Abstract: This review examines relevant impact assessments identified by a literature search from 1991to date. A bibliographic search was applied to the CAB Abstracts database with a given searchstring. Resultant papers were checked for relevance, based on expert judgment. This yielded 91 papers, which were subjected to further analysis. Firstly, publication intensity over time and distribution by geographic location and cereal crop were examined. Next, for a given crop, the assessments and their outcomes were grouped by type and number of the change variables considered – that is, effects of climate change only, elevated CO 2 and technological progress(improved breeds, management). Finally, separately for individual countries/subregions and Europe as a whole, we examined whether and to what extent study results have changed over time, for example become more positive/negative. Based on our sample, we found that publication intensity increased exponentially during thelast 4 years, the majority of studies are Europe-wide, but some concentrated on a few countries(Italy, Spain and UK), whereby studies on wheat are clearly most popular. Taking the factor of technological progress into account has an overruling influence on results. Finally, over time, projected yield impacts have become more negative. This is in line with finding from global analyses, as reflected by the most recent comparison of agricultural impact chapters, of the 4thand 5th Assessment Reports of Intergovernmental Panel on Climate Change, Working Group II.In the future, there is particular need to consider impacts under various incremental and transformational adaptation measures in more depth (e.g. their interconnections across scales)and with more breadth (e.g. anticipated new breeds). Follow-up reviews should also examine how projected impacts are changing with the new climate scenario data sets (CMIP5) and with improved impact models and assessment approaches.

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: We adapted a large area crop model, MCWLA-Wheat, to winter wheat Triticum aestivum L. and spring wheat in Finland. We then applied Bayesian probability inversion and a Markov Chain Monte Carlo technique to analyze uncertainties in parameter estimations and to optimize parameters. Finally, a super-ensemble-based probabilistic projection system was updated and applied to project the effects of climate change on wheat productivity and water use in Finland. The system used 6 climate scenarios and 20 sets of crop model parameters. We projected spatiotemporal changes of wheat productivity and water use due to climate change/variability during 2021-2040, 2041-2070, and 2071-2100. The results indicate that with a high probability wheat yields will increase substantially in Finland under the tested climate change scenarios, and spring wheat can benefit more from climate change than winter wheat. Nevertheless, in some areas of southern Finland, wheat production will face increasing risk of high temperature and drought, which can offset the benefits of climate change on wheat yield, resulting in an increase in yield variability and about 30% probability of yield decrease for spring wheat. Compared with spring wheat, the development, photosynthesis, and consequently yield will be much less enhanced for winter wheat, which, together with the risk of extreme weather, will result in an up to 56% probability of yield decrease in eastern parts of Finland. Our study explicitly para meterized the effects of extreme temperature and drought stress on wheat yields, and accounted for a wide range of wheat cultivars with contrasting phenological characteristics and thermal requirements.