My GeoServer WFSThis is a description of your Web Feature Server. The GeoServer is a full transactional Web Feature Server, you may wish to limit GeoServer to a Basic service level to prevent modificaiton of your geographic data.WFSWMSGEOSERVERWFS1.1.0NONENONE1.0.01.1.0text/xmlServiceIdentificationServiceProviderOperationsMetadataFeatureTypeListFilter_Capabilitiestext/xml; subtype=gml/3.1.1resultshitstext/xml; subtype=gml/3.1.1GML2KMLSHAPE-ZIPapplication/gml+xml; version=3.2application/jsonapplication/vnd.google-earth.kml xmlapplication/vnd.google-earth.kml+xmlcsvgml3gml32jsontext/csvtext/javascripttext/xml; subtype=gml/2.1.2text/xml; subtype=gml/3.22ALLSOMEresultshitstext/xml; subtype=gml/3.1.1GML2KMLSHAPE-ZIPapplication/gml+xml; version=3.2application/jsonapplication/vnd.google-earth.kml xmlapplication/vnd.google-earth.kml+xmlcsvgml3gml32jsontext/csvtext/javascripttext/xml; subtype=gml/2.1.2text/xml; subtype=gml/3.2text/xml; subtype=gml/3.1.1GenerateNewUseExistingReplaceDuplicateALLSOMEQueryInsertUpdateDeleteLockgeonode:geolocation_341dacec931591840e087ec78dd6db4522-years period from 1993 to 2014 with time series of daily pressure heads measured by automatic recording tensiometers. Continuouslongterm time series of daily soil contents measured for soil compartments 0-30 cm, 30-60 cm, 60-90, 990-120, 120-150 cm and 200 cm depth using Time-Domain-Reflectmetry (TDR)-probes for the timeperiod 1993 to 2014. Quality check of the time series= Field calibrationof the TDR-probes using gravimetric method and consistency check using meteorological time series of measured rainfall and pressure head and calculated evapotranspiration. This data set consist of continuous longterm time series of daily pressure heads measured at 30 cm, 60cm, 90 cm,120 cm , 150 cm and 200 cm depth using automatic recording tensimeters for the time period 1993 to 2014. The quality check of the time series consists of aplausibility check and correction using meteorological time series of measured rainfall and soil water contents, and calculated evapotranspiration.featuresgeolocation_341dacec931591840e087ec78dd6db45urn:x-ogc:def:crs:EPSG:432614.12285489130602 52.5159252542618714.12621301462753 52.51737853512295https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=c680a9d4-fe08-4e9c-81a2-4ae97414989e&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:mpl1_401_501_pressure_head_1993_201422-years period from 1993 to 2014 with time series of daily pressure heads measured by automatic recording tensiometers. Continuouslongterm time series of daily soil contents measured for soil compartments 0-30 cm, 30-60 cm, 60-90, 990-120, 120-150 cm and 200 cm depth using Time-Domain-Reflectmetry (TDR)-probes for the timeperiod 1993 to 2014. Quality check of the time series= Field calibrationof the TDR-probes using gravimetric method and consistency check using meteorological time series of measured rainfall and pressure head and calculated evapotranspiration. This data set consist of continuous longterm time series of daily pressure heads measured at 30 cm, 60cm, 90 cm,120 cm , 150 cm and 200 cm depth using automatic recording tensimeters for the time period 1993 to 2014. The quality check of the time series consists of aplausibility check and correction using meteorological time series of measured rainfall and soil water contents, and calculated evapotranspiration.featuresmpl1_401_501_pressure_head_1993_2014urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=de65a95e-25fb-404c-98b2-3dd2e0278739&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:mpl1_401_501_soil_water_content_1993_201422-years period from 1993 to 2014 with time series of daily pressure heads measured by automatic recording tensiometers. Continuouslongterm time series of daily soil contents measured for soil compartments 0-30 cm, 30-60 cm, 60-90, 990-120, 120-150 cm and 200 cm depth using Time-Domain-Reflectmetry (TDR)-probes for the timeperiod 1993 to 2014. Quality check of the time series= Field calibrationof the TDR-probes using gravimetric method and consistency check using meteorological time series of measured rainfall and pressure head and calculated evapotranspiration. This data set consist of continuous longterm time series of daily pressure heads measured at 30 cm, 60cm, 90 cm,120 cm , 150 cm and 200 cm depth using automatic recording tensimeters for the time period 1993 to 2014. The quality check of the time series consists of aplausibility check and correction using meteorological time series of measured rainfall and soil water contents, and calculated evapotranspiration.featuresmpl1_401_501_soil_water_content_1993_2014urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=e1f48364-f794-4d41-aee9-0b94316183bd&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:mpl2_402_502_pressure_head_1993_201422-years period from 1993 to 2014 with time series of daily pressure heads measured by automatic recording tensiometers. Continuouslongterm time series of daily soil contents measured for soil compartments 0-30 cm, 30-60 cm, 60-90, 990-120, 120-150 cm and 200 cm depth using Time-Domain-Reflectmetry (TDR)-probes for the timeperiod 1993 to 2014. Quality check of the time series= Field calibrationof the TDR-probes using gravimetric method and consistency check using meteorological time series of measured rainfall and pressure head and calculated evapotranspiration. This data set consist of continuous longterm time series of daily pressure heads measured at 30 cm, 60cm, 90 cm,120 cm , 150 cm and 200 cm depth using automatic recording tensimeters for the time period 1993 to 2014. The quality check of the time series consists of aplausibility check and correction using meteorological time series of measured rainfall and soil water contents, and calculated evapotranspiration.featuresmpl2_402_502_pressure_head_1993_2014urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=23b9aec8-77bc-44a3-91bc-5b16e8cd4ca8&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:mpl2_402_502_soil_water_content_1993_201422-years period from 1993 to 2014 with time series of daily pressure heads measured by automatic recording tensiometers. Continuouslongterm time series of daily soil contents measured for soil compartments 0-30 cm, 30-60 cm, 60-90, 990-120, 120-150 cm and 200 cm depth using Time-Domain-Reflectmetry (TDR)-probes for the timeperiod 1993 to 2014. Quality check of the time series= Field calibrationof the TDR-probes using gravimetric method and consistency check using meteorological time series of measured rainfall and pressure head and calculated evapotranspiration. This data set consist of continuous longterm time series of daily pressure heads measured at 30 cm, 60cm, 90 cm,120 cm , 150 cm and 200 cm depth using automatic recording tensimeters for the time period 1993 to 2014. The quality check of the time series consists of aplausibility check and correction using meteorological time series of measured rainfall and soil water contents, and calculated evapotranspiration.featuresmpl2_402_502_soil_water_content_1993_2014urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=59b4443f-b7c4-45d4-b2d2-ea4c78517c6a&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:mpl3_403_503_pressure_head_1993_201422-years period from 1993 to 2014 with time series of daily pressure heads measured by automatic recording tensiometers. Continuouslongterm time series of daily soil contents measured for soil compartments 0-30 cm, 30-60 cm, 60-90, 990-120, 120-150 cm and 200 cm depth using Time-Domain-Reflectmetry (TDR)-probes for the timeperiod 1993 to 2014. Quality check of the time series= Field calibrationof the TDR-probes using gravimetric method and consistency check using meteorological time series of measured rainfall and pressure head and calculated evapotranspiration. This data set consist of continuous longterm time series of daily pressure heads measured at 30 cm, 60cm, 90 cm,120 cm , 150 cm and 200 cm depth using automatic recording tensimeters for the time period 1993 to 2014. The quality check of the time series consists of aplausibility check and correction using meteorological time series of measured rainfall and soil water contents, and calculated evapotranspiration.featuresmpl3_403_503_pressure_head_1993_2014urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=e833be60-9701-49c1-9240-05e90cb7c369&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:mpl3_403_503_soil_water_content_1993_201422-years period from 1993 to 2014 with time series of daily pressure heads measured by automatic recording tensiometers. Continuouslongterm time series of daily soil contents measured for soil compartments 0-30 cm, 30-60 cm, 60-90, 990-120, 120-150 cm and 200 cm depth using Time-Domain-Reflectmetry (TDR)-probes for the timeperiod 1993 to 2014. Quality check of the time series= Field calibrationof the TDR-probes using gravimetric method and consistency check using meteorological time series of measured rainfall and pressure head and calculated evapotranspiration. This data set consist of continuous longterm time series of daily pressure heads measured at 30 cm, 60cm, 90 cm,120 cm , 150 cm and 200 cm depth using automatic recording tensimeters for the time period 1993 to 2014. The quality check of the time series consists of aplausibility check and correction using meteorological time series of measured rainfall and soil water contents, and calculated evapotranspiration.featuresmpl3_403_503_soil_water_content_1993_2014urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=942cc356-c913-40e7-b8d1-f32bf12828d0&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:2003_327_fertilization_becThe data set contains information about soil properties at 80 grid points of a 20 ha large agricultural used field in North Rhine-Westphalia. Each grid point was mapped and sampled up to the soil depth of 90 cm. Over a period of four years (1999 to 2002) soil analysis was conducted considering soil moisture and mineral nitrogen content before sowing and after harvest. Basic information about agricultural management, soil texture, soil organic carbon and annual yield are included.2003_327_fertilization_becspatial variabilitysoil nitrogensite-specific fertilisationfeaturesurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=7d8d475f-5bb0-483c-b4eb-b5d5a162aa26&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:2003_327_initial_partameters_becThe data set contains information about soil properties at 80 grid points of a 20 ha large agricultural used field in North Rhine-Westphalia. Each grid point was mapped and sampled up to the soil depth of 90 cm. Over a period of four years (1999 to 2002) soil analysis was conducted considering soil moisture and mineral nitrogen content before sowing and after harvest. Basic information about agricultural management, soil texture, soil organic carbon and annual yield are included.2003_327_initial_partameters_becspatial variabilitysoil nitrogensite-specific fertilisationfeaturesurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=1896bf1c-3709-4bfe-a90b-5b1b917cc000&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:2003_327_management_becThe data set contains information about soil properties at 80 grid points of a 20 ha large agricultural used field in North Rhine-Westphalia. Each grid point was mapped and sampled up to the soil depth of 90 cm. Over a period of four years (1999 to 2002) soil analysis was conducted considering soil moisture and mineral nitrogen content before sowing and after harvest. Basic information about agricultural management, soil texture, soil organic carbon and annual yield are included.spatial variabilitysoil nitrogen2003_327_management_becsite-specific fertilisationfeaturesurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=7c5f458a-2a8f-425a-bebc-bf72e1207efd&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:2003_327_soil_texture_becThe data set contains information about soil properties at 80 grid points of a 20 ha large agricultural used field in North Rhine-Westphalia. Each grid point was mapped and sampled up to the soil depth of 90 cm. Over a period of four years (1999 to 2002) soil analysis was conducted considering soil moisture and mineral nitrogen content before sowing and after harvest. Basic information about agricultural management, soil texture, soil organic carbon and annual yield are included.site-specific fertilisationfeaturesspatial variability2003_327_soil_texture_becsoil nitrogenurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=4aad5983-3310-45f1-9b2b-6349713a8061&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:2003_327_soil_variables_becThe data set contains information about soil properties at 80 grid points of a 20 ha large agricultural used field in North Rhine-Westphalia. Each grid point was mapped and sampled up to the soil depth of 90 cm. Over a period of four years (1999 to 2002) soil analysis was conducted considering soil moisture and mineral nitrogen content before sowing and after harvest. Basic information about agricultural management, soil texture, soil organic carbon and annual yield are included.featuressite-specific fertilisationspatial variabilitysoil nitrogen2003_327_soil_variables_becurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=ac434b6c-3c7b-4543-9305-1d50d9acccf1&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:2003_327_yields_becThe data set contains information about soil properties at 80 grid points of a 20 ha large agricultural used field in North Rhine-Westphalia. Each grid point was mapped and sampled up to the soil depth of 90 cm. Over a period of four years (1999 to 2002) soil analysis was conducted considering soil moisture and mineral nitrogen content before sowing and after harvest. Basic information about agricultural management, soil texture, soil organic carbon and annual yield are included.2003_327_yields_becspatial variabilitysoil nitrogensite-specific fertilisationfeaturesurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=3639dc82-7788-4bc7-912d-acf54d80d90b&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:geolocation_23b5e205a2a5f1f7c4cb29992a651a07The data set contains information about soil properties at 80 grid points of a 20 ha large agricultural used field in North Rhine-Westphalia. Each grid point was mapped and sampled up to the soil depth of 90 cm. Over a period of four years (1999 to 2002) soil analysis was conducted considering soil moisture and mineral nitrogen content before sowing and after harvest. Basic information about agricultural management, soil texture, soil organic carbon and annual yield are included.soil nitrogengeolocation_23b5e205a2a5f1f7c4cb29992a651a07featuresspatial variabilitysite-specific fertilisationurn:x-ogc:def:crs:EPSG:43267.990394473386695 51.746484307680917.996167312117166 51.75300496223323https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=19d504ef-1361-4072-ab2b-c3895e44d90a&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:geolocation_0ee24193ca873dafc8dc672ce5dddc71featuresgeolocation_0ee24193ca873dafc8dc672ce5dddc71urn:x-ogc:def:crs:EPSG:4326-10.7771472273596 34.718276979854832.163741211839 71.2267123723728https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=01f128e3-4d57-4551-a2b8-0c08b9c0b8d5&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:geolocation_37e232c3436a11f4de52bb786648e01ffeaturesgeolocation_37e232c3436a11f4de52bb786648e01furn:x-ogc:def:crs:EPSG:4326-10.7771472273596 34.718276979854832.163741211839 71.2267123723728https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=011c6aff-dde0-4263-8842-1e0aa1cafd11&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:geolocation_669903fe8c248abf156f5f447d5471d9The data set contains daily time-step observed and scenario climate data on a European grid with 25 km x 25 km spatial resolution and is intended to be used for crop modelling applications. The dataset covers the period 1980-2010 for observations (for a baseline period of 1981-2010 and the year 1980 for crop model simulations with sowing dates in the autumn) and the periods 2040-2069 and 2070-2099 for 5 GCMs x 2 forcing scenarios (RCP4.5 and RCP8.5) and 2 GCMs with RCP2.6. The Joint Research Centre's (JRC) Agri4Cast gridded dataset was used for the baseline. The scenarios have been calculated using an enhanced delta change method that applies changes in aspects of temperature and precipitation variability in addition to changes in mean climate.featuresgeolocation_669903fe8c248abf156f5f447d5471d9crop modelingurn:x-ogc:def:crs:EPSG:4326-10.7771472273596 34.718276979854832.163741211839 71.2267123723728https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=776e3f45-c145-48f2-a9cb-ae73e674b393&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:2017_316_gc_dataThe data set contains data from a full-factorial laboratory experiment, were three different designs and two modifications of typical manual closed chamber setups were tested for sealing integrity. Tests were performed using a simple method, based on injections of single CO2 pulses. Chamber designs differed in V:A-ratio nand chamber-collar sealing (water, rubber-foam, rubber-tube). All chambers were tested with and without pressure vent and fan. Included are measured CO2-concentration data as well as calculated sealing integrity in terms of chamber leakage. Results indicate significant differences in sealing integrity due to chamber-collar sealing strategy. The effect of pressure vent and fan, however, is of minor importance.2017_316_gc_datachamber leakagefeatureswind Shelternon-steady-state chamberchamber-collar sealingurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=20cbdec2-272e-4db2-9433-858e601db0c9&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:2017_316_irga_dataThe data set contains data from a full-factorial laboratory experiment, were three different designs and two modifications of typical manual closed chamber setups were tested for sealing integrity. Tests were performed using a simple method, based on injections of single CO2 pulses. Chamber designs differed in V:A-ratio nand chamber-collar sealing (water, rubber-foam, rubber-tube). All chambers were tested with and without pressure vent and fan. Included are measured CO2-concentration data as well as calculated sealing integrity in terms of chamber leakage. Results indicate significant differences in sealing integrity due to chamber-collar sealing strategy. The effect of pressure vent and fan, however, is of minor importance.2017_316_irga_datachamber leakagefeatureswind Shelternon-steady-state chamberchamber-collar sealingurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=2db712e3-7366-4d86-86c8-2aaf85a19d55&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:2017_316_sealing_integrityThe data set contains data from a full-factorial laboratory experiment, were three different designs and two modifications of typical manual closed chamber setups were tested for sealing integrity. Tests were performed using a simple method, based on injections of single CO2 pulses. Chamber designs differed in V:A-ratio nand chamber-collar sealing (water, rubber-foam, rubber-tube). All chambers were tested with and without pressure vent and fan. Included are measured CO2-concentration data as well as calculated sealing integrity in terms of chamber leakage. Results indicate significant differences in sealing integrity due to chamber-collar sealing strategy. The effect of pressure vent and fan, however, is of minor importance.chamber leakage2017_316_sealing_integrityfeatureswind Shelternon-steady-state chamberchamber-collar sealingurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=df420a4e-2c65-4cde-9443-17aa9e547ce5&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:geolocation_f3aef23e5dbe76185a4bcdaabe2fd3b2The data set contains data from a full-factorial laboratory experiment, were three different designs and two modifications of typical manual closed chamber setups were tested for sealing integrity. Tests were performed using a simple method, based on injections of single CO2 pulses. Chamber designs differed in V:A-ratio nand chamber-collar sealing (water, rubber-foam, rubber-tube). All chambers were tested with and without pressure vent and fan. Included are measured CO2-concentration data as well as calculated sealing integrity in terms of chamber leakage. Results indicate significant differences in sealing integrity due to chamber-collar sealing strategy. The effect of pressure vent and fan, however, is of minor importance.featureschamber-collar sealingnon-steady-state chambergeolocation_f3aef23e5dbe76185a4bcdaabe2fd3b2chamber leakagewind Shelterurn:x-ogc:def:crs:EPSG:432614.11899431971417 52.51620651350330614.11919431971417 52.51640651350331https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=a20c2b2d-f703-4051-b6f8-094e4b309451&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:2011_339_co2_flux_dataClosed chamber measurements are widely used for determining the CO2 exchange of different ecosystems. Among the chamber design and operational handling, the data processing procedure is a considerable source of uncertainty of obtained results. We developed a standardized automatic dataprocessing algorithm, based on the statistical computing environment R, which (i) calculates CO2 fluxes measured by closed chmabers, (ii) parameterizes temperature (Reco) and PAR (GPP) dependency models, (iii) optionally computes an adaptive temperature model, (iv) models Reco, GPP and NEE, and (v) evaluates model uncertainty. The dataset contains the developed R-script and the used test data, originating from measurments (2010-2012) at a cultivated fen situated in the northeast of Germany.Chamber measurement2011_339_co2_flux_datafeaturesR-scriptFlux calculationempirically modellingurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=827d5656-4975-4967-8aea-a15c0fa79864&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:2011_339_weather_stations_dataClosed chamber measurements are widely used for determining the CO2 exchange of different ecosystems. Among the chamber design and operational handling, the data processing procedure is a considerable source of uncertainty of obtained results. We developed a standardized automatic dataprocessing algorithm, based on the statistical computing environment R, which (i) calculates CO2 fluxes measured by closed chmabers, (ii) parameterizes temperature (Reco) and PAR (GPP) dependency models, (iii) optionally computes an adaptive temperature model, (iv) models Reco, GPP and NEE, and (v) evaluates model uncertainty. The dataset contains the developed R-script and the used test data, originating from measurments (2010-2012) at a cultivated fen situated in the northeast of Germany.Chamber measurement2011_339_weather_stations_datafeaturesR-scriptFlux calculationempirically modellingurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=529639ca-1843-4b79-9390-912b114bca83&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:geolocation_5dda05405dd8af371cc204d9acf15634Closed chamber measurements are widely used for determining the CO2 exchange of different ecosystems. Among the chamber design and operational handling, the data processing procedure is a considerable source of uncertainty of obtained results. We developed a standardized automatic dataprocessing algorithm, based on the statistical computing environment R, which (i) calculates CO2 fluxes measured by closed chmabers, (ii) parameterizes temperature (Reco) and PAR (GPP) dependency models, (iii) optionally computes an adaptive temperature model, (iv) models Reco, GPP and NEE, and (v) evaluates model uncertainty. The dataset contains the developed R-script and the used test data, originating from measurments (2010-2012) at a cultivated fen situated in the northeast of Germany.Chamber measurementfeaturesR-scriptFlux calculationempirically modellinggeolocation_5dda05405dd8af371cc204d9acf15634urn:x-ogc:def:crs:EPSG:432612.721039000000001 52.6875609999999912.721239 52.687760999999995https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=d3f78c67-9371-4519-8e07-20137561c63b&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:gps_mastertable_prieros2015We tracked adult Nyctalus noctula in July 2015 using Robin GPS loggers (CellGuide, Israel). Bats were taken from their artificial roosting boxes in the morning and loggers were attached with Sauer Hautkleber, then bats were placed back in their roosting boxes, which were located in a pine stand near Prieros / Germany. Loggers recorded GPS positions every 15 seconds. We retrieved useful GPS data for 9 animals. Data was analysed with respect to space use and the influence of moonlight on habitat choice of bats.habitat usemovement ecologyfeaturesbatsgps_mastertable_prieros2015moonlightflight altitudeLIDARurn:x-ogc:def:crs:EPSG:432613.696943 52.20624413.809247 52.248877https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=e0fb7277-7735-4c4c-9188-5beef79200c4&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:geolocation_d5eaf7cc2f22f9c195d6757c76c48b87Allelic data from neutral markers (microsatellites) to assess genetic connectivity and diversity of four wetland plant species occurring in kettle holes.markerfeaturesclonalitygenetic connectivityalleleslocigeolocation_d5eaf7cc2f22f9c195d6757c76c48b87genetic diversitykettle holeurn:x-ogc:def:crs:EPSG:432613.47096725586944 53.2944389887517413.85511279201188 53.42902016292231https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=a4aff8f6-93fd-433a-9019-1b23af7892bf&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:avg_climateWe analysed the abundance on plant ears and spore deposition patterns of Fusarium spp. and Alternaria spp. in a topographically heterogeneous field. Abundances were assessed genetically, using qPCR-based techniques, and passive spore traps were installed for quantifying the spore deposition at different plant heights. Data loggers were placed to measure the differences in microclimate across the field. Methods are described in details in the related paper.Fusarium head blightfeaturesFungal dispersalavg_climatemicrobiomeurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=a44577c9-27b3-45e8-b373-4b1c47cdce0a&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:geolocation_6eacfb2fa4a80eb36e1f8b06411af5eaWe analysed the abundance on plant ears and spore deposition patterns of Fusarium spp. and Alternaria spp. in a topographically heterogeneous field. Abundances were assessed genetically, using qPCR-based techniques, and passive spore traps were installed for quantifying the spore deposition at different plant heights. Data loggers were placed to measure the differences in microclimate across the field. Methods are described in details in the related paper.Fusarium head blightfeaturesgeolocation_6eacfb2fa4a80eb36e1f8b06411af5eaFungal dispersalmicrobiomeurn:x-ogc:def:crs:EPSG:432613.6097559308835 53.35121076747713.6171168133665 53.3566111205192https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=eed2ca26-f429-4e68-b85f-9aa538f2cf4d&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:qpcr_raakowWe analysed the abundance on plant ears and spore deposition patterns of Fusarium spp. and Alternaria spp. in a topographically heterogeneous field. Abundances were assessed genetically, using qPCR-based techniques, and passive spore traps were installed for quantifying the spore deposition at different plant heights. Data loggers were placed to measure the differences in microclimate across the field. Methods are described in details in the related paper.Fusarium head blightfeaturesFungal dispersalqpcr_raakowmicrobiomeurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=fb7f5ecd-2fa0-44df-b2fb-99cf8c3cc12a&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:spore_countsWe analysed the abundance on plant ears and spore deposition patterns of Fusarium spp. and Alternaria spp. in a topographically heterogeneous field. Abundances were assessed genetically, using qPCR-based techniques, and passive spore traps were installed for quantifying the spore deposition at different plant heights. Data loggers were placed to measure the differences in microclimate across the field. Methods are described in details in the related paper.Fusarium head blightfeaturesspore_countsFungal dispersalmicrobiomeurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=8644a32f-0802-454f-af71-87a96c79a9e3&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:co2_fluxes_allCurrent approaches to measure and quantify CO2 flux dynamics of agricultural landscapes e.g., eddy covariance, manual and automatic chamber systems have limitations in either accounting for small scale spatial heterogeneity (eddy covariance and automatic chambers) or short-term temporal variability (manual chambers). Although automatic chambers are in principle capable to detect small-scale spatial differences of CO2 flux dynamics in a sufficient temporal resolution, they are usually limited to only a few spatial repetitions. To overcome these limitations, a novel robotic chamber system for CO2 gas exchange measurements was developed which is capable to detect short term temporal dynamics and small scale spatial heterogeneity. This datasets contains data of weather conditions, measured CO2 flux(Net Ecosystem Exchange - NEE and ecosystem respiration - Reco) and modelled CO2 exchange obtained from the measurements that were conducted from 08/07/2019 to 9/09/2019 for spring barley within the hummocky ground moraine landscape of northeastern Germany (CarboZALF-D). The soil and erosion stages of the study area are classified as Calcaric Regosol (RZ; extremely eroded), Nudiargic Luvisol (eLL; strongly eroded) and Calcic Luvisol (LL; non-eroded).co2_fluxes_allGross primary productivityChamber measurementfeaturesLeaf Area IndexNet Ecosystem ExchangeEcosystem respirationurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=4338cb7d-7fd4-4323-8b3d-3c8628095767&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:geolocation_d216ca537d4256390d8e029299aaa50fCurrent approaches to measure and quantify CO2 flux dynamics of agricultural landscapes e.g., eddy covariance, manual and automatic chamber systems have limitations in either accounting for small scale spatial heterogeneity (eddy covariance and automatic chambers) or short-term temporal variability (manual chambers). Although automatic chambers are in principle capable to detect small-scale spatial differences of CO2 flux dynamics in a sufficient temporal resolution, they are usually limited to only a few spatial repetitions. To overcome these limitations, a novel robotic chamber system for CO2 gas exchange measurements was developed which is capable to detect short term temporal dynamics and small scale spatial heterogeneity. This datasets contains data of weather conditions, measured CO2 flux(Net Ecosystem Exchange - NEE and ecosystem respiration - Reco) and modelled CO2 exchange obtained from the measurements that were conducted from 08/07/2019 to 9/09/2019 for spring barley within the hummocky ground moraine landscape of northeastern Germany (CarboZALF-D). The soil and erosion stages of the study area are classified as Calcaric Regosol (RZ; extremely eroded), Nudiargic Luvisol (eLL; strongly eroded) and Calcic Luvisol (LL; non-eroded).Gross primary productivityChamber measurementgeolocation_d216ca537d4256390d8e029299aaa50ffeaturesLeaf Area IndexNet Ecosystem ExchangeEcosystem respirationurn:x-ogc:def:crs:EPSG:432613.78634446753544 53.3788417645312613.78728279165165 53.37921018597624https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=0d98f40a-674c-47ee-b0e2-642f05ca98f8&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:normal_model_allCurrent approaches to measure and quantify CO2 flux dynamics of agricultural landscapes e.g., eddy covariance, manual and automatic chamber systems have limitations in either accounting for small scale spatial heterogeneity (eddy covariance and automatic chambers) or short-term temporal variability (manual chambers). Although automatic chambers are in principle capable to detect small-scale spatial differences of CO2 flux dynamics in a sufficient temporal resolution, they are usually limited to only a few spatial repetitions. To overcome these limitations, a novel robotic chamber system for CO2 gas exchange measurements was developed which is capable to detect short term temporal dynamics and small scale spatial heterogeneity. This datasets contains data of weather conditions, measured CO2 flux(Net Ecosystem Exchange - NEE and ecosystem respiration - Reco) and modelled CO2 exchange obtained from the measurements that were conducted from 08/07/2019 to 9/09/2019 for spring barley within the hummocky ground moraine landscape of northeastern Germany (CarboZALF-D). The soil and erosion stages of the study area are classified as Calcaric Regosol (RZ; extremely eroded), Nudiargic Luvisol (eLL; strongly eroded) and Calcic Luvisol (LL; non-eroded).Gross primary productivityChamber measurementnormal_model_allfeaturesLeaf Area IndexNet Ecosystem ExchangeEcosystem respirationurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=657391c3-51b0-44e0-a082-157ae65b7e5b&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:weatherdata_halfhourlyCurrent approaches to measure and quantify CO2 flux dynamics of agricultural landscapes e.g., eddy covariance, manual and automatic chamber systems have limitations in either accounting for small scale spatial heterogeneity (eddy covariance and automatic chambers) or short-term temporal variability (manual chambers). Although automatic chambers are in principle capable to detect small-scale spatial differences of CO2 flux dynamics in a sufficient temporal resolution, they are usually limited to only a few spatial repetitions. To overcome these limitations, a novel robotic chamber system for CO2 gas exchange measurements was developed which is capable to detect short term temporal dynamics and small scale spatial heterogeneity. This datasets contains data of weather conditions, measured CO2 flux(Net Ecosystem Exchange - NEE and ecosystem respiration - Reco) and modelled CO2 exchange obtained from the measurements that were conducted from 08/07/2019 to 9/09/2019 for spring barley within the hummocky ground moraine landscape of northeastern Germany (CarboZALF-D). The soil and erosion stages of the study area are classified as Calcaric Regosol (RZ; extremely eroded), Nudiargic Luvisol (eLL; strongly eroded) and Calcic Luvisol (LL; non-eroded).Gross primary productivityChamber measurementfeaturesLeaf Area IndexNet Ecosystem Exchangeweatherdata_halfhourlyEcosystem respirationurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=780fa1b5-0b9e-472b-b8bb-5e9fe3513efe&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:swb_2013_2014_ponds_sed_chemSedimentation was studied in two kettle holes close to Rittgarten (RG) and Kraatz (KR), Uckermark, NE Germany. Pairs of cylindrical traps at three sites in each kettle hole were sampled biweekly (June 2013–July 2014). Mean fluxes decreased with decreasing water level. KR was Fe-dominated binding P, and had submersed macrophytes. RG was Ca dominated and had low Fe concentrations suggesting that both apatite and oxidized Fe compounds equilibrated P release, with finally a surplus in P.featuressedimentation rateswb_2013_2014_ponds_sed_chemurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=5f738f23-7107-4c7a-a986-6e469d296ec0&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:geolocation_7b661bb0c2b439b4a12851be6382e160Sedimentation was studied in two kettle holes close to Rittgarten (RG) and Kraatz (KR), Uckermark, NE Germany. Pairs of cylindrical traps at three sites in each kettle hole were sampled biweekly (June 2013–July 2014). Mean fluxes decreased with decreasing water level. KR was Fe-dominated binding P, and had submersed macrophytes. RG was Ca dominated and had low Fe concentrations suggesting that both apatite and oxidized Fe compounds equilibrated P release, with finally a surplus in P.featuresgeolocation_7b661bb0c2b439b4a12851be6382e160urn:x-ogc:def:crs:EPSG:432613.66579722222222 53.3840920001446113.70697899973657 53.39737777777778https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=a2400db5-37ad-45f5-ab73-98a05451123e&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:k_s_siteWater retention curveArtificial soilsfeaturesParticle size distributionk_s_siteurn:x-ogc:def:crs:EPSG:43269.98603313900006 50.899801111000079.98623313900006 50.90000111100008https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=0561e0ba-5471-475f-8706-7ddd10b9ccbf&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:bulk_densityThis dataset contains bulk density measurements for four different technosols collected at depths ranging from 0 to 0.64 m. Bulk density was determined from undisturbed soil core samples. A detailed description of the sampling procedure and the method used to determine bulk density is provided by Bilibio et al. (2022) (https://doi.org/10.1016/j.jenvman.2022.114654).bulk_densityWater retention curveArtificial soilsfeaturesParticle size distributionurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=7008f1de-c85d-4497-8991-0c6f45f5b861&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:gwc_ph_ecThe dataset includes measurements of the gravimetric water content (GWC), soil pH, and electrical conductivity (EC), collected from soil depths ranging from 0 to 3 m in a lysimeter experiment evaluating different technosols in Heringen, Germany. The measurements were obtained during 2014 and 2016. The sampling procedures and analytical methods are thoroughly described by Bilibio et al. (2022) (https://doi.org/10.1016/j.jenvman.2022.114654).Water retention curvefeaturesArtificial soilsParticle size distributiongwc_ph_ecurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=9e2ca606-e0f8-4d35-9218-62f4a9da2e33&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:ksat_53abd35dc322377e2e1671fb93bcac30This dataset contains measurements of saturated hydraulic conductivity (Ksat) for four different technosols collected at soil depths ranging from 0 to 0.64 m. The sampling procedure and the method used to determine saturated hydraulic conductivity are thoroughly described by Bilibio et al. (2022) (https://doi.org/10.1016/j.jenvman.2022.114654).Particle size distributionksat_53abd35dc322377e2e1671fb93bcac30Artificial soilsWater retention curvefeaturesurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=34a3247d-e025-443d-9de8-40e7aff687ee&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:particle_densityThis dataset contains particle density measurements for two different technosols (Technosols 1 and 4). The samples were collected in 2014 at soil depths of 0.20–0.26 m and 0.40–0.46 m. The method used to determine particle density is comprehensively described by Bilibio et al. (2022) (https://doi.org/10.1016/j.jenvman.2022.114654).Water retention curvefeaturesArtificial soilsParticle size distributionparticle_densityurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=86001199-0807-4625-9aa5-e32f27f516ca&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:particle_sizeThe dataset contains particle size distribution measurements for four different technosols. Information on the composition of the technosols, as well as the methods used to determine particle size distribution, is described in Bilibio et al. (2022; https://doi.org/10.1016/j.jenvman.2022.114654).Particle size distributionArtificial soilsWater retention curveparticle_sizefeaturesurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=4eb182bf-46c7-4a15-8ba7-8d1c91ea6886&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:pf_curveBewertung der physikalischen Eigenschaften von vier Technosolen als Evapotranspirationsabdeckungen für Kalirückstandshaldenpf_curveWater retention curvefeaturesArtificial soilsParticle size distributionurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=3f3efdf5-0095-4ca1-b4f9-0d46fe144019&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:res_lab_chemical_analysisThis table contains the results of the physical and chemical characterization of selected soil samples from different transects and depths. Parameters include particle-size distribution, soil texture, total carbon and nitrogen, organic and inorganic carbon, and pH. The data provide information on small-scale soil heterogeneity and the pedological conditions of the Agri-PV site.Agri PVResearch Station Münchebergfeaturespenetration resistancesandy soilsolar energyres_lab_chemical_analysisurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=9a2e80ae-b352-445b-ab97-4fbde77de209&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:ag_pv_pr_v1This table contains penetration resistance measurements (MPa) recorded with an electronic penetrologger along seven transects to a maximum depth of 80 cm. Measurements recorded at 1 cm depth resolution were aggregated into the same five depth intervals used for bulk density assessment. Volumetric soil moisture measured simultaneously in the upper 5 cm is provided as an additional parameter.Agri PVResearch Station Münchebergag_pv_pr_v1featurespenetration resistancesandy soilsolar energyurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=ce46e86a-3900-4d0a-a934-9c4a893fc15f&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:aufstaenderungen_2d_25833_a8c8344c22f2f87fa58d31fca802dce7featuresaufstaenderungen_2d_25833_a8c8344c22f2f87fa58d31fca802dce7penetration resistanceResearch Station MünchebergAgri PVsandy soilsolar energyurn:x-ogc:def:crs:EPSG:2583314.118887084153272 52.5165968549540214.120390731098192 52.517484815040135https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=ff5501de-4b1d-4e51-b881-17b4ec9ed192&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:aufstaenderungen_3d__project_2bd83c5abd475b5bde35faaf0e19af85solar energyResearch Station Münchebergsandy soilaufstaenderungen_3d__project_2bd83c5abd475b5bde35faaf0e19af85Agri PVfeaturespenetration resistanceurn:x-ogc:def:crs:EPSG:432614.120170823551291 52.51619646684072514.120933920231856 52.51666953261826https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=70115694-1422-41ca-81a3-588dd7efc6cd&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:penetration_measurementsolar energypenetration_measurementResearch Station Münchebergsandy soilAgri PVfeaturespenetration resistanceurn:x-ogc:def:crs:EPSG:432614.119200000000035 52.5161333330000314.121016667000049 52.51736666700003https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=71ef7279-9c8e-41ff-894d-8b708009b8c8&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:ag_pv_bd_v1This table contains measured and stone-corrected bulk density values for five depth intervals ranging from 4–9 until 70–75 cm. Samples were collected along five transects at agricultural positions between panels, near panel construction posts, and at reference locations. Gravimetric soil moisture and soil porosity values are provided as complementary parameters.Agri PVResearch Station Münchebergfeaturespenetration resistancesandy soilsolar energyag_pv_bd_v1urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=dcba36d4-f120-4784-91bf-7122f0768d43&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:flux_and_environTo better assess ecosystem C budgets of croplands and understand their potential response to climate and management changes, detailed information on the mechanisms and environmental controls driving the individual C flux components are needed. This accounts in particular for the ecosystem respiration (Reco) and its components, the autotrophic (Ra) and heterotrophic respiration (Rh), which vary tremendously in time and space. This dataset presents results of a study, which uses a combination of a root exclusion experimental design and continous automatic chamber and below soil porous tube measurements to derive the Reco flux components. Measurements were carried out for winter wheat (Triticum aestivum L.) during the crop season 2015 at an experimental plot located in the hummocky ground moraine landscape of NE Germany.carbon dioxidefeaturesheterotrophic respiration (Rh)root exclusiongreenhouse gasesautothrophic respiration (Ra)automatic porous tube measurementsnon-steady-state automatic chamber measurementsEcosystem respiration (Reco)flux_and_environurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=2b008ad4-6030-401e-b074-40a4d7f19f86&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:geolocation_fe7563d3b72cad7df7d7d4ff155da219To better assess ecosystem C budgets of croplands and understand their potential response to climate and management changes, detailed information on the mechanisms and environmental controls driving the individual C flux components are needed. This accounts in particular for the ecosystem respiration (Reco) and its components, the autotrophic (Ra) and heterotrophic respiration (Rh), which vary tremendously in time and space. This dataset presents results of a study, which uses a combination of a root exclusion experimental design and continous automatic chamber and below soil porous tube measurements to derive the Reco flux components. Measurements were carried out for winter wheat (Triticum aestivum L.) during the crop season 2015 at an experimental plot located in the hummocky ground moraine landscape of NE Germany.carbon dioxidefeaturesheterotrophic respiration (Rh)geolocation_fe7563d3b72cad7df7d7d4ff155da219root exclusiongreenhouse gasesautothrophic respiration (Ra)automatic porous tube measurementsnon-steady-state automatic chamber measurementsEcosystem respiration (Reco)urn:x-ogc:def:crs:EPSG:432613.7837905690383 53.3801114462069513.78386235472606 53.38016361295505https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=f52f93e2-5931-460a-b2c5-6b3f6326e2f5&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:root_growthTo better assess ecosystem C budgets of croplands and understand their potential response to climate and management changes, detailed information on the mechanisms and environmental controls driving the individual C flux components are needed. This accounts in particular for the ecosystem respiration (Reco) and its components, the autotrophic (Ra) and heterotrophic respiration (Rh), which vary tremendously in time and space. This dataset presents results of a study, which uses a combination of a root exclusion experimental design and continous automatic chamber and below soil porous tube measurements to derive the Reco flux components. Measurements were carried out for winter wheat (Triticum aestivum L.) during the crop season 2015 at an experimental plot located in the hummocky ground moraine landscape of NE Germany.carbon dioxidefeaturesroot_growthheterotrophic respiration (Rh)root exclusiongreenhouse gasesautothrophic respiration (Ra)automatic porous tube measurementsnon-steady-state automatic chamber measurementsEcosystem respiration (Reco)urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=8105a7be-2e3f-420b-b350-692d0d4ae66d&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:separated_fluxesTo better assess ecosystem C budgets of croplands and understand their potential response to climate and management changes, detailed information