2018, Webber, Heidi, Ewert, Frank, Olesen, Jørgen E., Müller, Christoph, Fronzek, Stefan, Ruane, Alex C., Bourgault, Maryse, Martre, Pierre, Ababaei, Behnam, Bindi, Marco, Ferrise, Roberto, Finger, Robert, Fodor, Nándor, Gabaldón-Leal, Clara, Gaiser, Thomas, Jabloun, Mohamed, Kersebaum, Kurt-Christian, Lizaso, Jon I., Lorite, Ignacio J., Manceau, Loic, Moriondo, Marco, Nendel, Claas, Rodríguez, Alfredo, Ruiz-Ramos, Margarita, Semenov, Mikhail A., Siebert, Stefan, Stella, Tommaso, Stratonovitch, Pierre, Trombi, Giacomo, Wallach, Daniel

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.

2017, Ruane, Alex C., Rosenzweig, Cynthia, Asseng, Senthold, Boote, Kenneth J., Elliott, Joshua, Ewert, Frank, Jones, James W., Martre, Pierre, McDermid, Sonali P., Müller, Christoph, Snyder, Abigail, Thorburn, Peter J.

Integrated assessment models (IAMs) hold great potential to assess how future agricultural systems will be shaped by socioeconomic development, technological innovation, and changing climate conditions. By coupling with climate and crop model emulators, IAMs have the potential to resolve important agricultural feedback loops and identify unintended consequences of socioeconomic development for agricultural systems. Here we propose a framework to develop robust representation of agricultural system responses within IAMs, linking downstream applications with model development and the coordinated evaluation of key climate responses from local to global scales. We survey the strengths and weaknesses of protocol-based assessments linked to the Agricultural Model Intercomparison and Improvement Project (AgMIP), each utilizing multiple sites and models to evaluate crop response to core climate changes including shifts in carbon dioxide concentration, temperature, and water availability, with some studies further exploring how climate responses are affected by nitrogen levels and adaptation in farm systems. Site-based studies with carefully calibrated models encompass the largest number of activities; however they are limited in their ability to capture the full range of global agricultural system diversity. Representative site networks provide more targeted response information than broadly-sampled networks, with limitations stemming from difficulties in covering the diversity of farming systems. Global gridded crop models provide comprehensive coverage, although with large challenges for calibration and quality control of inputs. Diversity in climate responses underscores that crop model emulators must distinguish between regions and farming system while recognizing model uncertainty. Finally, to bridge the gap between bottom-up and top-down approaches we recommend the deployment of a hybrid climate response system employing a representative network of sites to bias-correct comprehensive gridded simulations, opening the door to accelerated development and a broad range of applications.