Package 'BioCro'

Description

Four A/Ci (assimilation vs. intercellular CO2) curves.

Format

A data frame with 32 observations on the following 7 variables.

list('ID')

Identification for each curve. a numeric vector

list('Photo')

Assimilation. a numeric vector

list('PARi')

Incident Photosynthetic Active Radiation. a numeric vector

list('Tleaf')

Temperature of the leaf. a numeric vector

list('RH_S')

Realtive humidity. a numeric vector

list('Ci')

Intercellular CO2. a numeric vector

list('CO2_R')

Reference CO2. a numeric vector

Details

Measurements taken on Miscanthus x giganteus.

Source

Measurements taken by Dandan Wang.

References

Dandan Wang

Examples

data(aci)
plotAC(aci)

Description

The first column is the thermal time. The second, third, fourth and fifth columns are miscanthus stem, leaf, root and rhizome dry biomass in Mg ha$^{-1}$ (root is missing). The sixth column is the leaf area index. The annualDB.c version is altered so that root biomass is not missing and LAI is smaller. The purpose of this last modification is for testing some functions.

Format

data frame of dimensions 5 by 6.

Source

Clive Beale and Stephen Long. (1997) Seasonal dynamics of nutrient accumulation and partitioning in the perennial C4 grasses Miscanthus x giganteus and Spartina cynosuroides. Biomass and Bioenergy. 419-428.

Description

Example of A/Q curves which serves as a template for using the Opc4photo and mOpc4photo functions.

Format

A data frame with 64 observations on the following 6 variables.

list('ID')

a numeric vector

list('trt')

a factor with levels mxg swg

list('A')

a numeric vector. Assimilation

list('PARi')

a numeric vector. Photosynthetic Active Radiation (incident).

list('Tleaf')

a numeric vector. Temperature of the leaf.

list('RH_S')

a numeric vector. Relative humidity (fraction).

Details

swg stand for switchgrass (Panicum virgatum) mxg stands for miscanthus (Miscanthus x gignateus)

Source

Data based on measurements made by Dandan Wang. a publication or URL from which the data were obtained ~~

References

Dandan Wang

Examples

data(aq)
plotAQ(aq)

Description

Description of the package goes here

Description

Simulates dry biomass growth during an entire growing season. It represents an integration of the photosynthesis function c4photo, canopy evapo/transpiration CanA, the multilayer canopy model sunML and a dry biomass partitioning calendar and senescence. It also considers, carbon and nitrogen cycles and water and nitrogen limitations.

Usage

BioGro(WetDat, day1 = NULL, dayn = NULL, timestep = 1, lat = 40,
  iRhizome = 7, iLeaf = iRhizome * 1e-04, iStem = iRhizome * 0.001,
  iRoot = iRhizome * 0.001, canopyControl = list(),
  seneControl = list(), photoControl = list(), phenoControl = list(),
  soilControl = list(), nitroControl = list(),
  centuryControl = list())

Arguments

Value

a list structure with components

• DayofYear Day of the year

• Hour Hour for each day

• CanopyAssim Hourly canopy assimilation, (Mg $ha^-1$ ground $hr^-1$).

• CanopyTrans Hourly canopy transpiration, (Mg $ha^-1$ ground $hr^-1$).

• Leaf leaf dry biomass (Mg $ha^-1$).

• Stem stem dry biomass(Mg $ha^-1$).

• Root root dry biomass (Mg $ha^-1$).

• Rhizome rhizome dry biomass (Mg $ha^-1$).

• LAI leaf area index ($m^2$ $m^-2$).

• ThermalT thermal time (Celsius $day^-1$).

• StomatalCondCoefs Coefficeint which determines the effect of water stress on stomatal conductance and photosynthesis.

• LeafReductionCoefs Coefficient which determines the effect of water stress on leaf expansion reduction.

• LeafNitrogen Leaf nitrogen.

• AboveLitter Above ground biomass litter (Leaf + Stem).

• BelowLitter Below ground biomass litter (Root + Rhizome).

• VmaxVec Value of Vmax during the growing season.

• AlphaVec Value of alpha during the growing season.

• SpVec Value of the specific leaf area.

• MinNitroVec Nitrogen in the mineral pool.

• RespVec Soil respiration.

• SoilEvaporation Soil Evaporation.

Examples

## Not run: 
data(cmi04)

res <- BioGro(cmi04)

plot(res)
plot(res, plot.kind = "SW") 
plot(res, plot.kind = "ET")
plot(res, plot.kind = "cumET")
plot(res, plot.kind = "stress")


## End(Not run)

Description

It represents an integration of the photosynthesis function c3photo, canopy evapo/transpiration and the multilayer canopy model sunML.

