Fitting light responses of net CO2 assimilation
Fitting light responses of net CO2 assimilation
fit_aq_response( data, varnames = list(A_net = "A_net", PPFD = "PPFD"), usealpha_Q = FALSE, alpha_Q = 0.84, title = NULL )
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 |
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.
Marshall B, Biscoe P. 1980. A model for C3 leaves describing the dependence of net photosynthesis on irradiance. J Ex Bot 31:29-39
# 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
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