Abstract: Protection of natural or semi-natural ecosystems is an important part of societal strategies for maintaining biodiversity, ecosystem services, and achieving overall sustainable development. The assessment of multiple emerging land use trade-offs is complicated by the fact that land use changes occur and have consequences at local, regional, and even global scale. Outcomes also depend on the underlying socio-economic trends. We apply a coupled, multi-scale modelling system to assess an increase in nature protection areas as a key policy option in the European Union (EU). The main goal of the analysis is to understand the interactions between policy-induced land use changes across different scales and sectors under two contrasting future socio-economic pathways. We demonstrate how complementary insights into land system change can be gained by coupling land use models for agriculture, forestry, and urban areas for Europe, in connection with other world regions. The simulated policy case of nature protection shows how the allocation of a certain share of total available land to newly protected areas, with specific management restrictions imposed, may have a range of impacts on different land-based sectors until the year 2040. Agricultural land in Europe is slightly reduced, which is partly compensated for by higher management intensity. As a consequence of higher costs, total calorie supply per capita is reduced within the EU. While wood harvest is projected to decrease, carbon sequestration rates increase in European forests. At the same time, imports of industrial roundwood from other world regions are expected to increase. Some of the aggregate effects of nature protection have very different implications at the local to regional scale in different parts of Europe. Due to nature protection measures, agricultural production is shifted from more productive land in Europe to on average less productive land in other parts of the world. This increases, at the global level, the allocation of land resources for agriculture, leading to a decrease in tropical forest areas, reduced carbon stocks, and higher greenhouse gas emissions outside of Europe. The integrated modelling framework provides a method to assess the land use effects of a single policy option while accounting for the trade-offs between locations, and between regional, European, and global scales.

Abstract: Working with ensembles of crop models is a recent but important development in crop modeling which promises to lead to better uncertainty estimates for model projections and predictions, better predictions using the ensemble mean or median, and closer collaboration within the modeling community. There are numerous open questions about the best way to create and analyze such ensembles. Much can be learned from the field of climate modeling, given its much longer experience with ensembles. We draw on that experience to identify questions and make propositions that should help make ensemble modeling with crop models more rigorous and informative. The propositions include defining criteria for acceptance of models in a crop MME, exploring criteria for evaluating the degree of relatedness of models in a MME, studying the effect of number of models in the ensemble, development of a statistical model of model sampling, creation of a repository for MME results, studies of possible differential weighting of models in an ensemble, creation of single model ensembles based on sampling from the uncertainty distribution of parameter values or inputs specifically oriented toward uncertainty estimation, the creation of super ensembles that sample more than one source of uncertainty, the analysis of super ensemble results to obtain information on total uncertainty and the separate contributions of different sources of uncertainty and finally further investigation of the use of the multi-model mean or median as a predictor.

Abstract: To improve climate change impact estimates and to quantify their uncertainty, multi-model ensembles (MMEs) have been suggested. Model improvements can improve the accuracy of simulations and reduce the uncertainty of climate change impact assessments. Furthermore, they can reduce the number of models needed in a MME. Herein, 15 wheat growth models of a larger MME were improved through re-parameterization and/or incorporating or modifying heat stress effects on phenology, leaf growth and senescence, biomass growth, and grain number and size using detailed field experimental data from the USDA Hot Serial Cereal experiment (calibration data set). Simulation results from before and after model improvement were then evaluated with independent field experiments from a CIMMYT world-wide field trial network (evaluation data set). Model improvements decreased the variation (10th to 90th model ensemble percentile range) of grain yields simulated by the MME on average by 39% in the calibration data set and by 26% in the independent evaluation data set for crops grown in mean seasonal temperatures >24 °C. MME mean squared error in simulating grain yield decreased by 37%. A reduction in MME uncertainty range by 27% increased MME prediction skills by 47%. Results suggest that the mean level of variation observed in field experiments and used as a benchmark can be reached with half the number of models in the MME. Improving crop models is therefore important to increase the certainty of model-based impact assessments and allow more practical, i.e. smaller MMEs to be used effectively.