Usage

c3CanA(lai, doy, hr, solar, temp, rh, windspeed, lat = 40, nlayers = 8,
  kd = 0.1, heightFactor = 3, c3photoControl = list(),
  lnControl = list(), StomWS = 1)

Arguments

Details

The photosynthesis function is modeled after the version in WIMOVAC. This is based on the well known Farquar model.

Value

list

returns a list with several elements

CanopyAssim: hourly canopy assimilation ($Mg ha^{-1}$ $hr^{-1}$)

CanopyTrans: hourly canopy transpiration ($Mg ha^{-1}$ $hr^{-1}$)

CanopyCond: hourly canopy conductance (units ?)

TranEpen: hourly canopy transpiration according to Penman ($Mg ha^{-1}$ $hr^{-1}$)

TranEpen: hourly canopy transpiration according to Priestly ($Mg ha^{-1}$ $hr^{-1}$)

LayMat: hourly by Layer matrix containing details of the calculations by layer (each layer is a row). col1: Direct Irradiance col2: Diffuse Irradiance col3: Leaf area in the sun col4: Leaf area in the shade col5: Transpiration of leaf area in the sun col6: Transpiration of leaf area in the shade col7: Assimilation of leaf area in the sun col8: Assimilation of leaf area in the shade col9: Difference in temperature between the leaf and the air (i.e. TLeaf - TAir) for leaves in sun. col10: Difference in temperature between the leaf and the air (i.e. TLeaf - TAir) for leaves in shade. col11: Stomatal conductance for leaves in the sun col12: Stomatal conductance for leaves in the shade col13: Nitrogen concentration in the leaf (g m^-2) col14: Vmax value as depending on leaf nitrogen

Author(s)

Fernando E. Miguez

References

Farquhar model here ~

Examples

data(doy124)
tmp <- numeric(24)

for(i in 1:24){
   lai <- doy124[i,1]
   doy <- doy124[i,3]
   hr  <- doy124[i,4]
 solar <- doy124[i,5]
  temp <- doy124[i,6]
    rh <- doy124[i,7]
    ws <- doy124[i,8]

  tmp[i] <- c3CanA(lai,doy,hr,solar,temp,rh,ws)$CanopyAssim

}

plot(c(0:23),tmp,
            type='l',lwd=2,
            xlab='Hour',
            ylab=expression(paste('Canopy assimilation (Mg  ',
            ha^-2,' ',h^-1,')')))

Description

Simulates coupled assimilation and stomatal conductance based on Farquhar and Ball-Berry.

Usage

c3photo(Qp, Tl, RH, vcmax = 100, jmax = 180, Rd = 1.1, Catm = 380,
  O2 = 210, b0 = 0.08, b1 = 5, theta = 0.7, StomWS = 1,
  ws = c("gs", "vmax"))

Arguments

Value

A list

• Gs Stomatal Conductance

• Assim CO2 Assimilation

• Ci Intercellular CO2

Note

~~further notes~~ ## Additional notes about assumptions

Author(s)

Fernando E. Miguez

References

Farquhar (1980) Ball-Berry (1987)

See Also

See Also Opc3photo

Examples

## Testing the c3photo function
## First example: looking at the effect of changing Vcmax
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 <- c3photo(dat1$Qp,dat1$Tl,dat1$RH) ## default Vcmax = 100
res2 <- c3photo(dat1$Qp,dat1$Tl,dat1$RH,vcmax=120)

## Plot comparing alpha 0.04 vs. 0.06 for a range of conditions
lattice::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('Vcmax 100','Vcmax 120')),
              lines=TRUE,col=c('blue','green'),lwd=2))

## Second example: looking at the effect of changing Jmax
## Plot comparing Jmax 300 vs. 100 for a range of conditions

res1 <- c3photo(dat1$Qp,dat1$Tl,dat1$RH) ## default Jmax = 300
res2 <- c3photo(dat1$Qp,dat1$Tl,dat1$RH,jmax=100)

lattice::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 300','Jmax 100')),
              lines=TRUE,col=c('blue','green'),lwd=2))

## A/Ci curve

Ca <- seq(20,1000,length.out=50)
dat2 <- data.frame(Qp=rep(700,50), Tl=rep(25,50), rh=rep(0.7,50))
res1 <- c3photo(dat2$Qp, dat2$Tl, dat2$rh, Catm = Ca)
res2 <- c3photo(dat2$Qp, dat2$Tl, dat2$rh, Catm = Ca, vcmax = 70)

lattice::xyplot(res1$Assim ~ res1$Ci,
           lwd=2,
           panel = function(x,y,...){
                   lattice::panel.xyplot(x,y,type='l',col='blue',...)
                   lattice::panel.xyplot(res2$Ci,res2$Assim, type='l', col =
           'green',...)
           },
            ylab=expression(paste('Assimilation (',
                 mu,mol,' ',m^-2,' ',s^-1,')')))

Description

The mathematical model is based on Collatz et al (1992) (see References). Stomatal conductance is based on code provided by Joe Berry.