on the mechanisms and environmental controls driving the individual C flux components are needed. This accounts in particular for the ecosystem respiration (Reco) and its components, the autotrophic (Ra) and heterotrophic respiration (Rh), which vary tremendously in time and space. This dataset presents results of a study, which uses a combination of a root exclusion experimental design and continous automatic chamber and below soil porous tube measurements to derive the Reco flux components. Measurements were carried out for winter wheat (Triticum aestivum L.) during the crop season 2015 at an experimental plot located in the hummocky ground moraine landscape of NE Germany.carbon dioxidefeaturesheterotrophic respiration (Rh)non-steady-state automatic chamber measurementsroot exclusiongreenhouse gasesautothrophic respiration (Ra)automatic porous tube measurementsseparated_fluxesEcosystem respiration (Reco)urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=43e97abd-e467-407c-a10c-8dd920c43249&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:shoot_growthTo better assess ecosystem C budgets of croplands and understand their potential response to climate and management changes, detailed information on the mechanisms and environmental controls driving the individual C flux components are needed. This accounts in particular for the ecosystem respiration (Reco) and its components, the autotrophic (Ra) and heterotrophic respiration (Rh), which vary tremendously in time and space. This dataset presents results of a study, which uses a combination of a root exclusion experimental design and continous automatic chamber and below soil porous tube measurements to derive the Reco flux components. Measurements were carried out for winter wheat (Triticum aestivum L.) during the crop season 2015 at an experimental plot located in the hummocky ground moraine landscape of NE Germany.carbon dioxidefeaturesshoot_growthheterotrophic respiration (Rh)root exclusiongreenhouse gasesautothrophic respiration (Ra)automatic porous tube measurementsnon-steady-state automatic chamber measurementsEcosystem respiration (Reco)urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=eb2c8a2f-108a-4e9a-aa9b-6c4de3ae4d07&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:witzenhausenRidge agriculturewater savingfeaturesCarrots cultivationwitzenhausenWater distributionIrrigationWater contenturn:x-ogc:def:crs:EPSG:43269.864344444000071 51.345733333000039.864544444000071 51.34593333300003https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=f72ada8f-2714-46b8-82b0-cc1a9a5e38ab&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:ksatThis dataset contains measurements of the soil saturated hydraulic conductivity (Ksat), determined using an Eijkelkamp 09.03 Hauben water permeameter and the falling-head method. Measurements were performed on undisturbed soil samples collected at a depth of 0.10–0.14 m in ridges that had been constructed 30 days earlier.Ridge agriculturewater savingfeaturesCarrots cultivationksatWater distributionIrrigationWater contenturn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=6af46c81-56bf-4671-b972-8f999cdb4088&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:vwc_sprinkler_irrigationThis dataset contains measurements of the volumetric soil water content taken before irrigation, immediately after irrigation, and 24 and 48 hours after irrigation. Measurements were performed at three positions on the ridges (furrow, ridge side, and ridge crest) under a sprinkler irrigation system.Ridge agriculturewater savingfeaturesCarrots cultivationvwc_sprinkler_irrigationWater distributionIrrigationWater contenturn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=846fa40c-4a7f-40eb-99c4-49d9964e65db&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:vwc_strip_irrigationThis dataset contains measurements of the volumetric soil water content taken before irrigation, immediately after irrigation, and 24 and 48 hours after irrigation. Measurements were performed at three positions on the ridges (furrow, ridge side, and ridge crest) under a strip irrigation system.Ridge agriculturewater savingfeaturesCarrots cultivationvwc_strip_irrigationWater distributionIrrigationWater contenturn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=cfc9325e-5a1f-41b7-b7d4-dd4d29b19ce6&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:water_retention_curveThis dataset contains measurements of the soil water retention curve obtained from undisturbed soil samples collected at a depth of 0.10–0.14 m in ridges that had been constructed 30 days earlier. Soil water retention was determined using a sand bath at matric potentials of −10 and −30 hPa, suction plates at −60, −300, and −500 hPa, and a pressure chamber at −15,000 hPa. Bulk density was calculated from the oven-dry mass and the volume of each soil core. Total porosity was estimated from the bulk density and an assumed particle density of 2.65 g cm⁻³.water savingCarrots cultivationWater distributionwater_retention_curveRidge agricultureIrrigationfeaturesWater contenturn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=64fa0565-48c1-4e65-9ac4-e4cbb56eebac&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:yieldThis dataset includes measurements of carrot yield and key yield components, including root mass, root length, and root diameter, for two irrigation systems: sprinkler irrigation and strip irrigation.water savingCarrots cultivationWater distributionRidge agricultureIrrigationyieldfeaturesWater contenturn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=ef431d11-6ce6-42e2-8792-7ca59acfb653&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:geolocation_333e122a2e0cae21b7ba07f745b7ed25A six-year experimental data set for three field plots located at the Muencheberg Experimental Station with mainly sandy soils and the soil type Eutric Cambisol is documented in detail. These plots were managed at different levels of intensity such as intensive management on high level, organic management without chemicals in fertilization and pest management and without ploughing and extensive management. The data set contains coherent data for soil water and soil nitrogen content, crop growth,yield formation, weather at a different time solution as well as field management data.This data set was one basis for the international workshop "Modelling water and nutrient dynamics in soil-crop systems" in Müncheberg, Germany, in 2004, organized by the Institute of Landscape Systems Analysis of the ZALF Müncheberg.featuresgeolocation_333e122a2e0cae21b7ba07f745b7ed25urn:x-ogc:def:crs:EPSG:432614.122415 52.51576714.126497 52.517584https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=ba07dc17-37fd-4f84-a39c-a7d9e20a9e55&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:v004_crop_carbon_nigtrogenA six-year experimental data set for three field plots located at the Muencheberg Experimental Station with mainly sandy soils and the soil type Eutric Cambisol is documented in detail. These plots were managed at different levels of intensity such as intensive management on high level, organic management without chemicals in fertilization and pest management and without ploughing and extensive management. The data set contains coherent data for soil water and soil nitrogen content, crop growth,yield formation, weather at a different time solution as well as field management data.This data set was one basis for the international workshop "Modelling water and nutrient dynamics in soil-crop systems" in Müncheberg, Germany, in 2004, organized by the Institute of Landscape Systems Analysis of the ZALF Müncheberg.featuresv004_crop_carbon_nigtrogenurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=6abe078a-152a-4d4f-b21c-eb5377baefb5&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:v004_crop_ontogenesis_biomass_4ec45a4f01f05e2ec01269bfee9d5153A six-year experimental data set for three field plots located at the Muencheberg Experimental Station with mainly sandy soils and the soil type Eutric Cambisol is documented in detail. These plots were managed at different levels of intensity such as intensive management on high level, organic management without chemicals in fertilization and pest management and without ploughing and extensive management. The data set contains coherent data for soil water and soil nitrogen content, crop growth,yield formation, weather at a different time solution as well as field management data.This data set was one basis for the international workshop "Modelling water and nutrient dynamics in soil-crop systems" in Müncheberg, Germany, in 2004, organized by the Institute of Landscape Systems Analysis of the ZALF Müncheberg.featuresv004_crop_ontogenesis_biomass_4ec45a4f01f05e2ec01269bfee9d5153urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=4d8b3f9c-09d3-49d2-befe-1d7b5349ead3&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:v004_management_8eab222603d5a795709117ac15a521a4A six-year experimental data set for three field plots located at the Muencheberg Experimental Station with mainly sandy soils and the soil type Eutric Cambisol is documented in detail. These plots were managed at different levels of intensity such as intensive management on high level, organic management without chemicals in fertilization and pest management and without ploughing and extensive management. The data set contains coherent data for soil water and soil nitrogen content, crop growth,yield formation, weather at a different time solution as well as field management data.This data set was one basis for the international workshop "Modelling water and nutrient dynamics in soil-crop systems" in Müncheberg, Germany, in 2004, organized by the Institute of Landscape Systems Analysis of the ZALF Müncheberg.featuresv004_management_8eab222603d5a795709117ac15a521a4urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=67ee29e7-7ddd-4087-b4fc-a15a7155beb7&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:v004_muencheberg_weather_92_98A six-year experimental data set for three field plots located at the Muencheberg Experimental Station with mainly sandy soils and the soil type Eutric Cambisol is documented in detail. These plots were managed at different levels of intensity such as intensive management on high level, organic management without chemicals in fertilization and pest management and without ploughing and extensive management. The data set contains coherent data for soil water and soil nitrogen content, crop growth,yield formation, weather at a different time solution as well as field management data.This data set was one basis for the international workshop "Modelling water and nutrient dynamics in soil-crop systems" in Müncheberg, Germany, in 2004, organized by the Institute of Landscape Systems Analysis of the ZALF Müncheberg.featuresv004_muencheberg_weather_92_98urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=317db5ff-c81b-43e6-8381-fc23b6f94ce5&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:v004_soil_h20_n03_nh4_contentA six-year experimental data set for three field plots located at the Muencheberg Experimental Station with mainly sandy soils and the soil type Eutric Cambisol is documented in detail. These plots were managed at different levels of intensity such as intensive management on high level, organic management without chemicals in fertilization and pest management and without ploughing and extensive management. The data set contains coherent data for soil water and soil nitrogen content, crop growth,yield formation, weather at a different time solution as well as field management data.This data set was one basis for the international workshop "Modelling water and nutrient dynamics in soil-crop systems" in Müncheberg, Germany, in 2004, organized by the Institute of Landscape Systems Analysis of the ZALF Müncheberg.featuresv004_soil_h20_n03_nh4_contenturn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=de1f15ac-7713-49d4-ab47-e9e3f780f6bd&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:v004_soil_plot_2bf3d9d69bd969dae5bb35fa73d88a9bA six-year experimental data set for three field plots located at the Muencheberg Experimental Station with mainly sandy soils and the soil type Eutric Cambisol is documented in detail. These plots were managed at different levels of intensity such as intensive management on high level, organic management without chemicals in fertilization and pest management and without ploughing and extensive management. The data set contains coherent data for soil water and soil nitrogen content, crop growth,yield formation, weather at a different time solution as well as field management data.This data set was one basis for the international workshop "Modelling water and nutrient dynamics in soil-crop systems" in Müncheberg, Germany, in 2004, organized by the Institute of Landscape Systems Analysis of the ZALF Müncheberg.featuresv004_soil_plot_2bf3d9d69bd969dae5bb35fa73d88a9burn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=5242f4bc-c296-4369-bf3a-98ec1d3913f3&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:cost_sitesCroatiaRomaniaSpainSerbiaEstoniasoil healthPolandUnited KingdomNorwaySloveniaopendataSwitzerlandX-ray computed tomographyItalyLietzencost_sitesEuropeGermanyfeaturesFinnlandurn:x-ogc:def:crs:EPSG:4326-4.018484999999942 40.49947200000002626.045440000000042 61.42412940000003https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=c9cce94e-3696-461b-b7e6-affdf02f34ea&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:2007_290_data_cover_aggregatedBryophytes and lichen are important components of many boreal forest ecosystems, making the quantification of moss and lichen decomposition rates critical to understanding the C cycle of these forests. A total of 1560 litter samples were incubated for two years (2007-2009) in six in high-boreal Labrador black spruce forests stands of different disturbance origin: three old-growth stands of wildfire origin, and three recently clearcut stands. Liter samples were incubated using two different methods (litterbags and minicontainers) and retrieved after 6.5, 13, 47, 57 and 109 weeks of field incubation, subsequently analyzed for mass loss, C, N, nutrient, and fibre content.2007_290_data_cover_aggregatedfeaturesurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=db547d6d-5d75-41f7-a48b-db10ef63d8a0&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:2007_290_data_cover_plotBryophytes and lichen are important components of many boreal forest ecosystems, making the quantification of moss and lichen decomposition rates critical to understanding the C cycle of these forests. A total of 1560 litter samples were incubated for two years (2007-2009) in six in high-boreal Labrador black spruce forests stands of different disturbance origin: three old-growth stands of wildfire origin, and three recently clearcut stands. Liter samples were incubated using two different methods (litterbags and minicontainers) and retrieved after 6.5, 13, 47, 57 and 109 weeks of field incubation, subsequently analyzed for mass loss, C, N, nutrient, and fibre content.features2007_290_data_cover_ploturn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=1f2ec613-9cc2-4482-8f0d-9fb77a39fdb1&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:2007_290_data_decompositionBryophytes and lichen are important components of many boreal forest ecosystems, making the quantification of moss and lichen decomposition rates critical to understanding the C cycle of these forests. A total of 1560 litter samples were incubated for two years (2007-2009) in six in high-boreal Labrador black spruce forests stands of different disturbance origin: three old-growth stands of wildfire origin, and three recently clearcut stands. Liter samples were incubated using two different methods (litterbags and minicontainers) and retrieved after 6.5, 13, 47, 57 and 109 weeks of field incubation, subsequently analyzed for mass loss, C, N, nutrient, and fibre content.features2007_290_data_decompositionurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=199b1404-022c-474e-8ba4-8f60582e34e0&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:geolocation_9b1f6a3dcea17cd44ac83f20b43ee1c6Bryophytes and lichen are important components of many boreal forest ecosystems, making the quantification of moss and lichen decomposition rates critical to understanding the C cycle of these forests. A total of 1560 litter samples were incubated for two years (2007-2009) in six in high-boreal Labrador black spruce forests stands of different disturbance origin: three old-growth stands of wildfire origin, and three recently clearcut stands. Liter samples were incubated using two different methods (litterbags and minicontainers) and retrieved after 6.5, 13, 47, 57 and 109 weeks of field incubation, subsequently analyzed for mass loss, C, N, nutrient, and fibre content.featuresgeolocation_9b1f6a3dcea17cd44ac83f20b43ee1c6urn:x-ogc:def:crs:EPSG:4326-60.89459 53.2677-60.591174 53.62637https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=b7954ffb-9231-4def-823a-36141f3a3416&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:geolocation_e2f873f1868b350d9613912409053cf3This data set reports measurements and observations from a soybean cropping system experiment (2015-2018) investigating the effects of cultivars and irrigation on soybean yield and rotational effects on a subsequent cereal crop. The objective was to design novel soybean-supported cropping systems in north-eastern Germany. The data supports agronomic analyses, as well as dynamic simulation modelling and includes details on crop growth, soil characteristics and weather. In the first part of the experiment different soybean cultivars and the effect of irrigation were compared in a split block design with six replicates and the factors cultivar and irrigation during three consecutive years (2015-2017). The treatments included with and without irrigation and three early maturing soybean cultivars of maturity group 000 (cv. Merlin and cv. Sultana for feed use and cv. Protibus for human food use). Buckwheat and Narrow-leafed lupin (cv. Probor) were cultivated as references. Seeds were inoculated with HISTICK® soybean (BASF, Germany). Measurements included whole plant biomass, grain yield, one thousand seed weigh, macro nutrients (N, P, K) in the biomass and grain, and additional agronomic observations including number of plants and plant phenology. Irrigation water was applied with a sprinkler system using the Web-BEREST model (Mirschel et al., 2014) to determine the amounts and timing (data on the amounts and dates are provided). In the second part of the experiment, the pre-crop effect of soybean (without cover crop), narrow-leafed lupin (followed by turnip rape as a cover crop), and buckwheat (without cover crop) was tested on the grain yield and nutrient content in the following oat crop and on the nitrogen dynamics in the soil. Before winter (November) and in the subsequent spring (March), mineral nitrogen was measured in the soil at three depths (0-30 cm, 30-60 cm, and 60-90 cm) after the different pre-crops. A spring oat was established following the different pre-crops with 6 replicates during three consecutive years (2016-2018).geolocation_e2f873f1868b350d9613912409053cf3featuresurn:x-ogc:def:crs:EPSG:432614.12713478431887 52.5206280056175314.12896944133175 52.52227505186436https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=1a1b3db0-fe8d-4c23-964f-b04be8e54266&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:v402_crop_biomass_nitrogenThis data set reports measurements and observations from a soybean cropping system experiment (2015-2018) investigating the effects of cultivars and irrigation on soybean yield and rotational effects on a subsequent cereal crop. The objective was to design novel soybean-supported cropping systems in north-eastern Germany. The data supports agronomic analyses, as well as dynamic simulation modelling and includes details on crop growth, soil characteristics and weather. In the first part of the experiment different soybean cultivars and the effect of irrigation were compared in a split block design with six replicates and the factors cultivar and irrigation during three consecutive years (2015-2017). The treatments included with and without irrigation and three early maturing soybean cultivars of maturity group 000 (cv. Merlin and cv. Sultana for feed use and cv. Protibus for human food use). Buckwheat and Narrow-leafed lupin (cv. Probor) were cultivated as references. Seeds were inoculated with HISTICK® soybean (BASF, Germany). Measurements included whole plant biomass, grain yield, one thousand seed weigh, macro nutrients (N, P, K) in the biomass and grain, and additional agronomic observations including number of plants and plant phenology. Irrigation water was applied with a sprinkler system using the Web-BEREST model (Mirschel et al., 2014) to determine the amounts and timing (data on the amounts and dates are provided). In the second part of the experiment, the pre-crop effect of soybean (without cover crop), narrow-leafed lupin (followed by turnip rape as a cover crop), and buckwheat (without cover crop) was tested on the grain yield and nutrient content in the following oat crop and on the nitrogen dynamics in the soil. Before winter (November) and in the subsequent spring (March), mineral nitrogen was measured in the soil at three depths (0-30 cm, 30-60 cm, and 60-90 cm) after the different pre-crops. A spring oat was established following the different pre-crops with 6 replicates during three consecutive years (2016-2018).featuresv402_crop_biomass_nitrogenurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=7245d86c-3341-41d6-9fcd-c955e408ae00&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:v402_managementThis data set reports measurements and observations from a soybean cropping system experiment (2015-2018) investigating the effects of cultivars and irrigation on soybean yield and rotational effects on a subsequent cereal crop. The objective was to design novel soybean-supported cropping systems in north-eastern Germany. The data supports agronomic analyses, as well as dynamic simulation modelling and includes details on crop growth, soil characteristics and weather. In the first part of the experiment different soybean cultivars and the effect of irrigation were compared in a split block design with six replicates and the factors cultivar and irrigation during three consecutive years (2015-2017). The treatments included with and without irrigation and three early maturing soybean cultivars of maturity group 000 (cv. Merlin and cv. Sultana for feed use and cv. Protibus for human food use). Buckwheat and Narrow-leafed lupin (cv. Probor) were cultivated as references. Seeds were inoculated with HISTICK® soybean (BASF, Germany). Measurements included whole plant biomass, grain yield, one thousand seed weigh, macro nutrients (N, P, K) in the biomass and grain, and additional agronomic observations including number of plants and plant phenology. Irrigation water was applied with a sprinkler system using the Web-BEREST model (Mirschel et al., 2014) to determine the amounts and timing (data on the amounts and dates are provided). In the second part of the experiment, the pre-crop effect of soybean (without cover crop), narrow-leafed lupin (followed by turnip rape as a cover crop), and buckwheat (without cover crop) was tested on the grain yield and nutrient content in the following oat crop and on the nitrogen dynamics in the soil. Before winter (November) and in the subsequent spring (March), mineral nitrogen was measured in the soil at three depths (0-30 cm, 30-60 cm, and 60-90 cm) after the different pre-crops. A spring oat was established following the different pre-crops with 6 replicates during three consecutive years (2016-2018).featuresv402_managementurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=cab4d532-2abc-424f-982a-2ceed9960d6c&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:v402_soil_textureThis data set reports measurements and observations from a soybean cropping system experiment (2015-2018) investigating the effects of cultivars and irrigation on soybean yield and rotational effects on a subsequent cereal crop. The objective was to design novel soybean-supported cropping systems in north-eastern Germany. The data supports agronomic analyses, as well as dynamic simulation modelling and includes details on crop growth, soil characteristics and weather. In the first part of the experiment different soybean cultivars and the effect of irrigation were compared in a split block design with six replicates and the factors cultivar and irrigation during three consecutive years (2015-2017). The treatments included with and without irrigation and three early maturing soybean cultivars of maturity group 000 (cv. Merlin and cv. Sultana for feed use and cv. Protibus for human food use). Buckwheat and Narrow-leafed lupin (cv. Probor) were cultivated as references. Seeds were inoculated with HISTICK® soybean (BASF, Germany). Measurements included whole plant biomass, grain yield, one thousand seed weigh, macro nutrients (N, P, K) in the biomass and grain, and additional agronomic observations including number of plants and plant phenology. Irrigation water was applied with a sprinkler system using the Web-BEREST model (Mirschel et al., 2014) to determine the amounts and timing (data on the amounts and dates are provided). In the second part of the experiment, the pre-crop effect of soybean (without cover crop), narrow-leafed lupin (followed by turnip rape as a cover crop), and buckwheat (without cover crop) was tested on the grain yield and nutrient content in the following oat crop and on the nitrogen dynamics in the soil. Before winter (November) and in the subsequent spring (March), mineral nitrogen was measured in the soil at three depths (0-30 cm, 30-60 cm, and 60-90 cm) after the different pre-crops. A spring oat was established following the different pre-crops with 6 replicates during three consecutive years (2016-2018).featuresv402_soil_textureurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=2362ba20-7bb2-479b-9dc7-025f72fb9547&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:v410_crop_biomass_nitrogenThis data set reports measurements and observations from a soybean cropping system experiment (2015-2018) investigating the effects of cultivars and irrigation on soybean yield and rotational effects on a subsequent cereal crop. The objective was to design novel soybean-supported cropping systems in north-eastern Germany. The data supports agronomic analyses, as well as dynamic simulation modelling and includes details on crop growth, soil characteristics and weather. In the first part of the experiment different soybean cultivars and the effect of irrigation were compared in a split block design with six replicates and the factors cultivar and irrigation during three consecutive years (2015-2017). The treatments included with and without irrigation and three early maturing soybean cultivars of maturity group 000 (cv. Merlin and cv. Sultana for feed use and cv. Protibus for human food use). Buckwheat and Narrow-leafed lupin (cv. Probor) were cultivated as references. Seeds were inoculated with HISTICK® soybean (BASF, Germany). Measurements included whole plant biomass, grain yield, one thousand seed weigh, macro nutrients (N, P, K) in the biomass and grain, and additional agronomic observations including number of plants and plant phenology. Irrigation water was applied with a sprinkler system using the Web-BEREST model (Mirschel et al., 2014) to determine the amounts and timing (data on the amounts and dates are provided). In the second part of the experiment, the pre-crop effect of soybean (without cover crop), narrow-leafed lupin (followed by turnip rape as a cover crop), and buckwheat (without cover crop) was tested on the grain yield and nutrient content in the following oat crop and on the nitrogen dynamics in the soil. Before winter (November) and in the subsequent spring (March), mineral nitrogen was measured in the soil at three depths (0-30 cm, 30-60 cm, and 60-90 cm) after the different pre-crops. A spring oat was established following the different pre-crops with 6 replicates during three consecutive years (2016-2018).featuresv410_crop_biomass_nitrogenurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=cc605c5f-f07d-492f-8219-f92f39688293&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:v410_crop_ontogenesisThis data set reports measurements and observations from a soybean cropping system experiment (2015-2018) investigating the effects of cultivars and irrigation on soybean yield and rotational effects on a subsequent cereal crop. The objective was to design novel soybean-supported cropping systems in north-eastern Germany. The data supports agronomic analyses, as well as dynamic simulation modelling and includes details on crop growth, soil characteristics and weather. In the first part of the experiment different soybean cultivars and the effect of irrigation were compared in a split block design with six replicates and the factors cultivar and irrigation during three consecutive years (2015-2017). The treatments included with and without irrigation and three early maturing soybean cultivars of maturity group 000 (cv. Merlin and cv. Sultana for feed use and cv. Protibus for human food use). Buckwheat and Narrow-leafed lupin (cv. Probor) were cultivated as references. Seeds were inoculated with HISTICK® soybean (BASF, Germany). Measurements included whole plant biomass, grain yield, one thousand seed weigh, macro nutrients (N, P, K) in the biomass and grain, and additional agronomic observations including number of plants and plant phenology. Irrigation water was applied with a sprinkler system using the Web-BEREST model (Mirschel et al., 2014) to determine the amounts and timing (data on the amounts and dates are provided). In the second part of the experiment, the pre-crop effect of soybean (without cover crop), narrow-leafed lupin (followed by turnip rape as a cover crop), and buckwheat (without cover crop) was tested on the grain yield and nutrient content in the following oat crop and on the nitrogen dynamics in the soil. Before winter (November) and in the subsequent spring (March), mineral nitrogen was measured in the soil at three depths (0-30 cm, 30-60 cm, and 60-90 cm) after the different pre-crops. A spring oat was established following the different pre-crops with 6 replicates during three consecutive years (2016-2018).featuresv410_crop_ontogenesisurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=e793ecc9-2460-447a-a2a2-29f47d5ab6c6&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:v410_managementThis data set reports measurements and observations from a soybean cropping system experiment (2015-2018) investigating the effects of cultivars and irrigation on soybean yield and rotational effects on a subsequent cereal crop. The objective was to design novel soybean-supported cropping systems in north-eastern Germany. The data supports agronomic analyses, as well as dynamic simulation modelling and includes details on crop growth, soil characteristics and weather. In the first part of the experiment different soybean cultivars and the effect of irrigation were compared in a split block design with six replicates and the factors cultivar and irrigation during three consecutive years (2015-2017). The treatments included with and without irrigation and three early maturing soybean cultivars of maturity group 000 (cv. Merlin and cv. Sultana for feed use and cv. Protibus for human food use). Buckwheat and Narrow-leafed lupin (cv. Probor) were cultivated as references. Seeds were inoculated with HISTICK® soybean (BASF, Germany). Measurements included whole plant biomass, grain yield, one thousand seed weigh, macro nutrients (N, P, K) in the biomass and grain, and additional agronomic observations including number of plants and plant phenology. Irrigation water was applied with a sprinkler system using the Web-BEREST model (Mirschel et al., 2014) to determine the amounts and timing (data on the amounts and dates are provided). In the second part of the experiment, the pre-crop effect of soybean (without cover crop), narrow-leafed lupin (followed by turnip rape as a cover crop), and buckwheat (without cover crop) was tested on the grain yield and nutrient content in the following oat crop and on the nitrogen dynamics in the soil. Before winter (November) and in the subsequent spring (March), mineral nitrogen was measured in the soil at three depths (0-30 cm, 30-60 cm, and 60-90 cm) after the different pre-crops. A spring oat was established following the different pre-crops with 6 replicates during three consecutive years (2016-2018).featuresv410_managementurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=66b37809-9e3c-4f38-9493-e43ee0506eb2&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:v410_soil_h2o_nmin_contentThis data set reports measurements and observations from a soybean cropping system experiment (2015-2018) investigating the effects of cultivars and irrigation on soybean yield and rotational effects on a subsequent cereal crop. The objective was to design novel soybean-supported cropping systems in north-eastern Germany. The data supports agronomic analyses, as well as dynamic simulation modelling and includes details on crop growth, soil characteristics and weather. In the first part of the experiment different soybean cultivars and the effect of irrigation were compared in a split block design with six replicates and the factors cultivar and irrigation during three consecutive years (2015-2017). The treatments included with and without irrigation and three early maturing soybean cultivars of maturity group 000 (cv. Merlin and cv. Sultana for feed use and cv. Protibus for human food use). Buckwheat and Narrow-leafed lupin (cv. Probor) were cultivated as references. Seeds were inoculated with HISTICK® soybean (BASF, Germany). Measurements included whole plant biomass, grain yield, one thousand seed weigh, macro nutrients (N, P, K) in the biomass and grain, and additional agronomic observations including number of plants and plant phenology. Irrigation water was applied with a sprinkler system using the Web-BEREST model (Mirschel et al., 2014) to determine the amounts and timing (data on the amounts and dates are provided). In the second part of the experiment, the pre-crop effect of soybean (without cover crop), narrow-leafed lupin (followed by turnip rape as a cover crop), and buckwheat (without cover crop) was tested on the grain yield and nutrient content in the following oat crop and on the nitrogen dynamics in the soil. Before winter (November) and in the subsequent spring (March), mineral nitrogen was measured in the soil at three depths (0-30 cm, 30-60 cm, and 60-90 cm) after the different pre-crops. A spring oat was established following the different pre-crops with 6 replicates during three consecutive years (2016-2018).featuresv410_soil_h2o_nmin_contenturn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=c191828d-4781-4fda-813a-bb8941b9461e&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:v410_soil_textureThis data set reports measurements and observations from a soybean cropping system experiment (2015-2018) investigating the effects of cultivars and irrigation on soybean yield and rotational effects on a subsequent cereal crop. The objective was to design novel soybean-supported cropping systems in north-eastern Germany. The data supports agronomic analyses, as well as dynamic simulation modelling and includes details on crop growth, soil characteristics and weather. In the first part of the experiment different soybean cultivars and the effect of irrigation were compared in a split block design with six replicates and the factors cultivar and irrigation during three consecutive years (2015-2017). The treatments included with and without irrigation and three early maturing soybean cultivars of maturity group 000 (cv. Merlin and cv. Sultana for feed use and cv. Protibus for human food use). Buckwheat and Narrow-leafed lupin (cv. Probor) were cultivated as references. Seeds were inoculated with HISTICK® soybean (BASF, Germany). Measurements included whole plant biomass, grain yield, one thousand seed weigh, macro nutrients (N, P, K) in the biomass and grain, and additional agronomic observations including number of plants and plant phenology. Irrigation water was applied with a sprinkler system using the Web-BEREST model (Mirschel et al., 2014) to determine the amounts and timing (data on the amounts and dates are provided). In the second part of the experiment, the pre-crop effect of soybean (without cover crop), narrow-leafed lupin (followed by turnip rape as a cover crop), and buckwheat (without cover crop) was tested on the grain yield and nutrient content in the following oat crop and on the nitrogen dynamics in the soil. Before winter (November) and in the subsequent spring (March), mineral nitrogen was measured in the soil at three depths (0-30 cm, 30-60 cm, and 60-90 cm) after the different pre-crops. A spring oat was established following the different pre-crops with 6 replicates during three consecutive years (2016-2018).featuresv410_soil_textureurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=db5be9f4-0626-4d39-9b66-217fe189070a&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:geolocation_155c3112fb4a318b90b58749138f47e5Up to now, it has not been possible to determine the beet biomass of sugar beet non-destructive dynamically during the growth process. This value is important for the parameterisation of crop growth models. Studies on individual beets show that there is a close relationship between the beet diameter at the widest (thickest) point and the beet biomass. To quantify this relationship data were colleced. The data set contains four sugar beet varieties from the 1990s for three different locations (Müncheberg, Hohenfinow and Dedelow) in Brandenburg, Germany. The data set contains more than 6,700 data records. The measurements were realized in approx. 14-day intervals between rising and harvesting. Different cultivation systems (intensive, organic, extensive) were taken into account additionaly with and without use of irrigation.featuresgeolocation_155c3112fb4a318b90b58749138f47e5urn:x-ogc:def:crs:EPSG:432613.80423213913699 52.5166646056255814.12357478307664 53.36715648528616https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=062adc86-37a1-4a3f-88ee-ed78cbd7a23e&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:sbbmdehomuUp to now, it has not been possible to determine the beet biomass of sugar beet non-destructive dynamically during the growth process. This value is important for the parameterisation of crop growth models. Studies on individual beets show that there is a close relationship between the beet diameter at the widest (thickest) point and the beet biomass. To quantify this relationship data were colleced. The data set contains four sugar beet varieties from the 1990s for three different locations (Müncheberg, Hohenfinow and Dedelow) in Brandenburg, Germany. The data set contains more than 6,700 data records. The measurements were realized in approx. 14-day intervals between rising and harvesting. Different cultivation systems (intensive, organic, extensive) were taken into account additionaly with and without use of irrigation.featuressbbmdehomuurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=34650918-00b2-4072-875f-96c090af7def&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:dk_140_winter_wheat_irrigDetailed measurements on soil, plant and atmosphere are required for the development and validation of crop growth and agroecosystem models. These measurements should be available with a high temporal resolution. With the aim of creating a growth model for winter wheat, an experiment with winter wheat under integrated cultivation conditions was carried out at the intensive experimental field of the Müncheberg Research Centre for Soil Fertility, Germany, between 1979 and 1981, both with and without irrigation. Field chambers were used for daily measurements of the CO2 balance of the crop stand. The daily evaporation was measured with two different evaporation pans. The different biomass components of the winter wheat crop stand were measured in weekly intervals from April to harvest in July/August. The different biomass components were analysed in the laboratory concerning their carbon, nitrogen, phosphorus and potassium content. Based on this coherent data set, the growth model TRITSIM for winter wheat was developed at the Müncheberg Research Centre for Soil Fertility in the 1980s. TRITSIM was incorporated into the complex agroecosystem model AGROSIM-WHEAT of the Research Institute of Plant Protection Eberswalde, Germany, for the identification of optimal plant protection measures under practical field conditions. The data set presented here can also be the basis for the verification and validation of further winter wheat growth and/or agroecosystem models.featuresdk_140_winter_wheat_irrigurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=ea097267-4b74-4f02-a1a2-0e1be07e20c3&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:dk_140_winter_wheat_irrigation_amountDetailed measurements on soil, plant and atmosphere are required for the development and validation of crop growth and agroecosystem models. These measurements should be available with a high temporal resolution. With the aim of creating a growth model for winter wheat, an experiment with winter wheat under integrated cultivation conditions was carried out at the intensive experimental field of the Müncheberg Research Centre for Soil Fertility, Germany, between 1979 and 1981, both with and without irrigation. Field chambers were used for daily measurements of the CO2 balance of the crop stand. The daily evaporation was measured with two different evaporation pans. The different biomass components of the winter wheat crop stand were measured in weekly intervals from April to harvest in July/August. The different biomass components were analysed in the laboratory concerning their carbon, nitrogen, phosphorus and potassium content. Based on this coherent data set, the growth model TRITSIM for winter wheat was developed at the Müncheberg Research Centre for Soil Fertility in the 1980s. TRITSIM was incorporated into the complex agroecosystem model AGROSIM-WHEAT of the Research Institute of Plant Protection Eberswalde, Germany, for the identification of optimal plant protection measures under practical field conditions. The data set presented here can also be the basis for the verification and validation of further winter wheat growth and/or agroecosystem models.featuresdk_140_winter_wheat_irrigation_amounturn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=947d7846-82fb-4afd-a758-3a1edab986b5&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:dk_140_winter_wheat_no_irrigDetailed measurements on soil, plant and atmosphere are required for the development and validation of crop growth and agroecosystem models. These measurements should be available with a high temporal resolution. With the aim of creating a growth model for winter wheat, an experiment with winter wheat under integrated cultivation conditions was carried out at the intensive experimental field of the Müncheberg Research Centre for Soil Fertility, Germany, between 1979 and 1981, both with and without irrigation. Field chambers were used for daily measurements of the CO2 balance of the crop stand. The daily evaporation was measured with two different evaporation pans. The different biomass components of the winter wheat crop stand were measured in weekly intervals from April to harvest in July/August. The different biomass components were analysed in the laboratory concerning their carbon, nitrogen, phosphorus and potassium content. Based on this coherent data set, the growth model TRITSIM for winter wheat was developed at the Müncheberg Research Centre for Soil Fertility in the 1980s. TRITSIM was incorporated into the complex agroecosystem model AGROSIM-WHEAT of the Research Institute of Plant Protection Eberswalde, Germany, for the identification of optimal plant protection measures under practical field conditions. The data set presented here can also be the basis for the verification and validation of further winter wheat growth and/or agroecosystem models.featuresdk_140_winter_wheat_no_irrigurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=2d7cd516-61fb-4efb-8a5c-40a24ed287c2&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:geolocation_d06ee648b7986d631f877a9edfa67bc3Detailed measurements on soil, plant and atmosphere are required for the development and validation of crop growth and agroecosystem models. These measurements should be available with a high temporal resolution. With the aim of creating a growth model for winter wheat, an experiment with winter wheat under integrated cultivation conditions was carried out at the intensive experimental field of the Müncheberg Research Centre for Soil Fertility, Germany, between 1979 and 1981, both with and without irrigation. Field chambers were used for daily measurements of the CO2 balance of the crop stand. The daily evaporation was measured with two different evaporation pans. The different biomass components of the winter wheat crop stand were measured in weekly intervals from April to harvest in July/August. The different biomass components were analysed in the laboratory concerning their carbon, nitrogen, phosphorus and potassium content. Based on this coherent data set, the growth model TRITSIM for winter wheat was developed at the Müncheberg Research Centre for Soil Fertility in the 1980s. TRITSIM was incorporated into the complex agroecosystem model AGROSIM-WHEAT of the Research Institute of Plant Protection Eberswalde, Germany, for the identification of optimal plant protection measures under practical field conditions. The data set presented here can also be the basis for the verification and validation of further winter wheat growth and/or agroecosystem models.featuresgeolocation_d06ee648b7986d631f877a9edfa67bc3urn:x-ogc:def:crs:EPSG:432613.47096725586944 53.2944389887517413.85511279201188 53.42902016292231https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=66bea075-1119-4bef-bebf-7ec259c5145d&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:cluster_analysisThe data set reveals consumers' attitudes towards ethical issues in chicken production. The focus is on the killing practice and dual purpose chickens. It provides information on consumer purchase pattern of eggs and chicken meat, perception of animal welfare and protection issues, knowledge and perception of killing day-old chicks, attitudes towards dual purpose chickens, and socio-demographic characteristics. The raw data contains standardized responds of 1000 telephone interviews with German consumers (Berlin and Brandenburg) from 2016. A cluster analysis was performed