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27 results for “surplus”
Century Long Reconstruction of Gridded Phosphorus Surplus Across Europe (1850-2019)
<h4><strong>Publication</strong></h4> <p>Please cite this publication if you use the dataset:</p> <p>Batool, M., Sarrazin, F. J. and Kumar, R. Century Long Reconstruction of Gridded Phosphorus Surplus Across Europe (1850-2019), submitted to Earth System Science Data.</p> <p>Please also refer to the above publication for methodological details.</p> <h4><strong>License</strong></h4> <p>The "Century Long Reconstruction of Gridded Phosphorus Surplus Across Europe (1850-2019)" is freely available under an Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0, https://creativecommons.org/licenses/by-nc-sa/4.0), in compliance with the terms of use of the Food and Agriculture Organization of the United Nations (FAO).</p> <p><strong>Data description (v1):</strong></p> <p>This dataset consists of annual long-term reconstruction of total P surplus (both agricultural and non-agricultural soils) across Europe at a 5 arcmin spatial resolution for the period 1850 to 2019. The dataset consists of 48 P surplus estimates that account for the uncertainties resulting from methodological choices and coefficients in major components of the P surplus. This dataset offers the flexibility of aggregating the P surplus at any spatial scale of relevance to support water and land management strategies. Notably, our P surplus dataset has been developed consistently with our N surplus dataset (Batool et al. 2022), enabling joint analysis of N and P budgets across Europe, thereby facilitating holistic nutrient management studies.<br> </p> <p>1. Gridded P surplus data (NetCDF format): 48 files, each of them containing 170 years (1850-2019) of gridded data P surplus</p> <p>2. Aggregated P surplus at European NUTS level (csv format) : 3 files (NUTS 1, NUTS 2, NUTS 3), each of them containing 170 years (1850-2019) of P surplus (mean and standard deviation of 48 estimates). Additionally, a readme file is provided for the NUTS ID's.</p> <p>3. Aggregated P surplus at European river basins (CSV format): 1 file, each of them containing 170 years (1850-2019) of P surplus (mean and standard deviation of 48 estimates). Additionally, a readme file is provided for the river basin ID's.</p> <p>Unit: kg/ha/yr (ha = physical area of grid cell/NUTS/river basins)</p> <p>Time period: 1850-2019</p> <h4><strong>Data description (v2):</strong></h4> <p>We have updated the dataset (v1) and creared v2, which includes improvements and additional components for a more comprehensive phosphorus surplus analysis across Europe. The updates are as follows:</p> <ul> <li><strong>Refined P input estimates from mineral fertilizers (1850–1960):</strong> We have revised our methodology for historical P inputs from mineral fertilizers. In the revised estimates, instead of relying on nitrogen (N) fertilizer trends as a proxy for changes in P fertilizer, we have now incorporated a global dataset that traces the historical sources of phosphorus fertilizers from phosphate rock (1800–2000). This dataset provides a more reliable temporal trend for P fertilizer use. </li> <li><strong>Exclusion of chemical weathering inputs to urban areas:</strong> This adjustment better reflects phosphorus dynamics in urban regions.</li> <li><strong>Expanded data components:</strong> In addition to P surplus, the dataset now includes detailed estimates of P inputs (e.g., mineral fertilizers, manure) and P outputs , offering a more granular view of phosphorus flows.</li> </ul> <ol> <li>Gridded datasets <ol> <li>Total P surplus data (NetCDF format): 48 files, each of them containing 170 years (1850-2019) of gridded P surplus data</li> <li>Total P inputs data (NetCDF format): 1 file, containing 170 years (1850-2019) of gridded P inputs data</li> <li>Total P output data (NetCDF format): 1 file, containing 170 years (1850-2019) of gridded P outputs data</li> <li>P fertilizer (NetCDF format): 2 files, each of them containing 170 years (1850-2019) of gridded P inputs from mineral fertilizer data</li> <li>P animal manure (NetCDF format): 6 files, each of them containing 170 years (1850-2019) of gridded P inputs from animal manure data</li> </ol> </li> <li> <p>Aggregated P surplus at European NUTS level (csv format) : 3 files (NUTS 1, NUTS 2, NUTS 3), each of them containing 170 years (1850-2019) of P surplus (mean and standard deviation of 48 estimates). Additionally, a readme file is provided for the NUTS ID's.</p> </li> <li> <p>Aggregated P surplus at European river basins (CSV format): 1 file, each of them containing 170 years (1850-2019) of P surplus (mean and standard deviation of 48 estimates). Additionally, a readme file is provided for the river basin ID's.</p> </li> </ol> <p>Unit: kg/ha/yr (ha = physical area of grid cell/NUTS/river basins)</p> <p>Time period: 1850-2019</p> <h4><strong>Acknowledgments and underlying datasets</strong></h4> <p>Partial support for this work was provided by the Global Water Quality Analysis and Service Platform (GlobeWQ) project financed by the German Ministry for Education and Research (grant number 02WGR1527A) and the Development Bank of Saxony, Research Project Funding on Resilient Zero-Pollution Wastewater Systems in Climate Change – Case Study Saxony (Project No. 100669418). We are also thankful to UFZ for providing computing power and technical support to the EVE supercomputing facility. We would like to thank people from various organizations and projects for kindly providing us with the data that were used in this study, which includes among others: FAO, Eurostat, HYDE, and IFA.</p> <h4><strong>Contact</strong></h4> <p>Further queries regarding these datasets can be directed to Masooma Batool (masooma.batool@ufz.de) and Rohini Kumar (rohini.kumar@ufz.de).</p>
