스크립트 언어를 사용한 DSSAT 모델 기반 격자형 작물 생육 모의 시스템 개발

The gridded simulation of crop growth, which would be useful for shareholders and policy makers, often requires specialized computation tasks for preparation of weather input data and operation of a given crop model. Here we developed an automated system to allow for crop growth simulation over a region using the DSSAT (Decision Support System for Agrotechnology Transfer) model. The system consists of modules implemented using R and shell script languages. One of the modules has a functionality to create weather input files in a plain text format for each cell. Another module written in R script was developed for GIS data processing and parallel computing. The other module that launches the crop model automatically was implemented using the shell script language. As a case study, the automated system was used to determine the maximum soybean yield for a given set of management options in Illinois state in the US. The AgMERRA dataset, which is reanalysis data for agricultural models, was used to prepare weather input files during 1981 - 2005. It took 7.38 hours to create 1,859 weather input files for one year of soybean growth simulation in Illinois using a single CPU core. In contrast, the processing time decreased considerably, e.g., 35 minutes, when 16 CPU cores were used. The automated system created a map of the maturity group and the planting date that resulted in the maximum yield in a raster data format. Our results indicated that the automated system for the DSSAT model would help spatial assessments of crop yield at a regional scale.