[Objective] The objective of this study was to predict regional wheat productivity, which would lay a foundation for quantitative evaluating agricultural productivity and precision management. [Method] Considering the approaches related to scaling-up the crop model from plot to region level, a regional wheat model was developed by integrating the WheatGrow model with GIS. To address the spatial variability, grid-based data as model input were generated in advance by interpolation and overlaying with the aid of GIS. When all the necessary input data were available, the target region was partitioned into smaller and relatively homogeneous spatial grids, and crop yields were simulated with WheatGrow for each grid cell. The regional wheat productivity of Jiangsu Province in 2000 was simulated. [Result] The distribution of wheat yield that simulated by the regional wheat model based on grid input data, was coincident with statistic data. The mean RMSE of wheat yield between simulated result and statistics was 496.48 kg·hm~(-2). [Conclusion] The results indicated that the GIS based regional wheat model has a good performance in predicting regional wheat productivity.%[目的]准确预测区域小麦生产力,可以为农业生产的定量评价、环境变化影响的评估等提供关键技术支持.[方法]基于课题组研制的小麦生长模拟模型,探讨生长模型在区域化应用过程中升尺度连接的方法,进一步将模型与GIS相耦合,建立小麦区域生产力预测模拟模型;并以江苏省2000年小麦产量的区域模拟为例,借助GIS技术完成了气象、土壤特性、管理措施等模型基本输入数据的空间栅格化预处理,通过将研究区域划分为许多均质栅格,解决了研究区域内环境条件的空间变异,实现了小麦生产力的区域模拟;并将江苏省内的栅格模拟产量空间变异图和平均模拟结果同实际产量变异和统计产量进行了比较.[结果]模型模拟的产量空间分布图可以较好的反映江苏省区域内实际小麦产量的变化规律,通过归并得到的江苏省县市平均产量与统计产量的均方根差为496.48 kg·hm~(-2).[结论]表明基于模型和GIS对不同生态条件下的小麦生产力进行模拟,具有较好的可行性.
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