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The Thünen agricultural atlas

Project image: The Thünen agricultural atlas
© Thünen-Institut 2014
Project image: The Thünen agricultural atlas

The German agricultural statistics are an important source of information to assess environmental impacts and to estimate agricultural trends. Unfortunately, the access to this information is limited through data protection rules. This was the reason why the project Agraratlas was initialized, to generate a data set at community level on land use and live stocks, without restrictions but fulfilling all data security issues at the same time.

Background and Objective

The German agricultural statistics on land use and live stocks is an important indicator to assess environmental impacts. The statistics are the basis to estimate trends and policy impacts in the Thünen-Modellverbund. The access at small-scale level, like at community level, is limited by data security issues and the renewal of regional borderlines or new classification of data collection characters make it difficult to compile comparisons for longer time periods. Consequently, it is challenging to derive a data set, which is coherent over time.To solve this challenge is the aim of the project Thünen agricultural atlas: we intend to create such a data set at community level from 1999 until present by using the Farm Structure Survey, agricultural statistics and geo-referenced land use data and fulfill all data security issues, which allows for making the data set publicly available. The results of the project are part of the work regarding geo-referenced data infrastructure at the Thünen-Institute and will be published in this context.


The following working steps are needed to create a consistent data set without restrictions:

  • Getting permission of the federal states for the access to data at community level
  • Analyzing the data at the Forschungsdatenzentrum (FDZ) at community level via cluster analysis to fulfill the data security issues
  • Linking the following data sets using the Bayesian-estimation procedure: i) public agricultural statistics ii) unconditional data generated by the cluster analysis iii) geo-referenced (GIS) land use data of the German landscape model (DLM)
  • Validating the results at the FDZ with the observed community data
  • Publishing the data at the Thünen geo-referenced data infrastructure (GDI)
  • Development of a visualization tool ( to analyze the land use and live stock densities

Data and Methods

We use geographic and geo-referenced methods, linked to the tools if the data preparation for the model analysis, to harmonize breaks in structures and new classifications, to close data gaps, and to derive a data set which is consistent over time and regions.

Data sources
2010 – district data of the Farm Structure Survey (FSS) of the statistical offices of the federal states and cluster estimations on basis of the Forschungsdatenzentrums (FDZ), the FSS of the statistical offices of the federation and the federal states;
1999 until 2007 – district data and cluster estimators on basis of the FDZ, the statistical offices of the federation and the federal states – AfiD-Panel Agrar;
1999 until 2010 Basis-DLM – Bundesamt für Katographie und Geodäsie (BKG).

Preliminary Results

The results of the project are published at the web site The methodological approach and the validation results can be found in the publications listed below. 

Publications to the project

  1. 0

    Neuenfeldt S, Gocht A (2017) Unterschiede in der Gruppierung der Gemeinden hinsichtlich ausgewählter Betriebstypen in Deutschland und ihrer regionalen Verteilung : AFiD-Nutzerkonferenz. Z Amtl Statistik Berlin Brandenb 11(2):52-55

  2. 1

    Gocht A, Meyer-Borstel H, Röder N (2015) Vertraue nur der Statistik, die du selbst schätzt : Geodaten und Agrarstatistik. Forschungsreport Ernähr Landwirtsch Verbrauchersch(2):24-27

  3. 2

    Gocht A, Röder N (2014) Using a Bayesian estimator to combine information from a cluster analysis and remote sensing data to estimate high-resolution data for agricultural production in Germany. Int J Geogr Inf Sci 28(9):1744-1764, doi:10.1080/13658816.2014.897348

  4. 3

    Röder N, Gocht A (2013) Recovering localised information on agricultural structures while observing data confidentiality regulations - the potential of different data aggregation and segregation techniques. J Land Use Sci 8(1):31-46, DOI:10.1080/1747423X.2011.605915

  5. 4

    Gocht A, Röder N (2011) Municipality disaggregation of German's agricultural sector model RAUMIS : paper prepared for the 122nd EAAE Seminar "Evidence-based agricultural and rural policy making: methodological and empirical challenges of policy evaluation" Ancona, February 17-18,2011. 15 p

  6. 5

    Gocht A, Röder N (2011) Salvage the treasure of geographic information in farm census data : paper prepared for presentation at the EAAE 2011 Congress "Change and Uncertainty, Challenges for Agriculture, Food and Natural Resources" ; August 30 to September 2, 2011 ; ETH Zurich, Zurich, Switzerland. 13 p

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