Usage

c4photo(Qp, Tl, RH, vmax = 39, alpha = 0.04, kparm = 0.7,
  theta = 0.83, beta = 0.93, Rd = 0.8, uppertemp = 37.5,
  lowertemp = 3, Catm = 380, b0 = 0.08, b1 = 3, StomWS = 1,
  ws = c("gs", "vmax"))

Arguments

Value

a list structure with components

• Gs stomatal conductance (mmol $m^-2$ $s^-$$1$).

• Assim Net Assimilation ($\mu$mol $m^-2$ $s^-1$).

• Ci Intercellular CO2 ($\mu$mol $mol^-1$).

References

G. Collatz, M. Ribas-Carbo, J. Berry. (1992). Coupled photosynthesis-stomatal conductance model for leaves of C4 plants. Australian Journal of Plant Physiology 519–538.

See Also

eC4photo

Examples

## Not run: 
     ## First example: looking at the effect of changing alpha
      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 <- c4photo(dat1$Qp,dat1$Tl,dat1$RH) ## default alpha = 0.04
      res2 <- c4photo(dat1$Qp,dat1$Tl,dat1$RH,alpha=0.06)

     ## Plot comparing alpha 0.04 vs. 0.06 for a range of conditions
     lattice::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('alpha 0.04','alpha 0.06')),
              lines=TRUE,col=c('blue','green'),lwd=2))

     ## Second example: looking at the effect of changing vmax
     ## Plot comparing Vmax 39 vs. 50 for a range of conditions

      res1 <- c4photo(dat1$Qp,dat1$Tl,dat1$RH) ## default Vmax = 39
      res2 <- c4photo(dat1$Qp,dat1$Tl,dat1$RH,vmax=50)

     lattice::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('Vmax 39','Vmax 50')),
              lines=TRUE,col=c('blue','green'),lwd=2))

     ## Small effect of low RH on  Assim
      Qps <- seq(0,2000,10)
      Tls <- seq(0,55,5)
      rhs <- c(0.2,0.9)
      dat1 <- data.frame(expand.grid(Qp=Qps,Tl=Tls,RH=rhs))
      res1 <- c4photo(dat1$Qp,dat1$Tl,dat1$RH)
     # plot for Assimilation and two RH
      lattice::xyplot(res1$Assim ~ Qp | factor(Tl) , data = dat1,
             groups=RH, 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('RH 20%','RH 90%')),
                        lines=TRUE,col=c('blue','green'),
                        lwd=2))

    ## Effect of the previous runs on Stomatal conductance

    lattice::xyplot(res1$Gs ~ Qp | factor(Tl) , data = dat1,
           type='l', groups=RH,
           col=c('blue','green'),lwd=2,
           ylab=expression(paste('Stomatal Conductance (',
                           mu,mol,' ',m^-2,' ',s^-1,')')),
           xlab=expression(paste('Quantum flux (',
                           mu,mol,' ',m^-2,' ',s^-1,')')),
           key=list(text=list(c('RH 20%','RH 90%')),
                     lines=TRUE,col=c('blue','green'),
                     lwd=2))


## A Ci curve for the Collatz model
## Assuming constant values of Qp, Temp, and RH
## Notice the effect of the different kparm
## The loop is needed because the length of Ca
## should be the same as Qp

Ca <- seq(15,400,5)

res1 <- numeric(length(Ca))
res2 <- numeric(length(Ca))
for(i in 1:length(Ca)){
  res1[i] <- c4photo(1500,25,0.7,Catm=Ca[i])$Assim
  res2[i] <- c4photo(1500,25,0.7,Catm=Ca[i],kparm=0.8)$Assim
}

lattice::xyplot(res1 + res2 ~ Ca ,type='l',lwd=2,
       col=c('blue','green'),
     xlab=expression(paste(CO[2],' (ppm)')),
     ylab=expression(paste('Assimilation (',
         mu,mol,' ',m^-2,' ',s^-1,')')),
     key=list(text=list(c('kparm 0.7','kparm 0.8')),
                        lines=TRUE,col=c('blue','green'),
                        lwd=2))

## Effect of Reduction in Assimilation due to
## water stress

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 <- c4photo(dat1$Qp,dat1$Tl,dat1$RH) ## default StomWS = 1 No stress
res2 <- c4photo(dat1$Qp,dat1$Tl,dat1$RH,StomWS=0.5)

## Plot comparing StomWS = 1 vs. 0.5 for a range of conditions
lattice::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('StomWS 1','StomWS 0.5')),
           lines=TRUE,col=c('blue','green'),lwd=2))


## Effect on Stomatal Conductance
## Plot comparing StomWS = 1 vs. 0.5 for a range of conditions
lattice::xyplot(res1$Gs + res2$Gs ~ Qp | factor(Tl) , data = dat1,
        type='l',col=c('blue','green'),lwd=2,
        ylab=expression(paste('Stomatal Conductance (mmol ',
          m^-2,' ',s^-1,')')),
        xlab=expression(paste('Quantum flux (',
          mu,mol,' ',m^-2,' ',s^-1,')')),
        key=list(text=list(c('StomWS 1','StomWS 0.5')),
           lines=TRUE,col=c('blue','green'),lwd=2))

## End(Not run)

Description

It represents an integration of the photosynthesis function c4photo, canopy evapo/transpiration and the multilayer canopy model sunML.