Global spatially explicit critical nitrogen surpluses and critical nitrogen inputs, and their exceedances
<p>Data files belonging to manuscript:</p> <p>Schulte-Uebbing, LF, AHW Beusen, AF Bouwman & W de Vries (2022): From planetary to regional boundaries for agricultural nitrogen pollution. <strong>Nature</strong>, Vol 610 (7932), https://doi.org/10.1038/s41586-022-05158-2</p> <p>* Input datafiles: Contains complete set of input files used in the calculations of global, spatially explicit critical nitrogen surpluses and critical nitrogen inputs. All input files are output from the IMAGE-GNM model. For further information on IMAGE-GNM, see: Beusen, A. H. W., Van Beek, L. P. H., Bouwman, A. F., Mogollón, J. M., & Middelburg, J. J. (2015). Coupling global models for hydrology and nutrient loading to simulate nitrogen and phosphorus retention in surface water - Description of IMAGE-GNM and analysis of performance. Geoscientific Model Development, 8(12), 4045–4067. https://doi.org/10.5194/gmd-8-4045-2015</p> <p>* Output datafiles: Selection of output datafiles, supporting results presented in the paper. </p> <p>For more information, see file "README.xlsx".</p>
Long-term annual soil nitrogen surplus across Europe (1850 – 2019)
<p>This dataset consists of annual long-term reconstruction of total N surplus (both agricultural and non-agricultural soils) across Europe at a 5 arcmin spatial resolution for the period 1850 to 2019. The dataset consists of 16 N surplus estimates that account for the uncertainties resulting from input data sources and methodological choices in major components of the N surplus.This dataset offers the flexibility of aggregating the N surplus at any spatial scale of relevance to support water and land management strategies.<br> </p> <p><strong>Data description:</strong></p> <p>1. Gridded N surplus data (NetCDF format): 16 files, each of them containing 170 years (1850-2019) of gridded data N surplus</p> <p>2. Aggregated N surplus at European NUTS level (csv format) : 3 files (NUTS 1, NUTS 2, NUTS 3), each of them containing 170 years (1850-2019) of N surplus (mean and standard deviation of 16 estimates). Additionally, a readme file is provided for the NUTS ID's.</p> <p>3. Aggregated N surplus at European river basins (csv format) : 1 file, each of them containing 170 years (1850-2019) of N surplus (mean and standard deviation of 16 estimates). Additionally, a readme file is provided for the river basin ID's.</p> <p>Unit: kg/ha/yr (ha = physical area of grid cell/NUTS/river basins)</p> <p>Time period: 1850-2019</p> <p><strong>Further information:</strong></p> <p>Details/Citation: Long-term annual soil nitrogen surplus across Europe (1850-2019) by M. Batool, F.J.Sarrazin, S. Attinger, N.B.Basu, K.Van Meter and R. Kumar.</p> <p>Further queries regardig these datasets can be directed to Masooma Batool (masooma.batool@ufz.de) and Rohini Kumar (rohini.kumar@ufz.de).</p>
Nitrogen Input, Nitrogen Surplus, and Nitrogen Use Efficiency Globally and for Selected Regions
<p>Data on nitrogen input, surplus and nitrogen use efficiency are provided globally, and for USA, European Union, China, Brazil and South Asia (i.e. India, Nepal, Bangladesh and Pakistan) for 1995 to 2013. Nitrogen input (in the file ninput_1995_2013.csv) is separated into synthetic fertiliser (FERT), manure (MANURE), biological nitrogen fixation (BNF), and NOx deposition from non-agricultural sources (NOx). These N-inputs are for the whole agricultural system, including crops and grassland. Also included is N-surplus (SURPLUS) for cropping systems, which is calculated as the difference between the total N-input and the N removed through harvest. NUE (in the file nue_1995_2013.csv) is N in harvest relative to the total N-input. </p> <p>Synthetic fertilizer application is based on the FAOSTAT dataset (http://www.fao.org/home/en/) with several inputs from the International Fertilizer Association (<a href="https://www.fertilizer.org/">https://www.fertilizer.org/</a>). Total animal excretion is calculated using the FAOSTAT livestock inventory and dynamic excretion factors, biological N fixation is calculated from crop productivities (Anglade et al., 2015)<sup> </sup>and atmospheric deposition was from Dentener et al. (2006). Grassland nitrogen fixation was based on the grassland production estimated following Lassaletta et al. (2016). N in harvested crops is based on crop productivity and N content of 177 crops, utilizing data from the FAOSTAT database.</p>
