Pythia: A gridded modeling framework for decision support system for agrotechnology transfer for multiple spatiotemporal scale applications

Abstract Crop models are essential tools to understand risks, vulnerabilities, and uncertainties in agricultural systems. Advancements in digital data acquisition and accessibility of crop and land area data with larger spatial coverage and higher spatial resolutions necessitate corresponding developments in flexible multi‐scale crop modeling application framework to facilitate decision‐making and policy formulation from sub‐national to global levels. While a few tools and approaches exist for spatial applications of crop models, their lesser flexibility owing to dependency on external programs, portability issues, complexity in files setup, and limited functionality thwarts users to employ crop models at larger scales. This paper aims to introduce Pythia, a novel gridded modeling framework for Decision Support System for Agrotechnology Transfer (DSSAT)‐cropping system model (CSM), and demonstrate its application. The objectives are to explain Pythia design, execution workflow, and to show its main functionalities. Inputs to the Pythia framework include (i) point vector files that specify the sites of weather data and simulation points, (ii) a raster map of soil‐profile identity numbers, (iii) a raster map of crop area (iv) a DSSAT FileX template, and (v) a configuration file to provide references to the required model input files and databases, and to set up the dynamic portions of the FileX template. A case study from maize cropping system in Ghana is used to demonstrate the applications of Pythia. Flexibilities in spatial coverage and parameterizing model inputs in Pythia provide DSSAT‐CSM users a useful tool to run spatial simulations in local machines and in high‐performance computing environment.