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Python package for geospatial Crop Simulations

Maryland_Yield

GeoEpic is an open source package that expands the capabilities of the EPIC crop simulation model, to simulate crop growth and development across large geographies, such as entire states or counties by leveraging openly availabe remote sensing products and geospatial databases. Additionally, the package features a unique calibration module that allows fine-tuning of model parameters to reflect specific local conditions or experimental results. This toolkit allows researchers to assess crop production potential, management scenarios and risks at broader scales, informing decision-making for sustainable agricultural practices.

What can you do with GeoEPIC?

1. Getting Input Files

GeoEPIC enables you to download and customize essential input files for crop simulations, including:

  • Soil Data: Access soil properties from sources like USDA SSURGO and ISRIC SoilGrids.
  • Site Data: Specify location parameters such as latitude, longitude, elevation, and slope.
  • Crop Management Data: Define planting schedules, irrigation, fertilization, and tillage operations.
  • Weather Data: Extract historical and forecasted weather information, including temperature, precipitation, and solar radiation, from datasets like AgERA5 on Google Earth Engine.

Modifying these inputs to reflect local conditions ensures more accurate simulation results.

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2. Site Simulation

GeoEPIC enhances the EPIC crop simulation model by enabling large-scale geospatial simulations. It allows users to model crop growth, yields, and water usage across extensive areas, such as entire states or counties. By integrating remote sensing data and geospatial databases, GeoEPIC facilitates the creation of detailed model inputs, supporting assessments of crop production potential and the evaluation of various management scenarios.

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3. Model Calibration

GeoEPIC's Calibration Module enables precise tuning of the EPIC model parameters to align simulations with observed data, such as Leaf Area Index (LAI), Net Ecosystem Exchange (NEE), crop yield, or biomass. This calibration process ensures that the model accurately reflects specific local conditions or experimental results, enhancing the reliability of simulations.

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4. Remote Sensing

GeoEPIC's Remote Sensing Module leverages satellite imagery and geospatial data to enhance crop simulation accuracy. By integrating data from platforms like Google Earth Engine, users can incorporate up-to-date environmental information into their models, leading to more precise assessments of crop growth and productivity.

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Additional Resources

Explore more about GeoEPIC through the following resources:

  1. Tutorials
    These tutorials provide detailed examples of what you can achieve using GeoEPIC, including site simulation, model calibration, and creating site input files. Explore the guides below to enhance your understanding:

  2. Python API Reference
    Python API Reference contains detailed information about the various functions and classes available in GeoEPIC. This helps you to import the package into your Python scripts and utilize its functionalities directly, enabling efficient geospatial crop simulations and data analysis.

  3. Contribution
    Contribution Guide contains instructions on how to contribute. This document outlines how to contribute to GeoEPIC through code changes, bug reports, feature requests, and other helpful contributions.

Contributors