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  • Müller, ChristophPotsdam Institute for Climate Impact Research (author)

Global gridded crop model evaluation : Benchmarking, skills, deficiencies and implications

  • Article/chapterEnglish2017

Publisher, publication year, extent ...

  • 2017-04-04
  • Copernicus GmbH,2017
  • 20 s.

Numbers

  • LIBRIS-ID:oai:lup.lub.lu.se:215787f8-b9dd-4025-8a7e-d98f02730ba7
  • https://lup.lub.lu.se/record/215787f8-b9dd-4025-8a7e-d98f02730ba7URI
  • https://doi.org/10.5194/gmd-10-1403-2017DOI

Supplementary language notes

  • Language:English
  • Summary in:English

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  • Subject category:art swepub-publicationtype
  • Subject category:ref swepub-contenttype

Notes

  • Crop models are increasingly used to simulate crop yields at the global scale, but so far there is no general framework on how to assess model performance. Here we evaluate the simulation results of 14 global gridded crop modeling groups that have contributed historic crop yield simulations for maize, wheat, rice and soybean to the Global Gridded Crop Model Intercomparison (GGCMI) of the Agricultural Model Intercomparison and Improvement Project (AgMIP). Simulation results are compared to reference data at global, national and grid cell scales and we evaluate model performance with respect to time series correlation, spatial correlation and mean bias. We find that global gridded crop models (GGCMs) show mixed skill in reproducing time series correlations or spatial patterns at the different spatial scales. Generally, maize, wheat and soybean simulations of many GGCMs are capable of reproducing larger parts of observed temporal variability (time series correlation coefficients (r) of up to 0.888 for maize, 0.673 for wheat and 0.643 for soybean at the global scale) but rice yield variability cannot be well reproduced by most models. Yield variability can be well reproduced for most major producing countries by many GGCMs and for all countries by at least some. A comparison with gridded yield data and a statistical analysis of the effects of weather variability on yield variability shows that the ensemble of GGCMs can explain more of the yield variability than an ensemble of regression models for maize and soybean, but not for wheat and rice. We identify future research needs in global gridded crop modeling and for all individual crop modeling groups. In the absence of a purely observation-based benchmark for model evaluation, we propose that the best performing crop model per crop and region establishes the benchmark for all others, and modelers are encouraged to investigate how crop model performance can be increased. We make our evaluation system accessible to all crop modelers so that other modeling groups can also test their model performance against the reference data and the GGCMI benchmark.

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Added entries (persons, corporate bodies, meetings, titles ...)

  • Elliott, JoshuaColumbia University,University of Chicago (author)
  • Chryssanthacopoulos, JamesColumbia University,University of Chicago (author)
  • Arneth, AlmutKarlsruhe Institute of Technology(Swepub:lu)nate-aar (author)
  • Balkovic, JurajInternational Institute for Applied Systems Analysis,Comenius University (author)
  • Ciais, PhilippeLaboratoire des Sciences du Climat et de l'Environnement (author)
  • Deryng, DelphineColumbia University,University of Chicago (author)
  • Folberth, ChristianInternational Institute for Applied Systems Analysis,Ludwig-Maximilian University of Munich (author)
  • Glotter, MichaelUniversity of Chicago (author)
  • Hoek, StevenWageningen University (author)
  • Iizumi, ToshichikaNational Agriculture and Food Research Organization (NARO) (author)
  • Izaurralde, Roberto C.University of Maryland,Texas A and M University (author)
  • Jones, CurtisUniversity of Maryland (author)
  • Khabarov, NikolayInternational Institute for Applied Systems Analysis (author)
  • Lawrence, PeterNational Center for Atmospheric Research (author)
  • Liu, WenfengEawag: Swiss Federal Institute of Aquatic Science and Technology (author)
  • Olin, StefanLund University,Lunds universitet,Institutionen för naturgeografi och ekosystemvetenskap,Naturvetenskapliga fakulteten,Dept of Physical Geography and Ecosystem Science,Faculty of Science(Swepub:lu)nate-sao (author)
  • Pugh, Thomas Alan MillerKarlsruhe Institute of Technology,University of Birmingham(Swepub:lu)th5432pu (author)
  • Ray, Deepak K.University of Minnesota (author)
  • Reddy, AshwanUniversity of Maryland (author)
  • Rosenzweig, CynthiaNASA Goddard Institute for Space Studies,Columbia University (author)
  • Ruane, Alex C.NASA Goddard Institute for Space Studies,Columbia University (author)
  • Sakurai, GenNational Agriculture and Food Research Organization (NARO) (author)
  • Schmid, ErwinUniversity of Natural Resources and Life Sciences, Vienna (author)
  • Skalsky, RastislavInternational Institute for Applied Systems Analysis (author)
  • Song, Carol X.Purdue University (author)
  • Wang, XuhuiLaboratoire des Sciences du Climat et de l'Environnement,Peking University (author)
  • De Wit, AllardWageningen University (author)
  • Yang, HongUniversity of Basel,Eawag: Swiss Federal Institute of Aquatic Science and Technology (author)
  • Potsdam Institute for Climate Impact ResearchColumbia University (creator_code:org_t)

Related titles

  • In:Geoscientific Model Development: Copernicus GmbH10:4, s. 1403-14221991-959X1991-9603

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