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The crop model based on physiology and ecology has been widely applied to the simulation of regional potential productivity. By determining the appropriate spatial resolution of meteorological data required for model simulation for different regions, we can reduce the difficulty of acquiring model input data, thereby improving the regional computing efficiency of the model and increasing the model applications. In this study, we investigated the appropriate spatial resolution of meteorological data needed for the regional potential productivity simulation of the WheatGrow model by scale effect index and verify the feasibility of using the landform to obtain the appropriate spatial resolution of meteorological data required by the potential productivity simulation for the winter wheat region of China. The research results indicated that the spatial variation of landforms in the winter wheat region of China is significantly correlated to the spatial variation of multi-year meteorological data. Based on the scale effect index, we can obtain a spatial distribution of appropriate spatial resolution for the meteorological data required for the regional potential productivity simulation of the WheatGrow model for the winter wheat region of China. Moreover, although we can use the spatial heterogeneity of landforms to guide the selection of appropriate spatial resolution for the meteorological data, in the regions where the spatial heterogeneity of the landform is relatively weak or relatively strong over a small range, the method of using a single heterogeneity index derived from semi-variogram cannot well reflect the scale effect of simulation results and needs further improvement.2 of 18 potential, identify the rule of variation in the upper limit of yield, optimize the planting system, and improve the use efficiency of agroclimatic resources, thereby providing a scientific reference for regional sustainable development [2,3]. At present, the simulation of the regional potential productivity of crops includes two methods: the empirical model and the mechanical model. The empirical model, often used includes the agro-ecological region method (AEZ) recommended by the United Nations Food and Agriculture Organization (FAO) [4,5]. However, the empirical model, which establishes a simple statistical equation based on statistical data, does not consider the genetic characteristics and growth and development of crops, and the interpretation and universality is relatively poor. The mechanical model is based on the eco-physiological process of crop growth and is a powerful tool to predict crop yield, manage agricultural resources, and assess the influence of climate change on agricultural production, and is most widely applied in the regional potential productivity simulation [6]. At present, the CERES-WHEAT model [7,8] WOFOST model [9,10] and WheatGrow model [11,12] of wheat growth have been applied to the simulation of the regional potential productivity in China.However, the lack of high-quality spatial input data is the mai...

Simulations based on site-specific crop growth models have been widely used to obtain regional yield potential estimates for food security assessments at the regional scale. By dividing a region into nonoverlapping basic spatial units using appropriate zonation schemes, the data required to run a crop growth model can be reduced, thereby improving the simulation efficiency. In this study, we explored the impacts of different zonation schemes on estimating the regional yield potential of the Chinese winter wheat area to obtain the most appropriate spatial zonation scheme of weather sites therein. Our simulated results suggest that the upscaled site-specific yield potential is affected by the zonation scheme and by the spatial distribution of sites. As such, the distribution of a small number of sites significantly affected the simulated regional yield potential under different zonation schemes, and the zonation scheme based on sunshine duration clustering zones could effectively guarantee the simulation accuracy at the regional scale. Using the most influential environmental variable of crop growth models for clustering can get the better zonation scheme to upscale the site-specific simulation results. In contrast, a large number of sites had little effect on the regional yield potential simulation results under the different zonation schemes.