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    cropdata – spatial yield productivity data base for the ten most cultivated crops in Germany from 1989 to 2020 - version 1.0
    (2022-09-19) Ellsäßer, Florian
    This 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.
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    cropdata – supplementary data (for spatial yield productivity data base for the ten most cultivated crops in Germany from 1989 to 2020) - version 1.0
    (2022-10-06) Ellsäßer, Florian
    This data set is a supplementary data set containing the phenological and agricultural management data expressed using the BBCH-scale and days of the year (DOY) as supplement for the data set: http://dx.doi.org/10.22029/jlupub-7177. It contains the sowing and harvesting dates and other key events of the crop phenology cycle for the following crops: winter wheat, rye and winter mixed crops, spring and winter barley, oat, silage maize, oilseed rape and sugar beet for the years 1989 to 2020. However, not all years or locations are available. The data set is based on the phenological observations of crops from sowing to harvest v006 by the DWD (DWD Climate Data Center (CDC): Phenological observations of crops from sowing to harvest (annual reporters, historical), Version v006, 2019)downloaded from: ftp://opendata.dwd.de/climate_environment/CDC/observations_germany/phenology/annual_reporters/crops ,the station data provided by DWD (DWD Climate Data Center (CDC): Eintrittsdaten verschiedener Entwicklungsstadien landwirtschaftlicherKulturpflanzen von der Bestellung bis zur Ernte (Jahresmelder, historisch), Version v006, 2019) downloaded from: https://opendata.dwd.de/climate_environment/CDC/observations_germany/phenology/annual_reporters/vine/historical/PH_Beschreibung_Phaenologie_Stationen_Jahresmelder.txt , the phase description keys also provided by DWD (DWD Climate Data Center (CDC): Eintrittsdaten verschiedener Entwicklungsstadien landwirtschaftlicherKulturpflanzen von der Bestellung bis zur Ernte (Jahresmelder, historisch), Version v006, 2019) and downloaded from: https://www.dwd.de/DE/klimaumwelt/klimaueberwachung/phaenologie/daten_deutschland/jahresmelder/jahresmelder_beobachtungsprogramm.pdf?__blob=publicationFile&v=5 as well as the Natural Area Data following Meynen and Schmithüsen as provided by Bundesamt für Naturschutz (Bundesamt für Naturschutz, Fachgebiet I 1.2, Naturschutzinformation, Geoinformation, Open Data) and downloaded from: https://ffalle.bfn.de:443/ssf/s/readFile/share/1040/-305838928916869590/publicLink/Naturr%C3%A4umliche%20Gliederung.zip . The county borders were kindly provided by the Bundesamt für Geographie und Geodäsie © GeoBasis-DE / BKG (2022) - Datenlizenz Deutschland Version 2.0. The data are available in the digital formats .csv and .netCDF. The .csv file contains the following column headers crop_type = the crop type, year = the harvest year, bbch = the BBCH value on the BBCH scale ( https://en.wikipedia.org/wiki/BBCH-scale ), reg_number = county or city identification number and doy = day of the year (DOI). This file can be opened e.g. with MS Excel or Libre office or any other software to open .csv files. The files use three coordinates: longitude, latitude and year. The data variables are built as follows: or_bbch_99 where the first two letters are an abbreviation for the crop and the last two letters define the BBCH value.
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    cropdata – yield anomaly catalogue for the ten most cultivated crops in Germany from 1989 to 2020 - version 1.0
    (2022-09-19) Ellsäßer, Florian
    This data set contains a .csv file yield_anomaly_catalogue_v001.csv that lists all areas affected by large or extreme yield anomalies. Large and extreme anomalies are defined according to the Standardized Yield Anomaly Index (SYAI) where “large” is defined as all values between ± 1 standard deviation (std) and ± 2 std and “extreme” values are defined as all values above or below ± 2 stds from the mean. The data set is organized by the following headers: year = harvest year of occurrence; crop = defining the crop type; attribute = where -2 and -1 are extreme and large negative yield anomalies respectively and 1 and 2 are large and extreme positive yield anomalies respectively; size = affected area in km²; relative_size = affected area in relative terms compared to the total area of Germany; average = average/mean value of all pixels in this affected area. 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.