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: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.featurescrop modelinggeolocation_669903fe8c248abf156f5f447d5471d9urn: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.chamber-collar sealingfeaturesnon-steady-state chamber2017_316_gc_datachamber leakagewind Shelterurn:x-ogc:def: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-collar sealingfeaturesnon-steady-state chamberchamber leakagewind Shelterurn:x-ogc:def: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-collar sealingfeaturesnon-steady-state chamberchamber leakagewind Shelter2017_316_sealing_integrityurn:x-ogc:def: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.geolocation_f3aef23e5dbe76185a4bcdaabe2fd3b2chamber leakagefeatureswind Shelternon-steady-state chamberchamber-collar sealingurn: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: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 blightfeaturesmicrobiomeFungal dispersalavg_climateurn:x-ogc:def: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 blightfeaturesmicrobiomeFungal dispersalgeolocation_6eacfb2fa4a80eb36e1f8b06411af5eaurn: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 blightfeaturesmicrobiomeFungal dispersalqpcr_raakowurn:x-ogc:def: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 blightfeaturesmicrobiomeFungal dispersalspore_countsurn:x-ogc:def: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).Gross primary productivityLeaf Area IndexfeaturesEcosystem respirationNet Ecosystem Exchangeco2_fluxes_allChamber measurementurn:x-ogc:def: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 productivityLeaf Area IndexfeaturesEcosystem respirationgeolocation_d216ca537d4256390d8e029299aaa50fNet Ecosystem ExchangeChamber measurementurn: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 productivityLeaf Area IndexfeaturesEcosystem respirationnormal_model_allNet Ecosystem ExchangeChamber measurementurn:x-ogc:def: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 productivityLeaf Area Indexfeaturesweatherdata_halfhourlyEcosystem respirationNet Ecosystem ExchangeChamber measurementurn:x-ogc:def: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: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: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: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).Conflict managementdataset_hotcoldspots_ecosystem_services_mol_bbSpatial Analysisfeaturesurn: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: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: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.PM10Inner MongoliafeaturesDust stormsMAGIMFine dust emissionscoordinates_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.PM10Inner MongoliafeaturesDust stormsMAGIMFine dust emissionsdust_depositionurn:x-ogc:def: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.PM10Inner MongoliafeaturesDust stormsMAGIMFine dust emissionsdust_transporturn:x-ogc:def: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.PM10Inner MongoliafeaturesDust stormsMAGIMFine dust emissionsgeolocation_767ea6e960abcdb42df960263cc710e1urn: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.PM10Inner MongoliafeaturesDust stormsMAGIMparticulate_matterFine dust emissionsurn:x-ogc:def: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.PM10Inner MongoliafeaturesDust stormsMAGIMFine dust emissionssamples_chemistryurn:x-ogc:def: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.PM10Inner MongoliafeaturesDust stormsMAGIMFine dust emissionstextureurn:x-ogc:def: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.PM10Inner MongoliafeaturesDust stormsMAGIMtopographyFine dust emissionsurn:x-ogc:def: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.vegetationPM10Inner MongoliafeaturesDust stormsMAGIMFine dust emissionsurn:x-ogc:def: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: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).Conflict managementSpatial analysisdataset_ecosystem_services_mol_bbfeaturesurn: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: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: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.yieldagro-ecosystemclimate datavalidationwinter barleywinter ryecrop modellingfeaturessoil nitrogenexperimental stationontogenesisgeolocation_400bb9a637a6d6fad181b3f111cc813esilage maizewinter oilseed rapefield experimentpotatoesbiomassmodelwinter wheatsoil waterurn: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.yieldagro-ecosystemclimate datavalidationwinter barleywinter ryecrop modellingfeaturessoil nitrogenexperimental stationontogenesissilage maizewinter oilseed rapefield experimentpotatoeshohe_cropbiomassmodelwinter wheatsoil 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=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.yieldagro-ecosystemclimate datavalidationwinter barleywinter ryecrop modellingfeaturessoil nitrogenexperimental stationontogenesissilage maizewinter oilseed rapefield experimenthohe_crop_contentspotatoesbiomassmodelwinter wheatsoil 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=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.yieldagro-ecosystemclimate datavalidationwinter barleywinter ryecrop modellingfeaturessoil nitrogenexperimental stationontogenesissilage maizewinter oilseed rapefield experimentpotatoesbiomasshohe_managementmodelwinter wheatsoil 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=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.yieldagro-ecosystemclimate datavalidationwinter barleywinter