You can find crop growth model scripts on the site Python Crop Simulation Environment:

https://pcse.readthedocs.io/en/stable/available_models.html

and for R:

Rwofost-package WOFOST Crop Growth Simulation Model https://cran.r-project.org/web/packages/Rwofost/Rwofost.pdf

Olumide Alabi Both R and Python are capable of handling crop growth modeling with controlled-climate data. The choice between the two largely depends on your familiarity with the programming languages and your specific requirements. Here's a brief overview:

1. R:

- R is known for its strong statistical and data analysis capabilities, making it suitable for working with agricultural data.

- It has packages like "agricolae," "crop," and "phytotools" that are specifically designed for crop modeling.

- You can utilize packages like "ggplot2" for data visualization, which can be helpful in understanding the results of your crop growth model.

- R's user-friendly interfaces like RStudio make it accessible for researchers with different backgrounds.

2. Python:

- Python is a versatile programming language with a wide range of libraries and frameworks.

- Libraries like "numpy," "pandas," and "scipy" provide robust data manipulation and scientific computing capabilities.

- "matplotlib" and "seaborn" are popular Python libraries for data visualization.

- Python offers machine learning libraries like "scikit-learn" that can be used for predictive modeling in agriculture.

- Integration with Jupyter notebooks allows for interactive data analysis and modeling.

For crop growth modeling with controlled-climate data, you can use either R or Python, depending on your personal preference and the specific tasks you need to perform. If you are comfortable with both languages, you may choose the one that aligns better with your existing workflow or research team's preferences. Additionally, consider the availability of relevant packages and resources in the chosen language to streamline your work.