Crop models are essential tools for assessing the threat of climate change to local and global food production 1 . Present models used to predict wheat grain yield are highly uncertain when simulating how crops respond to temperature 2 . Here we systematically tested 30 di erent wheat crop models of the Agricultural Model Intercomparison and Improvement Project against field experiments in which growing season mean temperatures ranged from 15 • C to 32 • C, including experiments with artificial heating. Many models simulated yields well, but were less accurate at higher temperatures. The model ensemble median was consistently more accurate in simulating the crop temperature response than any single model, regardless of the input information used. Extrapolating the model ensemble temperature response indicates that warming is already slowing yield gains at a majority of wheat-growing locations. Global wheat production is estimated to fall by 6% for each • C of further temperature increase and become more variable over space and time.Understanding how different climate factors interact and impact food production 3 is essential when reaching decisions on how to adapt to the effects of climate change. To implement such strategies the contribution of various climate variables on crop yields need to be separated and quantified. For instance, a change in temperature will require a different adaptation strategy than a change in rainfall 4 . Temperature changes alone are reported to have potentially large negative impacts on crop production 5 , and hotspots-locations where plants suffer from high temperature stress-have been identified across the globe 6,7 . Crop simulation models are useful tools in climate impact studies as they deal with multiple climate factors and how they interact with various crop growth and yield formation processes that are sensitive to climate. These models have been applied in many studies, including the assessment of temperature impacts on crop production 1,8 . However, none of the crop models have been tested systematically against experiments at different temperatures in field conditions. Although many glasshouse and controlled-environment temperature experiments have been described, they are often not suitable for model testing as the heating of root systems in pots 9 and effects on micro-climate differ greatly from field conditions 10 . Detailed information on field experiments with a wide range of sowing dates and infrared heating recently became available for wheat 11,12 . Such experiments are well suited for testing the ability of crop models to quantify temperature responses under field conditions. Testing the temperature responses of crop models is particularly important for assessing the impact of climate change on wheat production, because the largest uncertainty in simulated impacts on yield arises from increasing temperatures 2 .In a 'Hot Serial Cereal' (HSC) well-irrigated and fertilized experiment with a single cultivar, the observed days after sowing (DAS) to maturity declined...

This is a repository copy of Similar estimates of temperature impacts on global wheat yield by three independent methods.

Nendel 38 | Jørgen Eivind Olesen 37 | Taru Palosuo 44 | John R. Porter 42,45,46 | Eckart Priesack 39 | Dominique Ripoche 47 | Mikhail A. Semenov 48 | Claudio Stöckle 17 | Pierre Stratonovitch 48 | Thilo Streck 33 | Iwan Supit 49 | Fulu Tao 50,44 | Marijn Van der Velde 51 | Daniel Wallach 52 | Enli Wang 53 | Heidi Webber 30,38 AbstractWheat grain protein concentration is an important determinant of wheat quality for human nutrition that is often overlooked in efforts to improve crop production. We tested and applied a 32-multi-model ensemble to simulate global wheat yield and quality in a changing climate. Potential benefits of elevated atmospheric CO 2 concentration by 2050 on global wheat grain and protein yield are likely to be negated by impacts from rising temperature and changes in rainfall, but with considerable 156 |

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This study explored the utility of the impact response surface (IRS) approach for investigating model ensemble crop yield responses under a large range of changes in climate. IRSs of spring and winter wheat Triticum aestivum yields were constructed from a 26-member ensemble of process-based crop simulation models for sites in Finland, Germany and Spain across a latitudinal transect. The sensitivity of modelled yield to systematic increments of changes in temperature (-2 to + 9 degrees C) and precipitation (-50 to + 50%) was tested by modifying values of baseline (1981 to 2010) daily weather, with CO2 concentration fixed at 360 ppm. The IRS approach offers an effective method of portraying model behaviour under changing climate as well as advantages for analysing, comparing and presenting results from multi-model ensemble simulations. Though individual model behaviour occasionally departed markedly from the average, ensemble median responses across sites and crop varieties indicated that yields decline with higher temperatures and decreased precipitation and increase with higher precipitation. Across the uncertainty ranges defined for the IRSs, yields were more sensitive to temperature than precipitation changes at the Finnish site while sensitivities were mixed at the German and Spanish sites. Precipitation effects diminished under higher temperature changes. While the bivariate and multi-model characteristics of the analysis impose some limits to interpretation, the IRS approach nonetheless provides additional insights into sensitivities to inter-model and inter-annual variability. Taken together, these sensitivities may help to pinpoint processes such as heat stress, vernalisation or drought effects requiring refinement in future model developmen

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This study assesses the ability of 21 crop models to capture the impact of elevated CO2 concentration ([CO2]) on maize yield and water use as measured in a 2-year Free Air Carbon dioxide Enrichment experiment conducted at the Thünen Institute in Braunschweig, Germany (Manderscheid et al., 2014). Data for ambient [CO2] and irrigated treatments were provided to the 21 models for calibrating plant traits, including weather, soil and management data as well as yield, grain number, above ground biomass, leaf area index, nitrogen concentration in biomass and grain, water use and soil water content. Models differed in their representation of carbon assimilation and evapotranspiration processes. The models reproduced the absence of yield response to elevated [CO2] under well-watered conditions, as well as the impact of water deficit at ambient [CO2], with 50% of models within a range of +/−1 Mg ha−1 around the mean. The bias of the median of the 21 models was less than 1 Mg ha−1. However under water deficit in one of the two years, the models captured only 30% of the exceptionally high [CO2] enhancement on yield observed. Furthermore the ensemble of models was unable to simulate the very low soil water content at anthesis and the increase of soil water and grain number brought about by the elevated [CO2] under dry conditions. Overall, we found models with explicit stomatal control on transpiration tended to perform better. Our results highlight the need for model improvement with respect to simulating transpirational water use and its impact on water status during the kernel-set phase

61To improve climate change impact estimates and to quantify their uncertainty, multi-model ensembles 62 (MMEs) have been suggested. Model improvements can improve the accuracy of simulations and reduce 63 the uncertainty of climate change impact assessments. Furthermore, they can reduce the number of models 64 needed in a MME. Herein, 15 wheat growth models of a larger MME were improved through re-65 parameterization and/or incorporating or modifying heat stress effects on phenology, leaf growth and 66 senescence, biomass growth, and grain number and size using detailed field experimental data from the 67 USDA Hot Serial Cereal experiment (calibration data set). Simulation results from before and after model 68 improvement were then evaluated with independent field experiments from a CIMMYT world-wide field 69 trial network (evaluation data set). Model improvements decreased the variation (10 th to 90 th model 70 ensemble percentile range) of grain yields simulated by the MME on average by 39% in the calibration 71 data set and by 26% in the independent evaluation data set for crops grown in mean seasonal temperatures 72 >24°C. MME mean squared error in simulating grain yield decreased by 37%. A reduction in MME 73 uncertainty range by 27% increased MME prediction skills by 47%. Results suggest that the mean level of 74 variation observed in field experiments and used as a benchmark can be reached with half the number of 75 models in the MME. Improving crop models is therefore important to increase the certainty of model-76 based impact assessments and allow more practical, i.e. smaller MMEs to be used effectively. 77 78