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Biogeosciences An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/bg-2016-527
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
20 Dec 2016
Review status
This discussion paper is a preprint. It has been under review for the journal Biogeosciences (BG). The manuscript was not accepted for further review after discussion.
Uncertainties in global crop model frameworks: effects of cultivar distribution, crop management and soil handling on crop yield estimates
Christian Folberth1, Joshua Elliott2,3, Christoph Müller4, Juraj Balkovic1,5, James Chryssanthacopoulos3, Roberto C. Izaurralde6,7, Curtis D. Jones6, Nikolay Khabarov1, Wenfeng Liu8, Ashwan Reddy6, Erwin Schmid9, Rastislav Skalský1,10, Hong Yang8,11, Almut Arneth12, Philippe Ciais13, Delphine Deryng3, Peter J. Lawrence14, Stefan Olin15, Thomas A. M. Pugh12,16, Alex C. Ruane3,17, and Xuhui Wang13,18 1International Institute for Applied Systems Analysis , Ecosystem Services and Management Program, 2361 Laxenburg, Austria
2University of Chicago and ANL Computation Institute, Chicago, IL 60637, USA
3Columbia University Center for Climate Systems Research and NASA Goddard Institute for Space Studies, New York, NY 10025, USA
4Potsdam Institute for Climate Impact Research, 14473 Potsdam, Germany
5Comenius University in Bratislava, Department of Soil Science, 842 15 Bratislava, Slo vak Republic
6University of Maryland, Department of Geographical Sciences, College Park, MD 20742, USA
7Texas A&M University, Texas AgriLife Research and Extension, Temple, TX 76502, USA
8Eawag, Swiss Federal Institute of Aquatic Science and Technology, CH - 8600 Duebendorf, Switzerland
9University of Natural Resources and Life Sciences, Institute for Sustainable Economic Development, 1180 Vienna, Austria
10Soil Science and Conservation Research Institute, National Agricultural and Food Centre, 82713 Bratislava, Slovak Republic
11Department of Environmental Sciences, University of Basel, Petersplatz 1, CH-4003 Basel, Switzerland
12Karlsruhe Institute of Technology, IMK-IFU, 82467 Garmisch-Partenkirchen, Germany
13Laboratoire des Sciences du Climat et de l’En vironnement. CEA CNRS UVSQ Orme des Merisiers, F-91191 Gif-sur-Yvette, France
14National Center for Atmospheric Research, Earth System Laboratory, Boulder, CO 80307, USA
15Department of Physical Geography and Ecosystem Science, Lund University, 223 62 Lund, Sweden
16School of Geography, Earth & Environmental Science and Birmingham Institute of Forest Research, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom
17National Aeronautics and Space Administration Goddard Institute for Space Studies, New York, NY 10025, USA
18Peking University, Sino-French Institute of Earth System Sciences, 100871 Beijing, China
Abstract. Global gridded crop models (GGCMs) combine field-scale agronomic models or sets of plant growth algorithms with gridded spatial input data to estimate spatially explicit crop yields and agricultural externalities at the global scale. Differences in GGCM outputs arise from the use of different bio-physical models, setups, and input data. While algorithms have been in the focus of recent GGCM comparisons, this study investigates differences in maize and wheat yield estimates from five GGCMs based on the public domain field-scale model Environmental Policy Integrated Climate (EPIC) that participate in the AgMIP Global Gridded Crop Model Intercomparison (GGCMI) project. Albeit using the same crop model, the GGCMs differ in model version, input data, management assumptions, parameterization, geographic distribution of cultivars, and selection of subroutines e.g. for the estimation of potential evapotranspiration or soil erosion. The analyses reveal long-term trends and inter-annual yield variability in the EPIC-based GGCMs to be highly sensitive to soil parameterization and crop management. Absolute yield levels as well depend not only on nutrient supply but also on the parameterization and distribution of crop cultivars. All GGCMs show an intermediate performance in reproducing reported absolute yield levels or inter-annual dynamics. Our findings suggest that studies focusing on the evaluation of differences in bio-physical routines may require further harmonization of input data and management assumptions in order to eliminate background noise resulting from differences in model setups. For agricultural impact assessments, employing a GGCM ensemble with its widely varying assumptions in setups appears the best solution for bracketing such uncertainties as long as comprehensive global datasets taking into account regional differences in crop management, cultivar distributions and coefficients for parameterizing agro-environmental processes are lacking. Finally, we recommend improvements in the documentation of setups and input data of GGCMs in order to allow for sound interpretability, comparability and reproducibility of published results.
Citation: Folberth, C., Elliott, J., Müller, C., Balkovic, J., Chryssanthacopoulos, J., Izaurralde, R. C., Jones, C. D., Khabarov, N., Liu, W., Reddy, A., Schmid, E., Skalský, R., Yang, H., Arneth, A., Ciais, P., Deryng, D., Lawrence, P. J., Olin, S., Pugh, T. A. M., Ruane, A. C., and Wang, X.: Uncertainties in global crop model frameworks: effects of cultivar distribution, crop management and soil handling on crop yield estimates, Biogeosciences Discuss., https://doi.org/10.5194/bg-2016-527, 2016.
Christian Folberth et al.
Christian Folberth et al.

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Short summary
Global crop models differ in numerous aspects such as algorithms, parameterization, input data, and management assumptions. This study compares five global crop model frameworks, all based on the same field-scale model, to identify differences induced by the latter three. Results indicate that foremost nutrient supply, soil handling, and crop management induce substantial differences in crop yield estimates whereas crop cultivars primarily result in scaling of yield levels.
Global crop models differ in numerous aspects such as algorithms, parameterization, input data,...
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