Crop model definitions

In BioCro, a crop model is defined by sets of direct modules, differential modules, initial values, and parameters, along with an ordinary differential equation (ODE) solver. To run a model, these values, along with a set of weather data, are passed to the run_biocro function. For convenience, several crop model definitions are included in the BioCro R package. A full list can be obtained by typing ??crop_models into the R terminal.

Details

Each crop model definition is stored as a list with the following named elements:

direct_modules: A list of direct module names; can be passed to run_biocro as its direct_module_names argument.
differential_modules: A list of differential module names; can be passed to run_biocro as its differential_module_names argument.
ode_solver: A list specifying details of a numerical ODE solver; can be passed to run_biocro as its ode_solver argument.
initial_values: A list of named quantity values; can be passed to run_biocro as its initial_values argument.
parameters: A list of named quantity values; can be passed to run_biocro as its parameters argument, and also can be passed to evaluate_module and module_response_curve when investigating the behavior of one of the crop's modules.

These model definitions are not sufficient for running a simulation because run_biocro also requires drivers; for these crop growth models, the drivers should be sets of weather data. The soybean model is intended to be used along with the specialized soybean weather data (see cmi_soybean_weather_data). The other crops should be used with the other weather data (see cmi_weather_data).

Some quantities in the crop model definitions, such as the values of photosynthetic parameters, would remain the same in any location; others, such as the latitude or longitude, would need to change when simulating crop growth in different locations. Care must be taken to understand each input quantity before attempting to run simulations in other places or for other cultivars.

Typically, the modules in a crop model definition are defined as lists with some named elements; the names facilitate on-the-fly module swapping via the within function. For example, to change the soybean canopy photosynthesis module to the BioCro:ten_layer_rue_canopy module, one could pass within(soybean$direct_modules, {canopy_photosynthesis = "BioCro:ten_layer_rue_canopy"}) as the direct_module_names argument when calling run_biocro instead of soybean$direct_modules.

Because each crop model definition is stored as a list with named elements, it is possible to use the with function to save some typing when calling run_biocro or related functions such as partial_run_biocro or validate_dynamical_system_inputs. For an example, compare Example 1 and Example 2 below. Besides shortening the code, using with also makes it easy to modify a command to simulate the growth of a different crop; if the two models can use the same drivers, this switch can be accomplished with one small change (Example 3).

Examples

# Example 1: Simulating Miscanthus growth using its model definition list
result1 <- run_biocro(
  miscanthus_x_giganteus$initial_values,
  miscanthus_x_giganteus$parameters,
  get_growing_season_climate(weather$'2002'),
  miscanthus_x_giganteus$direct_modules,
  miscanthus_x_giganteus$differential_modules,
  miscanthus_x_giganteus$ode_solver
)

# Example 2: Performing the same simulation as in Example 1, but making use of
# the `with` command to reduce repeated references to the model definition list
result2 <- with(miscanthus_x_giganteus, {run_biocro(
  initial_values,
  parameters,
  get_growing_season_climate(weather$'2002'),
  direct_modules,
  differential_modules,
  ode_solver
)})

# Example 3: Simulating willow growth using the same weather data as Examples 1
# and 2, which just requires one change relative to Example 2
result3 <- with(willow, {run_biocro(
  initial_values,
  parameters,
  get_growing_season_climate(weather$'2002'),
  direct_modules,
  differential_modules,
  ode_solver
)})

Details

See also

Examples