2016
DOI: 10.5194/esd-7-893-2016
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Assessing uncertainties in global cropland futures using a conditional probabilistic modelling framework

Abstract: 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 i… Show more

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Cited by 40 publications
(81 citation statements)
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“…Finally, changes in total cropland are assumed to be proportional to changes in cereal land, using the actual proportions of cereal land to total cropland in 2000 (Engström et al, 2016b). In previous applications of PLUM (Engström et al, 2016a) the static feed ratio (assumption as to how much of the consumed meat is produced from cereal feeds vs. grazing) was identified as a cause for underestimation of cropland demand for scenarios with meat-rich diets.…”
Section: Land Use Model and Coupling To The Climate-economy Modelmentioning
confidence: 99%
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“…Finally, changes in total cropland are assumed to be proportional to changes in cereal land, using the actual proportions of cereal land to total cropland in 2000 (Engström et al, 2016b). In previous applications of PLUM (Engström et al, 2016a) the static feed ratio (assumption as to how much of the consumed meat is produced from cereal feeds vs. grazing) was identified as a cause for underestimation of cropland demand for scenarios with meat-rich diets.…”
Section: Land Use Model and Coupling To The Climate-economy Modelmentioning
confidence: 99%
“…The simulations capture the impact of climate change on yield developments on a 0.5 • × 0.5 • global grid through changes in precipitation, temperature patterns and CO 2 concentration (derived from the RCPs; see Engström et al, 2016a), through direct biophysical effect and also by adaptation of sowing time to changes in climate. No other changes (e.g.…”
Section: Ecosystem Model Downscaling Cropland and The Terrestrial Camentioning
confidence: 99%
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