ryecrop modellingfeaturessoil nitrogenexperimental stationontogenesissilage maizewinter oilseed rapefield experimentpotatoeshohe_soil_h2o_nbiomassmodelwinter wheatsoil 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=6a8b54b5-d97e-4e0c-b2df-c3aeb24b0405&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_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.featuressilage maizegeolocation_5c56887769653d0a73ebe11287e51004time seriesNUTS3official agricultural statisticsdistrictswinter barleywinter canolaurn: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: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-experimentdk_63_soilfeaturesurn:x-ogc:def: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-experimentdk_63_yieldfeaturesurn:x-ogc:def: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).long-term-experimentgeolocation_e235c3d3716a8d0def85235dae922a4bfeaturesurn: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: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.measurement methodgeolocation_31f820110c0d010f754e748df3e54c26featuresurn: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.measurement methodsugarbeetlafeaturesurn:x-ogc:def: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: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: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: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.nearest neighbour distancespaceuse_bvsfm_16home 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.featuresboldnessPCA explorationgeolocation_1f2b03310efe206c60afc20dc2691b0fmovement ecologyurn: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.featuresPCA explorationboldnessindividualdifferencescores_2017movement ecologyurn:x-ogc:def: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.featuresgeolocation_280d131bed75ee1c9d4f04d917dd5a40boldnessmovement ecologybayesian statisticsurn: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.featuresboldnessmovement ecologybayesian statisticsindividualdifferencescores_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: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_1dd200932b1de323d79834497a6b445bkettle holebiomassfeaturesurn: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: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.net ecosystem carbon balanceGross primary productivitygeolocation_f6b265bd7893a634bd07e451a6a0d92ffeaturesheterotrophic respirationecosystem respirationautotrophic respirationlight inhibition of leaf respirationurn: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.net ecosystem carbon balanceGross primary productivityfeaturesmeasured_co2_fluxes_1st_pot_expheterotrophic respirationecosystem respirationautotrophic 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.net ecosystem carbon balanceGross primary productivityfeaturesmeasured_co2_fluxes_2nd_pot_expheterotrophic respirationecosystem respirationautotrophic 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=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.net ecosystem carbon balanceGross primary productivityfeaturesheterotrophic respirationecosystem respirationautotrophic respirationlight inhibition of leaf respirationmeasured_daytime_ra_d_and_nighttime_ra_n_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=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.net ecosystem carbon balanceGross primary productivityfeaturesmodelled_co2_exchangeheterotrophic respirationecosystem respirationautotrophic 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=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.net ecosystem carbon balanceGross primary productivityfeaturessimulated_co2_exchangeheterotrophic respirationecosystem respirationautotrophic 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=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.net ecosystem carbon balanceGross primary productivityfeaturesheterotrophic respirationecosystem respirationautotrophic respirationlight inhibition of leaf respirationweather_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=dc3eed08-376a-48c5-b974-3268c809e3fe&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: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: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: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 methodbacteriaFusarium 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=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 methodenvFusarium 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=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 methodqpcrFusarium 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=f4805451-022c-4f3f-9b7e-a2c6363fa863&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.resource changetrackingfeaturesutilization range shiftfeldkartierung_bavaria_2014landscape structureEuropean brown hareurn: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.resource changetrackingfeaturesutilization range shiftfeldkartierung_bavaria_2015landscape structureEuropean brown hareurn: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.resource changefeldkartierung_brandenburg_2014_bis_2016utilization range shiftfeatureslandscape structureEuropean brown haretrackingurn: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.trackingfeaturesresource availabilityhome range sizedatatablendviEuropean brown hareurn:x-ogc:def: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_3d0f3dde46d70ac362d04b7b49fe7e63resource availabilityhome range sizeEuropean brown hareurn: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.trackingfeaturesresource