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    Authors: Srivastava, Amit Kumar;

    The yield gap for maize across the Ethiopia has been estimated using crop model LINTUL5 embedded into the modeling framework SIMPLACE (Scientific Impact Assessment and Modelling Platform for Advanced Crop and Ecosystem Management. The yield gap of a crop grown in a certain location and cropping system is defined as the difference between the yield and biomass under optimum management and the average yield achieved by farmers. Yield under optimum management is labeled as potential yield (Yp) under irrigated conditions or water-limited potential yield (Yw) under rain-fed conditions.Yp is location specific because of the climate, and not dependent on soil properties assuming that the required water and nutrients are non-limiting and can be added through management. Thus, in areas without major soil constraints, Yp is the most relevant benchmark for irrigated systems. Whereas, for rain-fed crops, Yw, equivalent to water-limited potential yield, is the most relevant benchmark. Both Yp and Yw are calculated for optimum planting dates, planting density and region-specific crop variety which is critical in determining the feasible growth duration, particularly in tropical climatic conditions where two or even three crops are produced each year on the same field. Purpose: To increase food production, identifying the regions with untapped production capacity is of prime importance and can be achieved by quantitative and spatially explicit estimates of Yield gaps, thus considering the spatial variation in environment and the production system. This dataset was first published on the institutional Repository "Zentrum für Entwicklungsforschung: ZEF Data Portal" with ID={c2bbd5ed-fd4c-4a3f-b0b1-113a5d4f3ddf}. The yield gaps plotted in the map were calculated as the average values of 7 years (the year 2004 -2010). The unit is Megagram per hectare (Mg ha-1) which is equivalent to tons ha-1. The climate data at the national scale was made available from the National Aeronautics and Space Administration (NASA), Goddard Institute of Space Studies(https://data.giss.nasa.gov/impacts/agmipcf/agmerra/), AgMERRA.The dataset is stored at 0.25°×0.25° horizontal resolution (~25km). Soil parameter values were extracted from the soil property maps of Africa at 1 km x 1 km resolution (http://www.isric.org/data/soil-property-maps-africa-1-km). Maize yields (Mg ha-1) and fertilizer application (Nitrogen and Phosphorus) rates over seven years (2004 - 2010) at administrative zone level have been collected from the Central Statistical Agency, Ethiopia.

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    https://dx.doi.org/10.60507/fk...
    Dataset . 2023
    License: CC BY SA
    Data sources: Datacite
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      https://dx.doi.org/10.60507/fk...
      Dataset . 2023
      License: CC BY SA
      Data sources: Datacite
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    Authors: Leybourne, Daniel J; Preedy, Katharine F; Valentine, Tracy A; Bos, Jorunn I B; +1 Authors

    1. Aphids are abundant in natural and managed vegetation, supporting a diverse community of organisms and causing damage to agricultural crops. Due to a changing climate, periods of drought are anticipated to increase, and the potential consequences of this for aphid-plant interactions are unclear. 2. Using a meta-analysis and synthesis approach, we aimed to advance understanding of how increased drought incidence will affect this ecologically and economically important insect group, and to characterise any potential underlying mechanisms. We used qualitative and quantitative synthesis techniques to determine whether drought stress has a negative, positive, or null effect on aphid fitness and examined these effects in relation to 1) aphid biology, 2) geographical region, 3) host plant biology. 3. Across all studies, aphid fitness is typically reduced under drought. Subgroup analysis detected no difference in relation to aphid biology, geographical region, or the aphid-plant combination, indicating the negative effect of drought on aphids is potentially universal. Furthermore, drought stress had a negative impact on plant vigour and increased plant concentrations of defensive chemicals, suggesting the observed response of aphids is associated with reduced plant vigour and increased chemical defence in drought-stressed plants. 4. We propose a conceptual model to predict drought effects on aphid fitness in relation to plant vigour and defence to stimulate further research. Please check the ReadMe for an explanation of the values included in the dataset. Please note that n/a values are included in the Global_Dataset tab for plant meta-analysis data (_Plant_Vigour, _Plant_Defence, and _Plant_Nutrition), these indicate studies that did not report these parameters. Data was collected and curated using standard systematic literature synthesis approaches. The effect size (Hedges' g) reported in the dataset was calculated from extracted means and standard deviations.

