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Earth System Dynamics An interactive open-access journal of the European Geosciences Union

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Earth Syst. Dynam., 7, 893-915, 2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
17 Nov 2016
Assessing uncertainties in global cropland futures using a conditional probabilistic modelling framework
Kerstin Engström1, Stefan Olin1, Mark D. A. Rounsevell2, Sara Brogaard3, Detlef P. van Vuuren4,5, Peter Alexander2, Dave Murray-Rust6, and Almut Arneth7 1Department of Physical Geography and Ecosystem Science, Lund University, Sölvegatan 12, 22362 Lund, Sweden
2School of GeoSciences, University of Edinburgh, Geography Building, Drummond Street, Edinburgh, EH89XP, UK
3Centre for Sustainability Studies, Lund University (LUCSUS), Biskopsgatan 5, 22362 Lund, Sweden
4PBL Netherlands Environmental Assessment Agency, Postbus 303, 3720 AH Bilthoven, the Netherlands
5Copernicus Institute for Sustainable Development, Faculty of Geosciences, Utrecht University, Heidelberglaan 2, 3584 CS Utrecht, the Netherlands
6School of Informatics, University of Edinburgh Appleton Tower, 11 Crichton Street, Edinburgh, EH8 9LE, UK
7Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research, Atmospheric Environmental Research (IMK-IFU), Kreuzeckbahnstr. 19, 82467 Garmisch-Partenkirchen, Germany
Abstract. We present a modelling framework to simulate probabilistic futures of global cropland areas that are conditional on the SSP (shared socio-economic pathway) scenarios. Simulations are based on the Parsimonious Land Use Model (PLUM) linked with the global dynamic vegetation model LPJ-GUESS (Lund–Potsdam–Jena General Ecosystem Simulator) using socio-economic data from the SSPs and climate data from the RCPs (representative concentration pathways). The simulated range of global cropland is 893–2380 Mha in 2100 (± 1 standard deviation), with the main uncertainties arising from differences in the socio-economic conditions prescribed by the SSP scenarios and the assumptions that underpin the translation of qualitative SSP storylines into quantitative model input parameters. Uncertainties in the assumptions for population growth, technological change and cropland degradation were found to be the most important for global cropland, while uncertainty in food consumption had less influence on the results. The uncertainties arising from climate variability and the differences between climate change scenarios do not strongly affect the range of global cropland futures. Some overlap occurred across all of the conditional probabilistic futures, except for those based on SSP3. We conclude that completely different socio-economic and climate change futures, although sharing low to medium population development, can result in very similar cropland areas on the aggregated global scale.

Citation: Engström, K., Olin, S., Rounsevell, M. D. A., Brogaard, S., van Vuuren, D. P., Alexander, P., Murray-Rust, D., and Arneth, A.: Assessing uncertainties in global cropland futures using a conditional probabilistic modelling framework, Earth Syst. Dynam., 7, 893-915,, 2016.
Publications Copernicus
Short summary
The development of global cropland in the future depends on how many people there will be, how much meat and milk we will eat, how much food we will waste and how well farms will be managed. Uncertainties in these factors mean that global cropland could decrease from today's 1500 Mha to only 893 Mha in 2100, which would free land for biofuel production. However, if population rises towards 12 billion and global yields remain low, global cropland could also increase up to 2380 Mha in 2100.
The development of global cropland in the future depends on how many people there will be, how...