Spatio-temporal water surplus and evapotranspiration in the catchment area of the Vögelsberg landslide (Tyrol, Austria)
<p>Multi-temporal maps of daily water surplus and evapotranspiration in the catchment area of the Vögelsberg landslide (Tyrol, Austria) based on the SVAT model LWF-Brook90 from 01/01/2008 to 31/12/2019. The spatio-temporal results represent the hydrological forcing of acceleration phases of the deep-seated landslide (see also Pfeiffer et al. 2021, <a href="https://doi.org/10.1002/esp.5129">https://doi.org/10.1002/esp.5129</a>). To investigate the feasibility of a modified land cover as nature-based solutions to reduce the landslide's activity, three land cover scenarios were considered (under current climatic conditions):</p> <p>- Current land cover conditions classified based on air-borne laser scanning data</p> <p>- Forest scenario: catchment area completely covered by forests (hypothetical scenario)</p> <p>- Pole timber scenario: open land above agricultural areas is replaced by areas of pole timber (considered realistic)</p> <p>Three land cover classes (open land, pole timber, mature forest), 11 soil types and 5 vertical meteorological domains were distinguished. Maps were produced with a spatial resolution of 10m (Projection: Austria GK West, EPSG: 31254). The maps are provided as raster stacks in tif-format with each layer representing one day.</p> <p>For further details see OPERANDUM deliverables D4.5 and D4.6.</p>
Food Flows of a Surplus Food Cafe Dataset
<p>Appendix Data to<em> A Pre and Post Analysis of Food and Carbon Flows of a Surplus Food Café initiative.</em></p>
Surplus Stock Measurement and Management Tool
<p>Surplus Stock Measurement and Management Tool will enable food chain retailers to identify the food waste at source and plan their procurement and operational processes to eliminate food waste by measuring it. Company-based information will be kept in a product-based cumulative structure and analysed based on region, city, and Storage Keeping Unit (SKU) group. The first part of the tool aims to provide more reliable and validated food waste data at the retailer level. The main purpose of this development represents SKU-based exploratory data analysis by establishing a relational database structure within SKU, regional-based, season-based, etc. The second part of the tool, the Management System, will be coded in line with the Food Recovery Hierarchy. The Food Recovery Hierarchy principle will be coded into the system as a part of the decision-making process. As a result, the system will create lists of products informing the beneficiary if they are suitable for human consumption (e.g. resell, donation), animal feed, biogas production, or recycling.</p>
Data from: Accounting for movement in spatial surplus production models: A case study of redfish on the Eastern Grand Banks of Newfoundland
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Appendix from: Understanding challenges and strengths in the post-dairy farm surplus calf value chain: An interview study
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Data from: Predator-dependent functional response in wolves: from food limitation to surplus killing
The functional response of a predator describes the change in per capita kill rate to changes in prey density. This response can be influenced by predator densities, giving a predator-dependent functional response. In social carnivores which defend a territory, kill rates also depend on the individual energetic requirements of group members and their contribution to the kill rate. This study aims to provide empirical data for the functional response of wolves Canis lupus to the highly managed moose Alces alces population in Scandinavia. We explored prey and predator dependence, and how the functional response relates to the energetic requirements of wolf packs. Winter kill rates of GPS-collared wolves and densities of cervids were estimated for a total of 22 study periods in 15 wolf territories. The adult wolves were identified as the individuals responsible for providing kills to the wolf pack, while pups could be described as inept hunters. The predator-dependent, asymptotic functional response models (i.e. Hassell-Varley type II and Crowley-Martin) performed best among a set of 23 competing linear, asymptotic and sigmoid models. Small wolf packs acquired > 3 times as much moose biomass as required to sustain their field metabolic rate (FMR), even at relatively low moose abundances. Large packs (6 - 9 wolves) acquired less biomass than required in territories with low moose abundance. We suggest the surplus-killing by small packs is a result of an optimal foraging strategy to consume only the most nutritious parts of easy accessible prey while avoiding the risk of being detected by humans. Food limitation may have a stabilizing effect on pack size in wolves, as supported by the observed negative relationship between body weight of pups and pack size.