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    ZENODO
    Dataset . 2021
    License: CC 0
    Data sources: ZENODO
    DRYAD
    Dataset . 2021
    License: CC 0
    Data sources: Datacite
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      ZENODO
      Dataset . 2021
      License: CC 0
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      DRYAD
      Dataset . 2021
      License: CC 0
      Data sources: Datacite
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  • Authors: Reinsch, S.; Koller, E.; Sowerby, A.; De Dato, G.; +17 Authors

    The data consists of annual measurements of standing aboveground plant biomass, annual aboveground net primary productivity and annual soil respiration between 1998 and 2012. Data were collected from seven European shrublands that were subject to the climate manipulations drought and warming. Sites were located in the United Kingdom (UK), the Netherlands (NL), Denmark ( two sites, DK-B and DK-M), Hungary (HU), Spain (SP) and Italy (IT). All field sites consisted of untreated control plots, plots where the plant canopy air is artificially warmed during night time hours, and plots where rainfall is excluded from the plots at least during the plants growing season. Standing aboveground plant biomass (grams biomass per square metre) was measured in two undisturbed areas within the plots using the pin-point method (UK, DK-M, DK-B), or along a transect (IT, SP, HU, NL). Aboveground net primary productivity was calculated from measurements of standing aboveground plant biomass estimates and litterfall measurements. Soil respiration was measured in pre-installed opaque soil collars bi-weekly, monthly, or in measurement campaigns (SP only). The datasets provided are the basis for the data analysis presented in Reinsch et al. (2017) Shrubland primary production and soil respiration diverge along European climate gradient. Scientific Reports 7:43952 https://doi.org/10.1038/srep43952 Standing biomass was measured using the non-destructive pin-point method to assess aboveground biomass. Measurements were conducted at the state of peak biomass specific for each site. Litterfall was measured annually using litterfall traps. Litter collected in the traps was dried and the weight was measured. Aboveground biomass productivity was estimated as the difference between the measured standing biomass in year x minus the standing biomass measured the previous year. Soil respiration was measured bi-weekly or monthly, or in campaigns (Spain only). It was measured on permanently installed soil collars in treatment plots. The Gaussen Index of Aridity (an index that combines information on rainfall and temperature) was calculated using mean annual precipitation, mean annual temperature. The reduction in precipitation and increase in temperature for each site was used to calculate the Gaussen Index for the climate treatments for each site. Data of standing biomass and soil respiration was provided by the site responsible. Data from all sites were collated into one data file for data analysis. A summary data set was combined with information on the Gaussen Index of Aridity Data were then exported from these Excel spreadsheet to .csv files for ingestion into the EIDC.

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    Authors: Arts, Gertie; van Smeden, J.; Wolters, M.F.; Belgers, J.D.M.; +6 Authors

    The dataset covers biotic and abiotic data from the aquatic habitat of a population of the sediment-rooted macrophyte Myriophyllum spicatum in the temperate climate region (The Netherlands). The growth of M. spicatum was monitored in 0.2025 m2 plant baskets installed in an experimental ditch. Parameters monitored included biomass (fresh and dry weight), shoot length, seasonal short-term growth rates of shoots, relevant environmental parameters and weather data. This dataset includes the 2-year experimental biotic (macrophyte biomass and growth data) and environmental data (water quality data, sediment data). A second file includes the statistical data. A third file includes the weather data.

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    4TU.ResearchData | science.engineering.design
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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    4TU.ResearchData | science.engineering.design
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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    Research@WUR
    Dataset . 2021
    Data sources: Research@WUR
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    Smithsonian figshare
    Dataset . 2021
    License: CC BY
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      4TU.ResearchData | science.engineering.design
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
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      4TU.ResearchData | science.engineering.design
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
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      Research@WUR
      Dataset . 2021
      Data sources: Research@WUR
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      Smithsonian figshare
      Dataset . 2021
      License: CC BY
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    Authors: Sriyana, Ignatius (author); de Gijt, J.G. (author); Parahyangsari, Sri Kumala (author); Niyomukiza, John Bosco (author);

