eC4photo: C4 photosynthesis simulation (von Caemmerer model)

In BioCro: Simulation of Biomass Crops

Description

Simulation of C4 photosynthesis based on the equations proposed by von Caemmerer (2000). At this point assimilation and stomatal conductance are not coupled and although, for example a lower relative humidity will lower stomatal conductance it will not affect assimilation. Hopefully, this will be improved in the future.

Usage

eC4photo(Qp, airtemp, rh, ca, oa, vcmax, vpmax, vpr, jmax)

Arguments

Qp	quantum flux (micro mol m-2 s-1).
airtemp	air temperature (Celsius).
rh	relative humidity in proportion (e.g. 0.7).
ca	atmospheric carbon dioxide concentration (ppm or microbar) (e.g. 380).
oa	atmospheric oxygen concentration (mbar) (e.g. 210).
vcmax	Maximum rubisco activity (micro mol m-2 s-1).
vpmax	Maximum PEP carboxylase activity (micro mol m-2 s-1).
vpr	PEP regeneration rate (micro mol m-2 s-1).
jmax	Maximal electron transport rate (micromol electrons m-2 s-1).

Details

The equations are taken from von Caemmerer (2000) for the assimilation part and stomatal conductance is based on FORTRAN code by Joe Berry (translated to C).

Value

a list structure with components

Assim	net assimilation rate (micro mol m-2 s-1).
Gs	stomatal conductance rate (micro mol m-2 s-1).
Ci	CO2 concentration in the bundle-sheath (microbar).
Os	oxygen evolution (mbar).

References

Susanne von Caemmerer (2000) Biochemical Models of Leaf Photosynthesis. CSIRO Publishing. (In particular chapter 4).

Examples

## Not run: 
## A simple example for the use of eC4photo
## This is the model based on von Caemmerer
## First we can compare the effect of varying
## Jmax. Notice that this is different from
## varying alpha in the Collatz model

Qps <- seq(0,2000,10)
Tls <- seq(0,55,5)
rhs <- c(0.7)
dat1 <- data.frame(expand.grid(Qp=Qps,Tl=Tls,RH=rhs))
res1 <- eC4photo(dat1$Qp,dat1$Tl,dat1$RH)
res2 <- eC4photo(dat1$Qp,dat1$Tl,dat1$RH,jmax=700)

## Plot comparing Jmax 400 vs. 700 for a range of conditions
xyplot(res1$Assim + res2$Assim ~ Qp | factor(Tl) , data = dat1,
            type="l",col=c("blue","green"),lwd=2,
            ylab=expression(paste("Assimilation (",
                 mu,mol," ",m^-2," ",s^-1,")")),
             xlab=expression(paste("Quantum flux (",
                 mu,mol," ",m^-2," ",s^-1,")")),
            key=list(text=list(c("Jmax 400","Jmax 700")),
              lines=TRUE,col=c("blue","green"),lwd=2))

## Second example is the effect of varying Vcmax

Qps <- seq(0,2000,10)
Tls <- seq(0,35,5)
rhs <- 0.7
vcmax <- seq(0,40,5)
dat1 <- data.frame(expand.grid(Qp=Qps,Tl=Tls,RH=rhs,vcmax=vcmax))
res1 <- numeric(nrow(dat1))
for(i in 1:nrow(dat1)){
res1[i] <- eC4photo(dat1$Qp[i],dat1$Tl[i],dat1$RH[i],vcmax=dat1$vcmax[i])$Assim
}

## Plot comparing different Vcmax
cols <- rev(heat.colors(9))
xyplot(res1 ~ Qp | factor(Tl) , data = dat1,col=cols,
            groups=vcmax,
            type="l",lwd=2,
            ylab=expression(paste("Assimilation (",
                 mu,mol," ",m^-2," ",s^-1,")")),
             xlab=expression(paste("Quantum flux (",
                 mu,mol," ",m^-2," ",s^-1,")")),
            key=list(text=list(as.character(vcmax)),
              lines=TRUE,col=cols,lwd=2))



## End(Not run)

BioCro documentation built on May 2, 2019, 6:15 p.m.