Parameterization-induced uncertainties and impacts of crop management harmonization in a global gridded crop model ensemble
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Christian Folberth aff001; Joshua Elliott aff002; Christoph Müller aff005; Juraj Balkovič aff001; James Chryssanthacopoulos aff003; Roberto C. Izaurralde aff007; Curtis D. Jones aff007; Nikolay Khabarov aff001; Wenfeng Liu aff009; Ashwan Reddy aff007; Erwin Schmid aff010; Rastislav Skalský aff001; Hong Yang aff008; Almut Arneth aff013; Philippe Ciais aff014; Delphine Deryng aff015; Peter J. Lawrence aff017; Stefan Olin aff018; Thomas A. M. Pugh aff019; Alex C. Ruane aff003; Xuhui Wang aff014
Působiště autorů:
International Institute for Applied Systems Analysis, Ecosystem Services and Management Program, Laxenburg, Austria
aff001; University of Chicago and ANL Computation Institute, Chicago, Illinois, United States of America
aff002; Columbia University, Center for Climate Systems Research, New York, New York, United States of America
aff003; National Aeronautics and Space Administration Goddard Institute for Space Studies, New York, New York, United States of America
aff004; Potsdam Institute for Climate Impact Research, Member of the Leibniz Association, Potsdam, Germany
aff005; Comenius University in Bratislava, Department of Soil Science, Bratislava, Slovak Republic
aff006; University of Maryland, Department of Geographical Sciences, College Park, Maryland, United States of America
aff007; Texas A&M University, Texas AgriLife Research and Extension, Temple, Texas, United States of America
aff008; Eawag, Swiss Federal Institute of Aquatic Science and Technology, Duebendorf, Switzerland
aff009; University of Natural Resources and Life Sciences, Institute for Sustainable Economic Development, Vienna, Austria
aff010; Soil Science and Conservation Research Institute, National Agricultural and Food Centre, Bratislava, Slovak Republic
aff011; Department of Environmental Sciences, University of Basel, Basel, Switzerland
aff012; Karlsruhe Institute of Technology, IMK-IFU, Garmisch-Partenkirchen, Germany
aff013; Laboratoire des Sciences du Climat et de l’Environnement, Gif-sur-Yvette, France
aff014; Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany
aff015; IRI THESys, Humboldt University of Berlin, Berlin, Germany
aff016; National Center for Atmospheric Research, Earth System Laboratory, Boulder, Colorado, United States of America
aff017; Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden
aff018; School of Geography, Earth & Environmental Sciences, University of Birmingham, Edgbaston, Birmingham, United Kingdom
aff019; Birmingham Institute of Forest Research, University of Birmingham, Edgbaston, Birmingham, United Kingdom
aff020; Peking University, Sino-French Institute of Earth System Sciences, Beijing, China
aff021
Vyšlo v časopise:
PLoS ONE 14(9)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0221862
Souhrn
Global gridded crop models (GGCMs) combine agronomic or plant growth models 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 biophysical models, setups, and input data. GGCM ensembles are frequently employed to bracket uncertainties in impact studies without investigating the causes of divergence in outputs. This study explores differences in maize 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 initiative. Albeit using the same crop model, the GGCMs differ in model version, input data, management assumptions, parameterization, and selection of subroutines affecting crop yield estimates via cultivar distributions, soil attributes, and hydrology among others. The analyses reveal inter-annual yield variability and absolute yield levels in the EPIC-based GGCMs to be highly sensitive to soil parameterization and crop management. All GGCMs show an intermediate performance in reproducing reported yields with a higher skill if a static soil profile is assumed or sufficient plant nutrients are supplied. An in-depth comparison of setup domains for two EPIC-based GGCMs shows that GGCM performance and plant stress responses depend substantially on soil parameters and soil process parameterization, i.e. hydrology and nutrient turnover, indicating that these often neglected domains deserve more scrutiny. For agricultural impact assessments, employing a GGCM ensemble with its widely varying assumptions in setups appears the best solution for coping with uncertainties from lack of comprehensive global data on crop management, cultivar distributions and coefficients for agro-environmental processes. However, the underlying assumptions require systematic specifications to cover representative agricultural systems and environmental conditions. Furthermore, the interlinkage of parameter sensitivity from various domains such as soil parameters, nutrient turnover coefficients, and cultivar specifications highlights that global sensitivity analyses and calibration need to be performed in an integrated manner to avoid bias resulting from disregarded core model domains. Finally, relating evaluations of the EPIC-based GGCMs to a wider ensemble based on individual core models shows that structural differences outweigh in general differences in configurations of GGCMs based on the same model, and that the ensemble mean gains higher skill from the inclusion of structurally different GGCMs. Although the members of the wider ensemble herein do not consider crop-soil-management interactions, their sensitivity to nutrient supply indicates that findings for the EPIC-based sub-ensemble will likely become relevant for other GGCMs with the progressing inclusion of such processes.
Klíčová slova:
Biology and life sciences – Agriculture – Agricultural soil science – Crop science – Crops – Crop management – Agrochemicals – Fertilizers – Organisms – Eukaryota – Plants – Grasses – Maize – Developmental biology – Plant science – Plant growth and development – Ecology and environmental sciences – Soil science – Edaphology – Research and analysis methods – Animal studies – Experimental organism systems – Model organisms – Plant and algal models – Earth sciences – Geomorphology – Erosion
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Článek vyšel v časopise
PLOS One
2019 Číslo 9