    In the current study, we examine the Indonesian government's watershed management program, which was established in 2001. In 2005, the Coordination Team for Rescue of Water Resources (CTRWR) was established to execute the program on a national level. However, at the time, field implementation was a sectoral interest due to the lack of program integration. To this end, the Indonesian government promoted integrated watershed management in 2009, which since then has been implemented by all stakeholders (in Top–Down management form), with application limited to preparing and planning documents. This is mainly driven by the stakeholders’ lack of understanding with regard to watershed systems as integrated management units. Field implementation results have not yet been realized, including the promotion of community-based watershed management (through Bottom–Up management). The purpose of our research was to determine the index numbers by measuring the level of cooperation between watershed management workers based on the Village Watershed Model (VWM) specifically surface water which includes six variables: planning, participation, institutional, fund sharing, gender, and management systems. The method used was an ordinal measure with the Likert scale. Our data showed successful watershed management, in which five of the six VWM variables—planning, participation, institutional, fund sharing, and management systems—were in the “good” category with indices ranging from 73.08 to 78.27. The gender variable index (69.12) was in the “medium” category.

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    International Soil and Water Conservation Research
    Article . 2020 . Peer-reviewed
    License: CC BY NC ND
    Data sources: Crossref
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    Authors: Luís Resende; Juan Flores; Cláudia Moreira; Diana Pacheco; +3 Authors

    Integrated multitrophic aquaculture (IMTA) is a versatile technology emerging as an ecological and sustainable solution for traditional monoculture aquacultures in terms of effluent treatment. Nevertheless, IMTA is still poorly applied in aquaculture industry due to, among other reasons, the lack of effective, low-investment and low-maintenance solutions. In this study, one has developed a practical and low maintenance IMTA-pilot system, settled in a semi-intensive coastal aquaculture. The optimisation and performance of the system was validated using Ulva spp., a macroalgae that naturally grows in the fishponds of the local aquaculture. Several cultivation experiments were performed at lab-scale and in the IMTA-pilot system, in static mode. The specific growth rate (SGR), yield, nutrient removal, N and C enrichment, protein and pigment content were monitored. Ulva spp. successfully thrived in effluent from the fish species sea bream (Sparus aurata) and sea bass (Dicentrarchus labrax) production tanks and significantly reduced inorganic nutrient load in the effluent, particularly, NH4+, PO43− and NO3−. The enrichment of nitrogen in Ulva spp.’s tissues indicated nitrogen assimilation by the algae, though, the cultivated Ulva spp. showed lower amounts of protein and pigments in comparison to the wild type. This study indicates that the designed IMTA-pilot system is an efficient solution for fish effluent treatment and Ulva spp., a suitable effluent remediator.

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    Applied Sciences
    Article . 2021 . Peer-reviewed
    License: CC BY
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    Applied Sciences
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    Applied Sciences
    Article . 2021
    Data sources: DOAJ
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    Estudo Geral
    Article . 2021
    Data sources: Estudo Geral
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      Applied Sciences
      Article . 2021 . Peer-reviewed
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      Applied Sciences
      Article . 2021
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      Estudo Geral
      Article . 2021
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  • image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Authors: Mohamed Samer; Omar Hijazi; Badr A. Mohamed; Essam M. Abdelsalam; +4 Authors

    Bioplastics are alternatives of conventional petroleum-based plastics. Bioplastics are polymers processed from renewable sources and are biodegradable. This study aims at conducting an environmental impact assessment of the bioprocessing of agricultural wastes into bioplastics compared to petro-plastics using an LCA approach. Bioplastics were produced from potato peels in laboratory. In a biochemical reaction under heating, starch was extracted from peels and glycerin, vinegar and water were added with a range of different ratios, which resulted in producing different samples of bio-based plastics. Nevertheless, the environmental impact of the bioplastics production process was evaluated and compared to petro-plastics. A life cycle analysis of bioplastics produced in laboratory and petro-plastics was conducted. The results are presented in the form of global warming potential, and other environmental impacts including acidification potential, eutrophication potential, freshwater ecotoxicity potential, human toxicity potential, and ozone layer depletion of producing bioplastics are compared to petro-plastics. The results show that the greenhouse gases (GHG) emissions, through the different experiments to produce bioplastics, range between 0.354 and 0.623 kg CO2 eq. per kg bioplastic compared to 2.37 kg CO2 eq. per kg polypropylene as a petro-plastic. The results also showed that there are no significant potential effects for the bioplastics produced from potato peels on different environmental impacts in comparison with poly-β-hydroxybutyric acid and polypropylene. Thus, the bioplastics produced from agricultural wastes can be manufactured in industrial scale to reduce the dependence on petroleum-based plastics. This in turn will mitigate GHG emissions and reduce the negative environmental impacts on climate change.