to categorize consumers according to purchasing criteria for dual chicken products. Clusters were described by means of socio-economic variables.FOS: Social sciences (Fields of Science and Technology (FOS))featuresFOS: Economics and business (Fields of Science and Technology (FOS))cluster_analysisurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=f37238ff-db75-4508-a42e-487a65322513&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:cluster_descriptionThe data set reveals consumers' attitudes towards ethical issues in chicken production. The focus is on the killing practice and dual purpose chickens. It provides information on consumer purchase pattern of eggs and chicken meat, perception of animal welfare and protection issues, knowledge and perception of killing day-old chicks, attitudes towards dual purpose chickens, and socio-demographic characteristics. The raw data contains standardized responds of 1000 telephone interviews with German consumers (Berlin and Brandenburg) from 2016. A cluster analysis was performed to categorize consumers according to purchasing criteria for dual chicken products. Clusters were described by means of socio-economic variables.cluster_descriptionFOS: Economics and business (Fields of Science and Technology (FOS))featuresFOS: Social sciences (Fields of Science and Technology (FOS))urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=490f4821-31ce-43c1-a647-4d026424d666&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:codes_raw_dataThe data set reveals consumers' attitudes towards ethical issues in chicken production. The focus is on the killing practice and dual purpose chickens. It provides information on consumer purchase pattern of eggs and chicken meat, perception of animal welfare and protection issues, knowledge and perception of killing day-old chicks, attitudes towards dual purpose chickens, and socio-demographic characteristics. The raw data contains standardized responds of 1000 telephone interviews with German consumers (Berlin and Brandenburg) from 2016. A cluster analysis was performed to categorize consumers according to purchasing criteria for dual chicken products. Clusters were described by means of socio-economic variables.FOS: Economics and business (Fields of Science and Technology (FOS))codes_raw_datafeaturesFOS: Social sciences (Fields of Science and Technology (FOS))urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=410158af-cf13-46a7-84d1-c34e3ac7d073&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:consumer_survey_raw_dataThe data set reveals consumers' attitudes towards ethical issues in chicken production. The focus is on the killing practice and dual purpose chickens. It provides information on consumer purchase pattern of eggs and chicken meat, perception of animal welfare and protection issues, knowledge and perception of killing day-old chicks, attitudes towards dual purpose chickens, and socio-demographic characteristics. The raw data contains standardized responds of 1000 telephone interviews with German consumers (Berlin and Brandenburg) from 2016. A cluster analysis was performed to categorize consumers according to purchasing criteria for dual chicken products. Clusters were described by means of socio-economic variables.consumer_survey_raw_dataFOS: Economics and business (Fields of Science and Technology (FOS))featuresFOS: Social sciences (Fields of Science and Technology (FOS))urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=9b4a9157-b64e-4fae-80a0-cddd2ac1836e&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:2013_298Large-scale column experiments were performed to quantify partitioning processes responsible for the retardation of carbon disulfide (CS2) vapor in partially saturated porous media. For experiments 1-16, fine glass beads (soda-lime glass, Sigmund Lindner, Warmensteinach, Germany) were used as medium; for experiments 17-37, Geba fine sand (Quarzsande GmbH, Eferding, Austria) was used. The experiments were conducted in large, vertical columns (i.d. = 0.109 m) of 2 m length packed with different porous media. A slug of CS2 vapor and the conservative tracer argon was injected at the bottom of the column followed by a nitrogen chase. Concentrations of CS2 and argon were measured at the top outlet of the column using two gas chromatographs (GC). The temporal-moment analysis (TMA) for step input was employed to evaluate concentration breakthrough curves and to calculate seepage velocity (v), dispersion (D) and retardation (R). The data sets contain for each experiment the raw data of the GC measurements of both CS2 and argon, temperature, pressure, and tensiometer recordings as well as the results of the TMA.featuresLaboratory ExperimentsPorous MediumVadose ZoneVOCVapor RetardationContaminant Transport2013_298Breakthrough Curveurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=e17f120a-44f0-47d4-bab9-c3254ea46750&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:dataset_hotcoldspots_ecosystem_services_mol_bbThe dataset contains the indicators of occurrence (0, 1) of hotspots and coldspots for six ecosystem services provision for the utilized agricultural area of the Märkisch-Oderland District (NUTS3) in east Brandenburg, Germany. The six ecosystem services are: i) biomass production (PRO), ii) water storage (WAS), iii) carbon stock total (CST), iv) carbon stock potential (CSP), v) habitat for species (HAB), and vi) landscape attractiveness (LAT). In addition, the dataset contains two composite indicators for the sum of the six ecosystem services' hotspots and coldspots (range 1-6), which indicate the total number of services of high or low quality, respectively. The data set has 140,116 entries, each one corresponding to a 1 ha size cell (100 m x 100 m), whose centroid coordinates are provided according to the EPSG:4839 - ETRS89/LCC Germany (N-E) - Projected coordinate system for Germany. In addition, the dataset provides information about landscape unit, dominant land cover, dominant soil and cadastral parcel (Digitales Feldblockkataster des Landes Brandenburg 2020, DFBK20/BB).featuresSpatial AnalysisConflict managementdataset_hotcoldspots_ecosystem_services_mol_bburn:x-ogc:def:crs:EPSG:432613.611291791000042 52.3752128220000414.631667718000074 52.87007475200005https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=fb061912-b6ac-4893-a2ba-1b6f6c260450&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:forst_joachimsthal The database contains measurement data on CO2 concentration and its isotopic signature (δ13C), temperature and moisture in different soil depths of a Scots pine forest (Pinus sylvestris) in the northeast German lowlands over the vegetation period of 2018. The investigated forest site represents an intensive monitoring plot in the ICP Forests programme (http://icp-forests.net).Details of the methods and results were presented in Jochheim et al. (2018), https://doi.org/10.1002/jpln.201700259 und Jochheim et al. (2022), https://doi.org/10.3389/ffgc.2022.826298.Soil CO2forst_joachimsthalfeaturesδ13Cisotopic signatureurn:x-ogc:def:crs:EPSG:432613.639900000000043 52.9799000000000713.640100000000043 52.98010000000008https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=cbdd4c6d-f2de-4db5-859e-2497218ba2e2&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:co2_d13c_st_swcfeaturesco2_d13c_st_swcurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=575bee91-e72c-4fd9-a469-8d6e26ac9efb&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:2017_322_biomass_samples_laiCarbon (C) sequestration in soils plays a key role in the global C cycle. It is therefore crucial to adequately monitor dynamics in soil organic carbon (deltaSOC) stocks when aiming to reveal underlying processes and potential drivers. However, small-scale spatial (10-30m) and temporal changes in SOC stocks, particularly pronounced in arable lands, are hard to assess. The main reasons for this are limitations of the well-established methods. On the one hand, repeated soil inventories, often used in long-term field trials, reveal spatial patterns and trends in deltaSOC but require a longer observation period and a sufficient number of repetitions. On the other hand, eddy covariance measurements of C fluxes towards a complete C budget of the soil-plant- atmosphere system may help to obtain temporal deltaSOC patterns but lack small-scale spatial resolution. To overcome these limitations, this study presents a reliable method to detect both short-term temporal dynamics as well as small-scale spatial differences of deltaSOC using measurements of the net ecosystem carbon balance (NECB) as a proxy. To estimate the NECB, a combination of automatic chamber (AC) measurements of CO2 exchange and empirically modelled aboveground biomass development (NPPshoot) were used.To verify the method, results were compared with deltaSOC observed by soil resampling. Soil resampling and AC measurements were performed from 2010 to 2014 at a colluvial depression located in the hummocky ground moraine landscape of northeastern Germany. The measurement site is characterized by a variable groundwater level (GWL) and pronounced small-scale spatial heterogeneity regarding SOC and nitrogen (Nt) stocks. Reported data sets contain: (1) weather data (PAR, air temperature, precipitation, groundwater level (GWL)); (2) calculated CO2-fluxes; (3) gap-filled and modelled daily sums of Reco, GPP, NEE, C content in aboveground biomass (NPPshoot) and NECB; (4) data regarding chamber position specific LAI-measurements, parcel biomass sampling campaigns and harvests at chamber position. Tendencies and magnitude of deltaSOC values derived by AC measurements and repeated soil inventories corresponded well. The period of maximum plant growth was identified as being most important for the development of spatial differences in annual deltaSOC. Hence, we were able to confirm that AC-based C budgets are able to reveal small-scale spatial differences and short-term temporal dynamics of deltaSOC.net ecosystem carbon balance (NECB)non-steady-state automatic chamberscarbon dioxidefeatures2017_322_biomass_samples_laiRecosoil organic carbon (SOC)GPPNEEurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=7836c6a0-e6db-4771-83b3-9045c7299452&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:2017_322_co2_biomass_necbCarbon (C) sequestration in soils plays a key role in the global C cycle. It is therefore crucial to adequately monitor dynamics in soil organic carbon (deltaSOC) stocks when aiming to reveal underlying processes and potential drivers. However, small-scale spatial (10-30m) and temporal changes in SOC stocks, particularly pronounced in arable lands, are hard to assess. The main reasons for this are limitations of the well-established methods. On the one hand, repeated soil inventories, often used in long-term field trials, reveal spatial patterns and trends in deltaSOC but require a longer observation period and a sufficient number of repetitions. On the other hand, eddy covariance measurements of C fluxes towards a complete C budget of the soil-plant- atmosphere system may help to obtain temporal deltaSOC patterns but lack small-scale spatial resolution. To overcome these limitations, this study presents a reliable method to detect both short-term temporal dynamics as well as small-scale spatial differences of deltaSOC using measurements of the net ecosystem carbon balance (NECB) as a proxy. To estimate the NECB, a combination of automatic chamber (AC) measurements of CO2 exchange and empirically modelled aboveground biomass development (NPPshoot) were used.To verify the method, results were compared with deltaSOC observed by soil resampling. Soil resampling and AC measurements were performed from 2010 to 2014 at a colluvial depression located in the hummocky ground moraine landscape of northeastern Germany. The measurement site is characterized by a variable groundwater level (GWL) and pronounced small-scale spatial heterogeneity regarding SOC and nitrogen (Nt) stocks. Reported data sets contain: (1) weather data (PAR, air temperature, precipitation, groundwater level (GWL)); (2) calculated CO2-fluxes; (3) gap-filled and modelled daily sums of Reco, GPP, NEE, C content in aboveground biomass (NPPshoot) and NECB; (4) data regarding chamber position specific LAI-measurements, parcel biomass sampling campaigns and harvests at chamber position. Tendencies and magnitude of deltaSOC values derived by AC measurements and repeated soil inventories corresponded well. The period of maximum plant growth was identified as being most important for the development of spatial differences in annual deltaSOC. Hence, we were able to confirm that AC-based C budgets are able to reveal small-scale spatial differences and short-term temporal dynamics of deltaSOC.featuresReconon-steady-state automatic chambers2017_322_co2_biomass_necbsoil organic carbon (SOC)GPPnet ecosystem carbon balance (NECB)carbon dioxideNEEurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=890b4925-a16d-466a-97d3-8dc156c5a764&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:2017_322_harvest_chambersCarbon (C) sequestration in soils plays a key role in the global C cycle. It is therefore crucial to adequately monitor dynamics in soil organic carbon (deltaSOC) stocks when aiming to reveal underlying processes and potential drivers. However, small-scale spatial (10-30m) and temporal changes in SOC stocks, particularly pronounced in arable lands, are hard to assess. The main reasons for this are limitations of the well-established methods. On the one hand, repeated soil inventories, often used in long-term field trials, reveal spatial patterns and trends in deltaSOC but require a longer observation period and a sufficient number of repetitions. On the other hand, eddy covariance measurements of C fluxes towards a complete C budget of the soil-plant- atmosphere system may help to obtain temporal deltaSOC patterns but lack small-scale spatial resolution. To overcome these limitations, this study presents a reliable method to detect both short-term temporal dynamics as well as small-scale spatial differences of deltaSOC using measurements of the net ecosystem carbon balance (NECB) as a proxy. To estimate the NECB, a combination of automatic chamber (AC) measurements of CO2 exchange and empirically modelled aboveground biomass development (NPPshoot) were used.To verify the method, results were compared with deltaSOC observed by soil resampling. Soil resampling and AC measurements were performed from 2010 to 2014 at a colluvial depression located in the hummocky ground moraine landscape of northeastern Germany. The measurement site is characterized by a variable groundwater level (GWL) and pronounced small-scale spatial heterogeneity regarding SOC and nitrogen (Nt) stocks. Reported data sets contain: (1) weather data (PAR, air temperature, precipitation, groundwater level (GWL)); (2) calculated CO2-fluxes; (3) gap-filled and modelled daily sums of Reco, GPP, NEE, C content in aboveground biomass (NPPshoot) and NECB; (4) data regarding chamber position specific LAI-measurements, parcel biomass sampling campaigns and harvests at chamber position. Tendencies and magnitude of deltaSOC values derived by AC measurements and repeated soil inventories corresponded well. The period of maximum plant growth was identified as being most important for the development of spatial differences in annual deltaSOC. Hence, we were able to confirm that AC-based C budgets are able to reveal small-scale spatial differences and short-term temporal dynamics of deltaSOC.net ecosystem carbon balance (NECB)non-steady-state automatic chamberscarbon dioxidefeatures2017_322_harvest_chambersRecosoil organic carbon (SOC)GPPNEEurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=9155e3c7-5133-460f-bdea-78f119fbcc4d&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:2017_322_laiCarbon (C) sequestration in soils plays a key role in the global C cycle. It is therefore crucial to adequately monitor dynamics in soil organic carbon (deltaSOC) stocks when aiming to reveal underlying processes and potential drivers. However, small-scale spatial (10-30m) and temporal changes in SOC stocks, particularly pronounced in arable lands, are hard to assess. The main reasons for this are limitations of the well-established methods. On the one hand, repeated soil inventories, often used in long-term field trials, reveal spatial patterns and trends in deltaSOC but require a longer observation period and a sufficient number of repetitions. On the other hand, eddy covariance measurements of C fluxes towards a complete C budget of the soil-plant- atmosphere system may help to obtain temporal deltaSOC patterns but lack small-scale spatial resolution. To overcome these limitations, this study presents a reliable method to detect both short-term temporal dynamics as well as small-scale spatial differences of deltaSOC using measurements of the net ecosystem carbon balance (NECB) as a proxy. To estimate the NECB, a combination of automatic chamber (AC) measurements of CO2 exchange and empirically modelled aboveground biomass development (NPPshoot) were used.To verify the method, results were compared with deltaSOC observed by soil resampling. Soil resampling and AC measurements were performed from 2010 to 2014 at a colluvial depression located in the hummocky ground moraine landscape of northeastern Germany. The measurement site is characterized by a variable groundwater level (GWL) and pronounced small-scale spatial heterogeneity regarding SOC and nitrogen (Nt) stocks. Reported data sets contain: (1) weather data (PAR, air temperature, precipitation, groundwater level (GWL)); (2) calculated CO2-fluxes; (3) gap-filled and modelled daily sums of Reco, GPP, NEE, C content in aboveground biomass (NPPshoot) and NECB; (4) data regarding chamber position specific LAI-measurements, parcel biomass sampling campaigns and harvests at chamber position. Tendencies and magnitude of deltaSOC values derived by AC measurements and repeated soil inventories corresponded well. The period of maximum plant growth was identified as being most important for the development of spatial differences in annual deltaSOC. Hence, we were able to confirm that AC-based C budgets are able to reveal small-scale spatial differences and short-term temporal dynamics of deltaSOC.net ecosystem carbon balance (NECB)non-steady-state automatic chamberscarbon dioxidefeatures2017_322_laiRecosoil organic carbon (SOC)GPPNEEurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=ca91c264-a21f-4b5e-ab7d-44fef6f5d69b&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:2017_322_measure_co2_fluxesCarbon (C) sequestration in soils plays a key role in the global C cycle. It is therefore crucial to adequately monitor dynamics in soil organic carbon (deltaSOC) stocks when aiming to reveal underlying processes and potential drivers. However, small-scale spatial (10-30m) and temporal changes in SOC stocks, particularly pronounced in arable lands, are hard to assess. The main reasons for this are limitations of the well-established methods. On the one hand, repeated soil inventories, often used in long-term field trials, reveal spatial patterns and trends in deltaSOC but require a longer observation period and a sufficient number of repetitions. On the other hand, eddy covariance measurements of C fluxes towards a complete C budget of the soil-plant- atmosphere system may help to obtain temporal deltaSOC patterns but lack small-scale spatial resolution. To overcome these limitations, this study presents a reliable method to detect both short-term temporal dynamics as well as small-scale spatial differences of deltaSOC using measurements of the net ecosystem carbon balance (NECB) as a proxy. To estimate the NECB, a combination of automatic chamber (AC) measurements of CO2 exchange and empirically modelled aboveground biomass development (NPPshoot) were used.To verify the method, results were compared with deltaSOC observed by soil resampling. Soil resampling and AC measurements were performed from 2010 to 2014 at a colluvial depression located in the hummocky ground moraine landscape of northeastern Germany. The measurement site is characterized by a variable groundwater level (GWL) and pronounced small-scale spatial heterogeneity regarding SOC and nitrogen (Nt) stocks. Reported data sets contain: (1) weather data (PAR, air temperature, precipitation, groundwater level (GWL)); (2) calculated CO2-fluxes; (3) gap-filled and modelled daily sums of Reco, GPP, NEE, C content in aboveground biomass (NPPshoot) and NECB; (4) data regarding chamber position specific LAI-measurements, parcel biomass sampling campaigns and harvests at chamber position. Tendencies and magnitude of deltaSOC values derived by AC measurements and repeated soil inventories corresponded well. The period of maximum plant growth was identified as being most important for the development of spatial differences in annual deltaSOC. Hence, we were able to confirm that AC-based C budgets are able to reveal small-scale spatial differences and short-term temporal dynamics of deltaSOC.net ecosystem carbon balance (NECB)non-steady-state automatic chambers2017_322_measure_co2_fluxescarbon dioxidefeaturesRecosoil organic carbon (SOC)GPPNEEurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=aa2451a3-53fe-4ee2-987a-7fb6e59f0f6b&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:2017_322_weather_dataCarbon (C) sequestration in soils plays a key role in the global C cycle. It is therefore crucial to adequately monitor dynamics in soil organic carbon (deltaSOC) stocks when aiming to reveal underlying processes and potential drivers. However, small-scale spatial (10-30m) and temporal changes in SOC stocks, particularly pronounced in arable lands, are hard to assess. The main reasons for this are limitations of the well-established methods. On the one hand, repeated soil inventories, often used in long-term field trials, reveal spatial patterns and trends in deltaSOC but require a longer observation period and a sufficient number of repetitions. On the other hand, eddy covariance measurements of C fluxes towards a complete C budget of the soil-plant- atmosphere system may help to obtain temporal deltaSOC patterns but lack small-scale spatial resolution. To overcome these limitations, this study presents a reliable method to detect both short-term temporal dynamics as well as small-scale spatial differences of deltaSOC using measurements of the net ecosystem carbon balance (NECB) as a proxy. To estimate the NECB, a combination of automatic chamber (AC) measurements of CO2 exchange and empirically modelled aboveground biomass development (NPPshoot) were used.To verify the method, results were compared with deltaSOC observed by soil resampling. Soil resampling and AC measurements were performed from 2010 to 2014 at a colluvial depression located in the hummocky ground moraine landscape of northeastern Germany. The measurement site is characterized by a variable groundwater level (GWL) and pronounced small-scale spatial heterogeneity regarding SOC and nitrogen (Nt) stocks. Reported data sets contain: (1) weather data (PAR, air temperature, precipitation, groundwater level (GWL)); (2) calculated CO2-fluxes; (3) gap-filled and modelled daily sums of Reco, GPP, NEE, C content in aboveground biomass (NPPshoot) and NECB; (4) data regarding chamber position specific LAI-measurements, parcel biomass sampling campaigns and harvests at chamber position. Tendencies and magnitude of deltaSOC values derived by AC measurements and repeated soil inventories corresponded well. The period of maximum plant growth was identified as being most important for the development of spatial differences in annual deltaSOC. Hence, we were able to confirm that AC-based C budgets are able to reveal small-scale spatial differences and short-term temporal dynamics of deltaSOC.net ecosystem carbon balance (NECB)non-steady-state automatic chamberscarbon dioxidefeaturesRecosoil organic carbon (SOC)GPPNEE2017_322_weather_dataurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=e0825af2-a85e-45ad-b81b-ccc4e5624f69&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:geolocation_d2620349e4b4a979897a363e128840b3Carbon (C) sequestration in soils plays a key role in the global C cycle. It is therefore crucial to adequately monitor dynamics in soil organic carbon (deltaSOC) stocks when aiming to reveal underlying processes and potential drivers. However, small-scale spatial (10-30m) and temporal changes in SOC stocks, particularly pronounced in arable lands, are hard to assess. The main reasons for this are limitations of the well-established methods. On the one hand, repeated soil inventories, often used in long-term field trials, reveal spatial patterns and trends in deltaSOC but require a longer observation period and a sufficient number of repetitions. On the other hand, eddy covariance measurements of C fluxes towards a complete C budget of the soil-plant- atmosphere system may help to obtain temporal deltaSOC patterns but lack small-scale spatial resolution. To overcome these limitations, this study presents a reliable method to detect both short-term temporal dynamics as well as small-scale spatial differences of deltaSOC using measurements of the net ecosystem carbon balance (NECB) as a proxy. To estimate the NECB, a combination of automatic chamber (AC) measurements of CO2 exchange and empirically modelled aboveground biomass development (NPPshoot) were used.To verify the method, results were compared with deltaSOC observed by soil resampling. Soil resampling and AC measurements were performed from 2010 to 2014 at a colluvial depression located in the hummocky ground moraine landscape of northeastern Germany. The measurement site is characterized by a variable groundwater level (GWL) and pronounced small-scale spatial heterogeneity regarding SOC and nitrogen (Nt) stocks. Reported data sets contain: (1) weather data (PAR, air temperature, precipitation, groundwater level (GWL)); (2) calculated CO2-fluxes; (3) gap-filled and modelled daily sums of Reco, GPP, NEE, C content in aboveground biomass (NPPshoot) and NECB; (4) data regarding chamber position specific LAI-measurements, parcel biomass sampling campaigns and harvests at chamber position. Tendencies and magnitude of deltaSOC values derived by AC measurements and repeated soil inventories corresponded well. The period of maximum plant growth was identified as being most important for the development of spatial differences in annual deltaSOC. Hence, we were able to confirm that AC-based C budgets are able to reveal small-scale spatial differences and short-term temporal dynamics of deltaSOC.geolocation_d2620349e4b4a979897a363e128840b3net ecosystem carbon balance (NECB)non-steady-state automatic chamberscarbon dioxidefeaturesRecosoil organic carbon (SOC)GPPNEEurn:x-ogc:def:crs:EPSG:432613.78361409432882 53.3799870273243413.78387235472606 53.38016061295505https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=c9ef314a-8e52-4bcd-8bb4-d174dc658918&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:geolocation_83f04da151a0878b45614b47e28aa6c3Dispersal between local communities is a hallmark of metacommunity theory. Evidence for the effects of dispersal rates and pathways on metacommunity dynamics under field conditions is often lacking. However, studying metacommunities of yeasts in floral nectar and yeasts transported by a mobile linker community offers the opportunity to understand how species pools of passively dispersing organisms affect the community assembly. We sampled flowers and pollinators of three common tree species, identified attached yeast species and observed flower visits of different pollinator groups. We found a high overlap in yeast communities between flower and pollinator for social insects and low overlap for solitary insects. Bumblebees and honeybees transported the most nectar-specialist species, wild bees and wasps the most insect-associated yeast species, and sawflies the most transient yeast species. We found strong environmental filtering for insect-associated yeast species. Our results show that the dispersal frequency of the mobile linker community plays an important role in determining the species richness and the overall species abundance of the metacommunity, whereas local processes like environmental filtering effects mainly shape species composition. This approach offers new insights into the role of ecological filters during dispersal and colonization processes and a better understanding of resulting metacommunities.featuresgeolocation_83f04da151a0878b45614b47e28aa6c3urn:x-ogc:def:crs:EPSG:432613.54201224831993 53.3157149336013113.81833789340925 53.3834289198704https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=ea285387-88d5-4a96-847e-c9972f88f519&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:v4_lbg_2014_mc_measured_co2_data_tableManual closed-chamber measurements are commonly used to quantify annual net CO2 ecosystem exchange (NEE) in a wide range of terrestrial ecosystems. However, differences in both the acquisition and gap filling of manual closed-chamber data are large in the existing literature, complicating inter-study comparisons and meta analyses. This data set contains data of a study which compares common approaches for quantifying CO2 exchange at three methodological levels. (i) The first level included two different CO2 flux measurement methods: one via measurements during mid-day applying net coverages (mid-day approach) and one via measurements from sunrise to noon (sunrise approach) to capture a span of light conditions for measurements of NEE with transparent chambers. (ii) The second level included three different methods of pooling measured ecosystem respiration (RECO) fluxes for empirical modeling of RECO: campaign-wise (single-measurement-day RECO models), season-wise (one RECO model for the entire study period), and cluster-wise (two RECO models representing a low and a high vegetation status). (iii) The third level included two different methods of deriving fluxes of gross primary production (GPP): by subtracting either proximately measured RECO fluxes (direct GPP modeling) or empirically modeled RECO fluxes from measured NEE fluxes (indirect GPP modeling). Measurements were made during 2013-2014 in a lucerne-clover-grass field in NE Germany. Besides modelled half-hourly RECO, NEE and GPP, measured CO2-fluxes, plant height and C content of aboveground biomass as well as weather conditions are given.v4_lbg_2014_mc_measured_co2_data_tableCarbon dioxidefeaturesNet ecosystem exchange (NEE)GrasslandGross primary productivity (GPP)Non-steady-state chambersEcosystem respiration (Reco)urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=535b6c91-e65a-444a-a62d-215dba2e0db0&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:v4_lbg_2014_mc_modelled_co2_data_tableManual closed-chamber measurements are commonly used to quantify annual net CO2 ecosystem exchange (NEE) in a wide range of terrestrial ecosystems. However, differences in both the acquisition and gap filling of manual closed-chamber data are large in the existing literature, complicating inter-study comparisons and meta analyses. This data set contains data of a study which compares common approaches for quantifying CO2 exchange at three methodological levels. (i) The first level included two different CO2 flux measurement methods: one via measurements during mid-day applying net coverages (mid-day approach) and one via measurements from sunrise to noon (sunrise approach) to capture a span of light conditions for measurements of NEE with transparent chambers. (ii) The second level included three different methods of pooling measured ecosystem respiration (RECO) fluxes for empirical modeling of RECO: campaign-wise (single-measurement-day RECO models), season-wise (one RECO model for the entire study period), and cluster-wise (two RECO models representing a low and a high vegetation status). (iii) The third level included two different methods of deriving fluxes of gross primary production (GPP): by subtracting either proximately measured RECO fluxes (direct GPP modeling) or empirically modeled RECO fluxes from measured NEE fluxes (indirect GPP modeling). Measurements were made during 2013-2014 in a lucerne-clover-grass field in NE Germany. Besides modelled half-hourly RECO, NEE and GPP, measured CO2-fluxes, plant height and C content of aboveground biomass as well as weather conditions are given.Carbon dioxidefeaturesNet ecosystem exchange (NEE)Grasslandv4_lbg_2014_mc_modelled_co2_data_tableGross primary productivity (GPP)Non-steady-state chambersEcosystem respiration (Reco)urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=169a626c-c28f-427d-ad06-aa7ccb1c15fc&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:v4_lbg_2014_mc_nppshoot_data_tableManual closed-chamber measurements are commonly used to quantify annual net CO2 ecosystem exchange (NEE) in a wide range of terrestrial ecosystems. However, differences in both the acquisition and gap filling of manual closed-chamber data are large in the existing literature, complicating inter-study comparisons and meta analyses. This data set contains data of a study which compares common approaches for quantifying CO2 exchange at three methodological levels. (i) The first level included two different CO2 flux measurement methods: one via measurements during mid-day applying net coverages (mid-day approach) and one via measurements from sunrise to noon (sunrise approach) to capture a span of light conditions for measurements of NEE with transparent chambers. (ii) The second level included three different methods of pooling measured ecosystem respiration (RECO) fluxes for empirical modeling of RECO: campaign-wise (single-measurement-day RECO models), season-wise (one RECO model for the entire study period), and cluster-wise (two RECO models representing a low and a high vegetation status). (iii) The third level included two different methods of deriving fluxes of gross primary production (GPP): by subtracting either proximately measured RECO fluxes (direct GPP modeling) or empirically modeled RECO fluxes from measured NEE fluxes (indirect GPP modeling). Measurements were made during 2013-2014 in a lucerne-clover-grass field in NE Germany. Besides modelled half-hourly RECO, NEE and GPP, measured CO2-fluxes, plant height and C content of aboveground biomass as well as weather conditions are given.Carbon dioxidefeaturesNet ecosystem exchange (NEE)v4_lbg_2014_mc_nppshoot_data_tableGrasslandGross primary productivity (GPP)Non-steady-state chambersEcosystem respiration (Reco)urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=0d18aaa9-7c0e-47b7-a2c7-860486798705&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:v4_lbg_2014_mc_plant_height_data_tableManual closed-chamber measurements are commonly used to quantify annual net CO2 ecosystem exchange (NEE) in a wide range of terrestrial ecosystems. However, differences in both the acquisition and gap filling of manual closed-chamber data are large in the existing literature, complicating inter-study comparisons and meta analyses. This data set contains data of a study which compares common approaches for quantifying CO2 exchange at three methodological levels. (i) The first level included two different CO2 flux measurement methods: one via measurements during mid-day applying net coverages (mid-day approach) and one via measurements from sunrise to noon (sunrise approach) to capture a span of light conditions for measurements of NEE with transparent chambers. (ii) The second level included three different methods of pooling measured ecosystem respiration (RECO) fluxes for empirical modeling of RECO: campaign-wise (single-measurement-day RECO models), season-wise (one RECO model for the entire study period), and cluster-wise (two RECO models representing a low and a high vegetation status). (iii) The third level included two different methods of deriving fluxes of gross primary production (GPP): by subtracting either proximately measured RECO fluxes (direct GPP modeling) or empirically modeled RECO fluxes from measured NEE fluxes (indirect GPP modeling). Measurements were made during 2013-2014 in a lucerne-clover-grass field in NE Germany. Besides modelled half-hourly RECO, NEE and GPP, measured CO2-fluxes, plant height and C content of aboveground biomass as well as weather conditions are given.Carbon dioxidefeaturesv4_lbg_2014_mc_plant_height_data_tableNet ecosystem exchange (NEE)GrasslandGross primary productivity (GPP)Non-steady-state chambersEcosystem respiration (Reco)urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=2fc998af-c136-4dc1-8d40-d3d13ff3aa76&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:v4_lbg_2014_mc_weather_data_data_tableManual closed-chamber measurements are commonly used to quantify annual net CO2 ecosystem exchange (NEE) in a wide range of terrestrial ecosystems. However, differences in both the acquisition and gap filling of manual closed-chamber data are large in the existing literature, complicating inter-study comparisons and meta analyses. This data set contains data of a study which compares common approaches for quantifying CO2 exchange at three methodological levels. (i) The first level included two different CO2 flux measurement methods: one via measurements during mid-day applying net coverages (mid-day approach) and one via measurements from sunrise to noon (sunrise approach) to capture a span of light conditions for measurements of NEE with transparent chambers. (ii) The second level included three different methods of pooling measured ecosystem respiration (RECO) fluxes for empirical modeling of RECO: campaign-wise (single-measurement-day RECO models), season-wise (one RECO model for the entire study period), and cluster-wise (two RECO models representing a low and a high vegetation status). (iii) The third level included two different methods of deriving fluxes of gross primary production (GPP): by subtracting either proximately measured RECO fluxes (direct GPP modeling) or empirically modeled RECO fluxes from measured NEE fluxes (indirect GPP modeling). Measurements were made during 2013-2014 in a lucerne-clover-grass field in NE Germany. Besides modelled half-hourly RECO, NEE and GPP, measured CO2-fluxes, plant height and C content of aboveground biomass as well as weather conditions are given.Carbon dioxidefeaturesNet ecosystem exchange (NEE)Grasslandv4_lbg_2014_mc_weather_data_data_tableGross primary productivity (GPP)Non-steady-state chambersEcosystem respiration (Reco)urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=b3009a6e-bf07-4cdd-a73f-9f0e2b676a0c&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:coordinates_magimThe dynamics of dust emission and deposition in the grasslands of Inner Mongolia were investigated during two measuring campaigns in spring months 2005 and 2006 in grazed and un-grazed plots in Inner Mongolia grassland. # Both processes are determined by the grazing intensity, whereas dust deposition rates are modified additionally by the topography. Because grazing intensity of the previous year influences the height and density of standing death vegetation, it could therefore be measured through the surface roughness length (z0). Fine dust concentrations and wind velocity were measured during dust storm events.featuresPM10MAGIMFine dust emissionsDust stormsInner Mongoliacoordinates_magimurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=ceb5bd74-3077-472b-97a3-4932265f39df&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:dust_depositionThe dynamics of dust emission and deposition in the grasslands of Inner Mongolia were investigated during two measuring campaigns in spring months 2005 and 2006 in grazed and un-grazed plots in Inner Mongolia grassland. # Both processes are determined by the grazing intensity, whereas dust deposition rates are modified additionally by the topography. Because grazing intensity of the previous year influences the height and density of standing death vegetation, it could therefore be measured through the surface roughness length (z0). Fine dust concentrations and wind velocity were measured during dust storm events.featuresPM10MAGIMFine dust emissionsdust_depositionDust stormsInner Mongoliaurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=bb473ce6-fa75-485e-af61-f6863ed4aa16&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:dust_transportThe dynamics of dust emission and deposition in the grasslands of Inner Mongolia were investigated during two measuring campaigns in spring months 2005 and 2006 in grazed and un-grazed plots in Inner Mongolia grassland. # Both processes are determined by the grazing intensity, whereas dust deposition rates are modified additionally by the topography. Because grazing intensity of the previous year influences the height and density of standing death vegetation, it could therefore be measured through the surface roughness length (z0). Fine dust concentrations and wind velocity were measured during dust storm events.featuresdust_transportPM10MAGIMFine dust emissionsDust stormsInner Mongoliaurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=528a6635-5070-487d-9ec5-7c25692d1b3d&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:geolocation_767ea6e960abcdb42df960263cc710e1The dynamics of dust emission and deposition in the grasslands of Inner Mongolia were investigated during two measuring campaigns in spring months 2005 and 2006 in grazed and un-grazed plots in Inner Mongolia grassland. # Both processes are determined by the grazing intensity, whereas dust deposition rates are modified additionally by the topography. Because grazing intensity of the previous year influences the height and density of standing death vegetation, it could therefore be measured through the surface roughness length (z0). Fine dust concentrations and wind velocity were measured during dust storm events.geolocation_767ea6e960abcdb42df960263cc710e1featuresPM10MAGIMFine dust emissionsDust stormsInner Mongoliaurn:x-ogc:def:crs:EPSG:4326116.6435340287599 43.54097307681807116.6778892231114 43.57986874775322https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=f2bc5723-5e63-4713-b11c-6e69ac17cb5f&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:particulate_matterThe dynamics of dust emission and deposition in the grasslands of Inner Mongolia were investigated during two measuring campaigns in spring months 2005 and 2006 in grazed and un-grazed plots in Inner Mongolia grassland. # Both processes are determined by the grazing intensity, whereas dust deposition rates are modified additionally by the topography. Because grazing intensity of the previous year influences the height and density of standing death vegetation, it could therefore be measured through the surface roughness length (z0). Fine dust concentrations and wind velocity were measured during dust storm events.particulate_matterfeaturesPM10MAGIMFine dust emissionsDust stormsInner Mongoliaurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=12089b06-3ee6-4eca-8d3f-2ec6afc763f7&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:samples_chemistryThe dynamics of dust emission and deposition in the grasslands of Inner Mongolia were investigated during two measuring campaigns in spring months 2005 and 2006 in grazed and un-grazed plots in Inner Mongolia grassland. # Both processes are determined by the grazing intensity, whereas dust deposition rates are modified additionally by the topography. Because grazing intensity of the previous year influences the height and density of standing death vegetation, it could therefore be measured through the surface roughness length (z0). Fine dust concentrations and wind velocity were measured during dust storm events.featuresPM10MAGIMFine dust emissionssamples_chemistryDust stormsInner Mongoliaurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=af059e6f-f520-4b2c-8073-82d61fa38028&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:textureThe dynamics of dust emission and deposition in the grasslands of Inner Mongolia were investigated during two measuring campaigns in spring months 2005 and 2006 in grazed and un-grazed plots in Inner Mongolia grassland. # Both processes are determined by the grazing intensity, whereas dust deposition rates are modified additionally by the topography. Because grazing intensity of the previous year influences the height and density of standing death vegetation, it could therefore be measured through the surface roughness length (z0). Fine dust concentrations and wind velocity were measured during dust storm events.texturefeaturesPM10MAGIMFine dust emissionsDust stormsInner Mongoliaurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=99736c92-04b1-4d24-9d77-cbb99bc30f7d&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:topographyThe dynamics of dust emission and deposition in the grasslands of Inner Mongolia were investigated during two measuring campaigns in spring months 2005 and 2006 in grazed and un-grazed plots in Inner Mongolia grassland. # Both processes are determined by the grazing intensity, whereas dust deposition rates are modified additionally by the topography. Because grazing intensity of the previous year influences the height and density of standing death vegetation, it could therefore be measured through the surface roughness length (z0). Fine dust concentrations and wind velocity were measured during dust storm events.topographyfeaturesPM10MAGIMFine dust emissionsDust stormsInner Mongoliaurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=a808a761-eb12-45d6-b89b-e22cc32f3766&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:vegetationThe dynamics of dust emission and deposition in the grasslands of Inner Mongolia were investigated during two measuring campaigns in spring months 2005 and 2006 in grazed and un-grazed plots in Inner Mongolia grassland. # Both processes are determined by the grazing intensity, whereas dust deposition rates are modified additionally by the topography. Because grazing intensity of the previous year influences the height and density of standing death vegetation, it could therefore be measured through the surface roughness length (z0). Fine dust concentrations and wind velocity were measured during dust storm events.vegetationfeaturesPM10MAGIMFine dust emissionsDust stormsInner Mongoliaurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=d2059c03-b3e3-45fe-9156-5bd646da37de&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:biodiversity_novelty_ecosystem_functioningWe evaluated the relationship between biodiversity, abiotic and biotic novelty and ecosystem functioning based on in situ measurements in non-manipulated grasslands along an urbanization gradient in Berlin. We measured plant aboveground biomass (AGB), intrinsic water-use efficiency (iWUE) and 15N enrichment factor (δ15N) as proxies for biomass production, water and N cycling, respectively. The measurements were done for the whole plant community, for plants with different introduction status, plants