格网化小麦生长模拟预测系统设计与实现

利用作物生长模型模拟小麦区域生产力,分析气候变化对农业生产的影响是研究粮食安全的热点问题之一。拥有操作方便、计算快速特点的小麦区域生产力模拟系统,可有效提高作物生长模型区域应用能力。该研究在分解小麦生长模型WheatGrow算法基础上,利用Python语言构建了格网化小麦生长模型,实现了基于空间格网数据的小麦区域生产力模拟。验证试验结果表明:模拟产量的均方根误差为1 070 kg/hm~2,标准均方根误差小于20%,系统所集成的WheatGrow模型具有较好的预测性;同时,结合格网数据分块构建区域模拟的并行计算策略,优化了区域模拟的性能。在此基础上,采用GIS组件式开发模式,在.NET平台下开发格网化小麦生长模拟预测系统,实现作物生长模型与GIS耦合,为研究区域小麦产量潜力,评估气候变化对小麦生长影响,制定农业决策提供软件工具。

Design and implementation of gridded simulation and prediction system for wheat growth

To simulate the regional wheat productivity can be an essential way to evaluate the impacts of climate change on food security. Generally, a crop growth model is available to the wheat productivity prediction at the regional and national scales for decision-making. However, a convenient and fast software system is necessary to improve the ability of the crop growth model, thereby to efficiently calculate regional wheat productivity. In this research, a simulation platform was designed to implement a gridded wheat growth model (Gridded WheatGrow) for regional wheat forecast, combining with the observed weather data. The Gridded WheatGrow model is derived from the WheatGrow model that invented by Nanjing Agricultural University, particularly for a process-based wheat growth simulation. The Gridded WheatGrow model can be used to integrate the gridded data and the simulation model within a geographical information system (GIS). The modified model can facilitate the acquisition of the input data, such as the daily meteorological data and soil data with a high spatial resolution, for the season forecasts, wheat productivity simulation, and other application of most previous open grid databases. The Gridded WheatGrow model can be a core component of a GIS. This is because the simulation system, not just an independent software, was designed based on the close integration of GIS and crop model. Furthermore, this design can simplify the data preparation, further to make it friendly to a non-professional user. A parallel computing method was adopted with the strategy of grid data partition based on Message Passing Interface (MPI), in order to solve the time-consuming and inconvenient problems caused from the modeling and calculation of grid data in the application of the regional productivity simulation. As such, the grid data can be dynamically segmented into a certain number of blocks, according to the number of the CPU cores in a computer and the size of the original grid data. Therefore, the computation of regional productivity simulation can efficiently utilize the full capacity of CPU in the computer, while reduce the consumption of stored physical memory. In the case of high efficiency, a normal personal computer can also be used to develop a gridded simulation system of wheat productivity. The proposed system was implemented based on the component geographic information system in development mode using the Microsoft(r) platform, together with net developer platform, C# and Python programming language. The Gridded WheatGrow model was also served as a specific geoprocessing tool for ESRI(r) ArcGIS. In ArcMap(c) module of the system, the customized code can be used to simulate the regional wheat productivity on the specific purpose. The proposed system was verified by the field data collected from the winter wheat area in China, and the root mean square error (RSME) and normalized root mean square error (NRSME) are1 070 kg/hm2and less than20%, respectively, showing an excellent performance. The typical system can be used to simulate the regional wheat productivity with a friendly user interface, while to reduce time and consumption of physical memory. Combining with the fundamental functions of GIS, the simulated data can be easily visualized and mapping for the later public use. All these features of the proposed system can prove that the Gridded WheatGrow simulation platform is an useful and reliable software on regional wheat productivity forecasts, and thereby it can be expected to evaluate the impacts of climate change on food security and decision making in modern agriculture.