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    Clean Technologies and Environmental Policy
    Article . 2021 . Peer-reviewed
    License: Springer TDM
    Data sources: Crossref
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      Clean Technologies and Environmental Policy
      Article . 2021 . Peer-reviewed
      License: Springer TDM
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    Energy Climate dataset consistent with ENTSO-E Pan-European Climatic Database (PECD 2021.3) in CSV and netCDF format TL;DR: this is a nationally aggregated hourly dataset for the capacity factors per unit installed capacity for storage hydropower plants and run-of-river hydropower plants in the European region. All the data is provided for 30 climatic years (1981-2010). Method Description The hydro inflow data is based on historical river runoff reanalysis data simulated by the E-HYPE model. E-HYPE is a pan-European model developed by The Swedish Meteorological and Hydrological Institute (SMHI), which describes hydrological processes including flow paths at the subbasin level. E-hype only provides the time series of daily river runoff entering the inlet of each European subbasin over 1981-2010. To match the operational resolution of the dispatch model, we linearly downscale these time series to hourly. By summing up runoff associated with the inlet subbasins of each country, we also obtain the country-level river runoff. The hydro inflow time series per country is defined as the normalized energy inflows (per unit installed capacity of hydropower) embodied in the country-level river runoff. A dispatch model can be used to decides whether the energy inflows are actually used for electricity generation, stored, or spilled (in case the storage reservoir is already full). Data coverage This dataset considers two types of hydropower plants, namely storage hydropower plant (STO) and run-of-river hydropower plant (ROR). Not all countries have both types of hydropower plants installed (see table). The countries and their acronyms for both technologies included in this dataset are: Country Run-of-River Storage Austria AT_ROR AT_STO Belgium BE_ROR BE_STO Bulgaria BG_ROR BG_STO Switzerland CH_ROR CH_STO Cyprus CZ_ROR CZ_STO Germany DE_ROR DE_STO Denmark DK_ROR Estonia EE_ROR Greece EL_ROR EL_STO Spain ES_ROR ES_STO Finland FI_ROR FI_STO France FR_ROR FR_STO Great Britain GB_ROR GB_STO Croatia HR_ROR HR_STO Hungary HU_ROR HU_STO Ireland IE_ROR IE_STO Italy IT_ROR IT_STO Luxembourg LU_ROR Latvia LV_ROR the Netherlands NL_ROR Norway NO_ROR NO_STO Poland PL_ROR PL_STO Portugal PT_ROR PT_STO Romania RO_ROR RO_STO Sweden SE_ROR SE_STO Slovenia SI_ROR SI_STO Slovakia SK_ROR SK_STO Data structure description The files is provided in CSV (.csv) format with a comma (,) as separator and double-quote mark (") as text indicator. The first row stores the column labels. The columns contain the following: first column (or A) contains the row number Label: unlabeled Contents: interger range [1,262968] second column (or B) contains the valid-time Label: T1h Contents represent time with text as [DD/MM/YYYY HH:MM]) column 3-52 (or C-AY) each contain the capacity factor for each valid combination of a country and hydropower plant type Label: XX_YYY the two letter country code (XX) and the hydropower plant type (YYY) acronym for storage hydropower plant (STO) and run-of-river hydropower plant (ROR) Contents represent the capacity factor as a floating value in the range [0,1], the decimal separator is a point (.). DISCLAIMER: the content of this dataset has been created with the greatest possible care. However, we invite to use the original data for critical applications and studies. The raw hydro data was generated as part of 'Evaluating sediment Delivery Impacts on Reservoirs in changing climaTe and society across scales and sectors (DIRT-X)', this project and therefor, Jing hu, received funding from the European Research Area Network (ERA-NET) under grant number 438.19.902. Laurens P. Stoop received funding from the Netherlands Organization for Scientific Research (NWO) under Grant No. 647.003.005.

