Asseng, S., Ewert, F., Rosenzweig, C., Jones, J. W., Hatfield, J. L., Ruane, A. C., et al. (2013). Uncertainty in simulating wheat yields under climate change. Nat. Clim. Change, 3(9), 827–832.
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.
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Rötter, R. P., Asseng, S., Ewert, F., Rosenzweig, C., Jones, J. W., Hatfield, J. L., et al. (2013). Quantifying Uncertainties in Modeling Crop Water Use under Climate Change..
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Nendel, C., Ewert, F., Rötter, R. P., Rosenzweig, C., Jones, J. W., Hatfield, J. L., et al. (2013). Addressing challenges and uncertainties for, the use of agro-ecosystem models to, assess climate change impact and food security across scales..
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Boote, K. J., Porter, C., Jones, J. W., Thorburn, P. J., Kersebaum, K. C., Hoogenboom, G., et al. (2015). Sentinel site data for crop model improvement – definition and characterization. In J. L. Hatfield, & D. Fleisher (Eds.), (Vol. Advances in Agricultural Systems Modeling (7)). Madison, WI: ASA, CSSA, and SSSA.
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Webber, H., White, J. W., Kimball, B. A., Ewert, F., Asseng, S., Rezaei, E. E., et al. (2018). Physical robustness of canopy temperature models for crop heat stress simulation across environments and production conditions. Field Crops Research, 216, 75–88.
Abstract: Despite widespread application in studying climate change impacts, most crop models ignore complex interactions among air temperature, crop and soil water status, CO2 concentration and atmospheric conditions that influence crop canopy temperature. The current study extended previous studies by evaluating Tc simulations from nine crop models at six locations across environmental and production conditions. Each crop model implemented one of an empirical (EMP), an energy balance assuming neutral stability (EBN) or an energy balance correcting for atmospheric stability conditions (EBSC) approach to simulate Tc. Model performance in predicting Tc was evaluated for two experiments in continental North America with various water, nitrogen and CO2 treatments. An empirical model fit to one dataset had the best performance, followed by the EBSC models. Stability conditions explained much of the differences between modeling approaches. More accurate simulation of heat stress will likely require use of energy balance approaches that consider atmospheric stability conditions.
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