Abstract: Projections of climate change impacts on crop yields are inherently uncertain(1). Uncertainty is often quantified when projecting future greenhouse gas emissions and their influence on climate(2). However, multi-model uncertainty analysis of crop responses to climate change is rare because systematic and objective comparisons among process-based crop simulation models(1,3) are difficult(4). Here we present the largest standardized model intercomparison for climate change impacts so far. We found that individual crop models are able to simulate measured wheat grain yields accurately under a range of environments, particularly if the input information is sufficient. However, simulated climate change impacts vary across models owing to differences in model structures and parameter values. A greater proportion of the uncertainty in climate change impact projections was due to variations among crop models than to variations among downscaled general circulation models. Uncertainties in simulated impacts increased with CO2 concentrations and associated warming. These impact uncertainties can be reduced by improving temperature and CO2 relationships in models and better quantified through use of multi-model ensembles. Less uncertainty in describing how climate change may affect agricultural productivity will aid adaptation strategy development and policymaking.

Abstract: Projections into the future, such as climate change impact projections on crop production for a given region, or, on global food prices and trade are inherently uncertain. Uncertainty does not fall within a single discipline but is dealt with by a wide variety of disciplines, themes and problem domains. Model uncertainty pertaining to the impact modelling chain from climate via crop and livestock to economic and trade modelling is only part of the overall uncertainty*. There is also scenario uncertainty and many other known and unknown “unknowns”1 to be considered in efforts such as MACSUR and its themes (CropM, LiveM, TradeM) to advance model-based integrated assessment of climate change risk assessment for agriculture and food security. Propagation of uncertainties along the climate change impact modelling chain has been portrayed as “uncertainty cascade” 2. We will present different basic approaches for evaluating uncertainty in models. So far, studies addressing quantification and reporting of uncertainties in impact projections still largely focus on two major sources, i.e. the shares originating from climate modelling and from crop modelling. However, a more comprehensive treatment of uncertainty and how it is reported is urgently needed.

Abstract: Projected changes in temperature, rainfall and soil moisture could threaten agricultural land use and crop productivity in Europe, with major consequences for food security (Daccache et al., 2014). We assessed the projected impacts of climate change on the yield of seven crops (viz wheat, barley, maize, potato, sugar beet, rice and rye) in Europe using a systematic review (SR) and meta-analysis of data reported in 67 original publications from an initial screening of 1424 studies. Whilst similar studies exist for Africa and South Asia (Roudier et al., 2011; Knox et al., 2012), surprisingly, no such comparable synthesis has been undertaken for Europe. Our study focussed on the biophysical impacts of climate change on productivity (i.e. yield per unit area) and did not consider ‘food production’ as this is dependent on many ‘non-biophysical’ factors, such as international trade policy and world markets. The data relate to the projected mean yield variations for each crop type, for all crop models, all GCM models and all time slices.For Europe, most studies projected a positive impact on yield; the reported increases largely being due to rising atmospheric CO2 concentrations enhancing both productivity and resource use efficiencies. Overall, a mean yield increase of +14% was identified, but with large differences between individual crops (e.g. wheat +22%; potato +12%) and regions (e.g. northern Europe +17%; southern Europe +7%). It is important to note that projected yield data were not available for all crops in all regions, so lack of a significant response may in part be due to the absence, or limited number of studies for certain crops and/or regions. Furthermore, the results include all reported yield projections, for all time slices, for all GCM combinations (whether single or ensemble) and for all crop modelling approaches (whether based on simple statistical trends or more complex biophysical modelling approaches). This highlights the magnitude of variability that exists when all possible sources of uncertainty are included. Further statistical analyses were conducted to disaggregate the data by time slice, climate and crop model to identify which factors were likely to contribute most to yield variations and uncertainty.The SR showed that evidence of climate change impacts on crop yield in Europe is extensive for wheat, maize, sugar beet and potato but very limited for barley, rice and rye. Interpreting the reported yield observations was compounded by ‘effect modifiers’ or reasons for heterogeneity. These included different emission scenarios and climate ensembles, implicit assumptions regarding crop varieties, the agricultural systems studied, and assumed levels of mechanization and crop husbandry. Despite its limitations, the SR helps identify where further research should be targeted and regions where adaptation will be most needed. It confirms that climate change is likely to increase productivity of Europe’s major agricultural cropping systems, with more favourable impacts in northern and central Europe. No Label