simpest

simpest is a Python package for coupled crop growth and plant disease/pest simulation. It runs a SIMPLACE/Lintul5 crop growth scenario, converts the daily crop trajectory into the inputs a disease and fungicide simulation model expects, and runs that model — optionally calibrating selected parameters against field reference observations — to produce daily and seasonal outputs. The disease/pest simulation logic is inspired by the FraNchEstYN model.

What it does

• Runs SIMPLACE crop growth scenarios and reads the resulting daily trajectory.
• Converts SIMPLACE weather, management, and crop-model outputs into the format the disease/fungicide simulation expects.
• Simulates daily crop growth, epidemiological (SEIR) disease dynamics, and fungicide efficacy, coupled through four damage mechanisms (light interception loss, radiation-use-efficiency reduction, assimilate loss, and accelerated senescence).
• Calibrates selected crop and disease parameters with a multi-start Nelder–Mead search against reference observations.
• Exports daily simulation records and per-season summaries (AUDPC, yield loss, peak severity, weather aggregates).

Core modules live under simpest/models.

Installation

pip install simpest

For an editable, development install:

git clone https://github.com/KaziJahidurRahaman/simpest
cd simpest
pip install -e .

Running the crop growth stage additionally requires a SIMPLACE installation and the simplace Python package (a Java/JVM bridge via jpype); see Installation for details.

Quickstart

The disease/fungicide simulation stage takes plain pandas DataFrames, so it can be exercised on its own once you have weather, management, crop-model, and reference data available (as CSVs or otherwise):

import pandas as pd
from simpest.models.franchestyn import FranchestynConfig, run_franchestyn

weather_df = pd.read_csv("weather.csv")
management_df = pd.read_csv("management.csv")
crop_model_df = pd.read_csv("crop_model.csv")
ref_df = pd.read_csv("reference.csv")

config = FranchestynConfig(
    crop_type="wheat",
    disease_type="septoria",
    fungicide_type=None,
    site="indiana",
    variety="Generic",
    disease="thisDisease",
    is_calibration=False,
)

result = run_franchestyn(
    start_year=2018,
    end_year=2019,
    config=config,
    weather_df=weather_df,
    management_df=management_df,
    crop_model_df=crop_model_df,
    ref_df=ref_df,
)
print(result["outputs"]["summary"])

For the full pipeline — running SIMPLACE first and converting its output into the DataFrames above — see the example notebooks at docs/examples/1_Run Simpest.ipynb and docs/examples/2_Plot.ipynb.

Directory layout

simpest/
|- docs/                # MkDocs documentation site and example notebooks
|- simpest/
|  |- common.py
|  |- simpest.py
|  |- models/
|     |- simplace.py         # SIMPLACE crop growth integration
|     |- franchestyn.py      # disease/fungicide simulation entry points
|     |- fr_runner.py        # end-to-end daily simulation loop
|     |- fr_crop_model.py    # daily crop growth + disease damage mechanisms
|     |- fr_disease_model.py # SEIR disease model
|     |- fr_fungicide_model.py
|     |- fr_optimizer.py     # multi-start Nelder-Mead calibration
|     |- fr_data.py          # input/output/parameter data structures
|     |- fr_*_reader.py      # weather, parameter, and reference data readers
|- tests/
|- mkdocs.yml
|- pyproject.toml
|- README.md

Documentation

Full documentation, including the API reference, is published at https://KaziJahidurRahaman.github.io/simpest. Locally:

• Installation
• Usage
• Examples
• API Reference
• Contributing
• Changelog

Contributing

Contributions are welcome — see Contributing for how to set up a development environment and the pull request process.

Project

• License: MIT
• Source: https://github.com/KaziJahidurRahaman/simpest
• Documentation: https://KaziJahidurRahaman.github.io/simpest