Uncertainty in Simulating Wheat Yields under Climate Change. Asseng, S., Ewert, F., Rosenzweig, C., Jones, J. W., Hatfield, J. L., Ruane, A. C., Boote, K. J., Thorburn, P. J., Rötter, R. P., Cammarano, D., Brisson, N., Basso, B., Martre, P., Aggarwal, P. K., Angulo, C., Bertuzzi, P., Biernath, C., Challinor, A. J., Doltra, J., Gayler, S., Goldberg, R., Grant, R., Heng, L., Hooker, J., Hunt, L. A., Ingwersen, J., Izaurralde, R. C., Kersebaum, K. C., Müller, C., Naresh Kumar, S., Nendel, C., O'Leary, G., Olesen, J. E., Osborne, T. M., Palosuo, T., Priesack, E., Ripoche, D., Semenov, M. A., Shcherbak, I., Steduto, P., Stöckle, C., Stratonovitch, P., Streck, T., Supit, I., Tao, F., Travasso, M., Waha, K., Wallach, D., White, J. W., Williams, J. R., & Wolf, J. 3(9):827–832.
Uncertainty in Simulating Wheat Yields under Climate Change [link]Paper  doi  abstract   bibtex   
Projections of climate change impacts on crop yields are inherently uncertain1. Uncertainty is often quantified when projecting future greenhouse gas emissions and their influence on climate2. However, multi-model uncertainty analysis of crop responses to climate change is rare because systematic and objective comparisons among process-based crop simulation models1, 3 are difficult4. 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 policy making.
@article{assengUncertaintySimulatingWheat2013,
  title = {Uncertainty in Simulating Wheat Yields under Climate Change},
  author = {Asseng, S. and Ewert, F. and Rosenzweig, C. and Jones, J. W. and Hatfield, J. L. and Ruane, A. C. and Boote, K. J. and Thorburn, P. J. and Rötter, R. P. and Cammarano, D. and Brisson, N. and Basso, B. and Martre, P. and Aggarwal, P. K. and Angulo, C. and Bertuzzi, P. and Biernath, C. and Challinor, A. J. and Doltra, J. and Gayler, S. and Goldberg, R. and Grant, R. and Heng, L. and Hooker, J. and Hunt, L. A. and Ingwersen, J. and Izaurralde, R. C. and Kersebaum, K. C. and Müller, C. and Naresh Kumar, S. and Nendel, C. and O'Leary, G. and Olesen, J. E. and Osborne, T. M. and Palosuo, T. and Priesack, E. and Ripoche, D. and Semenov, M. A. and Shcherbak, I. and Steduto, P. and Stöckle, C. and Stratonovitch, P. and Streck, T. and Supit, I. and Tao, F. and Travasso, M. and Waha, K. and Wallach, D. and White, J. W. and Williams, J. R. and Wolf, J.},
  date = {2013-06},
  journaltitle = {Nature Climate Change},
  volume = {3},
  pages = {827--832},
  issn = {1758-678X},
  doi = {10.1038/nclimate1916},
  url = {https://doi.org/10.1038/nclimate1916},
  abstract = {Projections of climate change impacts on crop yields are inherently uncertain1. Uncertainty is often quantified when projecting future greenhouse gas emissions and their influence on climate2. However, multi-model uncertainty analysis of crop responses to climate change is rare because systematic and objective comparisons among process-based crop simulation models1, 3 are difficult4. 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 policy making.},
  keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-12438735,agricultural-land,agricultural-resources,climate-change,crop-yield,integrated-natural-resources-modelling-and-management,integration-techniques,multiauthor,science-policy-interface,transdisciplinary-research,uncertainty,wheat},
  number = {9}
}
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