APSIM PARALLEL SIMULATOR
The CLM-APSIM model combines superior features in both Community Land Model (CLM) and Agricultural Production Systems sIMulator (APSIM), creating one of the most reliable tools for long-term crop prediction in the U.S. NCSA Professor Kaiyu Guan and NCSA postdoc fellow, Bin Peng have implemented and evaluated a new maize growth model. Researchers are using the Blue Waters supercomputer to create better tools for long-Term crop prediction. DOI 10.1016/j.envsoft.2012.08.007.Kaiyu Guan is an Assistant Professor in ecohydrology and geoinformatics at the University of Illinois at Urbana Champaign. (2013) “Large-scale, high-resolution agricultural systems modeling using a hybrid approach combining grid computing and parallel processing”, Environmental Modelling & Software, Vol. A., King, D., Luo, Z., Wang, E., Bende-Michl, U., Song, X. The case of route-To-pa sim online preference simulation“, Proceedings - Winter Simulation Conference, pp. (2017) "Optimal execution of large scale simulations in the cloud. (2018) "Processing of Big Data in Internet of Things and Precision Agriculture", Agrarian Perspectives XXVII.: Food Safety - Food Security, Proceedings of the 27th International Scientific Conference, pp. (2010) “Towards continuously updated simulation models: Combining automated raw data collection and automated data processing”, Proceedings - Winter Simulation Conference, pp. (2017) “Toward more efficient model development for farming systems research - An integrative review”, Computers And Electronics In Agriculture, Vol. (2002) “Does increasing horizontal resolution produce more skillful forecasts? The results of two years of real-time numerical weather prediction over the Pacific northwest”, Bulletin of the American Meteorological Society, Vol. (2018) “Visualization and data analytics challenges of large-scale high-fidelity numerical simulations of wind energy applications”, AIAA Aerospace Sciences Meeting. (2018) “START: A data preparation tool for crop simulation models using web-based soil databases”, Computers and Electronics in Agriculture, vol. (2013) “Parallel scaling properties from a basic block view”, ACM SIGMETRICS Performance Evaluation Review, Vol. (2018) “APSIM Next Generation: Overcoming Challenges in Modernising a Farming Systems Model”, Environmental Modelling & Software, Vol. I., Fainges, J., Brown, H., Zurcher, E., Cichota, R., Verrall, S., Herrmann, N. (2014) “APSIM - Evolution towards a New Generation of Agricultural Systems Simulation.” Environmental Modelling & Software, Vol. (1996) “Climate downscaling: Techniques and application” Climate Research, Vol. (2016) “Research Challenges in Parallel and Distributed Simulation”, ACM Transactions On Modeling And Computer Simulation, Vol. (2013) “Influence of climate change on short term management of field crops - A modelling approach”, Agricultural Systems, Vol. C., Steinbach, J., Reinmuth, E., Ingwersen, J.
APSIM PARALLEL SOFTWARE
KeywordsĪPSIM, big data, data processing, yield optimization, software automation, parallel processing. It also outlines initial testing and computing time estimations and discusses scheduling, parallel processing and other possible simulation optimization methods. This article specifically deals with the data acquisition, transformation and preparation process. Because of possible time savings when conducting large number of simulations at once, it is preferable to create all the input and settings files for all the simulations beforehand and process the simulations in batches as large as possible. The process of agricultural simulation using APSIM requires input meteorological data to be prepared in a specific format and the simulation setting file to be ready before the simulation processing starts. (2019) “Data Pre-processing for Agricultural Simulations", AGRIS on-line Papers in Economics and Informatics, Vol.