Uncertainty in Soil Data Can Outweigh Climate Impact Signals in Global Crop Yield Simulations. Folberth, C., Skalský, R., Moltchanova, E., Balkovi ̌c, J., Azevedo, L. B., Obersteiner, M., & van der Velde , M. Nature Communications, 7:11872+, June, 2016.
doi  abstract   bibtex   
Global gridded crop models (GGCMs) are increasingly used for agro-environmental assessments and estimates of climate change impacts on food production. Recently, the influence of climate data and weather variability on GGCM outcomes has come under detailed scrutiny, unlike the influence of soil data. Here we compare yield variability caused by the soil type selected for GGCM simulations to weather-induced yield variability. Without fertilizer application, soil-type-related yield variability generally outweighs the simulated inter-annual variability in yield due to weather. Increasing applications of fertilizer and irrigation reduce this variability until it is practically negligible. Importantly, estimated climate change effects on yield can be either negative or positive depending on the chosen soil type. Soils thus have the capacity to either buffer or amplify these impacts. Our findings call for improvements in soil data available for crop modelling and more explicit accounting for soil variability in GGCM simulations.
@article{folberthUncertaintySoilData2016,
  title = {Uncertainty in Soil Data Can Outweigh Climate Impact Signals in Global Crop Yield Simulations},
  author = {Folberth, Christian and Skalsk{\'y}, Rastislav and Moltchanova, Elena and Balkovi{\v c}, Juraj and Azevedo, Ligia B. and Obersteiner, Michael and {van der Velde}, Marijn},
  year = {2016},
  month = jun,
  volume = {7},
  pages = {11872+},
  issn = {2041-1723},
  doi = {10.1038/ncomms11872},
  abstract = {Global gridded crop models (GGCMs) are increasingly used for agro-environmental assessments and estimates of climate change impacts on food production. Recently, the influence of climate data and weather variability on GGCM outcomes has come under detailed scrutiny, unlike the influence of soil data. Here we compare yield variability caused by the soil type selected for GGCM simulations to weather-induced yield variability. Without fertilizer application, soil-type-related yield variability generally outweighs the simulated inter-annual variability in yield due to weather. Increasing applications of fertilizer and irrigation reduce this variability until it is practically negligible. Importantly, estimated climate change effects on yield can be either negative or positive depending on the chosen soil type. Soils thus have the capacity to either buffer or amplify these impacts. Our findings call for improvements in soil data available for crop modelling and more explicit accounting for soil variability in GGCM simulations.},
  journal = {Nature Communications},
  keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-14073273,~to-add-doi-URL,agricultural-resources,climate-change,deep-uncertainty,integration-techniques,soil-resources,uncertainty,uncertainty-propagation},
  lccn = {INRMM-MiD:c-14073273}
}
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