A simulation engine for models related to plants
Author VEZY
6 Stars
Updated Last
1 Year Ago
Started In
November 2022


Stable Dev Build Status Coverage ColPrac: Contributor's Guide on Collaborative Practices for Community Packages Aqua QA DOI JOSS


PlantSimEngine is a package for the simulation and modelling of plants, soil and atmosphere. It is designed to help researchers and practitioners prototype, implement, test plant/crop models at any scale, without the hassle of computer science technicality behind model coupling, running on several time-steps or objects.

The package defines a framework for declaring processes and implementing associated models for their simulation.

It focuses on key aspects of simulation and modeling such as:

  • Easy definition of new processes, such as light interception, photosynthesis, growth, soil water transfer...
  • Fast, interactive prototyping of models, with constraints to help users avoid errors, but sensible defaults to avoid over-complicating the model writing process
  • No hassle, the package manages automatically input and output variables, time-steps, objects, soft and hard coupling of models with a dependency graph
  • Switch between models without changing any code, with a simple syntax to define the model to use for a given process
  • Reduce the degrees of freedom by fixing variables, passing measurements, or using a simpler model for a given process
  • ๐Ÿš€(very) fast computation ๐Ÿš€, think of 100th of nanoseconds for one model, two coupled models (see this benchmark script), or the full energy balance of a leaf using PlantBiophysics.jl that uses PlantSimEngine
  • Out of the box Sequential, Parallel (Multi-threaded) or Distributed (Multi-Process) computations over objects, time-steps and independent processes (thanks to Floops.jl)
  • Easily scalable, with methods for computing over objects, time-steps and even Multi-Scale Tree Graphs
  • Composable, allowing the use of any types as inputs such as Unitful to propagate units, or MonteCarloMeasurements.jl to propagate measurement error


To install the package, enter the Julia package manager mode by pressing ] in the REPL, and execute the following command:

add PlantSimEngine

To use the package, execute this command from the Julia REPL:

using PlantSimEngine

Example usage

The package is designed to be easy to use, and to help users avoid errors when implementing, coupling and simulating models.

Simple example

Here's a simple example of a model that simulates the growth of a plant, using a simple exponential growth model:

# ] add PlantSimEngine
using PlantSimEngine

# Include the model definition from the examples folder:
include(joinpath(pkgdir(PlantSimEngine), "examples/ToyLAIModel.jl"))

# Define the model:
model = ModelList(
    status=(degree_days_cu=1.0:2000.0,), # Pass the cumulated degree-days as input to the model

run!(model) # run the model

status(model) # extract the status, i.e. the output of the model

Which gives:

TimeStepTable{Status{(:degree_days_cu, :LAI...}(1300 x 2):
โ”‚ Row โ”‚ degree_days_cu โ”‚        LAI โ”‚
โ”‚     โ”‚        Float64 โ”‚    Float64 โ”‚
โ”‚   1 โ”‚            1.0 โ”‚ 0.00560052 โ”‚
โ”‚   2 โ”‚            2.0 โ”‚ 0.00565163 โ”‚
โ”‚   3 โ”‚            3.0 โ”‚ 0.00570321 โ”‚
โ”‚   4 โ”‚            4.0 โ”‚ 0.00575526 โ”‚
โ”‚   5 โ”‚            5.0 โ”‚ 0.00580778 โ”‚
โ”‚  โ‹ฎ  โ”‚       โ‹ฎ        โ”‚     โ‹ฎ      โ”‚
                    1295 rows omitted

The ToyLAIModel is available from the examples folder, and is a simple exponential growth model. It is used here for the sake of simplicity, but you can use any model you want, as long as it follows PlantSimEngine interface.

Of course you can plot the outputs quite easily:

# ] add CairoMakie
using CairoMakie

lines(model[:degree_days_cu], model[:LAI], color=:green, axis=(ylabel="LAI (mยฒ mโปยฒ)", xlabel="Cumulated growing degree days since sowing (ยฐC)"))

LAI Growth

Model coupling

Model coupling is done automatically by the package, and is based on the dependency graph between the models. To couple models, we just have to add them to the ModelList. For example, let's couple the ToyLAIModel with a model for light interception based on Beer's law:

# ] add PlantSimEngine, DataFrames, CSV
using PlantSimEngine, PlantMeteo, DataFrames, CSV

# Include the model definition from the examples folder:
include(joinpath(pkgdir(PlantSimEngine), "examples/ToyLAIModel.jl"))
include(joinpath(pkgdir(PlantSimEngine), "examples/Beer.jl"))

