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Spatial Dataset of Climate Robust and High-Yield Agricultural Areas in Brandenburg: Results of a Classification Framework Using Bio-Economic Climate Simulations

Author

Listed:
  • Hannah Jona von Czettritz

    (Leibniz Centre for Agricultural Landscape Research (ZALF), 15374 Müncheberg, Germany)

  • Sandra Uthes

    (Leibniz Centre for Agricultural Landscape Research (ZALF), 15374 Müncheberg, Germany)

  • Johannes Schuler

    (Leibniz Centre for Agricultural Landscape Research (ZALF), 15374 Müncheberg, Germany)

  • Kurt-Christian Kersebaum

    (Leibniz Centre for Agricultural Landscape Research (ZALF), 15374 Müncheberg, Germany)

  • Peter Zander

    (Leibniz Centre for Agricultural Landscape Research (ZALF), 15374 Müncheberg, Germany)

Abstract

Coherent spatial data are crucial for informed land use and regional planning decisions, particularly in the context of securing a crisis-proof food supply and adapting to climate change. This dataset provides spatial information on climate-robust and high-yield agricultural arable land in Brandenburg, Germany, based on the results of a classification using bio-economic climate simulations. The dataset is intended to support regional planning and policy makers in zoning decisions (e.g., photovoltaic power plants) by identifying climate-robust arable land with high current and stable future production potential that should be reserved for agricultural use. The classification method used to generate the dataset includes a wide range of indicators, including established approaches, such as a soil quality index, drought, water, and wind erosion risk, as well as a dynamic approach, using bio-economic simulations, which determine the production potential under future climate scenarios. The dataset is a valuable resource for spatial planning and climate change adaptation, contributing to long-term food security especially in dry areas such as the state of Brandenburg facing increased production risk under future climatic conditions, thereby serving globally as an example for land use planning challenges related to climate change.

Suggested Citation

  • Hannah Jona von Czettritz & Sandra Uthes & Johannes Schuler & Kurt-Christian Kersebaum & Peter Zander, 2025. "Spatial Dataset of Climate Robust and High-Yield Agricultural Areas in Brandenburg: Results of a Classification Framework Using Bio-Economic Climate Simulations," Data, MDPI, vol. 10(3), pages 1-7, February.
    • Handle: RePEc:gam:jdataj:v:10:y:2025:i:3:p:32-:d:1598827
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    References listed on IDEAS

    as
    1. Kenneth Lee Copenhaver, 2022. "Combining Tabular and Satellite-Based Datasets to Better Understand Cropland Change," Land, MDPI, vol. 11(5), pages 1-20, May.
    2. Stevens, Mark R. & Senbel, Maged, 2017. "Are municipal land use plans keeping pace with global climate change?," Land Use Policy, Elsevier, vol. 68(C), pages 1-14.
    3. Hannah Jona von Czettritz & Seyed-Ali Hosseini-Yekani & Johannes Schuler & Kurt-Christian Kersebaum & Peter Zander, 2023. "Adapting Cropping Patterns to Climate Change: Risk Management Effectiveness of Diversification and Irrigation in Brandenburg (Germany)," Agriculture, MDPI, vol. 13(9), pages 1-17, September.
    Full references (including those not matched with items on IDEAS)

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