availabilityhome range sizeEuropean brown harehare_informationurn:x-ogc:def: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.trackingfeaturesimage_informationresource availabilityhome range sizeEuropean brown hareurn:x-ogc:def: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.movement ecologydataexperimentinteractions_2017featuresurn:x-ogc:def: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.movement ecologygeolocation_36f932b81d3c257fb846ec1437ae5e1efeaturesurn: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: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.kettle holepond_macrophytesfeaturesurn:x-ogc:def: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.kettle holepond_waterqualfeaturesurn:x-ogc:def: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:weatherstations_dedelow_till_oct_2025The 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.featuresweatherstations_dedelow_till_oct_2025urn:x-ogc:def:crs:EPSG:432613.802979605539438 53.366419927752513.803179605539437 53.36661992775251geonode: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 architecturefeaturescode_cropsWeed 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 architecturefeaturesWeed floracode_species_namesSpecies 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.coverage_per_species_replicatioplotyearCrop 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=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 floraSpecies compositioncropstandstructure_yearplotreplicationurn:x-ogc:def: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 architecturefeaturesWeed floraSpecies compositioncumulative_species_number_replicationplotyearurn:x-ogc:def: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 architecturefeaturesgeolocation_3b5bbe469e32d2318865736752aab4b3Weed 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.Crop stand architecturefeaturesWeed floraSpecies compositiongps_coordinates_plotreplicatesurn:x-ogc:def: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 architecturefeaturesWeed floraSpecies compositionmetadata_crop_replicationplotyearurn:x-ogc:def: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 architecturefeaturesspecies_number_coverage_replicationplotfieldyearmonthWeed 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.species_number_cropplotyearCrop 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=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 architecturefeaturesspecies_occurrence_yearplotreplicationWeed 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=fullgml:Envelopegml:Pointgml:LineStringgml:PolygonLessThanGreaterThanLessThanEqualToGreaterThanEqualToEqualToNotEqualToLikeBetweenNullCheckabsabs_2abs_3abs_4acosAddCoveragesAffineAggregateAndAreaarea2AreaGridarrayasinatanatan2attributeCountBandMergebandsBandSelectBarnesSurfacebetweenboundaryboundaryDimensionboundedByBoundsbufferBufferFeatureCollectionbufferWithSegmentsCategorizeceilcenterLinecentroidclassifyClassifyByRangeClipcollectGeometriesCollection_AverageCollection_BoundsCollection_CountCollection_MaxCollection_MedianCollection_MinCollection_NearestCollection_SumCollection_UniqueConcatenatecontainsContourcontrastconvertconvexHullConvolveCoveragecosCountCoverageClassStatsCropCoveragecrossesdarkendateDifferencedateFormatdateParsedensifydesaturatedifferencedimensiondisjointdisjoint3Ddistancedistance3Ddivdouble2boolDownloadDownloadAnimationDownloadEstimatorDownloadMapendAngleendPointenvenvelopeEqualAreaEqualIntervalequalsExactequalsExactToleranceequalToexpexteriorRingFeatureFeatureClassStatsfloorfootprintsgeometrygeometryTypegeomFromWKTgeomLengthGeorectifyCoverageGetCoveragesValueGetFullCoveragegetGeometryNgetXgetYgetzgrayscalegreaterEqualThangreaterThanGridGroupCandidateSelectionHeatmaphslidIEEEremainderif_then_elseImportinin10in2in3in4in5in6in7in8in9inArrayInclusionFeatureCollectionint2bboolint2ddoubleinteriorPointinteriorRingNInterpolateintersectionIntersectionFeatureCollectionintersectsintersects3DisCachedisClosedisCoverageisEmptyisInstanceOfisLikeisNullisometricisRingisSimpleisValidisWithinDistanceisWithinDistance3DJenksJifflejsonArrayContainsjsonPointerlabelPointlanguagelapplylengthlessEqualThanlessThanlightenlinlistlistMultiplylitemliteratelogLRSGeocodeLRSMeasureLRSSegmentmapGetmaxmax_2max_3max_4midAnglemidPointminmin_2min_3min_4mincircleminimumdiameterminrectanglemixmoduloMultiplyCoveragesNearestNormalizeCoveragenorthFixnotnotEqualTonownumberFormatnumberFormat2numGeometriesnumInteriorRingnumPointsoctagonalenvelopeoffsetOroverlapsPagedUniqueparameterparseBooleanparseDoubleparseIntparseLongparseTimepgNearestpiPointBufferspointNpointOnLinePointStackerPolygonExtractionpolygonizePolyLabellerpowpropertyPropertyExistsQuantileQueryqueryCollectionquerySinglerandomRangeLookupRasterAsPointCollectionRasterZonalStatisticsRasterZonalStatistics2RecodeRectangularCliprelaterelatePatternreprojectReprojectGeometryrescaleToPixelsrintroundround_2roundDoublesaturateScaleCoveragesetCRSshadesimplifysinsizeSnapSpatioTemporalZonalStatisticsspinsplitPolygonsqrtStandardDeviationstartAnglestartPointStoreCoveragestrAbbreviatestrCapitalizestrConcatstrDefaultIfBlankstrEndsWithstrEqualsIgnoreCasestrIndexOfstringTemplatestrLastIndexOfstrLengthstrMatchesstrPositionstrReplacestrStartsWithstrStripAccentsstrSubstringstrSubstringStartstrToLowerCasestrToUpperCasestrTrimstrTrim2strURLEncodeStyleCoveragesymDifferencetantinttoDegreestoRadianstouchestoWKTTransformTransparencyFillunionUnionFeatureCollectionUniqueUniqueIntervalVectorToRasterVectorZonalStatisticsverticeswithin