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    ZENODO
    Dataset . 2023
    License: CC BY SA
    Data sources: Datacite
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    ZENODO
    Dataset . 2023
    License: CC BY SA
    Data sources: Datacite
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    ZENODO
    Dataset . 2023
    License: CC BY SA
    Data sources: ZENODO
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      ZENODO
      Dataset . 2023
      License: CC BY SA
      Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY SA
      Data sources: Datacite
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      ZENODO
      Dataset . 2023
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      Data sources: ZENODO
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    Authors: Song, Bingnan; Weijma, Jan; van der Weijden, Renata; Buisman, Cees; +1 Authors

    Results belonging to paper "High-rate biological selenate reduction in a sequencing batch reactor for recovery of hexagonal selenium".Recovery of selenium (Se) from wastewater provides a solution for both securing Se supply and preventing Se pollution. Here, we developed a high-rate process for biological selenate reduction to elemental selenium. Distinctive from other studies, we aimed for a process with selenate as the main biological electron sink, with minimal formation of methane or sulfide. A sequencing batch reactor, fed with an influent containing 120 mgSe L-1 selenate and ethanol as electron donor and carbon source, was operated for 495 days. The high rates (419 �� 17 mgSe L-1 day-1) were recorded between day 446 and day 495 for a hydraulic retention time of 6h. The maximum conversion efficiency of selenate amounted to 96% with a volumetric conversion rate of 444 mgSe L-1 day-1, which is 6 times higher than the rates reported in the literature thus far. At the end of the experiment, a highly enriched selenate reducing biomass had developed, with a specific activity of 856��26 mgSe-1day-1gbiomass-1, which was nearly 1000-fold higher than that of the inoculum. No evidence was found for the formation of methane, sulfide, or volatile reduced selenium compounds like dimethyl-selenide or H2Se, revealing a high selectivity. Ethanol was incompletely oxidized to acetate. The produced elemental selenium partially accumulated in the reactor as pure (���80% Se of the total mixture of biomass sludge flocs and flaky aggregates, and ~100% of the specific flaky aggregates) selenium black hexagonal needles, with cluster sizes between 20-200 ��m. The new process may serve as the basis for a high-rate technology to remove and recover pure selenium from wastewater or process streams with high selectivity.

