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Global Maps of Agricultural Expansion Potential at a 300 m Resolution

Author

Listed:
  • Mirza Čengić

    (Department of Environmental Science, Radboud Institute for Biological and Environmental Sciences (RIBES), Radboud University, 6525 XZ Nijmegen, The Netherlands)

  • Zoran J. N. Steinmann

    (Department of Environmental Science, Radboud Institute for Biological and Environmental Sciences (RIBES), Radboud University, 6525 XZ Nijmegen, The Netherlands
    Environmental Systems Analysis, Department of Environmental Sciences, Wageningen University and Research, 6708 PB Wageningen, The Netherlands)

  • Pierre Defourny

    (Earth and Life Institute, Environmental Sciences, Université Catholique de Louvain, 1348 Louvain-la-Neuve, Belgium)

  • Jonathan C. Doelman

    (PBL Netherlands Environmental Assessment Agency, 2500 GH The Hague, The Netherlands)

  • Céline Lamarche

    (Earth and Life Institute, Environmental Sciences, Université Catholique de Louvain, 1348 Louvain-la-Neuve, Belgium)

  • Elke Stehfest

    (PBL Netherlands Environmental Assessment Agency, 2500 GH The Hague, The Netherlands)

  • Aafke M. Schipper

    (Department of Environmental Science, Radboud Institute for Biological and Environmental Sciences (RIBES), Radboud University, 6525 XZ Nijmegen, The Netherlands
    PBL Netherlands Environmental Assessment Agency, 2500 GH The Hague, The Netherlands)

  • Mark A. J. Huijbregts

    (Department of Environmental Science, Radboud Institute for Biological and Environmental Sciences (RIBES), Radboud University, 6525 XZ Nijmegen, The Netherlands)

Abstract

The global expansion of agricultural land is a leading driver of climate change and biodiversity loss. However, the spatial resolution of current global land change models is relatively coarse, which limits environmental impact assessments. To address this issue, we developed global maps representing the potential for conversion into agricultural land at a resolution of 10 arc-seconds (approximately 300 m at the equator). We created the maps using artificial neural network (ANN) models relating locations of recent past conversions (2007–2020) into one of three cropland categories (cropland only, mosaics with >50% crops, and mosaics with <50% crops) to various predictor variables reflecting topography, climate, soil, and accessibility. Cross-validation of the models indicated good performance with area under the curve (AUC) values of 0.88–0.93. Hindcasting of the models from 1992 to 2006 revealed a similar high performance (AUC of 0.83–0.91), indicating that our maps provide representative estimates of current agricultural conversion potential provided that the drivers underlying agricultural expansion patterns remain the same. Our maps can be used to downscale projections of global land change models to more fine-grained patterns of future agricultural expansion, which is an asset for global environmental assessments.

Suggested Citation

  • Mirza Čengić & Zoran J. N. Steinmann & Pierre Defourny & Jonathan C. Doelman & Céline Lamarche & Elke Stehfest & Aafke M. Schipper & Mark A. J. Huijbregts, 2023. "Global Maps of Agricultural Expansion Potential at a 300 m Resolution," Land, MDPI, vol. 12(3), pages 1-13, February.
  • Handle: RePEc:gam:jlands:v:12:y:2023:i:3:p:579-:d:1082981
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    References listed on IDEAS

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    2. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
    3. Melissa K. Ward Jones & Tobias Schwoerer & Glenna M. Gannon & Benjamin M. Jones & Mikhail Z. Kanevskiy & Iris Sutton & Brad St. Pierre & Christine St. Pierre & Jill Russell & David Russell, 2022. "Climate-driven expansion of northern agriculture must consider permafrost," Nature Climate Change, Nature, vol. 12(8), pages 699-703, August.
    4. Meiyappan, Prasanth & Dalton, Michael & O’Neill, Brian C. & Jain, Atul K., 2014. "Spatial modeling of agricultural land use change at global scale," Ecological Modelling, Elsevier, vol. 291(C), pages 152-174.
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