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The GitHub repository DSSAT-parallel-running contains a Python script designed to run simulations of the DSSAT model (Decision Support System for Agrotechnology Transfer) in parallel across multiple data points. This workflow is useful for large-scale agronomic studies that require simultaneous processing of numerous scenarios.

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DSSAT Batch Processing Workflow

This script is designed to run DSSAT simulations on multiple points, each represented in the POINTS_CSV file. The workflow involves the following steps:

1. Divide Points into Groups

The first step is to divide the points into groups based on the total number of points. The points are distributed equally among the groups defined by the NUMBER_GROUPS variable. Set this variable based on the core of your computer (core-1).

2. Create Directories and Batch Files

For each group, a new directory is created. Additionally, a batch file (BATCH_NAME) is written containing the necessary information for running the DSSAT simulations. This batch file specifies the experiment, crop, and other parameters.

3. Generate DSSAT Input Files for Each Point

For each point in a group, the function dssat_file_seq_creator generates an input file (.SQX) with the required data. This file is used by DSSAT to run the simulation.

4. Run DSSAT

After generating the .SQX file, the DSSAT model is run using the following command:

subprocess.run([MODEL_EXE, "Q", BATCH_NAME], cwd=gd, check=True)

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The GitHub repository DSSAT-parallel-running contains a Python script designed to run simulations of the DSSAT model (Decision Support System for Agrotechnology Transfer) in parallel across multiple data points. This workflow is useful for large-scale agronomic studies that require simultaneous processing of numerous scenarios.

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