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ARS Home » Pacific West Area » Maricopa, Arizona » U.S. Arid Land Agricultural Research Center » Plant Physiology and Genetics Research » Research » Publications at this Location » Publication #427229
Research Project: Analysis and Quantification of G x E x M Interactions for Sustainable Crop Production

Location: Plant Physiology and Genetics Research

Title: GeoEPIC: A comprehensive Python package for spatial implementation of EPIC crop simulation model

Author
item IRIGIREDDY, BHARATH - Oak Ridge Institute For Science And Education (ORISE)
item Bandaru, Varaprasad
item VELMURUGAN, SACHIN - Oak Ridge Institute For Science And Education (ORISE)
item KULKARNI, CHAITANYA - University Of Maryland

Submitted to: SoftwareX
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/24/2025
Publication Date: 1/5/2026
Citation: Irigireddy, B., Bandaru, V., Velmurugan, S., Kulkarni, C. 2026. GeoEPIC: A comprehensive Python package for spatial implementation of EPIC crop simulation model. SoftwareX. 33. Article 102500. https://doi.org/10.1016/j.softx.2025.102500.
DOI: https://doi.org/10.1016/j.softx.2025.102500

Interpretive Summary: The Environmental Policy Integrated Climate (EPIC) model is widely used process based model for assessing agricultural ecosystems at the field scale but struggles with large-scale regional simulations due to its original design to run on limited fields. To address this, the GeoEPIC package was developed, providing automated tools for input preparation, model calibration, simulation management, and output processing. GeoEPIC is organized into three modules for spatial data, input/output handling, and core simulations. This paper outlines GeoEPIC’s structure and demonstrates its use in modeling water use for irrigated soybeans in Nebraska. The package is publicly available on GitHub.

Technical Abstract: The Environmental Policy Integrated Climate (EPIC) model is a comprehensive, field-scale agro-ecosystem model widely used for both diagnostic and prognostic analyses in agriculture. However, because it was originally designed to simulate a limited number of fields, applying it at broader spatial scales for regional analysis remains challenging. To address this limitation, custom scripts in Python or R have been developed, but these are often inefficient, not standardized, and not available publicly to the broader modeling community. The GeoEPIC package was developed to overcome these challenges. It includes libraries for automated input file generation, model calibration, simulation management, and post-processing of model outputs. It is structured into three primary modules geoEpic.spatial, geoEpic.io and geoEpic.core to handle spatial data processing, input and output data handling, and the core components that drive model simulations, respectively. This paper presents the structure and components of GeoEPIC, demonstrating how it facilitates reproducible agricultural modeling workflows using EPIC. A case study is also included to showcase its application in simulating water use for irrigated soybean cultivated in a small region of Nebraska. The GeoEPIC can be accessed on GitHub: (https://github.com/smarsGroup/geo epic win).