# Import the example meteorological data:
meteo_day =, "examples/meteo_day.csv"), DataFrame, header=18)

# Define the list of models for coupling:
model = ModelList(
    status=(degree_days_cu=cumsum(meteo_day[:, :degree_days]),),  # Pass the cumulated degree-days as input to `ToyLAIModel`, this could also be done using another model

The ModelList couples the models by automatically computing the dependency graph of the models. The resulting dependency graph is:

โ•ญโ”€โ”€โ”€โ”€ Dependency graph โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ
โ”‚  โ•ญโ”€โ”€โ”€โ”€ LAI_Dynamic โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ  โ”‚
โ”‚  โ”‚  โ•ญโ”€โ”€โ”€โ”€ Main model โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ                              โ”‚  โ”‚
โ”‚  โ”‚  โ”‚  Process: LAI_Dynamic  โ”‚                              โ”‚  โ”‚
โ”‚  โ”‚  โ”‚  Model: ToyLAIModel    โ”‚                              โ”‚  โ”‚
โ”‚  โ”‚  โ”‚  Dep: nothing          โ”‚                              โ”‚  โ”‚
โ”‚  โ”‚  โ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏ                              โ”‚  โ”‚
โ”‚  โ”‚                  โ”‚  โ•ญโ”€โ”€โ”€โ”€ Soft-coupled model โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ  โ”‚  โ”‚
โ”‚  โ”‚                  โ”‚  โ”‚  Process: light_interception    โ”‚  โ”‚  โ”‚
โ”‚  โ”‚                  โ””โ”€โ”€โ”‚  Model: Beer                    โ”‚  โ”‚  โ”‚
โ”‚  โ”‚                     โ”‚  Dep: (LAI_Dynamic = (:LAI,),)  โ”‚  โ”‚  โ”‚
โ”‚  โ”‚                     โ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏ  โ”‚  โ”‚
โ”‚  โ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏ  โ”‚
# Run the simulation:
run!(model, meteo_day)


Which returns:

TimeStepTable{Status{(:degree_days_cu, :LAI...}(365 x 3):
โ”‚ Row โ”‚ degree_days_cu โ”‚        LAI โ”‚     aPPFD โ”‚
โ”‚     โ”‚        Float64 โ”‚    Float64 โ”‚   Float64 โ”‚
โ”‚   1 โ”‚            0.0 โ”‚ 0.00554988 โ”‚ 0.0476221 โ”‚
โ”‚   2 โ”‚            0.0 โ”‚ 0.00554988 โ”‚ 0.0260688 โ”‚
โ”‚   3 โ”‚            0.0 โ”‚ 0.00554988 โ”‚ 0.0377774 โ”‚
โ”‚   4 โ”‚            0.0 โ”‚ 0.00554988 โ”‚ 0.0468871 โ”‚
โ”‚   5 โ”‚            0.0 โ”‚ 0.00554988 โ”‚ 0.0545266 โ”‚
โ”‚  โ‹ฎ  โ”‚       โ‹ฎ        โ”‚     โ‹ฎ      โ”‚     โ‹ฎ     โ”‚
                                 360 rows omitted
# Plot the results:
using CairoMakie

fig = Figure(resolution=(800, 600))
ax = Axis(fig[1, 1], ylabel="LAI (mยฒ mโปยฒ)")
lines!(ax, model[:degree_days_cu], model[:LAI], color=:mediumseagreen)

ax2 = Axis(fig[2, 1], xlabel="Cumulated growing degree days since sowing (ยฐC)", ylabel="aPPFD (mol mโปยฒ dโปยน)")
lines!(ax2, model[:degree_days_cu], model[:aPPFD], color=:firebrick1)


LAI Growth and light interception

Projects that use PlantSimEngine

Take a look at these projects that use PlantSimEngine:

Make it yours

The package is developed so anyone can easily implement plant/crop models, use it freely and as you want thanks to its MIT license.

If you develop such tools and it is not on the list yet, please make a PR or contact me so we can add it! ๐Ÿ˜ƒ Make sure to read the community guidelines before in case you're not familiar with such things.


  • Look into locks for parallel computations over "independent" processes that can maybe call a model when both parents are being computed, so both are set to 0 and the model is never called