Potential climate-related impacts on future crop yield are a major societal concern. Previous projections of the Agricultural Model Intercomparison and Improvement Project's Global Gridded Crop Model Intercomparison based on CMIP5 identified substantial climate impacts on all major crops, but associated uncertainties were substantial. Here we report new 21st-century projections using ensembles of latest-generation crop and climate models. Results suggest markedly more pessimistic yield responses for maize, soybean and rice compared to the original ensemble. Mean end-of-century maize productivity is shifted from +5% to −6% (SSP126) and from +1% to −24% (SSP585)-explained by warmer climate projections and improved crop model sensitivities. In contrast, wheat shows stronger gains (+9% shifted to +18%, SSP585), linked to higher CO 2 concentrations and expanded high-latitude gains. The 'emergence' of climate impacts consistently occurs earlier in the new projectionsbefore 2040 for several main producing regions. While future yield estimates remain uncertain, these results suggest that major breadbasket regions will face distinct anthropogenic climatic risks sooner than previously anticipated.

Understanding the drivers of yield levels under climate change is required to support adaptation planning and respond to changing production risks. This study uses an ensemble of crop models applied on a spatial grid to quantify the contributions of various climatic drivers to past yield variability in grain maize and winter wheat of European cropping systems (1984–2009) and drivers of climate change impacts to 2050. Results reveal that for the current genotypes and mix of irrigated and rainfed production, climate change would lead to yield losses for grain maize and gains for winter wheat. Across Europe, on average heat stress does not increase for either crop in rainfed systems, while drought stress intensifies for maize only. In low-yielding years, drought stress persists as the main driver of losses for both crops, with elevated CO2 offering no yield benefit in these years.

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Crop yield can be affected by crop water use and vice versa, so when trying to simulate one or the other, it can be important that both are simulated well. In a prior inter-comparison among maize growth models, evapotranspiration (ET) predictions varied widely, but no observations of actual ET were available for comparison. Therefore, this follow-up study was initiated under the umbrella of AgMIP (Agricultural Model Inter-Comparison and Improvement Project). Observations of daily ET using the eddy covariance technique from an 8-year-long (2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)) experiment conducted at Ames, IA were used as the standard for comparison among models. Simulation results from 29 models are reported herein. In the first "blind" phase for which only weather, soils, phenology, and management information were provided to the modelers, estimates of seasonal ET varied from about 200 to about 700 mm. Subsequent three phases provided (1) leaf area indices for all years, (2) all daily ET and agronomic data for a typical year (2011), and (3) all data for all years, thus allowing the modelers to progressively calibrate their models as more information was provided, but the range among ET estimates still varied by a factor of two or more. Much of the variability among the models was due to differing estimates of potential evapotranspiration, which suggests an avenue for substantial model improvement. Nevertheless, the ensemble median values were generally close to the observations, and the medians were best (had the lowest mean squared deviations from observations, MSD) for several ET categories for inter-comparison, but not all. Further, the medians were best when considering both ET and agronomic parameters together. The best six models with the lowest MSDs were identified for several ET and agronomic categories, and they proved to vary widely in complexity in spite of having similar prediction accuracies. At the same time, other models with apparently similar approaches were not as accurate. The models that are widely used tended to perform better, leading us speculate that a larger number of users testing these models over a wider range of conditions likely has led to improvement. User experience and skill at calibration and dealing with missing input data likely were also a factor in determining the accuracy of model predictions. In several cases different versions of a model within the same family of models were run, and these within-family inter-comparisons identified particular approaches that were better while other factors were held constant. Thus, improvement is needed in many of the models with regard to their ability to simulate ET over a wide range of conditions, and several aspects for progress have been identified, especially in their simulation of potential ET.

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Crop residues contribute to the maintenance of soil organic carbon (SOC) stores, a key component of soil fertility and soil-based climate change mitigation strategies, such as the '4 per 1000' initiative. Residues are also in demand in sectors coupled to crop production, such as the supply chain of livestock and bioenergy production. Ongoing debate revolves around balancing these competing uses, but science-based assessments of the long-term sustainability of residue exploitation are rare. This work uses biophysical simulation modelling to explore the likely response of SOC to different management strategies, using the land area of North Rhine-Westphalia (Germany) as a case study. Four strategies are tested: zero, one third and 100% removal of cereal residues, plus an approach proposed by the State farm advisory service. Simulations are carried out for the period 1971-2050 and 19 crop rotations coincident with land use throughout the study area. Uncertainty associated with the modelled SOC changes is explored by sampling values of relevant parameters for SOC turnover and running an ensemble of model configurations. Simulated SOC is used to trace time-dependent response functions following a change in residue management under different soil textures, initial SOC levels and crop rotations. Results highlight a general exponential decrease in SOC, with relative changes in 2050 distributed between +10% and −40% with respect to a reference period. SOC loss can be buffered or offset by returning all crop residues to the soil. Under such management, an SOC increase can be achieved on clayey soils characterized by a low initial SOC. Under moderate crop residue removal, positive SOC trends are limited to a few crop rotations. In this context, 4 per 1000 increase rate in SOC appears largely out of reach through residue management, calling for additional measures to meet the targets of land-based mitigation of anthropogenic emissions.

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