Installing PlantSimEngine​#

Install Julia from the official download page, create a project environment (a folder that records the packages used for this simulation), open julia from this repository, and install the tutorial packages:

using Pkg
Pkg.activate(".")
Pkg.add(["PlantMeteo", "DataFrames", "CairoMakie", "PlantSimEngine.jl"])

PlantMeteo supplies weather data, DataFrames organizes the results, and CairoMakie draws the tutorial figures. Run these commands at Julia's interactive prompt, also called the REPL; my_simulation is the project directory created relative to your current working directory. In a later session, run Pkg.activate(".") from the same location before using the project again.

Julia makes reproducibility seemless: whenever you add a new package, it records the package versions you're using in two files called Project.toml and Manifest.toml.

First Simulation​#

This small example calculates how much light a canopy absorbs. Its leaf area index is 2 m² of leaves per m² of ground, and incoming photosynthetically active radiation (PAR) is 500 W m⁻² of ground. The canopy is one simulated object; individual leaves are not represented.

using PlantSimEngine, PlantMeteo, Dates
using PlantSimEngine.Examples

meteo = Atmosphere(
    T=20.0,
    Wind=1.0,
    Rh=0.65,
    Ri_PAR_f=500.0,
    duration=Hour(1),
)

model = CompositeModel(
    Beer(0.5);
    status=(LAI=2.0,),
    id=:canopy,
    scale=:Canopy,
    environment=meteo,
)

simulation = run!(model)
(absorbed_PAR_umol_m2_ground_s=final_state(simulation).aPPFD,)

using PlantSimEngine.Examples loads the example models, including Beer. These models are included for learning and testing.

The result is the amount of light absorbed by the canopy, expressed in μmol of photons per m² of ground per second. The inputs are illustrative, not a calibrated crop scenario. Next, couple three models over time, then collect and plot their outputs.