Selection of Appropriate Spatial Resolution for the Meteorological Data for Regional Winter Wheat Potential Productivity Simulation in China Based on WheatGrow Model

Abstract

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...

Impacts of Spatial Zonation Schemes on Yield Potential Estimates at the Regional Scale

“…The WheatGrow model includes five modules, the apical and phenological development of wheat, photosynthesis and dry matter production, dry matter partitioning and organogenesis, yield and quality formation, and soil moisture and nutrition balance modules [21][22][23][24][25][26][27].The WheatGrow model can simulate wheat growth and development conditions under three growth conditions: yield potential, water limitation, and nitrogen limitation [22,30]. The WheatGrow model has been validated in simulations of winter wheat at multiple ecological observation sites throughout the main winter wheat production area of China using field experiment data from different sowing dates, plant densities, and nitrogen fertilization strategies, and it is reported that the WheatGrow model displays good agreement between the predicted and observed values and can effectively capture the spatial variations of the yield at different regional scales [4,34,35].…”

“…Yield potential refers to potential productivity, which is entirely determined by temperature and photosynthetically active radiation when nutrients, moisture, soils, cultivars, and other agricultural technological parameters are at optimum conditions [1,2]. Yield potential estimations at the regional scale can identify the variations in the upper yield limit, optimize the planting system, and improve the use efficiency of agroclimatic resources, thereby providing information for agricultural impact and risk assessment to support policymaking [1][2][3][4][5]. Additionally, the process-based crop growth model as a robust, generic, and cost-effective tool to simulate yields under a range of agricultural and climatic scenarios, in this context, crop growth models have been extensively used to estimate yield potentials over large areas.…”

“…The first upscaling method links a site-specific crop growth model with regional gridded data (rain, temperature, radiation, soil, etc.) and runs the crop growth model for each grid cell, a basic homogeneous spatial unit, to obtain the regional yields [4,[6][7][8]. However, while this method can Agronomy 2020, 10, 631 2 of 15 reflect the spatial trends of the simulated yields, crop growth models are highly sensitive to changes in the spatial resolution of the data, and thus, it is necessary to preliminarily determine the appropriate spatial resolution [4,8].…”

“…and runs the crop growth model for each grid cell, a basic homogeneous spatial unit, to obtain the regional yields [4,[6][7][8]. However, while this method can Agronomy 2020, 10, 631 2 of 15 reflect the spatial trends of the simulated yields, crop growth models are highly sensitive to changes in the spatial resolution of the data, and thus, it is necessary to preliminarily determine the appropriate spatial resolution [4,8]. Additionally, this approach requires interpolation of the meteorological data inside each grid cell, which introduces errors in the data and imposes large data storage requirements and computational costs [4,8,9].…”

“…However, while this method can Agronomy 2020, 10, 631 2 of 15 reflect the spatial trends of the simulated yields, crop growth models are highly sensitive to changes in the spatial resolution of the data, and thus, it is necessary to preliminarily determine the appropriate spatial resolution [4,8]. Additionally, this approach requires interpolation of the meteorological data inside each grid cell, which introduces errors in the data and imposes large data storage requirements and computational costs [4,8,9]. The second upscaling method divides the study area into irregular basic spatial units that have the smallest spatial heterogeneities, and typical ecological sites within these units are then selected to adequately represent the area.…”