belonging to different functional groups and two single species: Calamagrostis epigejos and Plantago lanceolata. We found that approximately one third of the forbs were alien to Berlin and theymade up around 13% of the whole community aboground biomass. Nonetheless, community aboveground biomass was positively correlated with plant-species richness . In contrast, iWUE and δ15N were mostly correlated to urban parameters associated to abiotic novelty.urban ecologyfeaturesecosystem functioningplant invasionsbiomassecological noveltybiodiversity_novelty_ecosystem_functioninggrasslandbiodiversitywater use efficiencynitrogen cyclingfield experimenturn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=668e2291-d160-4efb-9e0d-6d55eb9680ad&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:data_light_curvesWe evaluated the relationship between biodiversity, abiotic and biotic novelty and ecosystem functioning based on in situ measurements in non-manipulated grasslands along an urbanization gradient in Berlin. We measured plant aboveground biomass (AGB), intrinsic water-use efficiency (iWUE) and 15N enrichment factor (δ15N) as proxies for biomass production, water and N cycling, respectively. The measurements were done for the whole plant community, for plants with different introduction status, plants belonging to different functional groups and two single species: Calamagrostis epigejos and Plantago lanceolata. We found that approximately one third of the forbs were alien to Berlin and theymade up around 13% of the whole community aboground biomass. Nonetheless, community aboveground biomass was positively correlated with plant-species richness . In contrast, iWUE and δ15N were mostly correlated to urban parameters associated to abiotic novelty.urban ecologyfeaturesecosystem functioningplant invasionsbiomassecological noveltygrasslandbiodiversitywater use efficiencynitrogen cyclingdata_light_curvesfield experimenturn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=ab3b65a3-b74f-4f70-b1b4-aa8948650af3&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:geolocation_0f6e540c6f946238a0c556617c617b32We evaluated the relationship between biodiversity, abiotic and biotic novelty and ecosystem functioning based on in situ measurements in non-manipulated grasslands along an urbanization gradient in Berlin. We measured plant aboveground biomass (AGB), intrinsic water-use efficiency (iWUE) and 15N enrichment factor (δ15N) as proxies for biomass production, water and N cycling, respectively. The measurements were done for the whole plant community, for plants with different introduction status, plants belonging to different functional groups and two single species: Calamagrostis epigejos and Plantago lanceolata. We found that approximately one third of the forbs were alien to Berlin and theymade up around 13% of the whole community aboground biomass. Nonetheless, community aboveground biomass was positively correlated with plant-species richness . In contrast, iWUE and δ15N were mostly correlated to urban parameters associated to abiotic novelty.urban ecologyfeaturesecosystem functioningplant invasionsbiomassgeolocation_0f6e540c6f946238a0c556617c617b32ecological noveltygrasslandbiodiversitywater use efficiencynitrogen cyclingfield experimenturn:x-ogc:def:crs:EPSG:432613.183282 52.4059721247829113.589183 52.58677900000001https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=72437e9f-1e0c-4705-a9c2-7567bc4e65f4&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:dataset_ecosystem_services_mol_bbThe dataset contains six standardized (0-1) indicators of ecosystem services provision for the utilized agricultural area of the Märkisch-Oderland District (NUTS3) in east Brandenburg, Germany. The six ecosystem services are i) habitat for species (HAB), ii) carbon stock total (CST), iii) carbon stock potential (CSP), iv) biomass production (PRO), v) landscape attractiveness (LAT), and vi) water storage (WAS). The data set has 140,116 entries, each one corresponding to a 1 ha size cell (100 m x 100 m), whose centroid coordinates are provided according to the EPSG:4839 - ETRS89/LCC Germany (N-E) – Projected coordinate system for Germany. Each indicator value is standardized as number in the range of 0 to 1. The maximum value observed in the study area is then set equal to 1, and the value 0 indicates the relative minimum in the area considered. For each entry, the dataset provides information about landscape unit, dominant land cover, dominant soil and cadastral parcel (Digitales Feldblockkataster des Landes Brandenburg 2020, DFBK20/BB).featuresConflict managementSpatial analysisdataset_ecosystem_services_mol_bburn:x-ogc:def:crs:EPSG:432613.611291791000042 52.3752128220000414.631667718000074 52.87007475200005https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=5e334dda-a148-4aa4-afa2-540e12c86130&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:ch4_flux_rawfeaturesfarming techniqueorganic farmingch4_flux_rawagricultural productionmixed farmingarable farmingclimate protectionurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=0e3365ec-4b83-4817-8465-69efa3a0cd33&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:co2_flux_raworganic farmingarable farmingagricultural productionco2_flux_rawmixed farmingfeaturesclimate protectionfarming techniqueurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=6d51685d-ec89-4f43-ad80-0e5c76786116&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:n2o_flux_rawfeaturesfarming techniqueorganic farmingagricultural productionn2o_flux_rawmixed farmingarable farmingclimate protectionurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=c1338066-13d2-43d4-bae6-1314d4c6b888&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:ch4_flux_gapfilledorganic farmingarable farmingch4_flux_gapfilledagricultural productionmixed farmingfeaturesclimate protectionfarming techniqueurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=11ef437f-7b37-454a-9108-fb17766a72f7&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:co2_flux_gapfilledfeaturesco2_flux_gapfilledfarming techniqueorganic farmingagricultural productionmixed farmingarable farmingclimate protectionurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=cb56d164-0fb0-46d7-a931-5b1205221966&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:dk_81_preperiodThe dataset contains measurment data from a weighable groundwater lysimeter in the Spreewald wetland. Water budget values and water levels were selected for 29 rainfall events > 10 mm between May 2010 and September 2012. The parameters were evaluated for different points of time and periods after and before the rainfall events. Special focus was set on water storage characteristics of the thin unsatureted zone of the shallow water table site. The data shows the effect of different assumptions (equilibrium conditions vs. dynamic conditions) on the estimated exhaustion of the unsaturated zone.featuresdk_81_preperiodurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=a694ad77-3894-4181-8924-04c856454182&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:dk_81_rawdataThe dataset contains measurment data from a weighable groundwater lysimeter in the Spreewald wetland. Water budget values and water levels were selected for 29 rainfall events > 10 mm between May 2010 and September 2012. The parameters were evaluated for different points of time and periods after and before the rainfall events. Special focus was set on water storage characteristics of the thin unsatureted zone of the shallow water table site. The data shows the effect of different assumptions (equilibrium conditions vs. dynamic conditions) on the estimated exhaustion of the unsaturated zone.featuresdk_81_rawdataurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=55098c58-289f-417e-a396-4995138dca2d&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:dk_81_resultsThe dataset contains measurment data from a weighable groundwater lysimeter in the Spreewald wetland. Water budget values and water levels were selected for 29 rainfall events > 10 mm between May 2010 and September 2012. The parameters were evaluated for different points of time and periods after and before the rainfall events. Special focus was set on water storage characteristics of the thin unsatureted zone of the shallow water table site. The data shows the effect of different assumptions (equilibrium conditions vs. dynamic conditions) on the estimated exhaustion of the unsaturated zone.featuresdk_81_resultsurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=c650e2cb-4c33-40f4-a8a8-2682ea7b96be&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:dk_81_timevalue_preperiodThe dataset contains measurment data from a weighable groundwater lysimeter in the Spreewald wetland. Water budget values and water levels were selected for 29 rainfall events > 10 mm between May 2010 and September 2012. The parameters were evaluated for different points of time and periods after and before the rainfall events. Special focus was set on water storage characteristics of the thin unsatureted zone of the shallow water table site. The data shows the effect of different assumptions (equilibrium conditions vs. dynamic conditions) on the estimated exhaustion of the unsaturated zone.dk_81_timevalue_preperiodfeaturesurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=0eb0c6de-9ac9-4c2a-b0df-ba8e809043c4&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:dk_81_timevaluesThe dataset contains measurment data from a weighable groundwater lysimeter in the Spreewald wetland. Water budget values and water levels were selected for 29 rainfall events > 10 mm between May 2010 and September 2012. The parameters were evaluated for different points of time and periods after and before the rainfall events. Special focus was set on water storage characteristics of the thin unsatureted zone of the shallow water table site. The data shows the effect of different assumptions (equilibrium conditions vs. dynamic conditions) on the estimated exhaustion of the unsaturated zone.featuresdk_81_timevaluesurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=39be55b3-a51c-45e1-8082-390dc7d7363a&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:geolocation_66cf61e201b790dd0c58e2a04de24adbThe dataset contains measurment data from a weighable groundwater lysimeter in the Spreewald wetland. Water budget values and water levels were selected for 29 rainfall events > 10 mm between May 2010 and September 2012. The parameters were evaluated for different points of time and periods after and before the rainfall events. Special focus was set on water storage characteristics of the thin unsatureted zone of the shallow water table site. The data shows the effect of different assumptions (equilibrium conditions vs. dynamic conditions) on the estimated exhaustion of the unsaturated zone.featuresgeolocation_66cf61e201b790dd0c58e2a04de24adburn:x-ogc:def:crs:EPSG:432614.0401328908437 51.8789840940346114.0403328908437 51.87918409403461https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=2a83d228-d7da-4344-9bd0-2cad4b6d5d5f&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:n2o_flux_gapfilledorganic farmingarable farmingn2o_flux_gapfilledagricultural productionmixed farmingfeaturesclimate protectionfarming techniqueurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=1049edc7-4657-48ba-8409-6884635f8ad1&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:witzenhausen_125a719b0f5eea1519b327ed1ffffa3bfeatureswitzenhausen_125a719b0f5eea1519b327ed1ffffa3burn:x-ogc:def:crs:EPSG:43269.864344444000071 51.345733333000039.864544444000071 51.34593333300003https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=07d9e4a3-160d-481c-b54c-17eabcff8b6d&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:gwcThis dataset contains measurements of the initial gravimetric water content of substrate materials collected from Frankfurter Entsorgungs- und Service GmbH (FES), Frankfurt, Germany; and Gesellschaft für die Aufbereitung und Verwertung von Reststoffen mbH (GFR), Würzburg, Germany. The substrates consisted of municipal solid waste incineration (MSWI) bottom ash and coal combustion residues. The materials, experimental design, and analytical methods are thoroughly described by Bilibio and Hensel (2023, Doi: https://doi.org/10.3389/fenvs.2023.1144043).featuresgwcurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=338b203e-2908-49f4-be86-33471530b2ab&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:ph_ecThis dataset contains measurements of pH and electrical conductivity (EC) for individual substrates and a substrate mixture composed of municipal solid waste incineration (MSWI) bottom ash and coal combustion residues. The materials, experimental design, and analytical methods are comprehensively described by Bilibio and Hensel (2023, Doi: https://doi.org/10.3389/fenvs.2023.1144043).featuresph_ecurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=1c5a2960-4ecb-459d-9d4a-c13f7606c0d8&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:tp_bdfeaturestp_bdurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=aeb470f8-c717-4593-8f6f-a332ad560176&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:volume_reductionThis dataset contains measurements of the volume reduction of the substrates. Volume reduction was determined by placing each substrate in a 1-L graduated measuring jug and subjecting it to 100 shaking cycles. The materials, experimental design, and analytical methods are thoroughly described by Bilibio and Hensel (2023, Doi: https://doi.org/10.3389/fenvs.2023.1144043).featuresvolume_reductionurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=eeb51b65-e0c1-4448-8d3d-6ced3dffdb9e&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:water_balanceThis dataset contains measurements of precipitation input and leachate, collected during a column leaching experiment. In addition, the pH and electrical conductivity (EC) of the leachate were measured and are included in the dataset. The materials, experimental design, and analytical methods are thoroughly described by Bilibio and Hensel (2023, Doi: https://doi.org/10.3389/fenvs.2023.1144043).featureswater_balanceurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=d1903855-38f9-46c4-81dc-b05ffd8c2e8d&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:bird_survey_1999_2002Farmland birds are used as an indicator for the overall biodiversity on agricultural lands summarizing the effects over the whole food pyramide. The composition, frequency and diversity of the bird communities at 117 sample points, representative for a whole landscape have been monitored over a periode of 4 years in 1999-2002. The monitoring was carried out over 5 month per year according to the stop-count-method, differntiated between two distances from the counting point.featuresbird_survey_1999_2002urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=9e6f03da-e325-4304-ab55-c93deb88ea7a&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:geolocation_a1f2a89115c3c894f4326a4cfa53d7dfFarmland birds are used as an indicator for the overall biodiversity on agricultural lands summarizing the effects over the whole food pyramide. The composition, frequency and diversity of the bird communities at 117 sample points, representative for a whole landscape have been monitored over a periode of 4 years in 1999-2002. The monitoring was carried out over 5 month per year according to the stop-count-method, differntiated between two distances from the counting point.featuresgeolocation_a1f2a89115c3c894f4326a4cfa53d7dfurn:x-ogc:def:crs:EPSG:432613.47096725586944 53.2944389887517413.85511279201188 53.42902016292231https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=49ea0882-3a7c-4edb-a32d-40684443bd97&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:bird_survey_2013_2015Farmland birds are used as indicator for the overall biodiversity on agricultural lands summarizing the effects over the whole food pyramide. The composition, frequency and diversity of the bird communities at 117 sample points, representative for a whole landscape have been monitored over a periode of 3 years in 2013-2015. The survey is a repication of the same survey proceed in 1999-2002. The monitoring was carried out over 5 month per year according to the stop-count-method, differntiated between two distances from the counting point.featuresbird_survey_2013_2015urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=e994bb16-0dd1-44a6-afad-2580f31b76db&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:geolocation_739c3fe9eaa1ac5d38ed812cbd066e3cFarmland birds are used as indicator for the overall biodiversity on agricultural lands summarizing the effects over the whole food pyramide. The composition, frequency and diversity of the bird communities at 117 sample points, representative for a whole landscape have been monitored over a periode of 3 years in 2013-2015. The survey is a repication of the same survey proceed in 1999-2002. The monitoring was carried out over 5 month per year according to the stop-count-method, differntiated between two distances from the counting point.featuresgeolocation_739c3fe9eaa1ac5d38ed812cbd066e3curn:x-ogc:def:crs:EPSG:432613.47096725586944 53.2944389887517413.85511279201188 53.42902016292231https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=5892390e-c81f-4bc1-908b-778c5511433b&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:geolocation_400bb9a637a6d6fad181b3f111cc813eFor the experimental site Hohenfinow (52.8115 N, 13.9266 E, altitude: 55 m), Germany, a data set of a six-field crop rotation (silage maize, winter rye, winter oilseed rape, winter barley, winter wheat, potatoes) with a field size of about 0.9 ha each is described in detail for the years 1992-1997. The Leibniz-Centre for Agricultural Landscape Research Müncheberg was responsible for carrying out the experiment and collecting all the data. The aim of the experiment was to investigate the dynamics and balances of the water and matter cycles on a deluvial site (soil type: Sandtieflehmfahlerde-Braunerde according KA5 (Albic Luvisol according WRB); site type: D3a; dry bulk density (g cm-3): 1. 48 (0-30 cm), 1.50 (30-60 cm), 1.51 (60-90 cm); field capacity (Vol%): 39.5 (0-30 cm), 39.3 (30-60 cm), 39.2 (60-90 cm); soil quality index: 36) in North-East Germany as a basis for the validation of agro-ecosystem models. Due to the location of the experimental station in the moraine landscape and the glacier history of its formation, the deposition with large stones is relatively high. Hohenfinow is located in the transition zone between maritime and continental climate. The average annual precipitation sum in 1951-1980 is 547 mm, the average annual mean temperature in the period 1951-1980 is 8.16 °C and the vegetation start in average is the 27 March. The weather data on the Experimental Station were continuously recorded using an automatic meteorological station. The agro-technical measures were managed in accordance with the recommendations of the "Computer-supported Advisory System for Agro Management (COBB". The experiment was carried out without irrigation. The agro-technical measures were recorded completely. In the experiment, soil and crop data were collected several times per vegetation period or year at short intervals. The data set presented here contains various data on soil (water, nitrogen (nitrate, ammonium) - at three different depths up to 90 cm), on crop (ontogenesis, plant, ear and tiller numbers, plant height, above-ground, root and yield biomasses, carbon, nitrogen, phosphorus, potassium and calcium contents in different biomass fractions), on daily weather (meteorological standard values) and on management measures (soil tillage, sowing, fertilisation, plant protection, harvest). The measured soil and plant values basis on six replicates. All measuring and bonitur methods used are briefly described.crop modellingexperimental stationwinter oilseed rapesilage maizeontogenesismodelfield experimentyieldwinter wheatwinter barleysoil waterclimate datageolocation_400bb9a637a6d6fad181b3f111cc813ebiomasssoil nitrogenagro-ecosystemwinter ryefeaturespotatoesvalidationurn:x-ogc:def:crs:EPSG:432613.9287358753 52.809619628213.931486135 52.8122475718https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=587bcbab-3cf5-4081-86f7-4a20e8c95cba&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:hohe_cropFor the experimental site Hohenfinow (52.8115 N, 13.9266 E, altitude: 55 m), Germany, a data set of a six-field crop rotation (silage maize, winter rye, winter oilseed rape, winter barley, winter wheat, potatoes) with a field size of about 0.9 ha each is described in detail for the years 1992-1997. The Leibniz-Centre for Agricultural Landscape Research Müncheberg was responsible for carrying out the experiment and collecting all the data. The aim of the experiment was to investigate the dynamics and balances of the water and matter cycles on a deluvial site (soil type: Sandtieflehmfahlerde-Braunerde according KA5 (Albic Luvisol according WRB); site type: D3a; dry bulk density (g cm-3): 1. 48 (0-30 cm), 1.50 (30-60 cm), 1.51 (60-90 cm); field capacity (Vol%): 39.5 (0-30 cm), 39.3 (30-60 cm), 39.2 (60-90 cm); soil quality index: 36) in North-East Germany as a basis for the validation of agro-ecosystem models. Due to the location of the experimental station in the moraine landscape and the glacier history of its formation, the deposition with large stones is relatively high. Hohenfinow is located in the transition zone between maritime and continental climate. The average annual precipitation sum in 1951-1980 is 547 mm, the average annual mean temperature in the period 1951-1980 is 8.16 °C and the vegetation start in average is the 27 March. The weather data on the Experimental Station were continuously recorded using an automatic meteorological station. The agro-technical measures were managed in accordance with the recommendations of the "Computer-supported Advisory System for Agro Management (COBB". The experiment was carried out without irrigation. The agro-technical measures were recorded completely. In the experiment, soil and crop data were collected several times per vegetation period or year at short intervals. The data set presented here contains various data on soil (water, nitrogen (nitrate, ammonium) - at three different depths up to 90 cm), on crop (ontogenesis, plant, ear and tiller numbers, plant height, above-ground, root and yield biomasses, carbon, nitrogen, phosphorus, potassium and calcium contents in different biomass fractions), on daily weather (meteorological standard values) and on management measures (soil tillage, sowing, fertilisation, plant protection, harvest). The measured soil and plant values basis on six replicates. All measuring and bonitur methods used are briefly described.crop modellingexperimental stationsilage maizewinter oilseed rapeontogenesisfield experimentmodelyieldwinter wheatwinter barleysoil waterclimate datahohe_cropbiomasssoil nitrogenagro-ecosystemwinter ryefeaturespotatoesvalidationurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=c16f230b-b000-46ed-9699-7e1f3cb5ef3d&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:hohe_crop_contentsFor the experimental site Hohenfinow (52.8115 N, 13.9266 E, altitude: 55 m), Germany, a data set of a six-field crop rotation (silage maize, winter rye, winter oilseed rape, winter barley, winter wheat, potatoes) with a field size of about 0.9 ha each is described in detail for the years 1992-1997. The Leibniz-Centre for Agricultural Landscape Research Müncheberg was responsible for carrying out the experiment and collecting all the data. The aim of the experiment was to investigate the dynamics and balances of the water and matter cycles on a deluvial site (soil type: Sandtieflehmfahlerde-Braunerde according KA5 (Albic Luvisol according WRB); site type: D3a; dry bulk density (g cm-3): 1. 48 (0-30 cm), 1.50 (30-60 cm), 1.51 (60-90 cm); field capacity (Vol%): 39.5 (0-30 cm), 39.3 (30-60 cm), 39.2 (60-90 cm); soil quality index: 36) in North-East Germany as a basis for the validation of agro-ecosystem models. Due to the location of the experimental station in the moraine landscape and the glacier history of its formation, the deposition with large stones is relatively high. Hohenfinow is located in the transition zone between maritime and continental climate. The average annual precipitation sum in 1951-1980 is 547 mm, the average annual mean temperature in the period 1951-1980 is 8.16 °C and the vegetation start in average is the 27 March. The weather data on the Experimental Station were continuously recorded using an automatic meteorological station. The agro-technical measures were managed in accordance with the recommendations of the "Computer-supported Advisory System for Agro Management (COBB". The experiment was carried out without irrigation. The agro-technical measures were recorded completely. In the experiment, soil and crop data were collected several times per vegetation period or year at short intervals. The data set presented here contains various data on soil (water, nitrogen (nitrate, ammonium) - at three different depths up to 90 cm), on crop (ontogenesis, plant, ear and tiller numbers, plant height, above-ground, root and yield biomasses, carbon, nitrogen, phosphorus, potassium and calcium contents in different biomass fractions), on daily weather (meteorological standard values) and on management measures (soil tillage, sowing, fertilisation, plant protection, harvest). The measured soil and plant values basis on six replicates. All measuring and bonitur methods used are briefly described.crop modellinghohe_crop_contentsexperimental stationsilage maizewinter oilseed rapeontogenesisfield experimentmodelyieldwinter wheatwinter barleysoil waterclimate databiomasssoil nitrogenagro-ecosystemwinter ryefeaturespotatoesvalidationurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=4616ad7a-8d36-43b6-b520-4efd02e13aef&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:hohe_managementFor the experimental site Hohenfinow (52.8115 N, 13.9266 E, altitude: 55 m), Germany, a data set of a six-field crop rotation (silage maize, winter rye, winter oilseed rape, winter barley, winter wheat, potatoes) with a field size of about 0.9 ha each is described in detail for the years 1992-1997. The Leibniz-Centre for Agricultural Landscape Research Müncheberg was responsible for carrying out the experiment and collecting all the data. The aim of the experiment was to investigate the dynamics and balances of the water and matter cycles on a deluvial site (soil type: Sandtieflehmfahlerde-Braunerde according KA5 (Albic Luvisol according WRB); site type: D3a; dry bulk density (g cm-3): 1. 48 (0-30 cm), 1.50 (30-60 cm), 1.51 (60-90 cm); field capacity (Vol%): 39.5 (0-30 cm), 39.3 (30-60 cm), 39.2 (60-90 cm); soil quality index: 36) in North-East Germany as a basis for the validation of agro-ecosystem models. Due to the location of the experimental station in the moraine landscape and the glacier history of its formation, the deposition with large stones is relatively high. Hohenfinow is located in the transition zone between maritime and continental climate. The average annual precipitation sum in 1951-1980 is 547 mm, the average annual mean temperature in the period 1951-1980 is 8.16 °C and the vegetation start in average is the 27 March. The weather data on the Experimental Station were continuously recorded using an automatic meteorological station. The agro-technical measures were managed in accordance with the recommendations of the "Computer-supported Advisory System for Agro Management (COBB". The experiment was carried out without irrigation. The agro-technical measures were recorded completely. In the experiment, soil and crop data were collected several times per vegetation period or year at short intervals. The data set presented here contains various data on soil (water, nitrogen (nitrate, ammonium) - at three different depths up to 90 cm), on crop (ontogenesis, plant, ear and tiller numbers, plant height, above-ground, root and yield biomasses, carbon, nitrogen, phosphorus, potassium and calcium contents in different biomass fractions), on daily weather (meteorological standard values) and on management measures (soil tillage, sowing, fertilisation, plant protection, harvest). The measured soil and plant values basis on six replicates. All measuring and bonitur methods used are briefly described.crop modellingexperimental stationsilage maizewinter oilseed rapeontogenesisfield experimentmodelyieldwinter wheatwinter barleysoil waterclimate databiomasssoil nitrogenagro-ecosystemhohe_managementwinter ryefeaturespotatoesvalidationurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=0f0943d6-80d4-4e1a-9ddc-a4d216ced00a&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:hohe_soil_h2o_nFor the experimental site Hohenfinow (52.8115 N, 13.9266 E, altitude: 55 m), Germany, a data set of a six-field crop rotation (silage maize, winter rye, winter oilseed rape, winter barley, winter wheat, potatoes) with a field size of about 0.9 ha each is described in detail for the years 1992-1997. The Leibniz-Centre for Agricultural Landscape Research Müncheberg was responsible for carrying out the experiment and collecting all the data. The aim of the experiment was to investigate the dynamics and balances of the water and matter cycles on a deluvial site (soil type: Sandtieflehmfahlerde-Braunerde according KA5 (Albic Luvisol according WRB); site type: D3a; dry bulk density (g cm-3): 1. 48 (0-30 cm), 1.50 (30-60 cm), 1.51 (60-90 cm); field capacity (Vol%): 39.5 (0-30 cm), 39.3 (30-60 cm), 39.2 (60-90 cm); soil quality index: 36) in North-East Germany as a basis for the validation of agro-ecosystem models. Due to the location of the experimental station in the moraine landscape and the glacier history of its formation, the deposition with large stones is relatively high. Hohenfinow is located in the transition zone between maritime and continental climate. The average annual precipitation sum in 1951-1980 is 547 mm, the average annual mean temperature in the period 1951-1980 is 8.16 °C and the vegetation start in average is the 27 March. The weather data on the Experimental Station were continuously recorded using an automatic meteorological station. The agro-technical measures were managed in accordance with the recommendations of the "Computer-supported Advisory System for Agro Management (COBB". The experiment was carried out without irrigation. The agro-technical measures were recorded completely. In the experiment, soil and crop data were collected several times per vegetation period or year at short intervals. The data set presented here contains various data on soil (water, nitrogen (nitrate, ammonium) - at three different depths up to 90 cm), on crop (ontogenesis, plant, ear and tiller numbers, plant height, above-ground, root and yield biomasses, carbon, nitrogen, phosphorus, potassium and calcium contents in different biomass fractions), on daily weather (meteorological standard values) and on management measures (soil tillage, sowing, fertilisation, plant protection, harvest). The measured soil and plant values basis on six replicates. All measuring and bonitur methods used are briefly described.crop modellingexperimental stationsilage maizewinter oilseed rapeontogenesisfield experimentmodelyieldhohe_soil_h2o_nwinter barleywinter wheatsoil waterclimate databiomasssoil nitrogenagro-ecosystemwinter ryefeaturespotatoesvalidationurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=6a8b54b5-d97e-4e0c-b2df-c3aeb24b0405&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:foraging_strategies_studyWe tracked weaned, young-of-the-year Nyctalus noctula with Vesper GPS loggers (A.S.D., Israel) including ultrasonic microphones. Tracked animals stemmed from colonies with artificial roosting boxes in a forest remnant near Falkenhagen / Uckermark / Germany and a pine stand near Prieros / Germany. Bats were removed from their artificial roosting boxes in the morning, loggers were attached with Sauer Hautkleber, and bats were soon replaced into their roosting boxes. Tracking took place in Falkenhagen in May 2016 and May 2018, in Prieros in May 2017 and May 2018. Loggers recorded GPS positions every 31 seconds, and ultrasound recordings every 10 seconds for the duration of 1.5 seconds. We retrieved useful data from 27 animals. The data was analysed with respect to space use, movement behavior, foraging activity, and habitat use.landscape competition (biological)movement ecologytrackingfeaturesbatsforaging_strategies_studycoexistencearea restrictedeavesdroppingurn:x-ogc:def:crs:EPSG:432613.6657595 51.9258114.129693 53.4237165https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=cf745ac0-fffa-4758-bb80-7b851927359c&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:site_neu_eichenberg_research_farmorganic farmingarable farmingagricultural productionmixed farmingfeaturessite_neu_eichenberg_research_farmclimate protectionfarming techniqueurn:x-ogc:def:crs:EPSG:43269.902007041929153 51.375156207929489.915742311798056 51.38174005629628https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=5fc07fef-fb18-4885-b97d-f9406ba7d04e&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:2009_361_eca_25_gn01_rawSoilscapes of the post-glacial morainic regions of the youngest glaciation are characterized by small hydrological kettle hole catchments forming hummocky soil landscapes. The spatial heterogeneity of subsurface structures as well as erosion-controlled pedogenesis under arable land use may complicate hydrological modeling. Our aim was to generate a soil landscape model for a small representative kettle hole catchment based on geoelectrical exploration and soil profile information. For a 1-ha catchment located in the northeastern German lowlands near the town of Prenzlau, electrical resistivity transects were determined by a multi electrode system (IMPETUS 12 Fs) and electrical conductivity (ECa) was mapped by using the electromagnetic induction (EMI) device EM38DD in both the vertical and horizontal modes. The 1-m digital elevation model (DEM) was obtained by kriging from high resolution manual elevation data determined with a leveling device (ZEISS Ni 40). Soil profile data from 26 boreholes distributed radially around the central pond were used to identify boundaries between soil horizons. The soil is characterized by varying topography and morphology of diagnostic horizons such as M- (colluvium), Bt- (clay illuviation), and C- (parent glacial till). By EMI mapping we identified (i) the boundary between erosive and colluvial areas around the kettle hole, and modeled (ii) the subsurface morphology of loamy horizons. Electrical resistivity tomography results coincide with these findings and allow for distinguishing between sandy and loamy dominated areas both in vertical and horizontal direction, respectively. This soil model of soil textural properties could be used for hydrological modeling.2009_361_eca_25_gn01_rawElectrical resistivity tomographyfeaturesElectrical resistivityElectromagnetic inductionurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=aaf2d724-2ac5-439d-a7b7-391de6d47534&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:2009_361_eca_khraw_so_gruenowSoilscapes of the post-glacial morainic regions of the youngest glaciation are characterized by small hydrological kettle hole catchments forming hummocky soil landscapes. The spatial heterogeneity of subsurface structures as well as erosion-controlled pedogenesis under arable land use may complicate hydrological modeling. Our aim was to generate a soil landscape model for a small representative kettle hole catchment based on geoelectrical exploration and soil profile information. For a 1-ha catchment located in the northeastern German lowlands near the town of Prenzlau, electrical resistivity transects were determined by a multi electrode system (IMPETUS 12 Fs) and electrical conductivity (ECa) was mapped by using the electromagnetic induction (EMI) device EM38DD in both the vertical and horizontal modes. The 1-m digital elevation model (DEM) was obtained by kriging from high resolution manual elevation data determined with a leveling device (ZEISS Ni 40). Soil profile data from 26 boreholes distributed radially around the central pond were used to identify boundaries between soil horizons. The soil is characterized by varying topography and morphology of diagnostic horizons such as M- (colluvium), Bt- (clay illuviation), and C- (parent glacial till). By EMI mapping we identified (i) the boundary between erosive and colluvial areas around the kettle hole, and modeled (ii) the subsurface morphology of loamy horizons. Electrical resistivity tomography results coincide with these findings and allow for distinguishing between sandy and loamy dominated areas both in vertical and horizontal direction, respectively. This soil model of soil textural properties could be used for hydrological modeling.2009_361_eca_khraw_so_gruenowElectrical resistivity tomographyfeaturesElectrical resistivityElectromagnetic inductionurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=29264d89-0a82-4d87-92b3-8fdb605da57d&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:2009_361_ert_2008Soilscapes of the post-glacial morainic regions of the youngest glaciation are characterized by small hydrological kettle hole catchments forming hummocky soil landscapes. The spatial heterogeneity of subsurface structures as well as erosion-controlled pedogenesis under arable land use may complicate hydrological modeling. Our aim was to generate a soil landscape model for a small representative kettle hole catchment based on geoelectrical exploration and soil profile information. For a 1-ha catchment located in the northeastern German lowlands near the town of Prenzlau, electrical resistivity transects were determined by a multi electrode system (IMPETUS 12 Fs) and electrical conductivity (ECa) was mapped by using the electromagnetic induction (EMI) device EM38DD in both the vertical and horizontal modes. The 1-m digital elevation model (DEM) was obtained by kriging from high resolution manual elevation data determined with a leveling device (ZEISS Ni 40). Soil profile data from 26 boreholes distributed radially around the central pond were used to identify boundaries between soil horizons. The soil is characterized by varying topography and morphology of diagnostic horizons such as M- (colluvium), Bt- (clay illuviation), and C- (parent glacial till). By EMI mapping we identified (i) the boundary between erosive and colluvial areas around the kettle hole, and modeled (ii) the subsurface morphology of loamy horizons. Electrical resistivity tomography results coincide with these findings and allow for distinguishing between sandy and loamy dominated areas both in vertical and horizontal direction, respectively. This soil model of soil textural properties could be used for hydrological modeling.Electrical resistivity tomographyfeaturesElectrical resistivityElectromagnetic induction2009_361_ert_2008urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=e44ce80a-6ffd-437c-8482-65a319756bca&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:2009_361_ert_2009Soilscapes of the post-glacial morainic regions of the youngest glaciation are characterized by small hydrological kettle hole catchments forming hummocky soil landscapes. The spatial heterogeneity of subsurface structures as well as erosion-controlled pedogenesis under arable land use may complicate hydrological modeling. Our aim was to generate a soil landscape model for a small representative kettle hole catchment based on geoelectrical exploration and soil profile information. For a 1-ha catchment located in the northeastern German lowlands near the town of Prenzlau, electrical resistivity transects were determined by a multi electrode system (IMPETUS 12 Fs) and electrical conductivity (ECa) was mapped by using the electromagnetic induction (EMI) device EM38DD in both the vertical and horizontal modes. The 1-m digital elevation model (DEM) was obtained by kriging from high resolution manual elevation data determined with a leveling device (ZEISS Ni 40). Soil profile data from 26 boreholes distributed radially around the central pond were used to identify boundaries between soil horizons. The soil is characterized by varying topography and morphology of diagnostic horizons such as M- (colluvium), Bt- (clay illuviation), and C- (parent glacial till). By EMI mapping we identified (i) the boundary between erosive and colluvial areas around the kettle hole, and modeled (ii) the subsurface morphology of loamy horizons. Electrical resistivity tomography results coincide with these findings and allow for distinguishing between sandy and loamy dominated areas both in vertical and horizontal direction, respectively. This soil model of soil textural properties could be used for hydrological modeling.Electrical resistivity tomographyfeaturesElectrical resistivityElectromagnetic induction2009_361_ert_2009urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=58bee435-1e22-45c2-9315-1fff3f00ce88&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:2009_361_gruenow_niv_pkteSoilscapes of the post-glacial morainic regions of the youngest glaciation are characterized by small hydrological kettle hole catchments forming hummocky soil landscapes. The spatial heterogeneity of subsurface structures as well as erosion-controlled pedogenesis under arable land use may complicate hydrological modeling. Our aim was to generate a soil landscape model for a small representative kettle hole catchment based on geoelectrical exploration and soil profile information. For a 1-ha catchment located in the northeastern German lowlands near the town of Prenzlau, electrical resistivity transects were determined by a multi electrode system (IMPETUS 12 Fs) and electrical conductivity (ECa) was mapped by using the electromagnetic induction (EMI) device EM38DD in both the vertical and horizontal modes. The 1-m digital elevation model (DEM) was obtained by kriging from high resolution manual elevation data determined with a leveling device (ZEISS Ni 40). Soil profile data from 26 boreholes distributed radially around the central pond were used to identify boundaries between soil horizons. The soil is characterized by varying topography and morphology of diagnostic horizons such as M- (colluvium), Bt- (clay illuviation), and C- (parent glacial till). By EMI mapping we identified (i) the boundary between erosive and colluvial areas around the kettle hole, and modeled (ii) the subsurface morphology of loamy horizons. Electrical resistivity tomography results coincide with these findings and allow for distinguishing between sandy and loamy dominated areas both in vertical and horizontal direction, respectively. This soil model of soil textural properties could be used for hydrological modeling.2009_361_gruenow_niv_pkteElectromagnetic inductionfeaturesElectrical resistivityElectrical resistivity tomographyurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=b79e9535-eb19-47d2-9ecd-061e9713d4dd&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:2009_361_soil_str_co_geophysSoilscapes of the post-glacial morainic regions of the youngest glaciation are characterized by small hydrological kettle hole catchments forming hummocky soil landscapes. The spatial heterogeneity of subsurface structures as well as erosion-controlled pedogenesis under arable land use may complicate hydrological modeling. Our aim was to generate a soil landscape model for a small representative kettle hole catchment based on geoelectrical exploration and soil profile information. For a 1-ha catchment located in the northeastern German lowlands near the town of Prenzlau, electrical resistivity transects were determined by a multi electrode system (IMPETUS 12 Fs) and electrical conductivity (ECa) was mapped by using the electromagnetic induction (EMI) device EM38DD in both the vertical and horizontal modes. The 1-m digital elevation model (DEM) was obtained by kriging from high resolution manual elevation data determined with a leveling device (ZEISS Ni 40). Soil profile data from 26 boreholes distributed radially around the central pond were used to identify boundaries between soil horizons. The soil is characterized by varying topography and morphology of diagnostic horizons such as M- (colluvium), Bt- (clay illuviation), and C- (parent glacial till). By EMI mapping we identified (i) the boundary between erosive and colluvial areas around the kettle hole, and modeled (ii) the subsurface morphology of loamy horizons. Electrical resistivity tomography results coincide with these findings and allow for distinguishing between sandy and loamy dominated areas both in vertical and horizontal direction, respectively. This soil model of soil textural properties could be used for hydrological modeling.Electromagnetic induction2009_361_soil_str_co_geophysfeaturesElectrical resistivityElectrical resistivity tomographyurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=7b753f21-dd8b-4629-ac16-67b051934c99&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:2009_361_str_soil_co_int_nameSoilscapes of the post-glacial morainic regions of the youngest glaciation are characterized by small hydrological kettle hole catchments forming hummocky soil landscapes. The spatial heterogeneity of subsurface structures as well as erosion-controlled pedogenesis under arable land use may complicate hydrological modeling. Our aim was to generate a soil landscape model for a small representative kettle hole catchment based on geoelectrical exploration and soil profile information. For a 1-ha catchment located in the northeastern German lowlands near the town of Prenzlau, electrical resistivity transects were determined by a multi electrode system (IMPETUS 12 Fs) and electrical conductivity (ECa) was mapped by using the electromagnetic induction (EMI) device EM38DD in both the vertical and horizontal modes. The 1-m digital elevation model (DEM) was obtained by kriging from high resolution manual elevation data determined with a leveling device (ZEISS Ni 40). Soil profile data from 26 boreholes distributed radially around the central pond were used to identify boundaries between soil horizons. The soil is characterized by varying topography and morphology of diagnostic horizons such as M- (colluvium), Bt- (clay illuviation), and C- (parent glacial till). By EMI mapping we identified (i) the boundary between erosive and colluvial areas around the kettle hole, and modeled (ii) the subsurface morphology of loamy horizons. Electrical resistivity tomography results coincide with these findings and allow for distinguishing between sandy and loamy dominated areas both in vertical and horizontal direction, respectively. This soil model of soil textural properties could be used for hydrological modeling.2009_361_str_soil_co_int_nameElectrical resistivityElectromagnetic inductionElectrical resistivity tomographyfeaturesurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=9601459f-4942-4f34-a288-1a3a54b1f0ad&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:2009_361_str_soil_coring_ka5Soilscapes of the post-glacial morainic regions of the youngest glaciation are characterized by small hydrological kettle hole catchments forming hummocky soil landscapes. The spatial heterogeneity of subsurface structures as well as erosion-controlled pedogenesis under arable land use may complicate hydrological modeling. Our aim was to generate a soil landscape model for a small representative kettle hole catchment based on geoelectrical exploration and soil profile information. For a 1-ha catchment located in the northeastern German lowlands near the town of Prenzlau, electrical resistivity transects were determined by a multi electrode system (IMPETUS 12 Fs) and electrical conductivity (ECa) was mapped by using the electromagnetic induction (EMI) device EM38DD in both the vertical and horizontal modes. The 1-m digital elevation model (DEM) was obtained by kriging from high resolution manual elevation data determined with a leveling device (ZEISS Ni 40). Soil profile data from 26 boreholes distributed radially around the central pond were used to identify boundaries between soil horizons. The soil is characterized by varying topography and morphology of diagnostic horizons such as M- (colluvium), Bt- (clay illuviation), and C- (parent glacial till). By EMI mapping we identified (i) the boundary between erosive and colluvial areas around the kettle hole, and modeled (ii) the subsurface morphology of loamy horizons. Electrical resistivity tomography results coincide with these findings and allow for distinguishing between sandy and loamy dominated areas both in vertical and horizontal direction, respectively. This soil model of soil textural properties could be used for hydrological modeling.Electromagnetic inductionfeaturesElectrical resistivityElectrical resistivity tomography2009_361_str_soil_coring_ka5urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=d4b62b59-1569-4193-884e-c1d8bf97c02c&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:geolocation_b6ce8b1f0b189dcef46762de8319e61aSoilscapes of the post-glacial morainic regions of the youngest glaciation are characterized by small hydrological kettle hole catchments forming hummocky soil landscapes. The spatial heterogeneity of subsurface structures as well as erosion-controlled pedogenesis under arable land use may complicate hydrological modeling. Our aim was to generate a soil landscape model for a small representative kettle hole catchment based on geoelectrical exploration and soil profile information. For a 1-ha catchment located in the northeastern German lowlands near the town of Prenzlau, electrical resistivity transects were determined by a multi electrode system (IMPETUS 12 Fs) and electrical conductivity (ECa) was mapped by using the electromagnetic induction (EMI) device EM38DD in both the vertical and horizontal modes. The 1-m digital elevation model (DEM) was obtained by kriging from high resolution manual elevation data determined with a leveling device (ZEISS Ni 40). Soil profile data from 26 boreholes distributed radially around the central pond were used to identify boundaries between soil horizons. The soil is characterized by varying topography and morphology of diagnostic horizons such as M- (colluvium), Bt- (clay illuviation), and C- (parent glacial till). By EMI mapping we identified (i) the boundary between erosive and colluvial areas around the kettle hole, and modeled (ii) the subsurface morphology of loamy horizons. Electrical resistivity tomography results coincide with these findings and allow for distinguishing between sandy and loamy dominated areas both in vertical and horizontal direction, respectively. This soil model of soil textural properties could be used for hydrological modeling.Electromagnetic inductionfeaturesElectrical resistivityElectrical resistivity tomographygeolocation_b6ce8b1f0b189dcef46762de8319e61aurn:x-ogc:def:crs:EPSG:432613.95521361566 53.3176527568989913.98374199498226 53.33239683806245https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=579ee247-b171-4dd6-8736-346bc983d772&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:geolocation_c6b9dbf03d80dc713d0badae4df84089We provide a dataset from a household survey in Mpanda region in Western Tanzania (N = 137) that was conducted in 2011. Household heads (or replacements) were interviewed. The topics addressed covered a broad range of socio-economic data and including, among others, household information (number of household members, age, sex, religion etc.), agricultural production (e.g. crops produced and livestock owned) including number and size of plots, income generation, energy access and owned assets.featuresgeolocation_c6b9dbf03d80dc713d0badae4df84089urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=13a648d1-cbb8-476c-ad01-462bc026a7ca&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:mpanda_region_geolocationWe provide a dataset from a household survey in Mpanda region in Western Tanzania (N = 137) that was conducted in 2011. Household heads (or replacements) were interviewed. The topics addressed covered a broad range of socio-economic data and including, among others, household information (number of household members, age, sex, religion etc.), agricultural production (e.g. crops produced and livestock owned) including number and size of plots, income generation, energy access and owned assets.featuresmpanda_region_geolocationurn:x-ogc:def:crs:EPSG:432629.903673172000026 -6.93923711699994831.187620164000123 -5.135649204999936https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=744e3548-ff7b-4a0c-9b7b-a04471db3c95&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:mpanda_data_set_anonymized_s01We provide a dataset from a household survey in Mpanda region in Western Tanzania (N = 137) that was conducted in 2011. Household heads (or replacements) were interviewed. The topics addressed covered a broad range of socio-economic data and including, among others, household information (number of household members, age, sex, religion etc.), agricultural production (e.g. crops produced and livestock owned) including number and size of plots, income generation, energy access and owned assets.featuresmpanda_data_set_anonymized_s01urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=5c8e4dc6-fe50-4101-a275-7089eff7f88d&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:mpanda_data_set_anonymized_s02We provide a dataset from a household survey in Mpanda region in Western Tanzania (N = 137) that was conducted in 2011. Household heads (or replacements) were interviewed. The topics addressed covered a broad range of socio-economic data and including, among others, household information (number of household members, age, sex, religion etc.), agricultural production (e.g. crops produced and livestock owned) including number and size of plots, income generation, energy access and owned assets.featuresmpanda_data_set_anonymized_s02urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=bcebc90d-8afa-4860-8495-75a0b04e3395&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:mpanda_data_set_anonymized_s03We provide a dataset from a household survey in Mpanda region in Western Tanzania (N = 137) that was conducted in 2011. Household heads (or replacements) were interviewed. The topics addressed covered a broad range of socio-economic data and including, among others, household information (number of household members, age, sex, religion etc.), agricultural production (e.g. crops produced and livestock owned) including number and size of plots, income generation, energy access and owned assets.mpanda_data_set_anonymized_s03featuresurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=7eaaad84-b292-44e9-ac6e-86f8d29b884f&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:mpanda_data_set_anonymized_s04We provide a dataset from a household survey in Mpanda region in Western Tanzania (N = 137) that was conducted in 2011. Household heads (or replacements) were interviewed. The topics addressed covered a broad range of socio-economic data and including, among others, household information (number of household members, age, sex, religion etc.), agricultural production (e.g. crops produced and livestock owned) including number and size of plots, income generation, energy access and owned assets.featuresmpanda_data_set_anonymized_s04urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=1f7ca6eb-7cf0-435d-b4ba-806e40a74238&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:mpanda_data_set_anonymized_s05We provide a dataset from a household survey in Mpanda region in Western Tanzania (N = 137) that was conducted in 2011. Household heads (or replacements) were interviewed. The topics addressed covered a broad range of socio-economic data and including, among others, household information (number of household members, age, sex, religion etc.), agricultural production (e.g. crops produced and livestock owned) including number and size of plots, income generation, energy access and owned assets.featuresmpanda_data_set_anonymized_s05urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=eecfa97e-203c-4def-bad2-252864fd9c20&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:mpanda_data_set_anonymized_s06We provide a dataset from a household survey in Mpanda region in Western Tanzania (N = 137) that was conducted in 2011. Household heads (or replacements) were interviewed. The topics addressed covered a broad range of socio-economic data and including, among others, household information (number of household members, age, sex, religion etc.), agricultural production (e.g. crops produced and livestock owned) including number and size of plots, income generation, energy access and owned assets.featuresmpanda_data_set_anonymized_s06urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=82f6dc35-c4d1-44ee-8357-70031ed8cff8&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:mpanda_data_set_anonymized_s07We provide a dataset from a household survey in Mpanda region in Western Tanzania (N = 137) that was conducted in 2011. Household heads (or replacements) were interviewed. The topics addressed covered a broad range of socio-economic data and including, among others, household information (number of household members, age, sex, religion etc.), agricultural production (e.g. crops produced and livestock owned) including number and size of plots, income generation, energy access and owned assets.featuresmpanda_data_set_anonymized_s07urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=be514d9a-9da6-4716-84f1-3b80b02ccca7&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:mpanda_data_set_anonymized_s08We provide a dataset from a household survey in Mpanda region in Western Tanzania (N = 137) that was conducted in 2011. Household heads (or replacements) were interviewed. The topics addressed covered a broad range of socio-economic data and including, among others, household information (number of household members, age, sex, religion etc.), agricultural production (e.g. crops produced and livestock owned) including number and size of plots, income generation, energy access and owned assets.featuresmpanda_data_set_anonymized_s08urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=5f2d57c4-b3e0-43e0-9b85-9a3c7c21cd12&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:mpanda_data_set_anonymized_s09We provide a dataset from a household survey in Mpanda region in Western Tanzania (N = 137) that was conducted in 2011. Household heads (or replacements) were interviewed. The topics addressed covered a broad range of socio-economic data and including, among others, household information (number of household members, age, sex, religion etc.), agricultural production (e.g. crops produced and livestock owned) including number and size of plots, income generation, energy access and owned assets.featuresmpanda_data_set_anonymized_s09urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=76bf5180-db0e-476a-aa3b-ffd3962ff909&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:mpanda_data_set_anonymized_s10We provide a dataset from a household survey in Mpanda region in Western Tanzania (N = 137) that was conducted in 2011. Household heads (or replacements) were interviewed. The topics addressed covered a broad range of socio-economic data and including, among others, household information (number of household members, age, sex, religion etc.), agricultural production (e.g. crops produced and livestock owned) including number and size of plots, income generation, energy access and owned assets.featuresmpanda_data_set_anonymized_s10urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=52ec9216-e46e-4eb2-96d4-3c798e2c5055&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:mpanda_data_set_anonymized_s11We provide a dataset from a household survey in Mpanda region in Western Tanzania (N = 137) that was conducted in 2011. Household heads (or replacements) were interviewed. The topics addressed covered a broad range of socio-economic data and including, among others, household information (number of household members, age, sex, religion etc.), agricultural production (e.g. crops produced and livestock owned) including number and size of plots, income generation, energy access and owned assets.featuresmpanda_data_set_anonymized_s11urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=17b645b5-8585-4649-a773-2f5e566baf5b&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:mpanda_data_set_anonymized_s12We provide a dataset from a household survey in Mpanda region in Western Tanzania (N = 137) that was conducted in 2011. Household heads (or replacements) were interviewed. The topics addressed covered a broad range of socio-economic data and including, among others, household information (number of household members, age, sex, religion etc.), agricultural production (e.g. crops produced and livestock owned) including number and size of plots, income generation, energy access and owned assets.featuresmpanda_data_set_anonymized_s12urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=e8cd3c66-0d3f-4ec0-b6e1-5bf1b5d6392f&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:mpanda_data_set_anonymized_s13We provide a dataset from a household survey in Mpanda region in Western Tanzania (N = 137) that was conducted in 2011. Household heads (or replacements) were interviewed. The topics addressed covered a broad range of socio-economic data and including, among others, household information (number of household members, age, sex, religion etc.), agricultural production (e.g. crops produced and livestock owned) including number and size of plots, income generation, energy access and owned assets.featuresmpanda_data_set_anonymized_s13urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=7377e538-c013-4a0c-8272-ca4ece2cc1f9&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:mpanda_data_set_anonymized_s14We provide a dataset from a household survey in Mpanda region in Western Tanzania (N = 137) that was conducted in 2011. Household heads (or replacements) were interviewed. The topics addressed covered a broad range of socio-economic data and including, among others, household information (number of household members, age, sex, religion etc.), agricultural production (e.g. crops produced and livestock owned) including number and size of plots, income generation, energy access and owned assets.mpanda_data_set_anonymized_s14featuresurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=c0351b21-0dde-466e-9803-506cd731ff82&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:mpanda_data_set_anonymized_s15We provide a dataset from a household survey in Mpanda region in Western Tanzania (N = 137) that was conducted in 2011. Household heads (or replacements) were interviewed. The topics addressed covered a broad range of socio-economic data and including, among others, household information (number of household members, age, sex, religion etc.), agricultural production (e.g. crops produced and livestock owned) including number and size of plots, income generation, energy access and owned assets.featuresmpanda_data_set_anonymized_s15urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=7b2b3dc7-bef6-4ed9-9d5a-1bd59d442b6d&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:mpanda_data_set_anonymized_s16We provide a dataset from a household survey in Mpanda region in Western Tanzania (N = 137) that was conducted in 2011. Household heads (or replacements) were interviewed. The topics addressed covered a broad range of socio-economic data and including, among others, household information (number of household members, age, sex, religion etc.), agricultural production (e.g. crops produced and livestock owned) including number and size of plots, income generation, energy access and owned assets.featuresmpanda_data_set_anonymized_s16urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=b72131bc-e95e-495f-97e7-06f516e0045c&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:mpanda_data_set_anonymized_s17We provide a dataset from a household survey in Mpanda region in Western Tanzania (N = 137) that was conducted in 2011. Household heads (or replacements) were interviewed. The topics addressed covered a broad range of socio-economic data and including, among others, household information (number of household members, age, sex, religion etc.), agricultural production (e.g. crops produced and livestock owned) including number and size of plots, income generation, energy access and owned assets.featuresmpanda_data_set_anonymized_s17urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=368b3a7a-9362-47cc-ab61-9ccfa1a36cb3&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:mpanda_data_set_anonymized_s18We provide a dataset from a household survey in Mpanda region in Western Tanzania (N = 137) that was conducted in 2011. Household heads (or replacements) were interviewed. The topics addressed covered a broad range of socio-economic data and including, among others, household information (number of household members, age, sex, religion etc.), agricultural production (e.g. crops produced and livestock owned) including number and size of plots, income generation, energy access and owned assets.featuresmpanda_data_set_anonymized_s18urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=93baab35-5f51-449d-8c05-70852079788b&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:mpanda_data_set_anonymized_s19We provide a dataset from a household survey in Mpanda region in Western Tanzania (N = 137) that was conducted in 2011. Household heads (or replacements) were interviewed. The topics addressed covered a broad range of socio-economic data and including, among others, household information (number of household members, age, sex, religion etc.), agricultural production (e.g. crops produced and livestock owned) including number and size of plots, income generation, energy access and owned assets.featuresmpanda_data_set_anonymized_s19urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=70cf9b48-3b3e-4476-8070-e5bc69bd7d03&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:mpanda_data_set_anonymized_s20We provide a dataset from a household survey in Mpanda region in Western Tanzania (N = 137) that was conducted in 2011. Household heads (or replacements) were interviewed. The topics addressed covered a broad range of socio-economic data and including, among others, household information (number of household members, age, sex, religion etc.), agricultural production (e.g. crops produced and livestock owned) including number and size of plots, income generation, energy access and owned assets.featuresmpanda_data_set_anonymized_s20urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=a5dc41f6-ec3b-4ba2-8986-7697dd3802cd&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:mpanda_data_set_anonymized_s21We provide a dataset from a household survey in Mpanda region in Western Tanzania (N = 137) that was conducted in 2011. Household heads (or replacements) were interviewed. The topics addressed covered a broad range of socio-economic data and including, among others, household information (number of household members, age, sex, religion etc.), agricultural production (e.g. crops produced and livestock owned) including number and size of plots, income generation, energy access and owned assets.featuresmpanda_data_set_anonymized_s21urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=495f8638-9c38-44a6-bc91-a0d9a503993b&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:mpanda_data_set_anonymized_s21aWe provide a dataset from a household survey in Mpanda region in Western Tanzania (N = 137) that was conducted in 2011. Household heads (or replacements) were interviewed. The topics addressed covered a broad range of socio-economic data and including, among others, household information (number of household members, age, sex, religion etc.), agricultural production (e.g. crops produced and livestock owned) including number and size of plots, income generation, energy access and owned assets.featuresmpanda_data_set_anonymized_s21aurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=6576ccc3-4f12-4144-adf1-b7d3517c3bdc&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:mpanda_data_set_anonymized_s22We provide a dataset from a household survey in Mpanda region in Western Tanzania (N = 137) that was conducted in 2011. Household heads (or replacements) were interviewed. The topics addressed covered a broad range of socio-economic data and including, among others, household information (number of household members, age, sex, religion etc.), agricultural production (e.g. crops produced and livestock owned) including number and size of plots, income generation, energy access and owned assets.featuresmpanda_data_set_anonymized_s22urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=0406cac8-77d3-4e25-bb51-5a5e67b37e06&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:geolocation_64e8c5ab25c37a04a16d8a8f88b796fbWe conducted playback experiments directed towards foraging Nyctalus noctula at waterbodies in the Uckermark / Germany in early and late summer 2016. Playback files consisted of superstimuli of hunting calls of conspecific Nyctalus noctula or heterospecific Pipistrellus nathusii. We recorded the reaction of hunting Nyctalus noctula towards these playbacks by means of ultrasonic activity. We simultaneously caught insects during the playback sessions using UV-traps. Data was analysed to test if the interaction of season and playback type influenced activity of hunting Nyctalus noctula, and if seasonal patterns could be explained by insect abundance.habitat usefeaturesbatsaerialkgeolocation_64e8c5ab25c37a04a16d8a8f88b796fbcoexistenceplaybackflighturn:x-ogc:def:crs:EPSG:432613.52655184220074 53.1742458716124514.146431 53.39917https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=23f95fff-d9de-4382-8cfb-8afb6a444c6d&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:insectsWe conducted playback experiments directed towards foraging Nyctalus noctula at waterbodies in the Uckermark / Germany in early and late summer 2016. Playback files consisted of superstimuli of hunting calls of conspecific Nyctalus noctula or heterospecific Pipistrellus nathusii. We recorded the reaction of hunting Nyctalus noctula towards these playbacks by means of ultrasonic activity. We simultaneously caught insects during the playback sessions using UV-traps. Data was analysed to test if the interaction of season and playback type influenced activity of hunting Nyctalus noctula, and if seasonal patterns could be explained by insect abundance.habitat usefeaturesbatsaerialkcoexistenceinsectsplaybackflighturn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=441c2599-4cb1-44ea-9d87-4e329f116446&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:playback_activityWe conducted playback experiments directed towards foraging Nyctalus noctula at waterbodies in the Uckermark / Germany in early and late summer 2016. Playback files consisted of superstimuli of hunting calls of conspecific Nyctalus noctula or heterospecific Pipistrellus nathusii. We recorded the reaction of hunting Nyctalus noctula towards these playbacks by means of ultrasonic activity. We simultaneously caught insects during the playback sessions using UV-traps. Data was analysed to test if the interaction of season and playback type influenced activity of hunting Nyctalus noctula, and if seasonal patterns could be explained by insect abundance.habitat usefeaturesbatsaerialkplayback_activitycoexistenceplaybackflighturn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=7af9055b-e026-4fd5-9b42-e1174eea5930&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:geolocation_ab2a95b5cae18975c1644dd3b87eae51We hypothesized that (i) NLE types differ in their impact on crop production and that (ii) this effect changes with proximity of the NLE to the examined agricultural field. We assessed winter wheat yields along transects with log-scaled distances from the field border into the agricultural field in two intensively managed agricultural landscapes in Germany (2014 near Göttingen, and 2015 - 2017 in the Uckermark). Transects either originated from a natural landscape element (forest, hedgerow, kettle hole) or a control (field-to-field border or agricultural road).geolocation_ab2a95b5cae18975c1644dd3b87eae51kettle natural habitsfeatureskettle holeurn:x-ogc:def:crs:EPSG:43269.955883347899421 51.571100007201713.79053987202249 53.3910122614669https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=1caf58e2-c15e-4a1a-9121-280a0878d849&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:up_biodiv_lr_2019_01We hypothesized that (i) NLE types differ in their impact on crop production and that (ii) this effect changes with proximity of the NLE to the examined agricultural field. We assessed winter wheat yields along transects with log-scaled distances from the field border into the agricultural field in two intensively managed agricultural landscapes in Germany (2014 near Göttingen, and 2015 - 2017 in the Uckermark). Transects either originated from a natural landscape element (forest, hedgerow, kettle hole) or a control (field-to-field border or agricultural road).featureskettle holeup_biodiv_lr_2019_01kettle natural habitsurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=b5edb7a4-05c3-490f-9c63-61e6c1e42c38&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:basicdataThe data set contains data about soil hydrological properties of North East and Central German soils. Included are measured data of the soil water retention curve and the unsaturated hydraulic conductivity function. Information to geo reference, soil type and horizon are given. Additional soil physical data like particle size distribution, dry bulk density, organic matter content and other variables are presented and its measurement is methodically described. The data base includes original measurement results of 497 mineral soil samples from 77 sites. The mineral soils cover a wide range of texture classes and dry bulk densities.featuresbasicdataurn:x-ogc:def:crs:EPSG:432611.935980885000049 51.2472435620000517.54287683700005 52.93957558500006https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=48cee64e-b339-4a8b-8119-53bcae06d84d&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:geolocation_f13a35f370debfd5560a7d10a67bde94Individual space use of bank voles (Myodes glareolus). Included are home range and core area sizes, intraspecific home range and core area overlaps,total distances moved and mean values of the ground cover and the maximum vegetation heights in home ranges and core areas for each trackedindividual.movement ecologygeolocation_f13a35f370debfd5560a7d10a67bde94featuresurn:x-ogc:def:crs:EPSG:432613.47096725586944 53.2944389887517413.85511279201188 53.42902016292231https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=85ee7a68-c138-471d-a402-848b7dcea4b6&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:spaceuse_bv_16Individual space use of bank voles (Myodes glareolus). Included are home range and core area sizes, intraspecific home range and core area overlaps,total distances moved and mean values of the ground cover and the maximum vegetation heights in home ranges and core areas for each trackedindividual.movement ecologyspaceuse_bv_16featuresurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=ab51a167-fa86-41bc-99f0-96486cc3e577&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:geolocation_019a0c35ea839e3f166b941e2a581b41One of the few laws in ecology is that communities consist of few common and many rare taxa. Functional traits may help to identify the underlying mechanisms of this community pattern, since they correlate with different niche dimensions. However, comprehensive studies are missing that investigat e the effects of species mean traits (niche position) and intraspecific trait variability (ITV, niche width) on species abundance. In this study, we tested three predictions: species abundance a) increases (or decreases) with species mean traits, b) is highest for intermediate species mean traits, and c) increases with ITV. We measured three plant functional traits (specific leaf area, leaf dry matter content, plant height) at 21 local dry grassland communities (10m x 10m) and analyzed the effect of these traits and their variation on species abundance at the local and regional scale. In our analyses we compared phylogenetic-corrected with standard models, in order to reveal whether the relationships between traits and species abundance are influenced by the species phylogeny. At the local scale, we found that species abundance increased with species mean leaf dry matter content and the intraspecific variations of leaf dry matter content and plant height. In contrast, at the regional scale, plants with a higher ITV of plant height were less abundant. We found no evidence that the consideration of phylogenetic-relationships influenced significantly our findings nor that species with intermediate traits were more abundant. Overall, our results indicate that the tolerance towards environmental conditions rather than competitive ability drives species abundance. Hereby, ITV may be particularly beneficial in heterogeneous environments at the local scale. However, the contrasting effect of ITV at the regional scale shows that trait-abundance relationships seem to be highly scale-dependent.featuresPlant functional traitscale dependencyspecies abundancetrait-environment relationshipgeolocation_019a0c35ea839e3f166b941e2a581b41competitionniche widthurn:x-ogc:def:crs:EPSG:432613.547935 53.253476513.9299655 53.433807https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=eb5049c7-2f5e-4704-80ff-7d12a9620ae7&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:localabundance_b3eeac6ee8a1627a2ed2cea78df944a0One of the few laws in ecology is that communities consist of few common and many rare taxa. Functional traits may help to identify the underlying mechanisms of this community pattern, since they correlate with different niche dimensions. However, comprehensive studies are missing that investigat e the effects of species mean traits (niche position) and intraspecific trait variability (ITV, niche width) on species abundance. In this study, we tested three predictions: species abundance a) increases (or decreases) with species mean traits, b) is highest for intermediate species mean traits, and c) increases with ITV. We measured three plant functional traits (specific leaf area, leaf dry matter content, plant height) at 21 local dry grassland communities (10m x 10m) and analyzed the effect of these traits and their variation on species abundance at the local and regional scale. In our analyses we compared phylogenetic-corrected with standard models, in order to reveal whether the relationships between traits and species abundance are influenced by the species phylogeny. At the local scale, we found that species abundance increased with species mean leaf dry matter content and the intraspecific variations of leaf dry matter content and plant height. In contrast, at the regional scale, plants with a higher ITV of plant height were less abundant. We found no evidence that the consideration of phylogenetic-relationships influenced significantly our findings nor that species with intermediate traits were more abundant. Overall, our results indicate that the tolerance towards environmental conditions rather than competitive ability drives species abundance. Hereby, ITV may be particularly beneficial in heterogeneous environments at the local scale. However, the contrasting effect of ITV at the regional scale shows that trait-abundance relationships seem to be highly scale-dependent.scale dependencycompetitionfeaturesspecies abundancetrait-environment relationshipPlant functional traitlocalabundance_b3eeac6ee8a1627a2ed2cea78df944a0niche widthurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=44fc7ffe-d221-4999-be57-5041eb84bcf7&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:regionalabundance_d22cb634ca4536c6b1d6594e59145940One of the few laws in ecology is that communities consist of few common and many rare taxa. Functional traits may help to identify the underlying mechanisms of this community pattern, since they correlate with different niche dimensions. However, comprehensive studies are missing that investigat e the effects of species mean traits (niche position) and intraspecific trait variability (ITV, niche width) on species abundance. In this study, we tested three predictions: species abundance a) increases (or decreases) with species mean traits, b) is highest for intermediate species mean traits, and c) increases with ITV. We measured three plant functional traits (specific leaf area, leaf dry matter content, plant height) at 21 local dry grassland communities (10m x 10m) and analyzed the effect of these traits and their variation on species abundance at the local and regional scale. In our analyses we compared phylogenetic-corrected with standard models, in order to reveal whether the relationships between traits and species abundance are influenced by the species phylogeny. At the local scale, we found that species abundance increased with species mean leaf dry matter content and the intraspecific variations of leaf dry matter content and plant height. In contrast, at the regional scale, plants with a higher ITV of plant height were less abundant. We found no evidence that the consideration of phylogenetic-relationships influenced significantly our findings nor that species with intermediate traits were more abundant. Overall, our results indicate that the tolerance towards environmental conditions rather than competitive ability drives species abundance. Hereby, ITV may be particularly beneficial in heterogeneous environments at the local scale. However, the contrasting effect of ITV at the regional scale shows that trait-abundance relationships seem to be highly scale-dependent.competitionscale dependencyfeaturesspecies abundancetrait-environment relationshipregionalabundance_d22cb634ca4536c6b1d6594e59145940Plant functional traitniche widthurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=e001a10c-a65b-4983-85ac-e1568b54b7a0&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:traitsOne of the few laws in ecology is that communities consist of few common and many rare taxa. Functional traits may help to identify the underlying mechanisms of this community pattern, since they correlate with different niche dimensions. However, comprehensive studies are missing that investigat e the effects of species mean traits (niche position) and intraspecific trait variability (ITV, niche width) on species abundance. In this study, we tested three predictions: species abundance a) increases (or decreases) with species mean traits, b) is highest for intermediate species mean traits, and c) increases with ITV. We measured three plant functional traits (specific leaf area, leaf dry matter content, plant height) at 21 local dry grassland communities (10m x 10m) and analyzed the effect of these traits and their variation on species abundance at the local and regional scale. In our analyses we compared phylogenetic-corrected with standard models, in order to reveal whether the relationships between traits and species abundance are influenced by the species phylogeny. At the local scale, we found that species abundance increased with species mean leaf dry matter content and the intraspecific variations of leaf dry matter content and plant height. In contrast, at the regional scale, plants with a higher ITV of plant height were less abundant. We found no evidence that the consideration of phylogenetic-relationships influenced significantly our findings nor that species with intermediate traits were more abundant. Overall, our results indicate that the tolerance towards environmental conditions rather than competitive ability drives species abundance. Hereby, ITV may be particularly beneficial in heterogeneous environments at the local scale. However, the contrasting effect of ITV at the regional scale shows that trait-abundance relationships seem to be highly scale-dependent.competitionscale dependencyfeaturestraitsspecies abundancetrait-environment relationshipPlant functional traitniche widthurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=49da114e-36d1-48a3-9fa9-5ac9bb97ac62&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:geolocation_5c56887769653d0a73ebe11287e51004The dataset compiles official long-term yield statistics (1996-2019) of four major crops in Germany: winter wheat, winter barley, silage maize, winter canola. Spatial aggregation represents the EU NUTS 3 level, which corresponds to districts in Germany. We used the actual district geometry which did not change in Germany since 2011.time serieswinter canolawinter barleyfeaturessilage maizedistrictsgeolocation_5c56887769653d0a73ebe11287e51004NUTS3official agricultural statisticsurn:x-ogc:def:crs:EPSG:43265.8676202445 47.255283595315.0361907094 55.04037662009999https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=631f0ad9-1f3d-497a-ba41-f4d8614bdf8c&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:geolocation_d216e82efdec35ea82b59097a1eab6aaOn set-aside areas, which there agricultural used for a long, the soil parameters have a special dynamic. After a long period of anthropogenic use they are moving back to a level typically for the site without any use. For an area on a sandy site (sandy loam-soil, German soil quality index for arable land: 25...35, mean(1988-2017) annual precipitation: 544 mm, mean (1988-2017) annual temperature: 9.3°C) in the Müncheberg area (Federal State of Brandenburg, Germany) for the period 1992-2017 measured values for the most important soil parameters with fast dynamics (NO3, NH4, Nan, soil water) and slow dynamics (pH, Nt, P, K, Mg, Ct) are available for three soil layers (0-30 cm, 30-60 cm and 60-90 cm). Generally these measured values are available annually in spring time. For the years 1992-1996, however, several measurements per year are available also until the end of the vegetation. The area, which has been set-aside since 1992, was used for agriculture until 1991 (recurrent winter rye cultivation). Since 1992 the set-aside area was mulched annually to prevent succession.featuresgeolocation_d216e82efdec35ea82b59097a1eab6aaurn:x-ogc:def:crs:EPSG:432614.1243337538498 52.5291657894357914.12887878339514 52.53098595888886https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=cc1e5258-8606-4813-8eb9-009662009897&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:saascOn set-aside areas, which there agricultural used for a long, the soil parameters have a special dynamic. After a long period of anthropogenic use they are moving back to a level typically for the site without any use. For an area on a sandy site (sandy loam-soil, German soil quality index for arable land: 25...35, mean(1988-2017) annual precipitation: 544 mm, mean (1988-2017) annual temperature: 9.3°C) in the Müncheberg area (Federal State of Brandenburg, Germany) for the period 1992-2017 measured values for the most important soil parameters with fast dynamics (NO3, NH4, Nan, soil water) and slow dynamics (pH, Nt, P, K, Mg, Ct) are available for three soil layers (0-30 cm, 30-60 cm and 60-90 cm). Generally these measured values are available annually in spring time. For the years 1992-1996, however, several measurements per year are available also until the end of the vegetation. The area, which has been set-aside since 1992, was used for agriculture until 1991 (recurrent winter rye cultivation). Since 1992 the set-aside area was mulched annually to prevent succession.featuressaascurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=7c60e0c1-68a0-419a-a93e-2e108e5864cc&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:geodata_decimalgradSite information for hydrometeorological observations. featuresgeodata_decimalgradurn:x-ogc:def:crs:EPSG:432612.72768 52.6918412.727879999999999 52.692040000000006https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=5a769ee1-d2b7-429c-b19c-0c24bb9f9ec3&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:soil_moisture_diff_depth_rain_et_et0_gwl_grabenUnderstanding the interactions between groundwater, soil moisture, evapotranspiration, and meteorological drivers is essential for assessing hydrological processes in peatland ecosystems and evaluating their response to climate variability and management interventions. The existing dataset contains measurements from hydrometeorological observations collected on a degraded fen area near Paulinenaue, Brandenburg (52°41′31″N, 12°43′40″E). The area is currently used as agricultural grassland by the Leibniz Centre for Agricultural Landscape Research (ZALF) e.V. The dataset includes daily precipitation (P), FAO grass reference evapotranspiration (ET0), actual evapotranspiration (AET), groundwater level (GWL) and ditch water level (DWL) measured at depths of 8, 15, 30, 45, and 60 cm between 1st January 2015 and 31st December 2024. The volumetric soil water content (qv) was measured between 1st of October 2020 and 31st December 2024. P was measured using an automatic rain gauge (Tipping Bucket Raingauge 52203, R.M. Young Company). ET0 was calculated following the FAO methodology (Allen et. Al, 1998) with the input parameters (net radiation – measured with CNR4, Kipp & Zonen; air temperature and relative air humidity – HC2S3, Rotronic AG; wind speed – IRGASON, Campbell Scientific). All sensors are part of an eddy covariance station (Campbell Scientific). AET was measured directly using an eddy covariance system with a combined high-speed sensor for water vapor and three-dimensional wind speed (Integrated CO2 and H2O Open-Path Gas Analyzer and 3-D Sonic Anemometer (IRGASON), Campbell Scientific). The measurement frequency was 10 Hz. The software package TK3 (Mauder and Foken, 2015) was used for the post-processing of the 10 Hz data. The energy balance gap was closed by the Bowen method (Twine et al., 2000) considering the measured latent heat flux, sensible heat flux, net radiation and soil heat flux (measured with heat flux plates – HFP01 and HFP01sc, Hukseflux Thermal Sensors). GWL and DWL were continuously monitored using automated datalogger systems (GWL – WPS5, Ackermann KG, DWL – Dipper-PT, Seba Hydrometrie GmbH & Co KG). Soil moisture measurements were obtained with water content reflectometers CS655 (Campbell Scientific) at multiple depths, providing information on vertical soil water dynamics. The sensors were used with a soil specific calibration function. All data were stored in a 30-minute interval on a data logger CR3000 (Campbell Scientific ) and summarized to daily values in a post-processing process. The dataset captures the coupled interactions between meteorological variables, vegetation water use, groundwater fluctuations, and soil moisture storage. It is suitable for hydrological model calibration and validation, ecohydrological studies, and peatland restoration assessments. References: Allen RG, Pereira LS, Raes D, Smith M. 1998. Crop evapotranspiration: Guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper 56. FAO, Rome. Mauder, M. and Foken, T. 2015. Documentation and Instruction Manual of the Eddy-Covariance Software Package TK3 (Update). Bayreuth, Germany, Univ. Bayreuth, Abt. Mikrometeorologie, 62 pp. Twine, T. E., Kustas, W. P., Norman, J. M., Cook, D. R., Houser, P., Meyers, T. P., Prueger, J. H., Starks, P. J., and Wesely, M. L. 2000. Correcting eddy-covariance flux underestimates over a grassland. Agric. For. Meteorol., 103, 279–300, https://doi.org/10.1016/S0168-1923(00)00123-4.featuressoil_moisture_diff_depth_rain_et_et0_gwl_grabenurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=510facf0-dfce-4358-8853-f6b1996f7a51&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:dk_63_managementThe data set contains yield data from a longterm field experiment. Thetreatments were tillage (ploughing/no-ploughing), mineral nitrogenfertiliser (70/120 kg N/ha a ) and organic fertiliser (100 t to 150t/ha acattle slurry). The crop rotation were sugar beet-springbarley-potato-winter wheat (1981-1990) and sugar beet-winter wheat-maize-triticale (1991-2001).long-term-experimentdk_63_managementfeaturesurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=12b80f30-a1d2-4312-8882-f51e74b1978d&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:dk_63_soilThe data set contains yield data from a longterm field experiment. Thetreatments were tillage (ploughing/no-ploughing), mineral nitrogenfertiliser (70/120 kg N/ha a ) and organic fertiliser (100 t to 150t/ha acattle slurry). The crop rotation were sugar beet-springbarley-potato-winter wheat (1981-1990) and sugar beet-winter wheat-maize-triticale (1991-2001).long-term-experimentfeaturesdk_63_soilurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=9f298027-bc8b-48f8-bc97-c0809b0b19f0&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:dk_63_yieldThe data set contains yield data from a longterm field experiment. Thetreatments were tillage (ploughing/no-ploughing), mineral nitrogenfertiliser (70/120 kg N/ha a ) and organic fertiliser (100 t to 150t/ha acattle slurry). The crop rotation were sugar beet-springbarley-potato-winter wheat (1981-1990) and sugar beet-winter wheat-maize-triticale (1991-2001).long-term-experimentfeaturesdk_63_yieldurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=b7f2aeff-506a-4999-b111-5d4eeba4b5b0&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:geolocation_e235c3d3716a8d0def85235dae922a4bThe data set contains yield data from a longterm field experiment. Thetreatments were tillage (ploughing/no-ploughing), mineral nitrogenfertiliser (70/120 kg N/ha a ) and organic fertiliser (100 t to 150t/ha acattle slurry). The crop rotation were sugar beet-springbarley-potato-winter wheat (1981-1990) and sugar beet-winter wheat-maize-triticale (1991-2001).featureslong-term-experimentgeolocation_e235c3d3716a8d0def85235dae922a4burn:x-ogc:def:crs:EPSG:432613.80004238481073 53.369375796748613.80371275993064 53.37035204341322https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=dece5158-8e30-42bc-812d-9c1c1e53f9b5&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:2011_325_dynamic_necbThe dataset contains information about dynamic and seasonal net ecosystem carbon balances (NECB) for maize for the growing season 2011, measured at five sites at the "CarboZALF-D" experimental field. Measurement sites differ regarding soil type (non-eroded Albic Luvisols, extremely eroded Calcaric Regosol and depositional Endogleyic Colluvic Regosol,) and N fertilization form (100% mineral fertilizer, 50% mineral and 50% organic fertilizer, 100% organic fertilizer). Fertilization treatments were established on the Albic Luvisol. Net ecosystem CO2 exchange (NEE) and ecosystem respiration (Reco) were measured every four weeks using a dynamic flow-through non-steady-state closed manual chamber system. Gap filling was performed based on empirical temperature and PAR dependency functions, used to derive daily NEE values. In parallel, daily above-ground biomass production (NPPshoot) was estimated using a sigmoidal growth function, based on periodic biomass sampling. Finally, NECB dynamics (as a proxy for soil C dynamics) were calculated as the balance of daily NEE and NPPshoot under consideration of the initial C input due to fertilization.Soil erosionNet ecosystem carbon balance (NECB)Gross primary productivity (GPP)Ecosystem respiration (Reco)2011_325_dynamic_necbBiogas fermentation residuesfeaturesNet ecosystem exchange (NEE)urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=4f13fd3c-97d1-4c50-b478-84f65cf11b70&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:2011_325_fertilizationThe dataset contains information about dynamic and seasonal net ecosystem carbon balances (NECB) for maize for the growing season 2011, measured at five sites at the "CarboZALF-D" experimental field. Measurement sites differ regarding soil type (non-eroded Albic Luvisols, extremely eroded Calcaric Regosol and depositional Endogleyic Colluvic Regosol,) and N fertilization form (100% mineral fertilizer, 50% mineral and 50% organic fertilizer, 100% organic fertilizer). Fertilization treatments were established on the Albic Luvisol. Net ecosystem CO2 exchange (NEE) and ecosystem respiration (Reco) were measured every four weeks using a dynamic flow-through non-steady-state closed manual chamber system. Gap filling was performed based on empirical temperature and PAR dependency functions, used to derive daily NEE values. In parallel, daily above-ground biomass production (NPPshoot) was estimated using a sigmoidal growth function, based on periodic biomass sampling. Finally, NECB dynamics (as a proxy for soil C dynamics) were calculated as the balance of daily NEE and NPPshoot under consideration of the initial C input due to fertilization.Net ecosystem exchange (NEE)Ecosystem respiration (Reco)Biogas fermentation residuesNet ecosystem carbon balance (NECB)features2011_325_fertilizationGross primary productivity (GPP)Soil erosionurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=b5b74d17-7f05-4adb-b897-673f02ac552c&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:2011_325_measured_co2The dataset contains information about dynamic and seasonal net ecosystem carbon balances (NECB) for maize for the growing season 2011, measured at five sites at the "CarboZALF-D" experimental field. Measurement sites differ regarding soil type (non-eroded Albic Luvisols, extremely eroded Calcaric Regosol and depositional Endogleyic Colluvic Regosol,) and N fertilization form (100% mineral fertilizer, 50% mineral and 50% organic fertilizer, 100% organic fertilizer). Fertilization treatments were established on the Albic Luvisol. Net ecosystem CO2 exchange (NEE) and ecosystem respiration (Reco) were measured every four weeks using a dynamic flow-through non-steady-state closed manual chamber system. Gap filling was performed based on empirical temperature and PAR dependency functions, used to derive daily NEE values. In parallel, daily above-ground biomass production (NPPshoot) was estimated using a sigmoidal growth function, based on periodic biomass sampling. Finally, NECB dynamics (as a proxy for soil C dynamics) were calculated as the balance of daily NEE and NPPshoot under consideration of the initial C input due to fertilization.2011_325_measured_co2Net ecosystem exchange (NEE)Ecosystem respiration (Reco)Biogas fermentation residuesNet ecosystem carbon balance (NECB)featuresGross primary productivity (GPP)Soil erosionurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=b7d5742e-39e0-4f4d-98c2-c70c8c51b379&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:2011_325_modelled_co2The dataset contains information about dynamic and seasonal net ecosystem carbon balances (NECB) for maize for the growing season 2011, measured at five sites at the "CarboZALF-D" experimental field. Measurement sites differ regarding soil type (non-eroded Albic Luvisols, extremely eroded Calcaric Regosol and depositional Endogleyic Colluvic Regosol,) and N fertilization form (100% mineral fertilizer, 50% mineral and 50% organic fertilizer, 100% organic fertilizer). Fertilization treatments were established on the Albic Luvisol. Net ecosystem CO2 exchange (NEE) and ecosystem respiration (Reco) were measured every four weeks using a dynamic flow-through non-steady-state closed manual chamber system. Gap filling was performed based on empirical temperature and PAR dependency functions, used to derive daily NEE values. In parallel, daily above-ground biomass production (NPPshoot) was estimated using a sigmoidal growth function, based on periodic biomass sampling. Finally, NECB dynamics (as a proxy for soil C dynamics) were calculated as the balance of daily NEE and NPPshoot under consideration of the initial C input due to fertilization.Soil erosionNet ecosystem carbon balance (NECB)Gross primary productivity (GPP)2011_325_modelled_co2Ecosystem respiration (Reco)Biogas fermentation residuesfeaturesNet ecosystem exchange (NEE)urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=a916eb57-84d4-4c89-a5a5-517a1701c001&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:2011_325_nppshootThe dataset contains information about dynamic and seasonal net ecosystem carbon balances (NECB) for maize for the growing season 2011, measured at five sites at the "CarboZALF-D" experimental field. Measurement sites differ regarding soil type (non-eroded Albic Luvisols, extremely eroded Calcaric Regosol and depositional Endogleyic Colluvic Regosol,) and N fertilization form (100% mineral fertilizer, 50% mineral and 50% organic fertilizer, 100% organic fertilizer). Fertilization treatments were established on the Albic Luvisol. Net ecosystem CO2 exchange (NEE) and ecosystem respiration (Reco) were measured every four weeks using a dynamic flow-through non-steady-state closed manual chamber system. Gap filling was performed based on empirical temperature and PAR dependency functions, used to derive daily NEE values. In parallel, daily above-ground biomass production (NPPshoot) was estimated using a sigmoidal growth function, based on periodic biomass sampling. Finally, NECB dynamics (as a proxy for soil C dynamics) were calculated as the balance of daily NEE and NPPshoot under consideration of the initial C input due to fertilization.Soil erosion2011_325_nppshootNet ecosystem carbon balance (NECB)Gross primary productivity (GPP)Ecosystem respiration (Reco)Biogas fermentation residuesfeaturesNet ecosystem exchange (NEE)urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=6eb684c5-fcf8-481b-9718-9a35566e946d&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:2011_325_weather_dataThe dataset contains information about dynamic and seasonal net ecosystem carbon balances (NECB) for maize for the growing season 2011, measured at five sites at the "CarboZALF-D" experimental field. Measurement sites differ regarding soil type (non-eroded Albic Luvisols, extremely eroded Calcaric Regosol and depositional Endogleyic Colluvic Regosol,) and N fertilization form (100% mineral fertilizer, 50% mineral and 50% organic fertilizer, 100% organic fertilizer). Fertilization treatments were established on the Albic Luvisol. Net ecosystem CO2 exchange (NEE) and ecosystem respiration (Reco) were measured every four weeks using a dynamic flow-through non-steady-state closed manual chamber system. Gap filling was performed based on empirical temperature and PAR dependency functions, used to derive daily NEE values. In parallel, daily above-ground biomass production (NPPshoot) was estimated using a sigmoidal growth function, based on periodic biomass sampling. Finally, NECB dynamics (as a proxy for soil C dynamics) were calculated as the balance of daily NEE and NPPshoot under consideration of the initial C input due to fertilization.2011_325_weather_dataSoil erosionNet ecosystem carbon balance (NECB)Gross primary productivity (GPP)Ecosystem respiration (Reco)Biogas fermentation residuesfeaturesNet ecosystem exchange (NEE)urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=15dcc069-6c64-405c-8281-1bf735ed147f&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:geolocation_c3fb2296cc6671977f95bc68066435ecThe dataset contains information about dynamic and seasonal net ecosystem carbon balances (NECB) for maize for the growing season 2011, measured at five sites at the "CarboZALF-D" experimental field. Measurement sites differ regarding soil type (non-eroded Albic Luvisols, extremely eroded Calcaric Regosol and depositional Endogleyic Colluvic Regosol,) and N fertilization form (100% mineral fertilizer, 50% mineral and 50% organic fertilizer, 100% organic fertilizer). Fertilization treatments were established on the Albic Luvisol. Net ecosystem CO2 exchange (NEE) and ecosystem respiration (Reco) were measured every four weeks using a dynamic flow-through non-steady-state closed manual chamber system. Gap filling was performed based on empirical temperature and PAR dependency functions, used to derive daily NEE values. In parallel, daily above-ground biomass production (NPPshoot) was estimated using a sigmoidal growth function, based on periodic biomass sampling. Finally, NECB dynamics (as a proxy for soil C dynamics) were calculated as the balance of daily NEE and NPPshoot under consideration of the initial C input due to fertilization.geolocation_c3fb2296cc6671977f95bc68066435ecSoil erosionNet ecosystem carbon balance (NECB)Gross primary productivity (GPP)Ecosystem respiration (Reco)Biogas fermentation residuesfeaturesNet ecosystem exchange (NEE)urn:x-ogc:def:crs:EPSG:432613.784370998322 53.3788446733607913.78763196352456 53.38043241087965https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=ad9bb8c7-5fbd-4157-b0b8-b74ca3a315b2&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:geolocation_31f820110c0d010f754e748df3e54c26For sugar beet growth models, knowledge of the development of the photosynthetically active leaf apparatus with its up to more than 50 individual leaves per beet plant as a source of assimilate production is very important. Because the measurement using leaf area index (LAI) meters from above allows only a limited measurement and does not cover the total leaf area of all existing individual leaves, it is necessary to carry out manually leaf area measurements for individual sugar beet plants over the whole growth period without destroying the leaves. For this reason, a special measuring methodology was developed and applied. On the experimental fields of the Research Centre for Soil Fertility Müncheberg (FZB Müncheberg) (location: 52°01`N, 14°07`E, 14°07`Eastern latitude; location type: D 2a; soil type: sandy-loamy soil; German soil quality index: 26; average (1951-1981) annual precipitation: 544 mm; average (1951-1981) annual temperature: 8.2°C) leaf area measurements were carried out in different experiments with sugar beets between 1979 and 1982. In each experimental variant, the leaf areas of 8 individual plants were measured between emergence and harvesting of the sugar beet at intervals of approximately 7 days. Between end of May and mid-October usually about 20 measurements were realized. The leaf area is given in cm2 per sugar beet plant. Experiments with different crop densities (60,000, 80,000 and 100,000 sugar beet plants per hectare) as well as without and with irrigation (based on pen evaporation or based on model-added irrigation scheduling system EDV-BB) were taken into account.featuresgeolocation_31f820110c0d010f754e748df3e54c26measurement methodurn:x-ogc:def:crs:EPSG:432614.12174911036021 52.5157849496236314.12385330831261 52.5170681408229https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=0948d8e0-b40b-4e75-b489-5076bab4f819&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:sugarbeetlaFor sugar beet growth models, knowledge of the development of the photosynthetically active leaf apparatus with its up to more than 50 individual leaves per beet plant as a source of assimilate production is very important. Because the measurement using leaf area index (LAI) meters from above allows only a limited measurement and does not cover the total leaf area of all existing individual leaves, it is necessary to carry out manually leaf area measurements for individual sugar beet plants over the whole growth period without destroying the leaves. For this reason, a special measuring methodology was developed and applied. On the experimental fields of the Research Centre for Soil Fertility Müncheberg (FZB Müncheberg) (location: 52°01`N, 14°07`E, 14°07`Eastern latitude; location type: D 2a; soil type: sandy-loamy soil; German soil quality index: 26; average (1951-1981) annual precipitation: 544 mm; average (1951-1981) annual temperature: 8.2°C) leaf area measurements were carried out in different experiments with sugar beets between 1979 and 1982. In each experimental variant, the leaf areas of 8 individual plants were measured between emergence and harvesting of the sugar beet at intervals of approximately 7 days. Between end of May and mid-October usually about 20 measurements were realized. The leaf area is given in cm2 per sugar beet plant. Experiments with different crop densities (60,000, 80,000 and 100,000 sugar beet plants per hectare) as well as without and with irrigation (based on pen evaporation or based on model-added irrigation scheduling system EDV-BB) were taken into account.featuresmeasurement methodsugarbeetlaurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=69424565-a894-4b9c-9227-b0b1b5325faa&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:geolocation_e5d1c267e5c40baf3e19032e6ff90927Six weighing lysimeters (1.5 m height; 1.0 m in diameter) with a soil surface area of 1.0 m² were installed on ground surface level at the Experimental Field Station Dedelow of the Leibniz Centre for Agricultural Landscape Research (ZALF). Water retention time series from 2012-2014 were obtained from tensiometer/MPS-1 sensors and TDR data measured in 10, 30, and 50 cm depths of six soil monoliths of lysimeters extracted from two differently managed field sites (lysimeters 1, 3, 5 from site Dedelow, lysimeters 2, 4, 6 from site Holzendorf).featuresgeolocation_e5d1c267e5c40baf3e19032e6ff90927urn:x-ogc:def:crs:EPSG:432613.802703000000001 53.36724713.802903 53.367447000000006https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=79ea07bb-e939-47e8-9370-5f0fb3a6a2eb&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:tereno_soil_probe_swr_mps_2012Six weighing lysimeters (1.5 m height; 1.0 m in diameter) with a soil surface area of 1.0 m² were installed on ground surface level at the Experimental Field Station Dedelow of the Leibniz Centre for Agricultural Landscape Research (ZALF). Water retention time series from 2012-2014 were obtained from tensiometer/MPS-1 sensors and TDR data measured in 10, 30, and 50 cm depths of six soil monoliths of lysimeters extracted from two differently managed field sites (lysimeters 1, 3, 5 from site Dedelow, lysimeters 2, 4, 6 from site Holzendorf).featurestereno_soil_probe_swr_mps_2012urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=9601f0c2-4bfc-4da2-b2f4-7ea0db72c37a&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:tereno_soil_probe_swr_mps_2013Six weighing lysimeters (1.5 m height; 1.0 m in diameter) with a soil surface area of 1.0 m² were installed on ground surface level at the Experimental Field Station Dedelow of the Leibniz Centre for Agricultural Landscape Research (ZALF). Water retention time series from 2012-2014 were obtained from tensiometer/MPS-1 sensors and TDR data measured in 10, 30, and 50 cm depths of six soil monoliths of lysimeters extracted from two differently managed field sites (lysimeters 1, 3, 5 from site Dedelow, lysimeters 2, 4, 6 from site Holzendorf).featurestereno_soil_probe_swr_mps_2013urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=cc73e191-9a81-49c8-8500-acb620297660&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:tereno_soil_probe_swr_mps_2014Six weighing lysimeters (1.5 m height; 1.0 m in diameter) with a soil surface area of 1.0 m² were installed on ground surface level at the Experimental Field Station Dedelow of the Leibniz Centre for Agricultural Landscape Research (ZALF). Water retention time series from 2012-2014 were obtained from tensiometer/MPS-1 sensors and TDR data measured in 10, 30, and 50 cm depths of six soil monoliths of lysimeters extracted from two differently managed field sites (lysimeters 1, 3, 5 from site Dedelow, lysimeters 2, 4, 6 from site Holzendorf).featurestereno_soil_probe_swr_mps_2014urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=3a5bcd44-bba0-48b1-9cc6-0627dad6bf50&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:tereno_soil_probe_swr_tdr_2012Six weighing lysimeters (1.5 m height; 1.0 m in diameter) with a soil surface area of 1.0 m² were installed on ground surface level at the Experimental Field Station Dedelow of the Leibniz Centre for Agricultural Landscape Research (ZALF). Water retention time series from 2012-2014 were obtained from tensiometer/MPS-1 sensors and TDR data measured in 10, 30, and 50 cm depths of six soil monoliths of lysimeters extracted from two differently managed field sites (lysimeters 1, 3, 5 from site Dedelow, lysimeters 2, 4, 6 from site Holzendorf).featurestereno_soil_probe_swr_tdr_2012urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=1106a0b8-cb84-45ab-acf7-116d30e7e466&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:tereno_soil_probe_swr_tdr_2013Six weighing lysimeters (1.5 m height; 1.0 m in diameter) with a soil surface area of 1.0 m² were installed on ground surface level at the Experimental Field Station Dedelow of the Leibniz Centre for Agricultural Landscape Research (ZALF). Water retention time series from 2012-2014 were obtained from tensiometer/MPS-1 sensors and TDR data measured in 10, 30, and 50 cm depths of six soil monoliths of lysimeters extracted from two differently managed field sites (lysimeters 1, 3, 5 from site Dedelow, lysimeters 2, 4, 6 from site Holzendorf).featurestereno_soil_probe_swr_tdr_2013urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=1f9fc7b6-97af-4eee-a038-5fbb93ec446a&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:tereno_soil_probe_swr_tdr_2014Six weighing lysimeters (1.5 m height; 1.0 m in diameter) with a soil surface area of 1.0 m² were installed on ground surface level at the Experimental Field Station Dedelow of the Leibniz Centre for Agricultural Landscape Research (ZALF). Water retention time series from 2012-2014 were obtained from tensiometer/MPS-1 sensors and TDR data measured in 10, 30, and 50 cm depths of six soil monoliths of lysimeters extracted from two differently managed field sites (lysimeters 1, 3, 5 from site Dedelow, lysimeters 2, 4, 6 from site Holzendorf).featurestereno_soil_probe_swr_tdr_2014urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=596b7081-ea1c-4be4-b8a6-d792ccd1a0da&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:tereno_soil_probe_swr_ts1_2012Six weighing lysimeters (1.5 m height; 1.0 m in diameter) with a soil surface area of 1.0 m² were installed on ground surface level at the Experimental Field Station Dedelow of the Leibniz Centre for Agricultural Landscape Research (ZALF). Water retention time series from 2012-2014 were obtained from tensiometer/MPS-1 sensors and TDR data measured in 10, 30, and 50 cm depths of six soil monoliths of lysimeters extracted from two differently managed field sites (lysimeters 1, 3, 5 from site Dedelow, lysimeters 2, 4, 6 from site Holzendorf).featurestereno_soil_probe_swr_ts1_2012urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=d687e66b-b45e-4a2b-b696-6c17544e5219&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:tereno_soil_probe_swr_ts1_2013Six weighing lysimeters (1.5 m height; 1.0 m in diameter) with a soil surface area of 1.0 m² were installed on ground surface level at the Experimental Field Station Dedelow of the Leibniz Centre for Agricultural Landscape Research (ZALF). Water retention time series from 2012-2014 were obtained from tensiometer/MPS-1 sensors and TDR data measured in 10, 30, and 50 cm depths of six soil monoliths of lysimeters extracted from two differently managed field sites (lysimeters 1, 3, 5 from site Dedelow, lysimeters 2, 4, 6 from site Holzendorf).featurestereno_soil_probe_swr_ts1_2013urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=44d48b56-e7f5-4d67-884e-86670ba0dad3&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:tereno_soil_probe_swr_ts1_2014Six weighing lysimeters (1.5 m height; 1.0 m in diameter) with a soil surface area of 1.0 m² were installed on ground surface level at the Experimental Field Station Dedelow of the Leibniz Centre for Agricultural Landscape Research (ZALF). Water retention time series from 2012-2014 were obtained from tensiometer/MPS-1 sensors and TDR data measured in 10, 30, and 50 cm depths of six soil monoliths of lysimeters extracted from two differently managed field sites (lysimeters 1, 3, 5 from site Dedelow, lysimeters 2, 4, 6 from site Holzendorf).featurestereno_soil_probe_swr_ts1_2014urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=c2671726-f3f9-4fa0-a4fd-1cacb283e1d9&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:dk_89The Leibniz Centre for Agricultural Landscape Research (ZALF) e.V. operated from 2007 until 2018 a lake water head monitoring in the Redernswalder See. The lake are located 7 km northwest of Angermünde, north of the stream Welse. Lake water heads were measured automatically every hour and and have been aggregated to daily mean values. The mean lake water Altitude in the series 2007-2018 was 52.5 m a.s.l.featuresdk_89urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=6a62a8ea-8ec4-4264-a93e-3da088df512c&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:geolocation_1bc13f8d9d0788f6d805e78b5e85bde9The Leibniz Centre for Agricultural Landscape Research (ZALF) e.V. operated from 2007 until 2018 a lake water head monitoring in the Redernswalder See. The lake are located 7 km northwest of Angermünde, north of the stream Welse. Lake water heads were measured automatically every hour and and have been aggregated to daily mean values. The mean lake water Altitude in the series 2007-2018 was 52.5 m a.s.l.featuresgeolocation_1bc13f8d9d0788f6d805e78b5e85bde9urn:x-ogc:def:crs:EPSG:432613.85023320120683 53.04500700687428613.85043320120683 53.04520700687429https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=864447d3-1fee-47e1-898c-e53f36baa752&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:dk148In the face of a changing climate, yield stability is becoming increasingly important for farmers and breeders. There are no commonly accepted guidelines for assessing yield stability and the large diversity of options impedes comparability of results and reduces confidence in conclusions. Here, we compile a unique list of measures available for yield stability analysis that can be used in agronomy and other disciplines. This data set is linked to the review paper “Methods of yield stability analysis in long-term field experiments. A review, that provides guidelines for quantifying yield stability in different settings. Consistent use of the suggested guidelines including the appropriate use of the measures listed in this data set may provide a basis for robust analyses of yield stability, and to subsequently design stable cropping systems that are better adapted to a changing climate.featuresexperimentationstabilitydk148yieldsurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=6665425e-af88-4da5-9615-1f7b50eed340&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:pc_base_fs_p_weather_editGeodata of patchCROP meteorological station. featurespc_base_fs_p_weather_editopendataurn:x-ogc:def:crs:EPSG:432614.135583200000042 52.44536600000003514.146795500000053 52.44879250000008https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=1583e015-8a2c-4604-a653-3c6bb0be66ed&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:meteorological_data_patchcropmeteorological_data_patchcropfeaturesopendataurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=c00b8097-38f3-4f8f-b277-cbe02184699c&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:geolocation_951f424c836c76f57ff3c0bea73bf54cTen kettle holes within six agricultural fields (crop: winter wheat) were selected for monitoring microclimatic conditions around the kettle holes. For this purpose we have established a gradient starting from the edge of the kettle holes up to 50 m into the surrounding wheat fields. Along these gradients microclimatic observation stations were installed at 4 different distances (1m, 5 m, 20 m, 50 m). At each point air temperature, air humidity, leaf wetness and soil moisture were monitored during the growing season of wheat plants (between March and July 2020).featuresgeolocation_951f424c836c76f57ff3c0bea73bf54curn:x-ogc:def:crs:EPSG:432613.47096725586944 53.2944389887517413.85511279201188 53.42902016292231https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=d776b6ad-e1c9-474c-9cc4-1d0ddaa1fc8f&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:table_mic_climate_2020_1Ten kettle holes within six agricultural fields (crop: winter wheat) were selected for monitoring microclimatic conditions around the kettle holes. For this purpose we have established a gradient starting from the edge of the kettle holes up to 50 m into the surrounding wheat fields. Along these gradients microclimatic observation stations were installed at 4 different distances (1m, 5 m, 20 m, 50 m). At each point air temperature, air humidity, leaf wetness and soil moisture were monitored during the growing season of wheat plants (between March and July 2020).featurestable_mic_climate_2020_1urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=dbc8a404-6256-4045-90b2-2dd70c2725ec&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:table_mic_climate_2020_2Ten kettle holes within six agricultural fields (crop: winter wheat) were selected for monitoring microclimatic conditions around the kettle holes. For this purpose we have established a gradient starting from the edge of the kettle holes up to 50 m into the surrounding wheat fields. Along these gradients microclimatic observation stations were installed at 4 different distances (1m, 5 m, 20 m, 50 m). At each point air temperature, air humidity, leaf wetness and soil moisture were monitored during the growing season of wheat plants (between March and July 2020).featurestable_mic_climate_2020_2urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=d114e36b-5613-4a18-9ef2-9e096ad2115e&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:table_mic_climate_2020_3Ten kettle holes within six agricultural fields (crop: winter wheat) were selected for monitoring microclimatic conditions around the kettle holes. For this purpose we have established a gradient starting from the edge of the kettle holes up to 50 m into the surrounding wheat fields. Along these gradients microclimatic observation stations were installed at 4 different distances (1m, 5 m, 20 m, 50 m). At each point air temperature, air humidity, leaf wetness and soil moisture were monitored during the growing season of wheat plants (between March and July 2020).featurestable_mic_climate_2020_3urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=33cf3d3b-4ccd-4433-8a4b-47e4d80710b1&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:table_mic_climate_2020_4Ten kettle holes within six agricultural fields (crop: winter wheat) were selected for monitoring microclimatic conditions around the kettle holes. For this purpose we have established a gradient starting from the edge of the kettle holes up to 50 m into the surrounding wheat fields. Along these gradients microclimatic observation stations were installed at 4 different distances (1m, 5 m, 20 m, 50 m). At each point air temperature, air humidity, leaf wetness and soil moisture were monitored during the growing season of wheat plants (between March and July 2020).featurestable_mic_climate_2020_4urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=8b780b7a-845a-41bb-b66b-b7cd2e8a303e&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:table_mic_climate_2020_5Ten kettle holes within six agricultural fields (crop: winter wheat) were selected for monitoring microclimatic conditions around the kettle holes. For this purpose we have established a gradient starting from the edge of the kettle holes up to 50 m into the surrounding wheat fields. Along these gradients microclimatic observation stations were installed at 4 different distances (1m, 5 m, 20 m, 50 m). At each point air temperature, air humidity, leaf wetness and soil moisture were monitored during the growing season of wheat plants (between March and July 2020).featurestable_mic_climate_2020_5urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=e1b7f4dc-acb6-4d92-9adc-f49168b1162c&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:table_mic_climate_2020_6Ten kettle holes within six agricultural fields (crop: winter wheat) were selected for monitoring microclimatic conditions around the kettle holes. For this purpose we have established a gradient starting from the edge of the kettle holes up to 50 m into the surrounding wheat fields. Along these gradients microclimatic observation stations were installed at 4 different distances (1m, 5 m, 20 m, 50 m). At each point air temperature, air humidity, leaf wetness and soil moisture were monitored during the growing season of wheat plants (between March and July 2020).featurestable_mic_climate_2020_6urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=b6beca2d-89aa-46ba-b7d3-d27b6491b816&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:heringenheringenminingfeaturesWater fluxgreeningPerennial grasscoal combustion residuesmunicipal wasteurn:x-ogc:def:crs:EPSG:43269.986033056000029 50.899801111000079.986233056000028 50.90000111100008https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=82cc2bcb-df5c-4f22-98ab-37d24cb9b11d&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:drainage_b5f87fb2d22f84745ee7148e2526b911This dataset presents the results of HYDRUS-1D simulations of root water uptake and drainage for four different substrates used in a lysimeter experiment conducted on the outskirts of Heringen, Germany. It also includes simulated water balance data covering 27 years based on meteorological records from Bad Hersfeld, Germany, using the soil hydraulic properties of substrate 1. In addition, the dataset provides analyses of total porosity and bulk density for substrate mixtures with varying proportions of fine and coarse materials. Detailed information on the substrate characteristics, experimental design, simulation procedures, and data analysis is available in Bilibio et al. (2021; https://cigrjournal.org/index.php/Ejounral/article/view/6649).Water fluxgreeningmunicipal wasteminingcoal combustion residuesPerennial grassfeaturesdrainage_b5f87fb2d22f84745ee7148e2526b911urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=8ffb6210-8380-418a-87a9-9392aa8594b0&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:tp_bd_af7673e68d60f80275ca9a85519c4446Water fluxtp_bd_af7673e68d60f80275ca9a85519c4446greeningmunicipal wasteminingPerennial grassfeaturescoal combustion residuesurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=333a193b-2aec-45b1-8c6e-aa2d89d42ea7&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:root_water_uptakeThis dataset contains simulated potential and actual root water uptake, together with substrate water storage, for four different substrates evaluated in a lysimeter experiment conducted on the outskirts of Heringen, Germany, during the 2014 and 2015 hydrological years. Detailed information on the substrate characteristics, experimental design, simulation procedures, and data analysis is provided in Bilibio et al. (2021; https://cigrjournal.org/index.php/Ejounral/article/view/6649).miningfeaturesroot_water_uptakeWater fluxgreeningPerennial grasscoal combustion residuesmunicipal wasteurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=b4774c4e-ba82-4a8f-b90e-00d27b88d14f&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:water_balance_bad_hersfeldThis dataset contains simulated water balance components for Bad Hersfeld, Germany, over 27 years based on the soil hydraulic properties of Substrate 1. Detailed information on the substrate characteristics, experimental design, simulation procedures, and data analysis is provided in Bilibio et al. (2021; https://cigrjournal.org/index.php/Ejounral/article/view/6649). Water fluxgreeningmunicipal wasteminingwater_balance_bad_hersfeldPerennial grassfeaturescoal combustion residuesurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=34837e7f-20e5-407e-aec6-96bfd8c64b91&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:weather_74ea878c8280adaf86ebe812ccec6e77This dataset contains meteorological data collected during the 2014 and 2015 hydrological years by a weather station installed on the outskirts of Heringen, Germany, as part of a lysimeter experiment. Detailed information on the substrate characteristics, experimental design, simulation procedures, and data analysis is provided in Bilibio et al. (2021; https://cigrjournal.org/index.php/Ejounral/article/view/6649).miningfeaturesWater fluxgreeningPerennial grassweather_74ea878c8280adaf86ebe812ccec6e77coal combustion residuesmunicipal wasteurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=ee1bd789-98db-49ac-92f4-1856a14e0177&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:k_s_site_b842ee1160c6ff7799656f829ba83bb9k_s_site_b842ee1160c6ff7799656f829ba83bb9Crop coefficientfeaturesRain gaugesPerennial grassPotash miningDrainageurn:x-ogc:def:crs:EPSG:43269.98603313900006 50.899801111000079.98623313900006 50.90000111100008https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=5ebe8872-a520-4540-a152-41932a875d3a&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:water_balance_c1dffb2de3523b61ba150e2557b3b13fThe dataset contains measurements of drainage and estimates of actual evapotranspiration for four different cover substrates installed in lysimeters during the monitoring period from 2013 to 2015.Crop coefficientfeaturesRain gaugeswater_balance_c1dffb2de3523b61ba150e2557b3b13fPerennial grassPotash miningDrainageurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=8b3a665e-cc54-4b8c-bb4a-dfe46161fab5&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:water_retention_curve_a78a98f4413f80ba55e248bce4468403The dataset contains the water retention characteristics (soil water retention curves) of two representative cover substrates, determined from undisturbed substrate samples collected in 2014.Crop coefficientfeaturesRain gaugeswater_retention_curve_a78a98f4413f80ba55e248bce4468403Perennial grassPotash miningDrainageurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=d07bcd92-d246-4ce9-ab9a-e9898b2e3f27&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:weather_data_1867f3f2faece70e3fa5fc6f937bf9ffThe dataset contains meteorological observations recorded by a Thies Clima weather station installed at the experimental site, together with precipitation measurements obtained from rain gauges positioned at ground level and at a 1-m height. The dataset also includes estimates of reference evapotranspiration (ET₀), Haude evapotranspiration, and crop evapotranspiration. The methods used to estimate these variables are described in Bilibio et al. (2017).Crop coefficientweather_data_1867f3f2faece70e3fa5fc6f937bf9fffeaturesRain gaugesPerennial grassPotash miningDrainageurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=fe626184-3c0f-495b-9f5e-d11730f47dfd&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:geolocation_60fde406d684315084b4a9b16bb18de5Mosquito monitoring data from active surveillance with BG-Sentinel traps from 2011 to 2017 and from passive surveillance - submissions to the "Mückenatlas" - between the years 2012 to 2017. The two different data sets are in one table, but separable by the table column "method". Locations of traps and submissions are not available due to data privacy regulations. The columns "bio.o" and "biocat" are only available for the passive surveillance entries as they depend on the participants notations about find spot that are not given in case of active surveillance by professional scientists.featuresgeolocation_60fde406d684315084b4a9b16bb18de5urn:x-ogc:def:crs:EPSG:43265.865998813138089 47.2703623267203815.03774333567461 55.05737470143498https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=8a07fc04-fca4-4a29-8ee7-a56e367ae709&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:monitoringMosquito monitoring data from active surveillance with BG-Sentinel traps from 2011 to 2017 and from passive surveillance - submissions to the "Mückenatlas" - between the years 2012 to 2017. The two different data sets are in one table, but separable by the table column "method". Locations of traps and submissions are not available due to data privacy regulations. The columns "bio.o" and "biocat" are only available for the passive surveillance entries as they depend on the participants notations about find spot that are not given in case of active surveillance by professional scientists.featuresmonitoringurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=983d4531-cb2b-4590-a51d-46d03efeeb79&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:v004_experiment_3ec834980c45d7f6c15461c6cf7661c5A multi-year (1993-1998) coherent data-set for a four-field crop rotation (sugar beet - winter wheat - winter barley - winter rye - catch crop) under organic and extensive cropping systems located at the Leibniz Centre for Agricultural Landscape Research (ZALF) Experimental Station, Müncheberg, Germany, is documented in detail. The experiment targets crop rotaion response to different cropping systems at sandy soils (Eutric Cambisol) taking into account rain-fed and irrigated conditions. Weather, soil and crop processes were intensively monitored and management actions were consistently recorded. The data set contains data for soil (water and nitrogen contents), crop (ontogenesis, plant, tiller and ear numbers, above-ground and root biomasses, yield, carbon and nitrogen content in biomass and their fractions, sugar content in beet), weather (standard meteorological variables) and management (soil tillage, sowing, fertilisation, irrigation, harvest). The methods for measurements and observations are briefly described.featuresv004_experiment_3ec834980c45d7f6c15461c6cf7661c5urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=c9289011-ac6f-4b00-bd70-a828506bfb74&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:geolocation_e7850a0b751b28ab8ad83c67c94b0da2Agricultural landscapes represent amajor component in the global carbon (C) cycle. Especially their behavior asCO2 sources or sinks is still under debate. In 2009 a long-term,interdisciplinary field experiment (CarboZALF-D) was established, toquantify all C fluxes at characteristic soil-plant-systems. Our researchfocused on the feedbacks of soil erosion on C fluxes, C balances and relateddrivers. Therefore, experimental plots were designed in a way to represent afull gradient of soil erosion: non-eroded, strongly eroded, extremelyeroded, and depositional soils. Here, we present data on basic physical andchemical soil properties, weather (incl. rainfall chemistry), treatments(farming practice), and crops (incl. LAI dynamics, biomass, yields,nutrients) of four representative plots over a seven-year period(2010-2016). Comprehensive scientific publications on complex interactionsin the soil-plant-atmosphere system are listed below.featuresgeolocation_e7850a0b751b28ab8ad83c67c94b0da2urn:x-ogc:def:crs:EPSG:432613.7835737792 53.379204032513.7878244754 53.3803019568https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=77ef1969-dc4f-454e-a19c-e347015f6c03&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:plantAgricultural landscapes represent amajor component in the global carbon (C) cycle. Especially their behavior asCO2 sources or sinks is still under debate. In 2009 a long-term,interdisciplinary field experiment (CarboZALF-D) was established, toquantify all C fluxes at characteristic soil-plant-systems. Our researchfocused on the feedbacks of soil erosion on C fluxes, C balances and relateddrivers. Therefore, experimental plots were designed in a way to represent afull gradient of soil erosion: non-eroded, strongly eroded, extremelyeroded, and depositional soils. Here, we present data on basic physical andchemical soil properties, weather (incl. rainfall chemistry), treatments(farming practice), and crops (incl. LAI dynamics, biomass, yields,nutrients) of four representative plots over a seven-year period(2010-2016). Comprehensive scientific publications on complex interactionsin the soil-plant-atmosphere system are listed below.featuresplanturn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=ac71559b-c694-42e8-b1bf-dab820a964e5&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:rainwaterAgricultural landscapes represent amajor component in the global carbon (C) cycle. Especially their behavior asCO2 sources or sinks is still under debate. In 2009 a long-term,interdisciplinary field experiment (CarboZALF-D) was established, toquantify all C fluxes at characteristic soil-plant-systems. Our researchfocused on the feedbacks of soil erosion on C fluxes, C balances and relateddrivers. Therefore, experimental plots were designed in a way to represent afull gradient of soil erosion: non-eroded, strongly eroded, extremelyeroded, and depositional soils. Here, we present data on basic physical andchemical soil properties, weather (incl. rainfall chemistry), treatments(farming practice), and crops (incl. LAI dynamics, biomass, yields,nutrients) of four representative plots over a seven-year period(2010-2016). Comprehensive scientific publications on complex interactionsin the soil-plant-atmosphere system are listed below.featuresrainwaterurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=0d17dd0f-a127-4281-b54b-d48d2f6fc36f&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:soilAgricultural landscapes represent amajor component in the global carbon (C) cycle. Especially their behavior asCO2 sources or sinks is still under debate. In 2009 a long-term,interdisciplinary field experiment (CarboZALF-D) was established, toquantify all C fluxes at characteristic soil-plant-systems. Our researchfocused on the feedbacks of soil erosion on C fluxes, C balances and relateddrivers. Therefore, experimental plots were designed in a way to represent afull gradient of soil erosion: non-eroded, strongly eroded, extremelyeroded, and depositional soils. Here, we present data on basic physical andchemical soil properties, weather (incl. rainfall chemistry), treatments(farming practice), and crops (incl. LAI dynamics, biomass, yields,nutrients) of four representative plots over a seven-year period(2010-2016). Comprehensive scientific publications on complex interactionsin the soil-plant-atmosphere system are listed below.featuressoilurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=7b8473c0-8c91-404e-9cbf-99c3ed747f9b&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:treatmentAgricultural landscapes represent amajor component in the global carbon (C) cycle. Especially their behavior asCO2 sources or sinks is still under debate. In 2009 a long-term,interdisciplinary field experiment (CarboZALF-D) was established, toquantify all C fluxes at characteristic soil-plant-systems. Our researchfocused on the feedbacks of soil erosion on C fluxes, C balances and relateddrivers. Therefore, experimental plots were designed in a way to represent afull gradient of soil erosion: non-eroded, strongly eroded, extremelyeroded, and depositional soils. Here, we present data on basic physical andchemical soil properties, weather (incl. rainfall chemistry), treatments(farming practice), and crops (incl. LAI dynamics, biomass, yields,nutrients) of four representative plots over a seven-year period(2010-2016). Comprehensive scientific publications on complex interactionsin the soil-plant-atmosphere system are listed below.featurestreatmenturn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=719fc09f-ef0e-4736-a266-31c551aa46c5&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:weatherAgricultural landscapes represent amajor component in the global carbon (C) cycle. Especially their behavior asCO2 sources or sinks is still under debate. In 2009 a long-term,interdisciplinary field experiment (CarboZALF-D) was established, toquantify all C fluxes at characteristic soil-plant-systems. Our researchfocused on the feedbacks of soil erosion on C fluxes, C balances and relateddrivers. Therefore, experimental plots were designed in a way to represent afull gradient of soil erosion: non-eroded, strongly eroded, extremelyeroded, and depositional soils. Here, we present data on basic physical andchemical soil properties, weather (incl. rainfall chemistry), treatments(farming practice), and crops (incl. LAI dynamics, biomass, yields,nutrients) of four representative plots over a seven-year period(2010-2016). Comprehensive scientific publications on complex interactionsin the soil-plant-atmosphere system are listed below.featuresweatherurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=1c79c652-276d-480b-a60e-832bf0feae30&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:geolocation_99b9e4c57a5ae38a1526fdcdeaf8f0e6Individual space use and spatial within and between species interactions of bank voles (Myodes glareolus) and striped field mice (Apodemus agrarius). Included are home range and core area sizes, intra- and interspecific home range and core area overlaps, the number of con- and heterospecific neighbours within the home ranges and core areas, the distance to the nearest neighbour, the distance to the nearest conspecific, the distance to the nearest heterospecific