Physical Sciences Division
Published April 4, 2017 | Version v1
Journal article Open

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

Creators

  • 1. Potsdam Institute for Climate Research
  • 2. University of Chicago
  • 3. Karlsruhe Institute of Technology
  • 4. University of Bratislava
  • 5. Laboratoire des Sciences du Climat et de l'Environnement
  • 6. Ludwig Maximilian University
  • 7. Alterra Wageningen University
  • 8. National Agriculture and Food Research Organization
  • 9. University of Maryland
  • 10. International Institute for Applied Systems Analysis
  • 11. National Center for Atmospheric Research
  • 12. Swiss Federal Institute of Aquatic Science and Technology
  • 13. Lund University
  • 14. University of Birmingham
  • 15. University of Minnesota
  • 16. Columbia University
  • 17. University of Natural Resources and Life Sciences
  • 18. Ecosystem Services and Management Program
  • 19. Purdue University
  • 20. Peking University

Description

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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Additional details

Identifiers

DOI
10.5194/gmd-10-1403-2017
Other
oai:uchicago.tind.io:14094

Funding

Agricultural Intercomparison and Improvement Project
German Federal Ministry of Education and Research
01LN1317A
European Commission
603542
Helmholtz Association
European Research Council
ERC-2013-SyG-610028

UChicago Information

Division(s)
Physical Sciences Division
Department(s)
Computer Science, Geophysical Sciences