Study On Simulating Regional Wheat Light Temperature Yield Potential Based On Coupling Crop Growth Model And GIS

Regional light temperature yield potential simulation can provide an important basis for agricultural planning and decision-making.At present,the process-based crop growth model has been widely used in regional light temperature yield potential simulation.However,the crop growth model is generally established and validated at the relatively uniform site or farm scales,while the spatial heterogeneity exists in the regional natural environment.So,the scale inconsistent exists in the regional application of the crop growth model.Therefore,establishing an up-scaling method for regional yield simulation is a key issue that needs to be solved urgently.In this study,we focused on China’s winter wheat region and studied the up-scaling methods in regional yield simulation.We investigated and determined the optimal spatial resolution and best zonation scheme in the regional application of the WheatGrowth model.We built a GIS-based platform for wheat regional productivity simulation and improved its efficiency.By constructing a nested sequence of spatial resolution,and we used the interpolation method to interpolate the site-specific meteorological data into the corresponding spatial resolution raster data.Then,the WheatGrow model was used to get the regional yield potential from 2000 to 2009.By constructing the scale effect index,we analyzed the influence of meteorological data with different spatial resolutions on the regional potential productivity simulation.We adopted the scale effect index threshold to determine the appropriate spatial resolution of the meteorological data required for the regional potential productivity simulation in the research area.Meanwhile,we analyzed the feasibility of using the spatial heterogeneity of the landform to obtain the appropriate spatial resolution of meteorological data required for the regional potential productivity simulation of WheatGrow.Results showed that we could obtain the spatial distribution of appropriate spatial resolution for the meteorological data required for the regional yield potential simulation of the WheatGrow model based on the scale effect index.Moreover,we could use landforms’ spatial heterogeneity to determine an appropriate spatial resolution for the meteorological data.However,in the regions where the landform’s spatial heterogeneity was relatively weak or relatively strong over a small range,using a single heterogeneity index derived from semivariograms cannot well reflect the scale effect of a simulation result.Limitations existed in obtaining an appropriate spatial resolution of meteorological data by landforms.Based on the simulated wheat yield potential at 538 sites throughout China’s main winter wheat production area from 2000 to 2009.Seventeen schemes were defined using spatial random sampling,and the number of sites per scheme ranged from 20 to 500 with an interval of 30.By combining six different zonation schemes,a total of 102 regional yield potential estimation scenarios were constructed.Then,the regional yield potential was calculated using the spatial weighted average method.The relative errors were calculated to quantitatively evaluate each zonation scheme’s impact on the accuracy of the simulation results.The standard deviations of the simulated yields for the sites in the basic spatial units were calculated and used to evaluate the effects of different zonation schemes on the simulation results’ stability.Finally,an appropriate zonation scheme for estimating the regional yield potential was obtained based on the site-specific simulation results.Results showed that the upscaled site-specific yield potential is affected by the zonation scheme and sites’ spatial distribution.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.In contrast,the large number of sites had little effect on the regional yield potential simulation results under the different zonation schemes.We built regional yield potential simulation meta-models based on machine learning methods,including multivariate linear regression,multilayer perceptron,random forests,and support vector regression.The meta-models used regional yield potential simulated by the gridded WheatGrow model as target variables and the regional easily available environmental data,which affect regional yield potential as feature variables.Building the meta-models aims to minimize environment data requirements for regional yield potential simulation of crop growth model and improve the efficiency of regional simulation under the premise of ensuring simulation accuracy.Results showed that the proposed meta-models could use monthly weather data instead of daily weather data.These meta-models’applications could effectively minimize the data requirements in regional light temperature yield potential simulation,and improve regional data availability.The four machine learning models could well reflect the mean regional yield potential.However,random forest modeling performed best,followed by multilayer perceptron,support vector regression,and multiple linear regression.The variables’importance and partial dependence of random forest showed that longitude,latitude,altitude,and maximum temperature in March(Tmax-3)were the most critical variables in the regional light temperature yield potential simulation.Training data influence the prediction ability of machine learning model.If the prediction data range exceeded the range of training data,the machine learning model’s prediction would be biased.Based on the analysis of the WheatGrow model,including input data,sub-model dependency,calculation process,and output data,the model was reconstructed by Python language.A gridded WheatGrow model was established to realize the wheat regional productivity simulation based on spatial grid data.Meanwhile,with the grid data partition strategy based on Message Passing Interface(MPI),a parallel computing method was adopted.The method could segment the grid data into a certain number of blocks dynamically based on the number of the CPU cores and the original grid data size.So,the computation of the regional productivity simulation could take advantage of the full CPU capacity and reduce the consumption of the physical memory stored.Finally,we implemented the gridded WheatGrow simulation system based on the existed GIS components by using the Microsoft.Net developer platform with C#and Python programming language together,realized the functions of regional light temperature yield potential simulation and yield gap calculation.The assembled system could provide a software tool for evaluating climate change impacts on food security and making agricultural decisions.