Estimate the operating point from an A-Ci curve — estimate_operating

Uses linear interpolation to estimate Cc, Ci, and An at atmospheric CO2 concentration from the data in the exdf object, which should represent a single A-Ci curve. This function can accomodate alternative column names for the variables taken from the data file in case they change at some point in the future. This function also checks the units of each required column and will produce an error if any units are incorrect.

Usage

estimate_operating_point(
    aci_exdf,
    Ca_atmospheric,
    type = 'c3',
    a_column_name = 'A',
    ca_column_name = 'Ca',
    cc_column_name = 'Cc',
    ci_column_name = 'Ci',
    pcm_column_name = 'PCm',
    return_list = FALSE
  )

Arguments

aci_exdf: An exdf object representing one CO2 response curve.
Ca_atmospheric: The atmospheric CO2 concentration (with units of micromol mol^(-1)); this will be used to estimate the operating point. For example, the approximate global average during the 2023 is 420 ppm, which would correspond to Ca_atmospheric = 420.
type: The type of photosynthesis: either 'c3' or 'c4'.
a_column_name: The name of the column in aci_exdf that contains the net assimilation in micromol m^(-2) s^(-1).
ca_column_name: The name of the column in aci_exdf that contains the ambient CO2 concentration in micromol mol^(-1).
cc_column_name: The name of the column in aci_exdf that contains the chloroplastic CO2 concentration in micromol mol^(-1).
ci_column_name: The name of the column in aci_exdf that contains the intercellular CO2 concentration in micromol mol^(-1).
pcm_column_name: The name of the column in aci_exdf that contains the partial pressure of CO2 in the mesophyll, expressed in microbar.
return_list: A logical value indicating whether or not to return the results as a list. Most users will only need to use return_list = TRUE; return_list = FALSE is used internally by other functions in the PhotoGEA package.

Details

When analyzing or interpreting A-Ci curves, it is often useful to determine the values of Ci and An that correspond to typical growth conditions (where Ca is set to the atmospheric value). Together, these special values of Ci and An specify the "operating point" of the leaf.

However, for a variety of practical reasons, most A-Ci curves do not actually contain a measurement point where Ca is at the atmospheric value. Nevertheless, it is possible to apply linear interpolation to the observed Ci - Ca and An - Ca relations to estimate the operating point. This function automates that procedure. It also calculates the operating values of Cc (for c3 A-Ci curves) and PCm (for c4 A-Ci curves).

This function assumes that aci_exdf represents a single A-Ci curve. Typically, this function is not directly called by users because the fitting functions fit_c3_aci and fit_c4_aci automatically use this function to determine the operating point.

Value

The return value depends on return_list and type.

When return_list is FALSE, this function returns an exdf object based on aci_exdf that includes its identifier columns as well as values of Ca_atmospheric, operating_Ci, operating_An, and operating_Cc (or operating_PCm) in columns with those names.

When return_list is TRUE, this function returns a list with the following named elements: Ca_atmospheric, operating_Ci, operating_An, operating_Cc (or operating_PCm), and operating_exdf. The first four are numeric values as described above, while operating_exdf is an exdf object with one row that can be passed to calculate_c3_assimilation or calculate_c4_assimilation in order to estimate the operating An from a photosynthesis model.

If Ca_atmospheric is outside the range of Ca values in aci_exdf, then the operating point cannot be reasonably estimated; in this case, a warning occurs and all calculated return values are set to NA. If Ca_atmospheric is NA, all calculated return values are set to NA but no warning occurs.

Examples

# Read an example Licor file included in the PhotoGEA package
licor_file <- read_gasex_file(
  PhotoGEA_example_file_path('c3_aci_1.xlsx')
)

# Define a new column that uniquely identifies each curve
licor_file[, 'species_plot'] <-
  paste(licor_file[, 'species'], '-', licor_file[, 'plot'] )

# Organize the data
licor_file <- organize_response_curve_data(
    licor_file,
    'species_plot',
    c(9, 10, 16),
    'CO2_r_sp'
)

# Specify an infinite mesophyll conductance (so `Cc` = `Ci`)
licor_file <- set_variable(
  licor_file,
  'gmc', 'mol m^(-2) s^(-1) bar^(-1)', value = Inf
)

# Calculate the total pressure in the Licor chamber
licor_file <- calculate_total_pressure(licor_file)

# Calculate Cc
licor_file <- apply_gm(licor_file)

# Determine the operating point for just one curve from the data set
one_result <- estimate_operating_point(
  licor_file[licor_file[, 'species_plot'] == 'tobacco - 1', , TRUE],
  Ca_atmospheric = 420
)

one_result[, 'operating_Cc']
#> [1] 294.7032
one_result[, 'operating_Ci']
#> [1] 294.7032
one_result[, 'operating_An']
#> [1] 37.51608