Parameter Fitting​#

Parameter fitting means finding parameter values that make a model agree with observations. PlantSimEngine provides Evaluation.fit as a common function name; each model package supplies the fitting method for its own models. Pass the model type first and a table of observations second. The table can be a DataFrame or another format supported by Tables.jl.

For the Beer model, we can calculate the light-extinction coefficient k from LAI, incoming light, and absorbed light:

J_to_umol = PlantMeteo.Constants().J_to_umol
incident_ppfd = J_to_umol .* data.Ri_PAR_f
f_abs = data.aPPFD ./ incident_ppfd
k = Statistics.mean(-log1p.(-f_abs) ./ data.LAI)

This snippet only shows how to rearrange Beer's equation to find k. The implementation in examples/Beer.jl validates every observation and returns (k=k,); do not use the snippet alone as an unchecked fitting method.

A fitting method returns named parameter values, for example (k=0.6,).

In this Beer example, Ri_PAR_f measures incoming light per unit ground area and aPPFD measures light absorbed by the whole canopy per unit ground area. LAI is leaf area per unit ground area. Do not use a PPFD expressed per unit leaf area in this calculation. The fit rejects empty data, non-finite or non-positive LAI and incident PAR, and absorbed fractions outside [0, 1).

using PlantSimEngine, PlantMeteo, Dates, DataFrames
using PlantSimEngine.Examples

meteo = Atmosphere(
    T=20.0,
    Wind=1.0,
    P=101.3,
    Rh=0.65,
    Ri_PAR_f=300.0,
    duration=Hour(1),
)

model = CompositeModel(
    Beer(0.6);
    status=(LAI=2.0,),
    id=:plant,
    scale=:Plant,
    environment=meteo,
)

simulation = run!(model)
plant = final_state(simulation, One(scale=:Plant))
data = DataFrame(
    aPPFD=[plant.aPPFD],
    LAI=[plant.LAI],
    Ri_PAR_f=[meteo.Ri_PAR_f[1]],
)

PlantSimEngine.Evaluation.fit(Beer, data)

This example recovers the parameter used to generate the synthetic observation. Real calibration methods can use any optimizer or uncertainty framework and may return additional diagnostics.