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    4TU.ResearchData | science.engineering.design
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    4TU.ResearchData | science.engineering.design
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    4TU.ResearchData | science.engineering.design
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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    Research@WUR
    Dataset . 2021
    Data sources: Research@WUR
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    Smithsonian figshare
    Dataset . 2021
    License: CC BY
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      4TU.ResearchData | science.engineering.design
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      4TU.ResearchData | science.engineering.design
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      4TU.ResearchData | science.engineering.design
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      Research@WUR
      Dataset . 2021
      Data sources: Research@WUR
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      Smithsonian figshare
      Dataset . 2021
      License: CC BY
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    Natural potentials for future cropland expansion The potential for the expansion of cropland is restricted by the availability of land resources and given local natural conditions. As a result, area that is highly suitable for agriculture according to the prevailing local biophysical conditions but is not under cultivation today has a high natural potential for expansion. Policy regulations can further restrict the availability of land for expansion by designating protected areas, although they may be suitable for agriculture. Conversely, by applying e.g. irrigation practices, land can be brought under cultivation, although it may naturally not be suitable. Here, we investigate the potentials for agricultural expansion for near future climate scenario conditions to identify the suitability of non-cropland areas for expansion according to their local natural conditions. We determine the available energy, water and nutrient supply for agricultural suitability from climate, soil and topography data, by using a fuzzy logic approach according to Zabel et al. (2014). It considers the 16 globally most important staple and energy crops. These are: barley, cassava, groundnut, maize, millet, oil palm, potato, rapeseed, rice, rye, sorghum, soy, sugarcane, sunflower, summer wheat, winter wheat. The parameterization of the membership functions that describe each of the crops’ specific natural requirements is taken from Sys et al. (1993). The considered natural conditions are: climate (temperature, precipitation, solar radiation), soil properties (texture, proportion of coarse fragments and gypsum, base saturation, pH content, organic carbon content, salinity, sodicity), and topography (elevation, slope). As a result of the fuzzy logic approach, values in a range between 0 and 1 describe the suitability of a crop for each of the prevailing natural conditions at a certain location. The smallest suitability value over all parameters finally determines the suitability of a crop. The daily climate data is provided by simulation results from the global climate model ECHAM5 (Jungclaus et al. 2006) for near future (2011-2040) SRES A1B climate scenario conditions. Soil data is taken from the Harmonized World Soil Database (HWSD) (FAO et al. 2012), and topography data is applied from the Shuttle Radar Topography Mission (SRTM) (Farr et al. 2007). In order to gather a general crop suitability, which does not refer to one specific crop, the most suitable crop with the highest suitability value is chosen at each pixel. In addition the natural biophysical conditions, we consider today’s irrigated areas according to (Siebert et al. 2013). We assume that irrigated areas globally remain constant until 2040, since adequate data on the development of irrigated areas do not exist, although it is likely that freshwater availability for irrigation could be limited in some regions, while in other regions surplus water supply could be used to expand irrigation practices (Elliott et al. 2014). However, it is difficult to project where irrigation practices will evolve, since it is driven by economic investment costs that are required to establish irrigation infrastructure. In principle, all agriculturally suitable land that is not used as cropland today has the natural potential to be converted into cropland. We assume that only urban and built-up areas are not available for conversion, although more than 80% of global urban areas are agriculturally suitable (Avellan et al. 2012). However, it seems unlikely that urban areas will be cleared at the large scale due to high investment costs, growing cities and growing demand for settlements. Concepts of urban and vertical farming usually are discussed under the aspects of cultivating fresh vegetables and salads for urban population. They are not designed to extensively grow staple crops such as wheat or maize for feeding the world in the near future. Urban farming would require one third of the total global urban area to meet only the global vegetable consumption of urban dwellers (Martellozzo et al. 2015). Thus, urban agriculture cannot substantially contribute to global agricultural production of staple crops. Protected areas or dense forested areas are not excluded from the calculation, in order not to lose any information in the further combination with the biodiversity patterns (see chapter 2.3). We use data on current cropland distribution by Ramankutty et al. (2008) and urban and built-up area according to the ESA-CCI land use/cover dataset (ESA 2014). From this data, we calculate the ‘natural expansion potential index’ (Iexp) that expresses the natural potential for an area to be converted into cropland as follows: Iexp = S * Aav The index is determined by the quality of agricultural suitability (S) (values between 0 and 1) multiplied with the amount of available area (Aav) for conversion (in percentage of pixel area). The available area includes all suitable area that is not cultivated today, and not classified as urban or artificial area. The index ranges between 0 and 100 and indicates where the conditions for cropland expansion are more or less favorable, when taking only natural conditions into account, disregarding socio-economic factors, policies and regulations that drive or inhibit cropland expansion. The index is a helpful indicator for identifying areas where cropland expansion could take place in the near future. Further information Detailled information are available in the following publication: Delzeit, R., F. Zabel, C. Meyer and T. Václavík (2017). Addressing future trade-offs between biodiversity and cropland expansion to improve food security. Regional Environmental Change 17(5): 1429-1441. DOI: 10.1007/s10113-016-0927-1 Contact Please contact: Dr. Florian Zabel, f.zabel@lmu.de, Department für Geographie, LMU München (www.geografie.uni-muenchen.de) This research was carried out within the framework of the GLUES (Global Assessment of Land Use Dynamics, Greenhouse Gas Emissions and Ecosystem Services) Project, which has been supported by the German Ministry of Education and Research (BMBF) program on sustainable land management (grant number: 01LL0901E).

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    ZENODO
    Dataset . 2016
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2016
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2016
    License: CC BY
    Data sources: ZENODO
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      ZENODO
      Dataset . 2016
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2016
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2016
      License: CC BY
      Data sources: ZENODO
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    Authors: Srivastava, Amit Kumar;

