Currently there is only support for fitting mesophyll conductance according to the variable J method from Harley et al. (1992).
library(dplyr)
library(photosynthesis)
# Read in your data
dat = system.file("extdata", "A_Ci_Q_data_1.csv", package = "photosynthesis") |>
read.csv() |>
rename(A_net = A, J_etr = ETR, C_i = Ci, PPFD = Qin, phi_PSII = PhiPS2)
# Note: there will be issues here if the alpha value used for calculating ETR is
# off, if GammaStar is incorrect, if Rd is incorrect.
fit = fit_g_mc_variableJ(dat, gamma_star = 46, R_d = 0.153, usealpha_Q = TRUE,
alpha_Q = 0.84, beta_Q = 0.5, P = 84)
# Note that many g_mc values from this method can be unreliable
# ggplot(fit, aes(x = CO2_s, y = g_mc, colour = reliable)) +
# labs(x = expression(CO[2]~"("*mu*mol~mol^{-1}*")"),
# y = expression(g[m]~"(mol"~m^{-2}~s^{-1}~Pa^{-1}*")")) +
# geom_point(size = 2) +
# theme_bw() +
# theme(legend.position = 'bottom')
# Plot QAQC graph according to Harley et al. 1992
# ggplot(fit, aes(x = CO2_s, y = dCcdA, colour = reliable)) +
# labs(x = expression(CO[2]~"("*mu*mol~mol^{-1}*")"),
# y = expression(delta*C[c]*"/"*delta*A)) +
# geom_hline(yintercept = 10) +
# geom_point(size = 2) +
# theme_bw() +
# theme(legend.position = 'bottom')
# ggplot(fit, aes(x = dCcdA, y = g_mc, colour = reliable)) +
# labs(x = expression(delta*C[c]*"/"*delta*A),
# y = expression(g[m]~"(mol"~m^{-2}~s^{-1}~Pa^{-1}*")")) +
# geom_point(size = 2) +
# theme_bw() +
# theme(legend.position = 'bottom')Harley PC, Loreto F, Di Marco G, Sharkey TD. 1992. Theoretical considerations when estimating mesophyll conductance to CO2 flux by analysis of the response of photosynthesis to CO2. Plant Physiology 98:1429-1436.