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

Xiong Wei

Vol.20, NO.1, 2009

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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.

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.

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

Xiong Wei^1,2

1.
Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing 100081
2.
Key Laboratory for Agro-environment and Climate Change, Ministry of Agriculture PRC, Beijing 100081

Received Date: 2008-01-30
Rev Recd Date: 2008-08-13
Publish Date: 2009-02-28

Abstract

Abstract

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.
- CERES-Wheat;
- regional simulation;
- performance;
- uncertainties

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References(22)

Figures and Tables

图 1 我国小麦种植生态区和播种面积(单位: 10³ hm²)

Fig. 1 Chinese wheat agro-ecological zones and its sowing area(unit:10³ hm²)

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图 2 1990—2000年各站点成熟期(a)和产量(b)的模拟值与实测值关系

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

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图 3 成熟期(a)和产量(b)的模拟值与实测值关系

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

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图 4 网格模拟产量与调查产量的关系

Fig. 4 Simulated and census yields for grids

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图 5 全国各网格模拟产量与调查产量的相对均方根误差

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

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表 1 各生态区模拟和实测的小麦产量统计比较

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

表 2 各区历年网格模拟的平均产量与调查平均产量的比较

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

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