and mean values of the ground cover and the maximum vegetation heights in home ranges and core areas for each tracked individual.geolocation_99b9e4c57a5ae38a1526fdcdeaf8f0e6nearest neighbour distancehome range sizefeaturesurn:x-ogc:def:crs:EPSG:432613.47096725586944 53.2944389887517413.85511279201188 53.42902016292231https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=b6f79659-c062-4d78-afab-fba50fab1cda&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:spaceuse_bvsfm_16Individual space use and spatial within and between species interactions of bank voles (Myodes glareolus) and striped field mice (Apodemus agrarius). Included are home range and core area sizes, intra- and interspecific home range and core area overlaps, the number of con- and heterospecific neighbours within the home ranges and core areas, the distance to the nearest neighbour, the distance to the nearest conspecific, the distance to the nearest heterospecific and mean values of the ground cover and the maximum vegetation heights in home ranges and core areas for each tracked individual.spaceuse_bvsfm_16nearest neighbour distancehome range sizefeaturesurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=9bd3d007-3e7f-4b75-9a05-13a872238681&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:geolocation_e1c7a18092dcb34df4ada67d9097192eWe analyzed bees and their food reserves after hibernation before they leave their nests. We did this for bees from three groups with different social organizations and hibernation strategies.featuresgeolocation_e1c7a18092dcb34df4ada67d9097192eurn:x-ogc:def:crs:EPSG:432613.47096725586944 53.2944389887517413.85511279201188 53.42902016292231https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=95858d94-a7a9-4871-a677-344f343a0b47&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:geolocation_1f2b03310efe206c60afc20dc2691b0fBoldness- and Exploration scores for individuals from experimental populations in semi natural enclosures. Scores are derived from a principle component analysis.boldnessmovement ecologygeolocation_1f2b03310efe206c60afc20dc2691b0ffeaturesPCA explorationurn:x-ogc:def:crs:EPSG:432613.47096725586944 53.2944389887517413.85511279201188 53.42902016292231https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=fd141663-fb3d-4c68-9d89-e018b1401add&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:individualdifferencescores_2017Boldness- and Exploration scores for individuals from experimental populations in semi natural enclosures. Scores are derived from a principle component analysis.boldnessmovement ecologyfeaturesindividualdifferencescores_2017PCA explorationurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=ab1b7dba-23e1-4d63-8cc5-b1bca811d829&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:geolocation_280d131bed75ee1c9d4f04d917dd5a40Boldness- and Exploration scores for individuals of free ranging populations in the Uckermark. Scores are calculated with Baysian models.bayesian statisticsboldnessmovement ecologyfeaturesgeolocation_280d131bed75ee1c9d4f04d917dd5a40urn:x-ogc:def:crs:EPSG:432613.47096725586944 53.2944389887517413.85511279201188 53.42902016292231https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=4d71af67-a43f-4374-ba03-bb4bffa13e7c&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:individualdifferencescores_2016Boldness- and Exploration scores for individuals of free ranging populations in the Uckermark. Scores are calculated with Baysian models.bayesian statisticsboldnessmovement ecologyfeaturesindividualdifferencescores_2016urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=d3042252-00d8-44e3-85af-b45e1aa44c06&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:hohenheim_sitefeatureshohenheim_siteurn:x-ogc:def:crs:EPSG:43269.2014 48.7375388888888859.2016 48.73773888888889https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=f1ef47bc-ef7b-4a0e-a25c-ab5ba4a19d16&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:site_research_station_dedelow_fieldsCentral EuropeBadensite_research_station_dedelow_fieldsDedelowGermanyfeaturesUckermarkBrandenburgurn:x-ogc:def:crs:EPSG:432613.782881006000025 53.3659981000000713.806251647000067 53.380984014000035https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=3fe256f7-6e50-49cd-a3ba-13f19b1c7d0e&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:data_chickpea_genotypesClimate change requires adaptation of cropping systems and crop choices. Due to its drought tolerance, chickpea (Cicer arietinum L.) is considered a promising grain legume for Central Europe. Chickpeas are commonly classified into Desi, Kabuli, and intermediate (Pea) types, although this classification is not always clear and their characterisation under Central European conditions remains limited. To evaluate genotype performance and type characteristics, contrasting chickpea genotypes were grown in a two-year field experiment (2023-2024) at two locations in Germany (UHOH in south-west Germany and Dedelow in north-east Germany). A total of 13 morphological and agronomic traits were assessed.featuresUckermarkBadenGermanyCentral Europedata_chickpea_genotypesDedelowBrandenburgurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=d5b07c14-95c0-4cd5-b513-acb0f1e370b9&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:environmentRecord of presence or absence of all plant species occurring in the amphibian (between open water body and agricultural matrix) and terrestrial zone of the kettle hole. Identification of the species were done in the field (mostly) according to Rothmaler (2011). Soil samples collected from a total of 20 kettle holes using a clean soil corer. Greenhouse experiments under controlled conditions of watering and temperature in a block design using two treatments: wet and flood.movement ecologyenvironmentfeaturesurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=0ec7ff4f-a77d-4443-9f66-03e3a17bbb30&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:geolocation_1dd200932b1de323d79834497a6b445bThe published file contains plant structural trait data for four typical macrophyte species of kettle holes (i.e. Carex riparia, Phalaris arundinacea, Rorippa amphibia and Persicaria amphibia) that where used in the article “Allometric relationships for selected macrophytes of kettle holes in northeast Germany as a basis for efficient biomass estimation using unmanned aerial systems (UAS)”. The surveyed kettle holes are located in the AgroScapeLab Quillow, an intensively cultivated agricultural young moraine landscape 90 km north of Berlin, Germany. In total, 12 kettle holes were chosen. Within each kettle hole three habitats were selected per species. In each of the three species specific habitats one sample (0.25 x 0.25 m plots) was taken monthly from May to July 2016.geolocation_1dd200932b1de323d79834497a6b445bbiomassfeatureskettle holeurn:x-ogc:def:crs:EPSG:432613.47096725586944 53.2944389887517413.85511279201188 53.42902016292231https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=0cb7736e-813e-4274-9e17-0c2209063405&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:quillow_chem_wqquillow_chem_wqQuillowR-scriptfeaturesstream waterurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=69110f32-b823-48f1-ab92-52df9aa278c6&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:quillow_gw_hco3_fe2featuresstream waterQuillowR-scriptquillow_gw_hco3_fe2urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=70f2b4e2-16ff-47a8-ad0e-8dd770ec4587&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:model_compl_cases_37c1d2615dacaa406c8ed47c19e72005The data available in connection with the publication are the data for the respective raster maps as well as the data table for modelling. (1) Raster data (count_covar_raster.grd): The data are stored as a raster stack with 9 layers. The layers are named as follows: Layer 1: "subs", the number of submissions Layer 2: "pop", the number of inhabitants Layer 3: "age", the average age of the population Layer 4: "fem", the percentage of the female population Layer 5: "temp", the average temperature in °C from March to November (2012-2017) Layer 6: "preci" the average rainfall in mm from March to November (2012-2017) Layer 7: "wind", the average wind speed in m/s Layer 8: "water", the presence of a larger, standing water body (yes=1, no=0) Layer 9: "east", the location of the grid cell in former political East Germany (yes=1, no=0) in the respective grid cell. (2) Raster data with complete cases for the predictors respected in the Automated modelling selection as data table (model_compl_cases.csv). The column names correspond to the raster data layer names. The variables are additionally described in the "Table Structure" section. The data used for the submission counts are an export of the nationwide database "CULBASE" that contained all data from mosquito monitoring since start of the nationwide monitoring programme in 2011. The CULBASE merged into the new database VECTORBASE in September 2020. Both databases are not publicly.featuresmodel_compl_cases_37c1d2615dacaa406c8ed47c19e72005urn:x-ogc:def:crs:EPSG:43265.89125035064801 47.33338359972470514.991250350647999 55.0333835997247https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=bf7ccea3-9cd5-4db3-b409-0018b8da1fca&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:geolocation_f6b265bd7893a634bd07e451a6a0d92fAgricultural landscapes represent amajor component in the global carbon (C) cycle. Especially their behavior asCO2 sources or sinks is still under debate. In 2009 a long-term,interdisciplinary field experiment (CarboZALF-D) was established, toquantify all C fluxes at characteristic soil-plant-systems. Our researchfocused on the feedbacks of soil erosion on C fluxes, C balances and relateddrivers. Therefore, experimental plots were designed in a way to represent afull gradient of soil erosion: non-eroded, strongly eroded, extremelyeroded, and depositional soils. Here, we present data on basic physical andchemical soil properties, weather (incl. rainfall chemistry), treatments(farming practice), and crops (incl. LAI dynamics, biomass, yields,nutrients) of four representative plots over a seven-year period(2010-2016). Comprehensive scientific publications on complex interactionsin the soil-plant-atmosphere system are listed below.featuresGross primary productivityecosystem respirationautotrophic respirationnet ecosystem carbon balanceheterotrophic respirationlight inhibition of leaf respirationgeolocation_f6b265bd7893a634bd07e451a6a0d92furn:x-ogc:def:crs:EPSG:432614.116524 52.51672314.116724 52.516923000000006https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=b3428424-7164-4801-b537-eb2bee931cfd&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:measured_co2_fluxes_1st_pot_expAgricultural landscapes represent amajor component in the global carbon (C) cycle. Especially their behavior asCO2 sources or sinks is still under debate. In 2009 a long-term,interdisciplinary field experiment (CarboZALF-D) was established, toquantify all C fluxes at characteristic soil-plant-systems. Our researchfocused on the feedbacks of soil erosion on C fluxes, C balances and relateddrivers. Therefore, experimental plots were designed in a way to represent afull gradient of soil erosion: non-eroded, strongly eroded, extremelyeroded, and depositional soils. Here, we present data on basic physical andchemical soil properties, weather (incl. rainfall chemistry), treatments(farming practice), and crops (incl. LAI dynamics, biomass, yields,nutrients) of four representative plots over a seven-year period(2010-2016). Comprehensive scientific publications on complex interactionsin the soil-plant-atmosphere system are listed below.featuresGross primary productivitymeasured_co2_fluxes_1st_pot_expecosystem respirationautotrophic respirationnet ecosystem carbon balanceheterotrophic respirationlight inhibition of leaf respirationurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=14cdfd60-b95b-406c-ad32-f3c8f71ad3c6&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:measured_co2_fluxes_2nd_pot_expAgricultural landscapes represent amajor component in the global carbon (C) cycle. Especially their behavior asCO2 sources or sinks is still under debate. In 2009 a long-term,interdisciplinary field experiment (CarboZALF-D) was established, toquantify all C fluxes at characteristic soil-plant-systems. Our researchfocused on the feedbacks of soil erosion on C fluxes, C balances and relateddrivers. Therefore, experimental plots were designed in a way to represent afull gradient of soil erosion: non-eroded, strongly eroded, extremelyeroded, and depositional soils. Here, we present data on basic physical andchemical soil properties, weather (incl. rainfall chemistry), treatments(farming practice), and crops (incl. LAI dynamics, biomass, yields,nutrients) of four representative plots over a seven-year period(2010-2016). Comprehensive scientific publications on complex interactionsin the soil-plant-atmosphere system are listed below.ecosystem respirationmeasured_co2_fluxes_2nd_pot_expGross primary productivityfeatureslight inhibition of leaf respirationautotrophic respirationheterotrophic respirationnet ecosystem carbon balanceurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=f710ad29-140c-4cc8-a481-0f03cde1ba1f&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:measured_daytime_ra_d_and_nighttime_ra_n_leaf_respirationAgricultural landscapes represent amajor component in the global carbon (C) cycle. Especially their behavior asCO2 sources or sinks is still under debate. In 2009 a long-term,interdisciplinary field experiment (CarboZALF-D) was established, toquantify all C fluxes at characteristic soil-plant-systems. Our researchfocused on the feedbacks of soil erosion on C fluxes, C balances and relateddrivers. Therefore, experimental plots were designed in a way to represent afull gradient of soil erosion: non-eroded, strongly eroded, extremelyeroded, and depositional soils. Here, we present data on basic physical andchemical soil properties, weather (incl. rainfall chemistry), treatments(farming practice), and crops (incl. LAI dynamics, biomass, yields,nutrients) of four representative plots over a seven-year period(2010-2016). Comprehensive scientific publications on complex interactionsin the soil-plant-atmosphere system are listed below.ecosystem respirationmeasured_daytime_ra_d_and_nighttime_ra_n_leaf_respirationGross primary productivityfeatureslight inhibition of leaf respirationautotrophic respirationheterotrophic respirationnet ecosystem carbon balanceurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=3d181272-7761-4bf7-91a7-8296fd774e62&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:modelled_co2_exchangeAgricultural landscapes represent amajor component in the global carbon (C) cycle. Especially their behavior asCO2 sources or sinks is still under debate. In 2009 a long-term,interdisciplinary field experiment (CarboZALF-D) was established, toquantify all C fluxes at characteristic soil-plant-systems. Our researchfocused on the feedbacks of soil erosion on C fluxes, C balances and relateddrivers. Therefore, experimental plots were designed in a way to represent afull gradient of soil erosion: non-eroded, strongly eroded, extremelyeroded, and depositional soils. Here, we present data on basic physical andchemical soil properties, weather (incl. rainfall chemistry), treatments(farming practice), and crops (incl. LAI dynamics, biomass, yields,nutrients) of four representative plots over a seven-year period(2010-2016). Comprehensive scientific publications on complex interactionsin the soil-plant-atmosphere system are listed below.ecosystem respirationGross primary productivityfeatureslight inhibition of leaf respirationautotrophic respirationheterotrophic respirationnet ecosystem carbon balancemodelled_co2_exchangeurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=6bce6216-bfcc-4031-bf7b-b76c9f9a08af&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:simulated_co2_exchangeAgricultural landscapes represent amajor component in the global carbon (C) cycle. Especially their behavior asCO2 sources or sinks is still under debate. In 2009 a long-term,interdisciplinary field experiment (CarboZALF-D) was established, toquantify all C fluxes at characteristic soil-plant-systems. Our researchfocused on the feedbacks of soil erosion on C fluxes, C balances and relateddrivers. Therefore, experimental plots were designed in a way to represent afull gradient of soil erosion: non-eroded, strongly eroded, extremelyeroded, and depositional soils. Here, we present data on basic physical andchemical soil properties, weather (incl. rainfall chemistry), treatments(farming practice), and crops (incl. LAI dynamics, biomass, yields,nutrients) of four representative plots over a seven-year period(2010-2016). Comprehensive scientific publications on complex interactionsin the soil-plant-atmosphere system are listed below.ecosystem respirationGross primary productivitysimulated_co2_exchangefeatureslight inhibition of leaf respirationautotrophic respirationheterotrophic respirationnet ecosystem carbon balanceurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=7452f6c3-c6e8-4055-a941-f7a51e45a8d1&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:weather_dataAgricultural landscapes represent amajor component in the global carbon (C) cycle. Especially their behavior asCO2 sources or sinks is still under debate. In 2009 a long-term,interdisciplinary field experiment (CarboZALF-D) was established, toquantify all C fluxes at characteristic soil-plant-systems. Our researchfocused on the feedbacks of soil erosion on C fluxes, C balances and relateddrivers. Therefore, experimental plots were designed in a way to represent afull gradient of soil erosion: non-eroded, strongly eroded, extremelyeroded, and depositional soils. Here, we present data on basic physical andchemical soil properties, weather (incl. rainfall chemistry), treatments(farming practice), and crops (incl. LAI dynamics, biomass, yields,nutrients) of four representative plots over a seven-year period(2010-2016). Comprehensive scientific publications on complex interactionsin the soil-plant-atmosphere system are listed below.ecosystem respirationGross primary productivityfeatureslight inhibition of leaf respirationautotrophic respirationheterotrophic respirationweather_datanet ecosystem carbon balanceurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=dc3eed08-376a-48c5-b974-3268c809e3fe&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:geolocation_a3493a19e44450079ef49362a42e0b65featuresgeolocation_a3493a19e44450079ef49362a42e0b65urn:x-ogc:def:crs:EPSG:432613.47096725586944 53.2944389887517413.85511279201188 53.42902016292231https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=59629266-b25a-454d-b8a7-cd611dd3de5f&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:bcaUltraviolet radiation (UVR) is an environmental stressor in several ecosystems and can affect organisms’ survival and reproduction, and community structure. Rotifers cope with UVR stress adopting preventive behavioral and metabolic mechanisms. However, the demonstration of an immediate behavioral response in rotifers is missing. We investigated the short-term response of rotifers to UVR, by combining video analysis and movement ecology methods, in three common species: Brachionus calyciflorus, Keratella cochlearis, and Keratella quadrata. We recorded the behavior of B. calyciflorus (both sexes), K. quadrata, and K. cochlearis (females) exposed to white light, and to intermittent cycles of UVR (30:30 s). Individual trajectories were extracted from videos with open-source software. We found that B. calyciflorus females exposed to UVR exhibited strong negative phototaxis with increased swimming speed, and a weak positive phototaxis in males. Keratella cochlearis and K. quadrata showed a weaker response. Our study reveals a species-specific behavioral response to UVR in rotifers. Furthermore, we highlight how sexual dimorphism in B. calyciflorus does not only occur in morphology and movement, but also in behavioral traits. Our results help to understand zooplankton community dynamics by providing a mechanistic explanation of UVR response in one major zooplankton taxonomic group.featuresbcaurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=fdd5bcb7-c295-45fe-b5fb-573828d4ebf5&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:kcoUltraviolet radiation (UVR) is an environmental stressor in several ecosystems and can affect organisms’ survival and reproduction, and community structure. Rotifers cope with UVR stress adopting preventive behavioral and metabolic mechanisms. However, the demonstration of an immediate behavioral response in rotifers is missing. We investigated the short-term response of rotifers to UVR, by combining video analysis and movement ecology methods, in three common species: Brachionus calyciflorus, Keratella cochlearis, and Keratella quadrata. We recorded the behavior of B. calyciflorus (both sexes), K. quadrata, and K. cochlearis (females) exposed to white light, and to intermittent cycles of UVR (30:30 s). Individual trajectories were extracted from videos with open-source software. We found that B. calyciflorus females exposed to UVR exhibited strong negative phototaxis with increased swimming speed, and a weak positive phototaxis in males. Keratella cochlearis and K. quadrata showed a weaker response. Our study reveals a species-specific behavioral response to UVR in rotifers. Furthermore, we highlight how sexual dimorphism in B. calyciflorus does not only occur in morphology and movement, but also in behavioral traits. Our results help to understand zooplankton community dynamics by providing a mechanistic explanation of UVR response in one major zooplankton taxonomic group.featureskcourn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=b17fc07f-8ec7-49f8-9ee7-7c0ac9337b91&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:kquUltraviolet radiation (UVR) is an environmental stressor in several ecosystems and can affect organisms’ survival and reproduction, and community structure. Rotifers cope with UVR stress adopting preventive behavioral and metabolic mechanisms. However, the demonstration of an immediate behavioral response in rotifers is missing. We investigated the short-term response of rotifers to UVR, by combining video analysis and movement ecology methods, in three common species: Brachionus calyciflorus, Keratella cochlearis, and Keratella quadrata. We recorded the behavior of B. calyciflorus (both sexes), K. quadrata, and K. cochlearis (females) exposed to white light, and to intermittent cycles of UVR (30:30 s). Individual trajectories were extracted from videos with open-source software. We found that B. calyciflorus females exposed to UVR exhibited strong negative phototaxis with increased swimming speed, and a weak positive phototaxis in males. Keratella cochlearis and K. quadrata showed a weaker response. Our study reveals a species-specific behavioral response to UVR in rotifers. Furthermore, we highlight how sexual dimorphism in B. calyciflorus does not only occur in morphology and movement, but also in behavioral traits. Our results help to understand zooplankton community dynamics by providing a mechanistic explanation of UVR response in one major zooplankton taxonomic group.featureskquurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=b3625a84-27f0-4fd4-9b54-bf1f85dc0039&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:2005_312_deu_ste_2_3_4_5_env_dataSap flux density was measured in 10 stems of a mature beech forest (Fagus sylvatica L.) in the northeastern lowlands of Germany during vegetation periods of years 2002 to 2005. The site represents an intensive monitoring core plot (DE1207) of the ICP Forests level II program (http://icp-forests.net/). The stand was growing on groundwater-distant, weakly degraded soil (Dystric Arenosol; FAO classification 1974). The humus type is moder. It was established in 1888 as a plantation under Scots pine (Pinus sylvestris L.) trees. Investigated trunks are described by stem diameter at breast height and tree height. Sap flow density of several depths of the trunks and data of the air temperature, air humidiy, global radiation, wind speed, and precipitation are given as hourly values. Xylem sap flux measurements performed to trees and tree stands allow differentiation of transpiration by different vegetation layers. The total transpiration of the canopy corresponds to the water flow through the trunk. Scaling from the sap flow measurement to stand transpiration is a two-step-procedure: firstly from sensor to tree and secondly from tree to stand. For the first step a radial profile of sap flow density is used. For scaling from tree to stand the structural inhomogeneity of the stand must be considered. The importance of this point is in even aged stands constricted, however, measurements should incorporate the differences of basal area and crown projected area of the trees.features2005_312_deu_ste_2_3_4_5_env_dataurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=970ed4bb-4133-4d3f-bdfe-ebf1a582372b&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:2005_312_deu_ste_sapflow_data_2002_2003Sap flux density was measured in 10 stems of a mature beech forest (Fagus sylvatica L.) in the northeastern lowlands of Germany during vegetation periods of years 2002 to 2005. The site represents an intensive monitoring core plot (DE1207) of the ICP Forests level II program (http://icp-forests.net/). The stand was growing on groundwater-distant, weakly degraded soil (Dystric Arenosol; FAO classification 1974). The humus type is moder. It was established in 1888 as a plantation under Scots pine (Pinus sylvestris L.) trees. Investigated trunks are described by stem diameter at breast height and tree height. Sap flow density of several depths of the trunks and data of the air temperature, air humidiy, global radiation, wind speed, and precipitation are given as hourly values. Xylem sap flux measurements performed to trees and tree stands allow differentiation of transpiration by different vegetation layers. The total transpiration of the canopy corresponds to the water flow through the trunk. Scaling from the sap flow measurement to stand transpiration is a two-step-procedure: firstly from sensor to tree and secondly from tree to stand. For the first step a radial profile of sap flow density is used. For scaling from tree to stand the structural inhomogeneity of the stand must be considered. The importance of this point is in even aged stands constricted, however, measurements should incorporate the differences of basal area and crown projected area of the trees.features2005_312_deu_ste_sapflow_data_2002_2003urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=afdfd93e-1f3f-42a9-a50d-1bda094cf4dc&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:2005_312_deu_ste_sapflow_data_2004_2005Sap flux density was measured in 10 stems of a mature beech forest (Fagus sylvatica L.) in the northeastern lowlands of Germany during vegetation periods of years 2002 to 2005. The site represents an intensive monitoring core plot (DE1207) of the ICP Forests level II program (http://icp-forests.net/). The stand was growing on groundwater-distant, weakly degraded soil (Dystric Arenosol; FAO classification 1974). The humus type is moder. It was established in 1888 as a plantation under Scots pine (Pinus sylvestris L.) trees. Investigated trunks are described by stem diameter at breast height and tree height. Sap flow density of several depths of the trunks and data of the air temperature, air humidiy, global radiation, wind speed, and precipitation are given as hourly values. Xylem sap flux measurements performed to trees and tree stands allow differentiation of transpiration by different vegetation layers. The total transpiration of the canopy corresponds to the water flow through the trunk. Scaling from the sap flow measurement to stand transpiration is a two-step-procedure: firstly from sensor to tree and secondly from tree to stand. For the first step a radial profile of sap flow density is used. For scaling from tree to stand the structural inhomogeneity of the stand must be considered. The importance of this point is in even aged stands constricted, however, measurements should incorporate the differences of basal area and crown projected area of the trees.features2005_312_deu_ste_sapflow_data_2004_2005urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=188cffeb-1d62-4d9a-aa68-9adced6c503d&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:geolocation_18f22356876142a9d5b57a10b8246d73Sap flux density was measured in 10 stems of a mature beech forest (Fagus sylvatica L.) in the northeastern lowlands of Germany during vegetation periods of years 2002 to 2005. The site represents an intensive monitoring core plot (DE1207) of the ICP Forests level II program (http://icp-forests.net/). The stand was growing on groundwater-distant, weakly degraded soil (Dystric Arenosol; FAO classification 1974). The humus type is moder. It was established in 1888 as a plantation under Scots pine (Pinus sylvestris L.) trees. Investigated trunks are described by stem diameter at breast height and tree height. Sap flow density of several depths of the trunks and data of the air temperature, air humidiy, global radiation, wind speed, and precipitation are given as hourly values. Xylem sap flux measurements performed to trees and tree stands allow differentiation of transpiration by different vegetation layers. The total transpiration of the canopy corresponds to the water flow through the trunk. Scaling from the sap flow measurement to stand transpiration is a two-step-procedure: firstly from sensor to tree and secondly from tree to stand. For the first step a radial profile of sap flow density is used. For scaling from tree to stand the structural inhomogeneity of the stand must be considered. The importance of this point is in even aged stands constricted, however, measurements should incorporate the differences of basal area and crown projected area of the trees.featuresgeolocation_18f22356876142a9d5b57a10b8246d73urn:x-ogc:def:crs:EPSG:432612.9999 53.199913.0001 53.200100000000006https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=de678af4-c27f-4266-ab67-6b8614abf1a6&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:exampleresultfile4bioeconomicmodellingThis data set contains files that are necessary for farm allocation method. They accompany the article "Re-creating the location of farms from non-spatial data: spatial allocation of single farm data" (authors: Sandra Uthes, Joachim Kiesel), submitted to the journal Agricultural Systems. The allocation methods was developed for modelling tasks requiring spatially-explicit farm locations, for example, to simulate land market decisions. Usually spatially-explicit farm data is not available for reasons of data protection and only farm typology data with aggregated spatial information (such as the share of arable land or the mean soil fertility of the farm) is at hand. Our method considers several landscape parameters for the allocation of farms, using a fine resolution (25m*25m) and introducing allocation quality indicators that allow for an assessment of the overall allocation result. The approach is implemented in the Geographical Information System (GIS) ArcInfo/ArcGIS by ESRI using the tools and script language (AML) offered by this software and operated on a BS Solares Unix Server.featuresexampleresultfile4bioeconomicmodellingurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=90f822d0-6af8-4079-a318-86c05c69907e&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:eva_soil_tbl_bo_koordThe data set contains co-ordinates, soil types with soil physical and chemical data and landscape as well as soil profile photographs from 11 experimental sites of stations participating in a FNR-project "Energy plants"featureseva_soil_tbl_bo_koordurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=2086bf9d-0468-40ed-b066-56b5d281b6f6&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:eva_soil_tbl_bo_profileThe data set contains co-ordinates, soil types with soil physical and chemical data and landscape as well as soil profile photographs from 11 experimental sites of stations participating in a FNR-project "Energy plants"featureseva_soil_tbl_bo_profileurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=4d4a82f7-f84c-403b-901e-467dc9d146d4&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:geolocation_ead4759ec2c135d3babb28d072b4df96The data set contains co-ordinates, soil types with soil physical and chemical data and landscape as well as soil profile photographs from 11 experimental sites of stations participating in a FNR-project "Energy plants"geolocation_ead4759ec2c135d3babb28d072b4df96featuresurn:x-ogc:def:crs:EPSG:43267.643498936435446 48.9320543863581913.21477837179484 53.81252364484133https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=b7edb4d0-428a-4cb4-881a-5f396f866edb&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:hortic_basicdataA set of 36 commercial horticultural substrates was selected and the hydraulic properties (water retention curve, unsaturated hydraulic conductivity function, capillary rise and shrinkage) were measured with the extended evaporation method (EEM). Additionally, the water drop penetration time was determined as a measure of wettability. Here the raw data are presented.featureshortic_basicdataurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=20819c7f-45f0-40aa-817f-46c6d9e57b1a&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:hortic_drybulkdA set of 36 commercial horticultural substrates was selected and the hydraulic properties (water retention curve, unsaturated hydraulic conductivity function, capillary rise and shrinkage) were measured with the extended evaporation method (EEM). Additionally, the water drop penetration time was determined as a measure of wettability. Here the raw data are presented.featureshortic_drybulkdurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=82567a50-bcca-479a-8756-b2a8f1e1da90&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:hortic_rawcon_pfA set of 36 commercial horticultural substrates was selected and the hydraulic properties (water retention curve, unsaturated hydraulic conductivity function, capillary rise and shrinkage) were measured with the extended evaporation method (EEM). Additionally, the water drop penetration time was determined as a measure of wettability. Here the raw data are presented.featureshortic_rawcon_pfurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=f1899194-7a5e-464f-9d4b-2abf63de8a33&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:hortic_rawcon_wcA set of 36 commercial horticultural substrates was selected and the hydraulic properties (water retention curve, unsaturated hydraulic conductivity function, capillary rise and shrinkage) were measured with the extended evaporation method (EEM). Additionally, the water drop penetration time was determined as a measure of wettability. Here the raw data are presented.featureshortic_rawcon_wcurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=afa57c68-9e86-4648-aa84-d8dbc0904fa5&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:hortic_rawretA set of 36 commercial horticultural substrates was selected and the hydraulic properties (water retention curve, unsaturated hydraulic conductivity function, capillary rise and shrinkage) were measured with the extended evaporation method (EEM). Additionally, the water drop penetration time was determined as a measure of wettability. Here the raw data are presented.featureshortic_rawreturn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=90d589ff-6e6d-4dad-aa48-d267cf7109e6&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:int_basicdataThis dataset contains a data base of 173 soil hydrological data (raw data) from 71 sites all over the world (Asia, Africa, Australia America and Europe). The samples were mainly collected and measured as part of research projects. The soils cover a wide range of texture classes and dry bulk densities. The data base consists of water retention and unsaturated hydraulic conductivity data. Additional data of the particle size distribution, the dry bulk density, the organic matter content and other variables are included. The hydraulic data were measured with the Extended Evaporation Method (EEM) and the HYPROP device in the range between saturation and close to the wilting point. Information to the geo reference, the soil type, the sampling depth and the horizon are given.featuresint_basicdataurn:x-ogc:def:crs:EPSG:4326-118.62430999999998 -43.55153399999995175.16022600000008 56.88583300000005https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=83058d7d-f297-468e-b3f2-45b676c041e1&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:int_rawpsdThis dataset contains a data base of 173 soil hydrological data (raw data) from 71 sites all over the world (Asia, Africa, Australia America and Europe). The samples were mainly collected and measured as part of research projects. The soils cover a wide range of texture classes and dry bulk densities. The data base consists of water retention and unsaturated hydraulic conductivity data. Additional data of the particle size distribution, the dry bulk density, the organic matter content and other variables are included. The hydraulic data were measured with the Extended Evaporation Method (EEM) and the HYPROP device in the range between saturation and close to the wilting point. Information to the geo reference, the soil type, the sampling depth and the horizon are given.featuresint_rawpsdurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=9d428965-f8f4-4925-8c15-e3ca8169a680&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:int_rawunsatThis dataset contains a data base of 173 soil hydrological data (raw data) from 71 sites all over the world (Asia, Africa, Australia America and Europe). The samples were mainly collected and measured as part of research projects. The soils cover a wide range of texture classes and dry bulk densities. The data base consists of water retention and unsaturated hydraulic conductivity data. Additional data of the particle size distribution, the dry bulk density, the organic matter content and other variables are included. The hydraulic data were measured with the Extended Evaporation Method (EEM) and the HYPROP device in the range between saturation and close to the wilting point. Information to the geo reference, the soil type, the sampling depth and the horizon are given.int_rawunsatfeaturesurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=e160dee8-6b27-42d0-8875-d9814ee08ce4&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:a_percremWe here examine how sufficiently high cell densities of nectar yeast can develop in a flower. In laboratory experiments, we determined the remaining fraction of nectar yeast cells after nectar removal, and used honeybees to determine the number of transmitted yeast cells from one flower to the next. The results of these experiments directly fed into a simulation model providing an insight into movement and colonization ecology of nectar yeasts. To understand the effect of many consecutive pollination events on population size and dispersal potential we developed a stochastic simulation model (NetLogo 5.3.1; Wilensky 1999) of nectar yeasts in one single flower. The model calculates the population size and the amount of dispersed cells of a single nectar yeast population over time, dependent on: pollination time and chance, inoculated cells during first pollination event, transmitted cells to the next flower, cells that remain in the flower during pollination, nectar production rate and growth rate of yeast cells with lag phase. Modelling was done with NetLogo 5.3.1 (Wilensky 1999). The model works stochastically, exclusively with global variables without individuals or space. One time step is one hour.a_percremfeaturesurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=e91a9f15-3346-4339-9d67-b0ae5d6d6381&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:b_transportationWe here examine how sufficiently high cell densities of nectar yeast can develop in a flower. In laboratory experiments, we determined the remaining fraction of nectar yeast cells after nectar removal, and used honeybees to determine the number of transmitted yeast cells from one flower to the next. The results of these experiments directly fed into a simulation model providing an insight into movement and colonization ecology of nectar yeasts. To understand the effect of many consecutive pollination events on population size and dispersal potential we developed a stochastic simulation model (NetLogo 5.3.1; Wilensky 1999) of nectar yeasts in one single flower. The model calculates the population size and the amount of dispersed cells of a single nectar yeast population over time, dependent on: pollination time and chance, inoculated cells during first pollination event, transmitted cells to the next flower, cells that remain in the flower during pollination, nectar production rate and growth rate of yeast cells with lag phase. Modelling was done with NetLogo 5.3.1 (Wilensky 1999). The model works stochastically, exclusively with global variables without individuals or space. One time step is one hour.featuresb_transportationurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=da13dcb4-d5bd-43f4-a52a-c0abf59a71ba&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:c_dispersalWe here examine how sufficiently high cell densities of nectar yeast can develop in a flower. In laboratory experiments, we determined the remaining fraction of nectar yeast cells after nectar removal, and used honeybees to determine the number of transmitted yeast cells from one flower to the next. The results of these experiments directly fed into a simulation model providing an insight into movement and colonization ecology of nectar yeasts. To understand the effect of many consecutive pollination events on population size and dispersal potential we developed a stochastic simulation model (NetLogo 5.3.1; Wilensky 1999) of nectar yeasts in one single flower. The model calculates the population size and the amount of dispersed cells of a single nectar yeast population over time, dependent on: pollination time and chance, inoculated cells during first pollination event, transmitted cells to the next flower, cells that remain in the flower during pollination, nectar production rate and growth rate of yeast cells with lag phase. Modelling was done with NetLogo 5.3.1 (Wilensky 1999). The model works stochastically, exclusively with global variables without individuals or space. One time step is one hour.featuresc_dispersalurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=1b448de3-1f13-4022-9b75-6dae02abb36a&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:geolocation_10c1e3cec908b81788b7f58fbba4031dWe here examine how sufficiently high cell densities of nectar yeast can develop in a flower. In laboratory experiments, we determined the remaining fraction of nectar yeast cells after nectar removal, and used honeybees to determine the number of transmitted yeast cells from one flower to the next. The results of these experiments directly fed into a simulation model providing an insight into movement and colonization ecology of nectar yeasts. To understand the effect of many consecutive pollination events on population size and dispersal potential we developed a stochastic simulation model (NetLogo 5.3.1; Wilensky 1999) of nectar yeasts in one single flower. The model calculates the population size and the amount of dispersed cells of a single nectar yeast population over time, dependent on: pollination time and chance, inoculated cells during first pollination event, transmitted cells to the next flower, cells that remain in the flower during pollination, nectar production rate and growth rate of yeast cells with lag phase. Modelling was done with NetLogo 5.3.1 (Wilensky 1999). The model works stochastically, exclusively with global variables without individuals or space. One time step is one hour.featuresgeolocation_10c1e3cec908b81788b7f58fbba4031durn:x-ogc:def:crs:EPSG:432613.47096725586944 53.2944389887517413.85511279201188 53.42902016292231https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=bbfd35ef-d421-4444-8f1f-daa232ee7b39&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:model_nectaryeast_growthWe here examine how sufficiently high cell densities of nectar yeast can develop in a flower. In laboratory experiments, we determined the remaining fraction of nectar yeast cells after nectar removal, and used honeybees to determine the number of transmitted yeast cells from one flower to the next. The results of these experiments directly fed into a simulation model providing an insight into movement and colonization ecology of nectar yeasts. To understand the effect of many consecutive pollination events on population size and dispersal potential we developed a stochastic simulation model (NetLogo 5.3.1; Wilensky 1999) of nectar yeasts in one single flower. The model calculates the population size and the amount of dispersed cells of a single nectar yeast population over time, dependent on: pollination time and chance, inoculated cells during first pollination event, transmitted cells to the next flower, cells that remain in the flower during pollination, nectar production rate and growth rate of yeast cells with lag phase. Modelling was done with NetLogo 5.3.1 (Wilensky 1999). The model works stochastically, exclusively with global variables without individuals or space. One time step is one hour.featuresmodel_nectaryeast_growthurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=5f755d16-7dd4-402f-984d-9e094f584dfe&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:data_puneThe survey is based on a questionnaire containing 56 closed questions that covers 111 home gardeners in Pune, India. Questions cover growing decisions and cultivation practice, including fertilization, pesticide use, irrigation and more, as well as the cultural and recreational use of the garden. Additionally socio-economic characteristics and motivations of gardeners are covered. The data was gathered by direct on-site interviews between January and May 2014. Respondents were recruited via snowball sampling starting with members of the local gardening club INORA (www.inora.in).featuresdata_puneurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=719a2c16-5d97-4be3-b278-62101022f635&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:geolocation_e2ea0b86bf652a4aa343a8d10d937360The survey is based on a questionnaire containing 56 closed questions that covers 111 home gardeners in Pune, India. Questions cover growing decisions and cultivation practice, including fertilization, pesticide use, irrigation and more, as well as the cultural and recreational use of the garden. Additionally socio-economic characteristics and motivations of