Development of an automated gridded crop growth simulation support system for distributed computing with virtual machines

Authors: KIM, JUNHWAN - Jeonju University; PARK, JINEW - Seoul National University; HYUN, SHIN WOO - Seoul National University; Fleisher, David; KIM, KWANG SOO - Seoul University

Submitted to: Environmental Modelling & Software
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/27/2019
Publication Date: 1/17/2020
Citation: Kim, J., Park, J., Hyun, S., Fleisher, D.H., Kim, K. 2020. Development of an automated gridded crop growth simulation support system for distributed computing with virtual machines. Environmental Modelling & Software. 169:1-8.

Interpretive Summary: Food security is a national priority that is influenced by changes in farmland availability, water for irrigation, fertilizer, and environmental inputs. Populations in most countries are also expected to continue to increase. These factors influence how much crops a country can, and needs to, grow. Scientists are using computers and software programs, such as mathematical crop and soil models, to study options to increase food security. These studies require tremendous amounts of data, computational time, and knowledge regarding information technology that scientists frequently do not have expertise in. A new computer system, called GROWLERS, was developed to help scientists more efficiently conducted these types of studies. GROWLERS links computer software with existing personal computers to automate most of the procedures in this type of food security research that would otherwise have to be conducted manually in a step by step fashion. A case study with rice production using GROWLERS showed that the time involved in this process could be reduced by as much as 88 percent over the manual approach. This can save several weeks or even months of time. The software and methodology was carefully documented in this study and can be made freely available. It can even function on older computers that may be available in most laboratories. This research can thus help scientists and food policy planners evaluate options for increasing food production while maintaining environmental stewardship needs involved in addressing United States food security concerns.

Technical Abstract: The spatial distribution of crop yield has been assessed under current and future climate conditions using gridded crop growth simulations. This task usually requires considerable efforts to prepare input data and post-process the outputs. In the present study, the Gridded cRop grOWth simuLation suppoRt System (GROWLERS) was developed to automate repetitive and tedious tasks using multiple PCs. In particular, the system was designed to aid researchers who have minimum knowledge on computer programming, network, and cluster management. An object oriented programming language, C++, was used to design and implement the GROWLERS, which would increase flexibility of a system while simplifying complexity including supports for different types of gridded data. Functionality of the GROWLERS includes preparation of weather input files, launch of crop growth model, and creation of gridded output files. Tools for the GROWLERS were installed on virtual machines connected through local network, which allows building of a cluster computer without dedicated workstations. In a case study, 5.8 x 107 simulations using the ORYZA2000 model were performed to examine spatial distribution of the optimum sowing date for rice under current climate conditions in Korea. The subsets of these simulations were allocated to groups of virtual machines hosted within five custom built personal computers of which the central processing unit was manufactured about 10 years ago. Weather input data were prepared automatically using the GROWLERS. A set of scripts were also prepared using the GROWLERS, which allowed to reduce the wall clock time by 88% using 16 processor cores for worker nodes. These results suggest that the GROWLERS would minimize researcher's time involved in preparation and operation of a large number of crop growth simulations. Still, the support for nested simulations using multi-scale datasets would be needed to improve the GROWLERS, which merits further development as a next step.