Xiong Wei. The performance of ceres-wheat model in wheat planting areas and its uncertainties. J Appl Meteor Sci, 2009, 20(1): 88-94.
Citation: Xiong Wei. The performance of ceres-wheat model in wheat planting areas and its uncertainties. J Appl Meteor Sci, 2009, 20(1): 88-94.

The Performance of CERES-Wheat Model in Wheat Planting Areas and Its Uncertainties

  • Received Date: 2008-01-30
  • Rev Recd Date: 2008-08-13
  • Publish Date: 2009-02-28
  • Crop models, coupling with climate data from climate models(GCMs, RCMs), are often employed to assess the impacts of climate change on crop production. However, there is a systematic mismatch of resolutions between climate models and crop models. Scaling up the crop model to regional scale is an appropriate method to resolve this problem. CERES Wheat crop model is used to simulate the wheat yields of 1981—2000 at 50 km×50 km grid scale. Performances of this simulation in wheat planting areas are evaluated based on the comparison of simulated yields to census values. The relative root mean square error(RMSE)between simulated and census yields for whole China is 27.9%, and the agreement index is 0.75. Of 2206 simulation units(50 km×50 km grid), 59.2% show relative RMSE less than 30%, in which 26.3% less than 15%. The performances differ among regions. Smallest bias occurs in agro-ecological zone 2(the largest wheat planting areas accounting for 39.9% of China's wheat planting area), with relative RMSE of 16.6% and D=0.68. To sum up, CERES Wheat crop model is able to produce reasonable results temporally and spatially. It can provide simulation information for policy making at macro scale despite existing uncertainties. The uncertainties of this regional simulation are ascribed to simplification and limitations of crop models, the aggregated inputs in wheat planting area, and errors in dataset etc, which need to be addressed in future.
  • Fig. 1  Chinese wheat agro-ecological zones and its sowing area(unit:103 hm2)

    Fig. 2  Comparison of observed and simulated duration from sowing to maturity(a) and yields(b)during 1990—2000

    Fig. 3  Comparison of observed and simulated duration from sowing to maturity(a)and yields(b)

    Fig. 4  Simulated and census yields for grids

    Fig. 5  Relative root mean square error of simulated to census wheat yields at grid level

    Table  1  Statistic of observed and simulated yields for each agro-ecological zone

    Table  2  Comparison of grid level simulated and census mean yields for agro-ecologial zones

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    • Received : 2008-01-30
    • Accepted : 2008-08-13
    • Published : 2009-02-28


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