Identifying where future landuse allocation in Europe is robust to climate and socio-economic uncertainty

Abstract

The spatial distribution of future European landuse will be influenced by yield changes arising from climate change and changes in profitability as a consequence of socio-economic change (arising from changing food demand; prices; technology etc).  To understand how these factors affect future land use allocation, a modelling system has been set up to predict agricultural land use across the EU under any scenario set of climate and socio- and techno-economic data. Metamodels of crop and forest yields, and optimal cropping and profit are derived from the outputs of the IMPEL, GOTILWA+, SFARMODand WaterGAP models. Profitability of each possible land use is modelled across the EU, assuming that use will change to the most profitable in the timescale being considered (2050). Land use in a grid is then allocated based on profit, with minimum profit thresholds set for intensive agriculture (arable or grassland), extensive agriculture, managed forest and finally unmanaged forest or unmanaged land.  The European demand for food as a function of population, imports, food preferences and bioenergy, is a production constraint, as is irrigation water available.  The model iterates prices until demand is satisfied (or cannot be met) and basin water usage for irrigation is not more than is available.

This presentation describes the application of the modelling system across future climate change uncertainty space (as given by 60 combinations of downscaled 10’x10’ gridded climate outputs from 5 Global Climate Models, 3 climate sensitivities and 4 emissions scenario) under both baseline and four future socio-economic scenarios to identify those areas of Europe in which the spatial allocation of agricultural landcovers are robust to this uncertainty.

See also: Holman IP, Brown C, Janes V, Sandars D (2017). Can we be certain about future land use change in Europe? A multi-scenario, integrated-assessment analysis.  Agricultural Systems 15: 126–135