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dc.contributorXoplaki, Elena
dc.contributor.authorEllsäßer, Florian
dc.date.accessioned2022-09-20T04:57:06Z
dc.date.available2022-09-20T04:57:06Z
dc.date.issued2022-09-19
dc.identifier.urihttps://jlupub.ub.uni-giessen.de//handle/jlupub/7746
dc.identifier.urihttp://dx.doi.org/10.22029/jlupub-7177
dc.description.abstractThis data set contains productivity data in decitons (100kg) per hectare (dt/ha) on the yield of the following crops (winter wheat, rye and winter mixed crops, spring and winter barley, oat, triticale, silage maize, oilseed rape, potato and sugar beet) for the years 1989 to 2020. The data are available in a (1) gap-filled and (2) a gap-filled and detrended version in the digital formats (a) .csv and (b) .netCDF. Therefore, the data set includes the files (1a) all_crops_gapfilled_with_indicators_v001.csv, (1b) all_crops_productivity_gapfilled_v001.nc4, (2a) all_crops_gapfilled_detrended_with_indicators_v001.csv and (2b) all_crops_productivity_gapfilled_detrended_v001.nc4. The files (1a) and (2a) use the following column headers: year = the harvest year, reg_number = county or city identification number, name = county or city name, description = description of the county or city, state_id = numerical id of the state following the same system than the reg_number, the crop names (winter_wheat, rye, winter_barley, summer_barley, oat, triticale, potato, silage_maize, sugar_beet, winter_oilseed_rape, where rye includes also winter mixed crops) listing the productivity data in dt/ha, the crop name with the _gap_filled ending indicates if this crop productivity value was gap-filled (indicated as True) or taken from an existing data set (indicated as False), geometry = Multipolygon or Polygon with geographic borders of the county or city area. Both files can be opened with MS Excel or Libre office or any other software to open .csv files. The files (1b) and (2b) use three coordinates: longitude, latitude and year. The data variables are similar to (1a) and (2a) including the ten crop productivity variables in (dt/ha) and the indicator of gap filling. However, the gap filled areas are indicated with a 1 and original values are indicated with a 0 here (instead of True and False respectively). Both files can be opened with software such as the MS NetCDF Viewer, however we recommend using the Python xarray package to work with the data. All files were gap-filled using a nearest neighbor gap-filling procedure where neighboring pixels were considered more than the locally typical values with a ratio of 3:1. Detrending was applied using a LOWESS regression, for all time-series where there was a significant trend (CI=95%). This data set is based on multiple data sets that were provided by the federal and state statistical offices of Germany and the Federal Agency for Cartography and Geodesy. The copyright statements and names of institutions are mentioned and listed in the following: For the spatial data set (county borders): © GeoBasis-DE / BKG (2022) - Datenlizenz Deutschland Version 2.0, further raw data from Regionaldatenbank Deutschland were used on federal level: © Statistisches Bundesamt (Destatis), 2022. Further data from individual states were collected and include the following copyright statements: Schleswig-Holstein: © Statistisches Amt für Hamburg und Schleswig-Holstein, Hamburg 2022; Niedersachsen: © Landesamt für Statistik Niedersachsen, Hannover 2020; Nordrhein-Westfalen: © Information und Technik Nordrhein-Westfalen, Düsseldorf 2020; Hessen: © Hessisches Statistisches Landesamt, Wiesbaden, 2022; Rheinland-Pfalz: © Statistisches Landesamt Rheinland-Pfalz, Bad Ems, 2022. Baden-Württemberg:© Statistisches Landesamt Baden-Württemberg, Stuttgart, 2020; Freistaat Bayern: © Bayerisches Landesamt für Statistik, Fürth, 2022; Saarland: © Statistisches Amt des Saarlandes, Saarbrücken; Brandenburg: © Amt für Statistik Berlin-Brandenburg, Potsdam, 2021; Mecklenburg-Vorpommern: © Statistisches Amt Mecklenburg-Vorpommern, Schwerin, 2022, Freistaat Sachsen: © Statistisches Landesamt des Freistaates Sachsen, Kamenz 2022; Sachsen-Anhalt: © Statistisches Landesamt Sachsen-Anhalt, Halle (Saale), 2020; Freistaat Thüringen: © Thüringer Landesamt für Statistik, Erfurt, 2021. Please refer to these institutions and copyright statements when publishing data or products that are based on this data set.de_DE
dc.description.sponsorshipBundesministerium für Bildung und Forschung (BMBF); ROR-ID:04pz7b180de_DE
dc.language.isoende_DE
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectcrop, productivity, (dt/ha), yield, Germany, spatial, county, NUTS-3, winter wheat, rye, mixed winter cereals, spring barley, winter barley, oat, triticale, silage maize, oilseed rape, potato, sugar beetde_DE
dc.subject.ddcddc:000de_DE
dc.subject.ddcddc:333.7de_DE
dc.subject.ddcddc:500de_DE
dc.subject.ddcddc:550de_DE
dc.subject.ddcddc:580de_DE
dc.subject.ddcddc:630de_DE
dc.titlecropdata – spatial yield productivity data base for the ten most cultivated crops in Germany from 1989 to 2020 - version 1.0de_DE
dc.title.alternativespatial yield productivity data base for winter wheat, rye and mixed winter cereals, spring and winter barley, oat, triticale, silage maize, oilseed rape, potato and sugar beet in Germany from 1989 to 2020 - version 1.0de_DE
dc.typeDatasetde_DE
local.affiliationZEU Zentrum für internationale Entwicklungs- und Umweltforschungde_DE
local.projectclimXtreme project https://www.climxtreme.net/index.php/en/ C3 CROP project (grant number 01LP1903C) https://www.uni-giessen.de/zeu/CROPde_DE


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