Assessing uncertainties in global cropland futures using a conditional probabilistic modelling framework

Kerstin Engström, Stefan Olin, Mark D A Rounsevell, Sara Brogaard, Detlef P. Van Vuuren, Peter Alexander, Dave Murray-Rust, Almut Arneth

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)893-915
Number of pages23
JournalEarth System Dynamics
Issue number4
Publication statusPublished - 2016 Nov 17

Subject classification (UKÄ)

  • Environmental Sciences related to Agriculture and Land-use


Dive into the research topics of 'Assessing uncertainties in global cropland futures using a conditional probabilistic modelling framework'. Together they form a unique fingerprint.

Cite this