We recently published a manuscript entitled “Enhancing crop model parameter estimation across computing environments: Utilizing the GLUE method and parallel computing for determining genetic coefficients” in Computers and Electronics in Agriculture. The study describes improvements in the Generalized Likelihood Uncertainty Estimation (GLUE) program, introducing a new parallel computing system for calibrating genetic parameters within the crop modules of the Cropping System Model (CSM) of DSSAT The Generalized Likelihood Uncertainty Estimation Parallelized (GLUEP) has been designed and tested for both conventional and High-Performance Computing (HPC) environments. It is available for most crop growth modules of CSM through a user-friendly graphical interface within DSSAT.GLUEP significantly accelerates the genetic-specific parameter calibration, allowing users to calibrate not only cultivar genetic coefficients (.CUL) but also ecotype parameters (.ECO), which were previously inaccessible for calibration using GLUE. The case studies show that GLUEP parallel processing can reduce calibration time by 87.4% to 95.4% compared to the previous version of the program, marking a substantial advancement in efficiency.
The software will be available in the upcoming DSSAT Version 4.8.5 release. If you plan to use the GLUE program for cultivar genetic coefficient calibration within DSSAT, we recommend that you include this manuscript as a reference in your future publications.
Ferreira, T. B., V. Shelia, C. Porter, P. M. Cadena, M. S. Cortasa, M. S. Khan, W. Pavan, G. Hoogenboom. 2024. Enhancing crop model parameter estimation across computing environments: Utilizing the GLUE method and parallel computing for determining genetic coefficients. Computers and Electronics in Agriculture, 227, 109513 (https://doi.org/10.1016/j.compag.2024.109513).
Free download of the manuscript until November 30, 2024, at https://authors.elsevier.com/c/1jvt5cFCSbHnP