Data from: Optimal foraging or surplus killing: selective consumption and discarding of salmon by brown bears
Selective consumption of prey by predators, observed in many animals, is often attributed to optimal foraging. Consistent with this idea, brown bears (Ursus arctos) often exhibit partial consumption, feeding exclusively on lipid-rich tissues of Pacific salmon (Oncorhynchus spp.), and discarding remains. However, bears also kill and abandon salmon without consuming any tissue. These discarded fish may be consistent with optimal foraging choices if they are of poor quality and if bears have easy access to better prey, or may reveal non-adaptive surplus killing behavior if fish are killed and discarded at random or solely based on prey abundance. Using 21 consecutive years of data from sockeye salmon (O. nerka) carcass surveys in Alaska, we found that foraging to maximize energy intake best explained prey discarding behavior. Specifically, discarding was more common under high prey abundance, late in the salmon run, and with low quality prey. Patterns of tissue consumption were consistent with these findings; bears were less likely to consume belly, body, and brain tissue when prey condition decreased. Other factors not quantified here (e.g., bear demography, alternative food resources) almost certainly influence prey discard and partial consumption, though the salmon-related factors explored here strongly influenced bear foraging decisions that were consistent with optimal foraging theory. We did not find clear evidence of surplus killing behavior in brown bears foraging on salmon, but prey selectivity manifested itself through both discarding and partial consumption, which contributes to our ability to predict transport of salmon nutrients by bears across ecosystem boundaries.
Data Figures Indicators of the 'wild seafood' provisioning ecosystem service based on the surplus production of commercial fish stocks
<p>This excel sheet contains the underlying data for the Figures that are presented by Piet al (2017).</p> <p>Piet, GJ, HMJ van Overzee, DCM Miller, E Royo Gelabert, 2017. Indicators of the ‘wild seafood’ provisioning ecosystem service based on the surplus production of commercial fish stocks. Ecological Indicators, Volume 72: 194-202.</p>
Data for "Energy Surplus and Atmosphere – Land-Surface "Tug of War" Induced by Climate Change Control Future Evapotranspiration"
<p>USGS gauges used in manuscript "<strong>Energy Surplus and An Atmosphere-Land-Surface “Tug of War” Control Future Evapotranspiration"</strong>. USGS_CTL15_Gage.mat contains the USGS gauge ID, and one can use retrieve_daily_streamflow.m to download the corresponding streamflow time series. </p>
Safely Disposing of Surplus Prescription Opioids
ClinicalTrials.gov study NCT03855241. IPD Sharing: NO. Countries: 1. Publications: 4.
Feasibility of Using Surplus qFIT Samples to Investigate the Gut Microbiota.
ClinicalTrials.gov study NCT06100549. IPD Sharing: NO. Countries: 1. Publications: 2.
Data from: Optimal foraging or surplus killing: selective consumption and discarding of salmon by brown bears
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Data from: Predator-dependent functional response in wolves: from food limitation to surplus killing
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Data from: The association between method of solicitation and patient permissions for use of surplus tissues and contact for future research
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Mycobacterium smegmatis - Comparison of amtR deletion strain YL1 under nitrogen surplus and starvation
GEO Series GSE30232. Mycolicibacterium smegmatis MC2 155; Mycolicibacterium smegmatis. 3 samples. Type: Expression profiling by array.
Mycobacterium smegmatis - Comparison of wild type SMR5 and amtR deletion strain YL1 under nitrogen surplus
GEO Series GSE30235. Mycolicibacterium smegmatis; Mycolicibacterium smegmatis MC2 155. 4 samples. Type: Expression profiling by array.
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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