    The yield gap for maize across the Ethiopia has been estimated using crop model LINTUL5 embedded into the modeling framework SIMPLACE (Scientific Impact Assessment and Modelling Platform for Advanced Crop and Ecosystem Management. The yield gap of a crop grown in a certain location and cropping system is defined as the difference between the yield and biomass under optimum management and the average yield achieved by farmers. Yield under optimum management is labeled as potential yield (Yp) under irrigated conditions or water-limited potential yield (Yw) under rain-fed conditions.Yp is location specific because of the climate, and not dependent on soil properties assuming that the required water and nutrients are non-limiting and can be added through management. Thus, in areas without major soil constraints, Yp is the most relevant benchmark for irrigated systems. Whereas, for rain-fed crops, Yw, equivalent to water-limited potential yield, is the most relevant benchmark. Both Yp and Yw are calculated for optimum planting dates, planting density and region-specific crop variety which is critical in determining the feasible growth duration, particularly in tropical climatic conditions where two or even three crops are produced each year on the same field. Purpose: To increase food production, identifying the regions with untapped production capacity is of prime importance and can be achieved by quantitative and spatially explicit estimates of Yield gaps, thus considering the spatial variation in environment and the production system. This dataset was first published on the institutional Repository "Zentrum für Entwicklungsforschung: ZEF Data Portal" with ID={c2bbd5ed-fd4c-4a3f-b0b1-113a5d4f3ddf}. The yield gaps plotted in the map were calculated as the average values of 7 years (the year 2004 -2010). The unit is Megagram per hectare (Mg ha-1) which is equivalent to tons ha-1. The climate data at the national scale was made available from the National Aeronautics and Space Administration (NASA), Goddard Institute of Space Studies(https://data.giss.nasa.gov/impacts/agmipcf/agmerra/), AgMERRA.The dataset is stored at 0.25°×0.25° horizontal resolution (~25km). Soil parameter values were extracted from the soil property maps of Africa at 1 km x 1 km resolution (http://www.isric.org/data/soil-property-maps-africa-1-km). Maize yields (Mg ha-1) and fertilizer application (Nitrogen and Phosphorus) rates over seven years (2004 - 2010) at administrative zone level have been collected from the Central Statistical Agency, Ethiopia.

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    https://dx.doi.org/10.60507/fk...
    Dataset . 2023
    License: CC BY SA
    Data sources: Datacite
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      https://dx.doi.org/10.60507/fk...
      Dataset . 2023
      License: CC BY SA
      Data sources: Datacite
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    Authors: Leybourne, Daniel J; Preedy, Katharine F; Valentine, Tracy A; Bos, Jorunn I B; +1 Authors

    1. Aphids are abundant in natural and managed vegetation, supporting a diverse community of organisms and causing damage to agricultural crops. Due to a changing climate, periods of drought are anticipated to increase, and the potential consequences of this for aphid-plant interactions are unclear. 2. Using a meta-analysis and synthesis approach, we aimed to advance understanding of how increased drought incidence will affect this ecologically and economically important insect group, and to characterise any potential underlying mechanisms. We used qualitative and quantitative synthesis techniques to determine whether drought stress has a negative, positive, or null effect on aphid fitness and examined these effects in relation to 1) aphid biology, 2) geographical region, 3) host plant biology. 3. Across all studies, aphid fitness is typically reduced under drought. Subgroup analysis detected no difference in relation to aphid biology, geographical region, or the aphid-plant combination, indicating the negative effect of drought on aphids is potentially universal. Furthermore, drought stress had a negative impact on plant vigour and increased plant concentrations of defensive chemicals, suggesting the observed response of aphids is associated with reduced plant vigour and increased chemical defence in drought-stressed plants. 4. We propose a conceptual model to predict drought effects on aphid fitness in relation to plant vigour and defence to stimulate further research. Please check the ReadMe for an explanation of the values included in the dataset. Please note that n/a values are included in the Global_Dataset tab for plant meta-analysis data (_Plant_Vigour, _Plant_Defence, and _Plant_Nutrition), these indicate studies that did not report these parameters. Data was collected and curated using standard systematic literature synthesis approaches. The effect size (Hedges' g) reported in the dataset was calculated from extracted means and standard deviations.

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    ZENODO
    Dataset . 2021
    License: CC 0
    Data sources: ZENODO
    DRYAD
    Dataset . 2021
    License: CC 0
    Data sources: Datacite
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      ZENODO
      Dataset . 2021
      License: CC 0
      Data sources: ZENODO
      DRYAD
      Dataset . 2021
      License: CC 0
      Data sources: Datacite
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