photosynthesis (version 2.0.3)

fit_aq_response: Fitting light responses of net CO2 assimilation

Description

Fitting light responses of net CO2 assimilation

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 et al. 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
)
# }