gardeners are covered. The data was gathered by direct on-site interviews between January and May 2014. Respondents were recruited via snowball sampling starting with members of the local gardening club INORA (www.inora.in).featuresgeolocation_e2ea0b86bf652a4aa343a8d10d937360urn:x-ogc:def:crs:EPSG:432673.85664369999999 18.520330373.8568437 18.5205303https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=446afd1c-e0e8-483c-b7fa-dfe3e250a28b&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:bacteriaWe selected a wheat field characterized by a diversified topography, to be responsible for variations in productivity and in canopy-driven microclimate. Fusarium and Alternaria mycotoxins where quantified in wheat ears at three sampling dates between flowering and harvest at 40 points. Tenuazonic acid (TeA), alternariol (AOH), alternariol monomethyl ether (AME), tentoxin (TEN), deoxynivalenol (DON), zearalenone (ZEN) and deoxynivalenol-3-Glucoside (DON.3G) were quantified. In canopy temperature, air and soil humidity were recorded for each point with data-loggers. Fusarium spp. as trichothecene producers, Alternaria spp. and fungal abundances were assessed using qPCR. Pseudomonas fluorescens bacteria were quantified with a culture based methodFusarium head blightfeaturesbacteriaurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=30abd1f9-c896-44e4-a2bd-6ae24407bb9c&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:envWe selected a wheat field characterized by a diversified topography, to be responsible for variations in productivity and in canopy-driven microclimate. Fusarium and Alternaria mycotoxins where quantified in wheat ears at three sampling dates between flowering and harvest at 40 points. Tenuazonic acid (TeA), alternariol (AOH), alternariol monomethyl ether (AME), tentoxin (TEN), deoxynivalenol (DON), zearalenone (ZEN) and deoxynivalenol-3-Glucoside (DON.3G) were quantified. In canopy temperature, air and soil humidity were recorded for each point with data-loggers. Fusarium spp. as trichothecene producers, Alternaria spp. and fungal abundances were assessed using qPCR. Pseudomonas fluorescens bacteria were quantified with a culture based methodFusarium head blightenvfeaturesurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=9e49501e-3e46-4870-8920-0b636066ec67&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:geolocation_8dfbb3adf5db6b7115f15823e5ccfaaeWe selected a wheat field characterized by a diversified topography, to be responsible for variations in productivity and in canopy-driven microclimate. Fusarium and Alternaria mycotoxins where quantified in wheat ears at three sampling dates between flowering and harvest at 40 points. Tenuazonic acid (TeA), alternariol (AOH), alternariol monomethyl ether (AME), tentoxin (TEN), deoxynivalenol (DON), zearalenone (ZEN) and deoxynivalenol-3-Glucoside (DON.3G) were quantified. In canopy temperature, air and soil humidity were recorded for each point with data-loggers. Fusarium spp. as trichothecene producers, Alternaria spp. and fungal abundances were assessed using qPCR. Pseudomonas fluorescens bacteria were quantified with a culture based methodgeolocation_8dfbb3adf5db6b7115f15823e5ccfaaeFusarium head blightfeaturesurn:x-ogc:def:crs:EPSG:432613.6097559308835 53.35121076747713.6171168133665 53.3566111205192https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=22799e5b-9931-4f8b-b8cf-d65befaac48a&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:mycotoxinsWe selected a wheat field characterized by a diversified topography, to be responsible for variations in productivity and in canopy-driven microclimate. Fusarium and Alternaria mycotoxins where quantified in wheat ears at three sampling dates between flowering and harvest at 40 points. Tenuazonic acid (TeA), alternariol (AOH), alternariol monomethyl ether (AME), tentoxin (TEN), deoxynivalenol (DON), zearalenone (ZEN) and deoxynivalenol-3-Glucoside (DON.3G) were quantified. In canopy temperature, air and soil humidity were recorded for each point with data-loggers. Fusarium spp. as trichothecene producers, Alternaria spp. and fungal abundances were assessed using qPCR. Pseudomonas fluorescens bacteria were quantified with a culture based methodmycotoxinsFusarium head blightfeaturesurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=593c9d1e-2975-49bc-bf01-fa0c6c678d37&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:qpcrWe selected a wheat field characterized by a diversified topography, to be responsible for variations in productivity and in canopy-driven microclimate. Fusarium and Alternaria mycotoxins where quantified in wheat ears at three sampling dates between flowering and harvest at 40 points. Tenuazonic acid (TeA), alternariol (AOH), alternariol monomethyl ether (AME), tentoxin (TEN), deoxynivalenol (DON), zearalenone (ZEN) and deoxynivalenol-3-Glucoside (DON.3G) were quantified. In canopy temperature, air and soil humidity were recorded for each point with data-loggers. Fusarium spp. as trichothecene producers, Alternaria spp. and fungal abundances were assessed using qPCR. Pseudomonas fluorescens bacteria were quantified with a culture based methodFusarium head blightqpcrfeaturesurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=f4805451-022c-4f3f-9b7e-a2c6363fa863&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:fen_soilsThe data set contains the results of water retention measurements in drained and cultivated fen soils all over Germany. The data set is considered as supplementary material according to the publication Wallor et al. (2017): Hydraulic properties of drained and cultivated fen soils Part I: Horizon-based evaluation of van Genuchten parameters considering the state of moorsh-forming process. Geoderma.featuresfen_soilsurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=b6d73256-c691-41e4-afdb-e6a2a67fa55b&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:feldkartierung_bavaria_2014Agricultural landscapes cover significant areas across ecosystems worldwide. These spatially and temporally dynamic areas force wildlife to interact with agricultural machinery, and with sudden changes in resource availability during harvests and mowing events. Animals may avoid agricultural machinery and the changed habitat after management events to search for undisturbed habitat. Whether this search is successful depends on the landscape structure, which can influence the animals" movement behaviour. Here we study how agricultural management events affect animal movement behaviour in two contrastingly structured agricultural landscapes. In 2014 and 2015 we collared 36 European brown hares (Lepus europaeus) with GPS-tags and accelerometers in a simple (large fields, few landscape elements) and a complex (small fields, many landscape elements) landscape in Germany. We recorded hare" movement behaviour for 4 days before and after agricultural management events with (harvest and mowing) and without resource changes (e.g. application of fertilizer). We used four proxies for movement behaviour: the number of GPS points on the focal field, utilization range shift, utilization range size, and energy expenditure (measured as overall dynamic body acceleration). The results show that hares adjust their behaviour in relation to crop type, management type and landscape structure. We found more GPS locations on the focal field after the harvest of maize, rape seed and wheat, but not on grasslands. Hares showed longer utilization range shifts after management with and without resource changes. Utilization range size was only affected in wheat fields in the simple landscape. It increased after harvest and decreased after agricultural management without resource changes. Energy expenditure was unaffected by agricultural management. Hares profit from harvested fields, as they find food in form of fallen grains and improve their predator detection probability. The reaction to agricultural management events without resource change might depend on the precise type of management, as inorganic fertilizer can foster different movement reactions than liquid manure. Landscape structure plays an important role as utilization range sizes increase due to the necessity to reach distant alternative habitats. The provision of high crop diversity and set-asides with high quality forage throughout the year will help to increase hare and other farmland animal populations.trackingfeaturesfeldkartierung_bavaria_2014utilization range shiftlandscape structureEuropean brown hareresource changeurn:x-ogc:def:crs:EPSG:432611.742879586177137 48.41714049513386411.902038314796282 48.51246079970808https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=43a99696-527d-438f-bb24-258ea072924e&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:feldkartierung_bavaria_2015Agricultural landscapes cover significant areas across ecosystems worldwide. These spatially and temporally dynamic areas force wildlife to interact with agricultural machinery, and with sudden changes in resource availability during harvests and mowing events. Animals may avoid agricultural machinery and the changed habitat after management events to search for undisturbed habitat. Whether this search is successful depends on the landscape structure, which can influence the animals" movement behaviour. Here we study how agricultural management events affect animal movement behaviour in two contrastingly structured agricultural landscapes. In 2014 and 2015 we collared 36 European brown hares (Lepus europaeus) with GPS-tags and accelerometers in a simple (large fields, few landscape elements) and a complex (small fields, many landscape elements) landscape in Germany. We recorded hare" movement behaviour for 4 days before and after agricultural management events with (harvest and mowing) and without resource changes (e.g. application of fertilizer). We used four proxies for movement behaviour: the number of GPS points on the focal field, utilization range shift, utilization range size, and energy expenditure (measured as overall dynamic body acceleration). The results show that hares adjust their behaviour in relation to crop type, management type and landscape structure. We found more GPS locations on the focal field after the harvest of maize, rape seed and wheat, but not on grasslands. Hares showed longer utilization range shifts after management with and without resource changes. Utilization range size was only affected in wheat fields in the simple landscape. It increased after harvest and decreased after agricultural management without resource changes. Energy expenditure was unaffected by agricultural management. Hares profit from harvested fields, as they find food in form of fallen grains and improve their predator detection probability. The reaction to agricultural management events without resource change might depend on the precise type of management, as inorganic fertilizer can foster different movement reactions than liquid manure. Landscape structure plays an important role as utilization range sizes increase due to the necessity to reach distant alternative habitats. The provision of high crop diversity and set-asides with high quality forage throughout the year will help to increase hare and other farmland animal populations.trackingfeaturesutilization range shiftlandscape structureEuropean brown harefeldkartierung_bavaria_2015resource changeurn:x-ogc:def:crs:EPSG:432611.762866151288566 48.4228108146791711.893282388657513 48.53538913134911https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=f42372ae-3630-4f76-ba86-47056cf88560&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:feldkartierung_brandenburg_2014_bis_2016Agricultural landscapes cover significant areas across ecosystems worldwide. These spatially and temporally dynamic areas force wildlife to interact with agricultural machinery, and with sudden changes in resource availability during harvests and mowing events. Animals may avoid agricultural machinery and the changed habitat after management events to search for undisturbed habitat. Whether this search is successful depends on the landscape structure, which can influence the animals" movement behaviour. Here we study how agricultural management events affect animal movement behaviour in two contrastingly structured agricultural landscapes. In 2014 and 2015 we collared 36 European brown hares (Lepus europaeus) with GPS-tags and accelerometers in a simple (large fields, few landscape elements) and a complex (small fields, many landscape elements) landscape in Germany. We recorded hare" movement behaviour for 4 days before and after agricultural management events with (harvest and mowing) and without resource changes (e.g. application of fertilizer). We used four proxies for movement behaviour: the number of GPS points on the focal field, utilization range shift, utilization range size, and energy expenditure (measured as overall dynamic body acceleration). The results show that hares adjust their behaviour in relation to crop type, management type and landscape structure. We found more GPS locations on the focal field after the harvest of maize, rape seed and wheat, but not on grasslands. Hares showed longer utilization range shifts after management with and without resource changes. Utilization range size was only affected in wheat fields in the simple landscape. It increased after harvest and decreased after agricultural management without resource changes. Energy expenditure was unaffected by agricultural management. Hares profit from harvested fields, as they find food in form of fallen grains and improve their predator detection probability. The reaction to agricultural management events without resource change might depend on the precise type of management, as inorganic fertilizer can foster different movement reactions than liquid manure. Landscape structure plays an important role as utilization range sizes increase due to the necessity to reach distant alternative habitats. The provision of high crop diversity and set-asides with high quality forage throughout the year will help to increase hare and other farmland animal populations.trackingfeldkartierung_brandenburg_2014_bis_2016featureslandscape structureutilization range shiftEuropean brown hareresource changeurn:x-ogc:def:crs:EPSG:432613.508009127176656 53.2844198579615813.877740240277852 53.437541349167866https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=26ac26f1-e1b9-41f1-aba8-8a2b5131102a&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:datatablendviMovement is one of the key mechanisms for animals to deal with changes within their habitats. Therefore, resource variability can impact animals' home range formation, especially in spatially and temporally highly dynamic landscapes, such as farmland. However, the movement response to resource variability might depend on the underlying landscape structure. We investigated whether a given landscape structure affects the level of home range size adaptation in response to resource variability. We tested whether increasing resource variability forces herbivorous mammals to increase their home ranges. Hares in simple landscapes showed increasing home range sizes with increasing resource variability, whereas hares in complex landscapes did not enlarge their home range. Animals in complex landscapes have the possibility to include various landscape elements within their home ranges and are more resilient against resource variability. But animals in simple landscapes with few elements experience shortcomings when resource variability becomes high. The increase in home range size, the movement related increase in energy expenditure, and a decrease in hare abundances can have severe implications for conservation of mammals in anthropogenic landscapes. Hence, conservation management could benefit from a better knowledge about fine-scaled effects of resource variability on movement behaviour.trackingfeaturesEuropean brown harehome range sizeresource availabilitydatatablendviurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=9ecd1750-af09-4a6f-90fc-d1b24c13cfea&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:geolocation_3d0f3dde46d70ac362d04b7b49fe7e63Movement is one of the key mechanisms for animals to deal with changes within their habitats. Therefore, resource variability can impact animals' home range formation, especially in spatially and temporally highly dynamic landscapes, such as farmland. However, the movement response to resource variability might depend on the underlying landscape structure. We investigated whether a given landscape structure affects the level of home range size adaptation in response to resource variability. We tested whether increasing resource variability forces herbivorous mammals to increase their home ranges. Hares in simple landscapes showed increasing home range sizes with increasing resource variability, whereas hares in complex landscapes did not enlarge their home range. Animals in complex landscapes have the possibility to include various landscape elements within their home ranges and are more resilient against resource variability. But animals in simple landscapes with few elements experience shortcomings when resource variability becomes high. The increase in home range size, the movement related increase in energy expenditure, and a decrease in hare abundances can have severe implications for conservation of mammals in anthropogenic landscapes. Hence, conservation management could benefit from a better knowledge about fine-scaled effects of resource variability on movement behaviour.trackingfeaturesgeolocation_3d0f3dde46d70ac362d04b7b49fe7e63European brown harehome range sizeresource availabilityurn:x-ogc:def:crs:EPSG:432613.47096725586944 53.2944389887517413.85511279201188 53.42902016292231https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=4b47a7fa-f172-4cee-ad80-d11cf17d2e09&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:hare_informationMovement is one of the key mechanisms for animals to deal with changes within their habitats. Therefore, resource variability can impact animals' home range formation, especially in spatially and temporally highly dynamic landscapes, such as farmland. However, the movement response to resource variability might depend on the underlying landscape structure. We investigated whether a given landscape structure affects the level of home range size adaptation in response to resource variability. We tested whether increasing resource variability forces herbivorous mammals to increase their home ranges. Hares in simple landscapes showed increasing home range sizes with increasing resource variability, whereas hares in complex landscapes did not enlarge their home range. Animals in complex landscapes have the possibility to include various landscape elements within their home ranges and are more resilient against resource variability. But animals in simple landscapes with few elements experience shortcomings when resource variability becomes high. The increase in home range size, the movement related increase in energy expenditure, and a decrease in hare abundances can have severe implications for conservation of mammals in anthropogenic landscapes. Hence, conservation management could benefit from a better knowledge about fine-scaled effects of resource variability on movement behaviour.trackingfeatureshare_informationEuropean brown harehome range sizeresource availabilityurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=589f5065-1832-4bf3-9ad0-10dbf02f00fd&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:image_informationMovement is one of the key mechanisms for animals to deal with changes within their habitats. Therefore, resource variability can impact animals' home range formation, especially in spatially and temporally highly dynamic landscapes, such as farmland. However, the movement response to resource variability might depend on the underlying landscape structure. We investigated whether a given landscape structure affects the level of home range size adaptation in response to resource variability. We tested whether increasing resource variability forces herbivorous mammals to increase their home ranges. Hares in simple landscapes showed increasing home range sizes with increasing resource variability, whereas hares in complex landscapes did not enlarge their home range. Animals in complex landscapes have the possibility to include various landscape elements within their home ranges and are more resilient against resource variability. But animals in simple landscapes with few elements experience shortcomings when resource variability becomes high. The increase in home range size, the movement related increase in energy expenditure, and a decrease in hare abundances can have severe implications for conservation of mammals in anthropogenic landscapes. Hence, conservation management could benefit from a better knowledge about fine-scaled effects of resource variability on movement behaviour.trackingfeaturesEuropean brown harehome range sizeimage_informationresource availabilityurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=aca95ace-0b0d-4ee2-8cda-78547425813a&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:dataexperimentinteractions_2017Effects of experimental manipulation on the spatial interactions within and between species in bank voles (Myodes glareolus) and striped field mice (Apodemus agrarius). Included are the home range and core area sizes, intra- and interspecific home range overlap and intra- and interspecific proximity values for each individual.dataexperimentinteractions_2017movement ecologyfeaturesurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=ac301e44-3fe1-49f8-a9d2-1da68463ae70&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:geolocation_36f932b81d3c257fb846ec1437ae5e1eEffects of experimental manipulation on the spatial interactions within and between species in bank voles (Myodes glareolus) and striped field mice (Apodemus agrarius). Included are the home range and core area sizes, intra- and interspecific home range overlap and intra- and interspecific proximity values for each individual.geolocation_36f932b81d3c257fb846ec1437ae5e1emovement ecologyfeaturesurn:x-ogc:def:crs:EPSG:432613.47096725586944 53.2944389887517413.85511279201188 53.42902016292231https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=fac042a8-3a5f-4ffc-b749-e11a5f77e7fc&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:geolocation_476190bc69bec6a122378d3b6c65011bAbstract: The published Excel files contain the macrophyte and water quality data that where analysed in the article "How much information do we gain from multiple-year sampling in natural pond research?" The ponds were located in three regions in the intensively cultivated agricultural young moraine landscape of the state of Brandenburg, Germany. Sampling was performed once a year at the climax of the macrophyte growing season (between the end of July and the end of August) in the period from 1994 and 1999. We considered nine physicochemical water quality parameters, i.e. pH, electric conductivity, water temperature, oxygen concentration, chloride, sulphate, (ammonium) nitrogen, soluble reactive phosphorous and total phosphorus. Macrophyte species and coverage (i.e., aggregated coverage of vascular plants, Characeae and filamentous algae) growing in the inundated area of the kettle hole were mapped. Macrophyte coverage was determined in 1 to 5% percentage steps and converted to the 14-part Londo scale.geolocation_476190bc69bec6a122378d3b6c65011bfeatureskettle holeurn:x-ogc:def:crs:EPSG:432614.13139217339713 52.4665638659881514.15397191797154 52.48686161451376https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=59b7f1b5-49c4-4637-86ad-841e36d58712&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:pond_macrophytesAbstract: The published Excel files contain the macrophyte and water quality data that where analysed in the article "How much information do we gain from multiple-year sampling in natural pond research?" The ponds were located in three regions in the intensively cultivated agricultural young moraine landscape of the state of Brandenburg, Germany. Sampling was performed once a year at the climax of the macrophyte growing season (between the end of July and the end of August) in the period from 1994 and 1999. We considered nine physicochemical water quality parameters, i.e. pH, electric conductivity, water temperature, oxygen concentration, chloride, sulphate, (ammonium) nitrogen, soluble reactive phosphorous and total phosphorus. Macrophyte species and coverage (i.e., aggregated coverage of vascular plants, Characeae and filamentous algae) growing in the inundated area of the kettle hole were mapped. Macrophyte coverage was determined in 1 to 5% percentage steps and converted to the 14-part Londo scale.featureskettle holepond_macrophytesurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=ccc33b27-4e02-4466-9bea-6e32c54ef14a&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:pond_waterqualAbstract: The published Excel files contain the macrophyte and water quality data that where analysed in the article "How much information do we gain from multiple-year sampling in natural pond research?" The ponds were located in three regions in the intensively cultivated agricultural young moraine landscape of the state of Brandenburg, Germany. Sampling was performed once a year at the climax of the macrophyte growing season (between the end of July and the end of August) in the period from 1994 and 1999. We considered nine physicochemical water quality parameters, i.e. pH, electric conductivity, water temperature, oxygen concentration, chloride, sulphate, (ammonium) nitrogen, soluble reactive phosphorous and total phosphorus. Macrophyte species and coverage (i.e., aggregated coverage of vascular plants, Characeae and filamentous algae) growing in the inundated area of the kettle hole were mapped. Macrophyte coverage was determined in 1 to 5% percentage steps and converted to the 14-part Londo scale.featurespond_waterqualkettle holeurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=62232d56-f6c3-42f2-9586-8c4b52ac9ea5&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:dedelow_1991_2026The agrometeorological weather station Dedelow was installed in 1991 by the Leibniz Centre for Agricultural Landscape Research (ZALF) e.V. and is managed by the research station of ZALF in Dedelow. The station is located within the municipality Dedelow, district Uckermark, state Brandenburg, Germany. Altitude in meter: 49 NN, Geographic latitude: 53,3665 N, Geographic longitude: 13,8030 E,Type: FMA 86. In 1991, data have been collected for: soil temperature in 20cm depth (°C); global radiation (J/cm²); relative humidity (%); air temperature, 20cm above ground (°C); air temperature, 2m above ground (°C); precipitation (mm); wind velocity (m/s); evaporation (mm). Data are saved by the logger of the station and are automatically transferred onto a PC which uses the Software MeteoWare Pro 1.02. In 1992-2014, data have been collected for: soil temperature in 5/20/50 cm depth (°C); global radiation (J/cm²); relative humidity (%); air temperature, 20cm above ground (°C); air temperature, 2m above ground (°C); precipitation (mm); wind velocity (m/s); evaporation (mm). Data are saved by the logger of the station and are automatically transferred onto a PC which uses the Software MeteoWare Pro 1.02. In 2015, data have been collected for: relative humidity (%); air temperature, 20cm above ground (°C); air temperature, 2m above ground (°C); precipitation (mm); wind velocity (m/s). Data are saved by the logger of the station and are automatically transferred onto a PC which uses the Software MeteoWare Pro 1.02. In 2016 until May, data have been collected for: relative humidity (%); air temperature, 20cm above ground (°C); air temperature, 2m above ground (°C); precipitation (mm); wind velocity (m/s). Data are saved by the logger of the station and are automatically transferred onto a PC which uses the Software MeteoWare Pro 1.02.The new agrometeorological weather station Dedelow was installed in 2016 . Geographic latitude: 53.3665 N, Geographic longitude: 13.8030 E, Type: SYMNET-LOG. From April 2016, data has been collected for: relative humidity (%); air temperature, 20cm above ground (°C); air temperature, 2m above ground (°C); precipitation (mm); wind velocity (m/s), wind direction (°), evapotranspiration (mm), precipitation (mm), air pressure (hPa) and solar radiation (J/m2). In 2017, data have been collected with SYMNET-LOG for: relative humidity (%); air temperature, 20cm above ground (°C); air temperature, 2m above ground (°C); precipitation (mm); wind velocity (m/s), wind direction (°), evapotranspiration (mm), precipitation (mm), air pressure (hPa) and solar radiation (J/m2).This datacollection consists of the original DOIs listed in related identifiers.featuresdedelow_1991_2026urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=f75b5b68-e373-4148-b8e3-5071b74eaae8&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:stationThe agrometeorological weather station Dedelow was installed in 1991 by the Leibniz Centre for Agricultural Landscape Research (ZALF) e.V. and is managed by the research station of ZALF in Dedelow. The station is located within the municipality Dedelow, district Uckermark, state Brandenburg, Germany. Altitude in meter: 49 NN, Geographic latitude: 53,3665 N, Geographic longitude: 13,8030 E,Type: FMA 86. In 1991, data have been collected for: soil temperature in 20cm depth (°C); global radiation (J/cm²); relative humidity (%); air temperature, 20cm above ground (°C); air temperature, 2m above ground (°C); precipitation (mm); wind velocity (m/s); evaporation (mm). Data are saved by the logger of the station and are automatically transferred onto a PC which uses the Software MeteoWare Pro 1.02. In 1992-2014, data have been collected for: soil temperature in 5/20/50 cm depth (°C); global radiation (J/cm²); relative humidity (%); air temperature, 20cm above ground (°C); air temperature, 2m above ground (°C); precipitation (mm); wind velocity (m/s); evaporation (mm). Data are saved by the logger of the station and are automatically transferred onto a PC which uses the Software MeteoWare Pro 1.02. In 2015, data have been collected for: relative humidity (%); air temperature, 20cm above ground (°C); air temperature, 2m above ground (°C); precipitation (mm); wind velocity (m/s). Data are saved by the logger of the station and are automatically transferred onto a PC which uses the Software MeteoWare Pro 1.02. In 2016 until May, data have been collected for: relative humidity (%); air temperature, 20cm above ground (°C); air temperature, 2m above ground (°C); precipitation (mm); wind velocity (m/s). Data are saved by the logger of the station and are automatically transferred onto a PC which uses the Software MeteoWare Pro 1.02.The new agrometeorological weather station Dedelow was installed in 2016 . Geographic latitude: 53.3665 N, Geographic longitude: 13.8030 E, Type: SYMNET-LOG. From April 2016, data has been collected for: relative humidity (%); air temperature, 20cm above ground (°C); air temperature, 2m above ground (°C); precipitation (mm); wind velocity (m/s), wind direction (°), evapotranspiration (mm), precipitation (mm), air pressure (hPa) and solar radiation (J/m2). In 2017, data have been collected with SYMNET-LOG for: relative humidity (%); air temperature, 20cm above ground (°C); air temperature, 2m above ground (°C); precipitation (mm); wind velocity (m/s), wind direction (°), evapotranspiration (mm), precipitation (mm), air pressure (hPa) and solar radiation (J/m2).This datacollection consists of the original DOIs listed in related identifiers.featuresstationurn:x-ogc:def:crs:EPSG:432613.802900000000001 53.366413.8031 53.366600000000005https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=f5916438-1ad2-48fa-8ba3-514abcfe0c38&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:station_6478942a2d502841e7de3cf7e01c710bfeaturesstation_6478942a2d502841e7de3cf7e01c710burn:x-ogc:def:crs:EPSG:432613.88791 52.9565213.88811 52.956720000000004https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=d4214631-9d51-4a37-a2d1-77ca5673672b&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:wetterstation_gross_ziehtenData table.featureswetterstation_gross_ziehtenurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=49229e23-2323-4d52-b938-1f73f425da87&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:code_cropsThe dataset contains data from a yearly monitoring on at all 43 agricultural fields in a typical agricultural landscape located in the North east of the German lowlands. Three different indicators are included in the data: (i) species richness of weed flora, (ii) abundance of single species per plot and year and (iii) data on crop stands structure. The data set contains information on the spatial arrangements of the investigated plots and on the crop stands per investigational plot and year as well as code tables for the used abbreviations in the data tables.Crop stand architecturecode_cropsfeaturesWeed floraSpecies compositionurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=a01943ec-c54c-452d-957e-ec6a0baf16a4&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:code_species_namesThe dataset contains data from a yearly monitoring on at all 43 agricultural fields in a typical agricultural landscape located in the North east of the German lowlands. Three different indicators are included in the data: (i) species richness of weed flora, (ii) abundance of single species per plot and year and (iii) data on crop stands structure. The data set contains information on the spatial arrangements of the investigated plots and on the crop stands per investigational plot and year as well as code tables for the used abbreviations in the data tables.Crop stand architecturecode_species_namesfeaturesWeed floraSpecies compositionurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=ac432dc3-0ac2-4ac6-8bd3-35a3506fcf19&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:coverage_per_species_replicatioplotyearThe dataset contains data from a yearly monitoring on at all 43 agricultural fields in a typical agricultural landscape located in the North east of the German lowlands. Three different indicators are included in the data: (i) species richness of weed flora, (ii) abundance of single species per plot and year and (iii) data on crop stands structure. The data set contains information on the spatial arrangements of the investigated plots and on the crop stands per investigational plot and year as well as code tables for the used abbreviations in the data tables.Crop stand architecturefeaturesWeed floracoverage_per_species_replicatioplotyearSpecies compositionurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=12b4d093-9b6d-447e-a17b-6d0d28bcdc75&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:cropstandstructure_yearplotreplicationThe dataset contains data from a yearly monitoring on at all 43 agricultural fields in a typical agricultural landscape located in the North east of the German lowlands. Three different indicators are included in the data: (i) species richness of weed flora, (ii) abundance of single species per plot and year and (iii) data on crop stands structure. The data set contains information on the spatial arrangements of the investigated plots and on the crop stands per investigational plot and year as well as code tables for the used abbreviations in the data tables.Crop stand architecturefeaturesWeed floracropstandstructure_yearplotreplicationSpecies compositionurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=0ae32bf2-5f1d-4b50-b462-0b8ab72217f0&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:cumulative_species_number_replicationplotyearThe dataset contains data from a yearly monitoring on at all 43 agricultural fields in a typical agricultural landscape located in the North east of the German lowlands. Three different indicators are included in the data: (i) species richness of weed flora, (ii) abundance of single species per plot and year and (iii) data on crop stands structure. The data set contains information on the spatial arrangements of the investigated plots and on the crop stands per investigational plot and year as well as code tables for the used abbreviations in the data tables.Crop stand architecturecumulative_species_number_replicationplotyearfeaturesWeed floraSpecies compositionurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=a9523332-9c21-40be-b065-15f3a49ed1ef&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:geolocation_3b5bbe469e32d2318865736752aab4b3The dataset contains data from a yearly monitoring on at all 43 agricultural fields in a typical agricultural landscape located in the North east of the German lowlands. Three different indicators are included in the data: (i) species richness of weed flora, (ii) abundance of single species per plot and year and (iii) data on crop stands structure. The data set contains information on the spatial arrangements of the investigated plots and on the crop stands per investigational plot and year as well as code tables for the used abbreviations in the data tables.Crop stand architecturegeolocation_3b5bbe469e32d2318865736752aab4b3featuresWeed floraSpecies compositionurn:x-ogc:def:crs:EPSG:432613.47096725586944 53.2944389887517413.85511279201188 53.42902016292231https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=e340d8d2-a61f-495d-936c-c1bd50bd6ac1&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:gps_coordinates_plotreplicatesThe dataset contains data from a yearly monitoring on at all 43 agricultural fields in a typical agricultural landscape located in the North east of the German lowlands. Three different indicators are included in the data: (i) species richness of weed flora, (ii) abundance of single species per plot and year and (iii) data on crop stands structure. The data set contains information on the spatial arrangements of the investigated plots and on the crop stands per investigational plot and year as well as code tables for the used abbreviations in the data tables.gps_coordinates_plotreplicatesCrop stand architecturefeaturesWeed floraSpecies compositionurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=0c5a2380-5f67-400f-8eb5-dadf6caa803c&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:metadata_crop_replicationplotyearThe dataset contains data from a yearly monitoring on at all 43 agricultural fields in a typical agricultural landscape located in the North east of the German lowlands. Three different indicators are included in the data: (i) species richness of weed flora, (ii) abundance of single species per plot and year and (iii) data on crop stands structure. The data set contains information on the spatial arrangements of the investigated plots and on the crop stands per investigational plot and year as well as code tables for the used abbreviations in the data tables.Crop stand architecturemetadata_crop_replicationplotyearfeaturesWeed floraSpecies compositionurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=1b557d49-c766-4072-a606-15fb334f6262&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:species_number_coverage_replicationplotfieldyearmonthThe dataset contains data from a yearly monitoring on at all 43 agricultural fields in a typical agricultural landscape located in the North east of the German lowlands. Three different indicators are included in the data: (i) species richness of weed flora, (ii) abundance of single species per plot and year and (iii) data on crop stands structure. The data set contains information on the spatial arrangements of the investigated plots and on the crop stands per investigational plot and year as well as code tables for the used abbreviations in the data tables.Crop stand architecturespecies_number_coverage_replicationplotfieldyearmonthfeaturesWeed floraSpecies compositionurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=832ae2c4-a473-4e14-8f8b-ee2e814fea35&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:species_number_cropplotyearThe dataset contains data from a yearly monitoring on at all 43 agricultural fields in a typical agricultural landscape located in the North east of the German lowlands. Three different indicators are included in the data: (i) species richness of weed flora, (ii) abundance of single species per plot and year and (iii) data on crop stands structure. The data set contains information on the spatial arrangements of the investigated plots and on the crop stands per investigational plot and year as well as code tables for the used abbreviations in the data tables.Crop stand architecturefeaturesspecies_number_cropplotyearWeed floraSpecies compositionurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=10e96cf1-5c42-4055-82fb-f0412e076ccf&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:species_occurrence_yearplotreplicationThe dataset contains data from a yearly monitoring on at all 43 agricultural fields in a typical agricultural landscape located in the North east of the German lowlands. Three different indicators are included in the data: (i) species richness of weed flora, (ii) abundance of single species per plot and year and (iii) data on crop stands structure. The data set contains information on the spatial arrangements of the investigated plots and on the crop stands per investigational plot and year as well as code tables for the used abbreviations in the data tables.Crop stand architecturespecies_occurrence_yearplotreplicationfeaturesWeed floraSpecies compositionurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=41dd834a-dd15-4895-a8b3-49810cd3d520&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:2012_329_modelled_ch4The dataset contains information about a two-year study (August 2010 - August 2012), regarding the effect of a newly established black alder plantation on the net GHG balance of a degraded fen in northeastern Germany. In detail, an black alder plantation (Awet) is compared with an extensively used meadow (Mwet) both characterized by very moist soil conditions and a drier reference meadow (Mmoist) characterized by moderately moist soil conditions. CO2 , CH4 and N2O fluxes were measured monthly to bimonthly with the manual closed chamber method. Fluxes were calculated using a modular R script and gap filled to obtain continuous daily fluxes.MethaneCarbon dioxidefeaturesBlack alder plantation (Alnus glutinosa)2012_329_modelled_ch4PaludicultureNitrous oxideNon-steady-state chamber measurementsGreenhouse gasesCarbon balanceurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=b1ecf552-74a9-44dd-8513-3d33d9038471&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:2012_329_precipitationThe dataset contains information about a two-year study (August 2010 - August 2012), regarding the effect of a newly established black alder plantation on the net GHG balance of a degraded fen in northeastern Germany. In detail, an black alder plantation (Awet) is compared with an extensively used meadow (Mwet) both characterized by very moist soil conditions and a drier reference meadow (Mmoist) characterized by moderately moist soil conditions. CO2 , CH4 and N2O fluxes were measured monthly to bimonthly with the manual closed chamber method. Fluxes were calculated using a modular R script and gap filled to obtain continuous daily fluxes.Methane2012_329_precipitationCarbon dioxidefeaturesBlack alder plantation (Alnus glutinosa)PaludicultureNitrous oxideNon-steady-state chamber measurementsGreenhouse gasesCarbon balanceurn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=248b041f-3d98-4a01-87d7-d25687056063&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:geolocation_fa1db586cb60ae0eb33781036fd8351fThe dataset contains information about a two-year study (August 2010 - August 2012), regarding the effect of a newly established black alder plantation on the net GHG balance of a degraded fen in northeastern Germany. In detail, an black alder plantation (Awet) is compared with an extensively used meadow (Mwet) both characterized by very moist soil conditions and a drier reference meadow (Mmoist) characterized by moderately moist soil conditions. CO2 , CH4 and N2O fluxes were measured monthly to bimonthly with the manual closed chamber method. Fluxes were calculated using a modular R script and gap filled to obtain continuous daily fluxes.MethaneCarbon dioxidefeaturesBlack alder plantation (Alnus glutinosa)PaludicultureNitrous oxidegeolocation_fa1db586cb60ae0eb33781036fd8351fNon-steady-state chamber measurementsGreenhouse gasesCarbon balanceurn:x-ogc:def:crs:EPSG:432613.3039 53.6740096087576313.30464925104058 53.674809https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=a48afc3d-9f16-4068-bcbe-8f53c2033dfc&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:data_bitSample to see the data structure.featuresdata_biturn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=40de3554-564c-4b6c-be3a-9084bcb4857e&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:jrc_soil_macsur_v3Mapping of modelled data with soil data from JRC (Joint Research Centre - European Soil Database). https://esdac.jrc.ec.europa.eu/content/european-soil-database-derived-data Format: Idrisi raster formatReference system: ETRS 89 LAEARows: 5900Columns: 4600Min. X: 1500000.0Max. X: 7400000.0Min. Y: 900000.0Max. Y: 5500000.0   Soil Property                Topsoil (Filename)    Subsoil (Filename)    UnitArea of STU allocation                STU_EU_ALLOCATE                unitlessDepth available to roots            STU_EU_DEPTH_ROOTS            cmClay content                STU_EU_T_CLAY        STU_EU_S_CLAY        %Sand content                STU_EU_T_SAND        STU_EU_S_SAND        %Silt content                STU_EU_T_SILT        STU_EU_S_SILT        %Organic carbon content        STU_EU_T_OC        STU_EU_S_OC        %Bulk density                STU_EU_T_BD        STU_EU_S_BD        g cm-3Coarse Fragments            STU_EU_T_GRAVEL        STU_EU_S_GRAVEL        %Total available water content from PTR    SMU_EU_T_TAWC        SMU_EU_S_TAWC        mmTotal available water content from PTF    STU_EU_T_TAWC        STU_EU_S_TAWC        mm Note: Please follow instructions on the website for citationsfeaturesjrc_soil_macsur_v3urn:x-ogc:def:crs:EPSG:43260.0 0.0-1.0 -1.0https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=851ea999-511e-4297-b3c9-7d3bde3e2d6c&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:lpg_daten_no_heightlpg_daten_no_heightfeaturesurn:x-ogc:def:crs:EPSG:432613.52829773035603 53.1383500536121614.174503295091029 53.44340561617628https://repository.zalf.de/catalogue/csw?request=GetRecordById&service=CSW&version=2.0.2&id=ec17fc27-1221-4528-943c-46f5a8a8f561&outputschema=http%3A%2F%2Fwww.opengis.net%2Fcat%2Fcsw%2Fcsdgm&elementsetname=fullgeonode:lpg_punkt_datenfeatureslpg_punkt_datenurn:x-ogc:def:crs:EPSG:432613.585839445000033 53.1671447190000814.120545804000074 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