photosynthesis (version 2.1.5)

fit_aq_response: Fitting light responses of net CO2 assimilation

Description

Please use fit_aq_response2().

Usage

fit_aq_response(
  data,
  varnames = list(A_net = "A_net", PPFD = "PPFD"),
  usealpha_Q = FALSE,
  alpha_Q = 0.84,
  title = NULL
)

Value

fit_aq_response fits the light response of net CO2 assimilation. Output is a dataframe containing light saturated net CO2 assimilation, quantum yield of CO2 assimilation (phi_J), curvature of the light response (theta_J), respiration (Rd), light compensation point (LCP), and residual sum of squares (resid_SS). Note that Rd fitted in this way is essentially the same as the Kok method, and represents a respiration value in the light that may not be accurate. Rd output should thus be interpreted more as a residual parameter to ensure an accurate fit of the light response parameters. Model originally from Marshall & Biscoe 1980.

Arguments

data

Dataframe containing CO2 assimilation light response

varnames

Variable names where varnames = list(A_net = "A_net", PPFD = "PPFD"). A_net is net CO2 assimilation in umol m-2 s-1, PPFD is incident irradiance. PPFD can be corrected for light absorbance by using useapha_Q and setting alpha_Q.

usealpha_Q

Correct light intensity for absorbance? Default is FALSE.

alpha_Q

Absorbance of incident light. Default value is 0.84.

title

Title for graph

References

Marshall B, Biscoe P. 1980. A model for C3 leaves describing the dependence of net photosynthesis on irradiance. J Ex Bot 31:29-39

Examples

# \donttest{
# Read in your data
# Note that this data is coming from data supplied by the package
# hence the complicated argument in read.csv()
# This dataset is a CO2 by light response curve for a single sunflower
data = read.csv(system.file("extdata", "A_Ci_Q_data_1.csv",
  package = "photosynthesis"
))

# Fit many AQ curves
# Set your grouping variable
# Here we are grouping by CO2_s and individual
data$C_s = (round(data$CO2_s, digits = 0))

# For this example we need to round sequentially due to CO2_s setpoints
data$C_s = as.factor(round(data$C_s, digits = -1))

# To fit one AQ curve
fit = fit_aq_response(data[data$C_s == 600, ],
  varnames = list(
    A_net = "A",
    PPFD = "Qin"
  )
)

# Print model summary
summary(fit[[1]])

# Print fitted parameters
fit[[2]]

# Print graph
fit[[3]]

# Fit many curves
fits = fit_many(
  data = data,
  varnames = list(
    A_net = "A",
    PPFD = "Qin",
    group = "C_s"
  ),
  funct = fit_aq_response,
  group = "C_s"
)

# Look at model summary for a given fit
# First set of double parentheses selects an individual group value
# Second set selects an element of the sublist
summary(fits[[3]][[1]])

# Print the parameters
fits[[3]][[2]]

# Print the graph
fits[[3]][[3]]

# Compile graphs into a list for plotting
fits_graphs = compile_data(fits,
  list_element = 3
)

# Compile parameters into dataframe for analysis
fits_pars = compile_data(fits,
  output_type = "dataframe",
  list_element = 2
)
# }