Usage

CanA(lai, doy, hr, solar, temp, rh, windspeed, lat = 40, nlayers = 8,
  kd = 0.1, StomataWS = 1, chi.l = 1, leafwidth = 0.04,
  heightFactor = 3, photoControl = list(), lnControl = list(),
  units = c("kg/m2/hr", "Mg/ha/hr"))

Arguments

Value

list

returns a list with several elements

CanopyAssim: hourly canopy assimilation ($kg m^{-2}$ $hr^{-1}$) or canopy assimilation ($Mg ha^{-1}$ $hr^{-1}$)

CanopyTrans: hourly canopy transpiration ($kg m^{-2}$ $hr^{-1}$) or canopy transpiration ($Mg ha^{-1}$ $hr^{-1}$)

CanopyCond: hourly canopy conductance (units ?)

TranEpen: hourly canopy transpiration according to Penman ($kg m^{-2}$ $hr^{-1}$) or canopy transpiration according to Penman ($Mg ha^{-1}$ $hr^{-1}$)

TranEpen: hourly canopy transpiration according to Priestly ($kg m^{-2}$ $hr^{-1}$) canopy transpiration according to Priestly ($Mg ha^{-1}$ $hr^{-1}$)

LayMat: hourly by Layer matrix containing details of the calculations by layer (each layer is a row). col1: Direct Irradiance col2: Diffuse Irradiance col3: Leaf area in the sun col4: Leaf area in the shade col5: Transpiration of leaf area in the sun col6: Transpiration of leaf area in the shade col7: Assimilation of leaf area in the sun col8: Assimilation of leaf area in the shade col9: Difference in temperature between the leaf and the air (i.e. TLeaf - TAir) for leaves in sun. col10: Difference in temperature between the leaf and the air (i.e. TLeaf - TAir) for leaves in shade. col11: Stomatal conductance for leaves in the sun col12: Stomatal conductance for leaves in the shade col13: Nitrogen concentration in the leaf (g m^-2) col14: Vmax value as depending on leaf nitrogen

Examples

## Not run: 
data(doy124)
tmp <- numeric(24)

for(i in 1:24){
   lai <- doy124[i,1]
   doy <- doy124[i,3]
   hr  <- doy124[i,4]
 solar <- doy124[i,5]
  temp <- doy124[i,6]
    rh <- doy124[i,7]
    ws <- doy124[i,8]

  tmp[i] <- CanA(lai,doy,hr,solar,temp,rh,ws)$CanopyAssim

}

plot(c(0:23),tmp,
            type='l',lwd=2,
            xlab='Hour',
            ylab=expression(paste('Canopy assimilation (kg  ',
            m^-2,' ',h^-1,')')))


## End(Not run)

Description

Calculates flows of soil organic carbon and nitrogen based on the Century model. There are two versions one written in R (Century) and one in C (CenturyC) they should give the same result. The C version only runs at weekly timesteps.

Usage

Century(LeafL, StemL, RootL, RhizL, smoist, stemp, precip, leachWater,
  centuryControl = list(), verbose = FALSE)

Arguments

Details

Most of the details can be found in the papers by Parton et al. (1987, 1988, 1993)

Value

A list with,

• SCs Soil carbon pools 1-9.

• SNs Soil nitrogen pools 1-9.

• MinN Mineralized nitrogen.

• Resp Soil respiration.

Author(s)

Fernando E. Miguez

References

~put references to the literature/web site here ~

Examples

Century(0,0,0,0,0.3,5,2,2)$Resp
Century(0,0,0,0,0.3,5,2,2)$MinN

Description

This fuction checks if year is a leap year. If yes, then returns 1 or 0.

Usage

CheckLeapYear(year)

Description

selected weather data corresponding to the Champaign weather station (IL, U.S.). It has two years: 2005 and 2006. Dimensions: 730 by 11. The columns correspond to the input necessary for the weach function.

Format

data frame of dimensions 730 by 11.

Source

http://www.sws.uiuc.edu/warm/

Description

Layer data for evapo/transpiration. Simulated data to show the result of the EvapoTrans function.

Format

data frame of dimensions 384 by 9.

Details

lfClass: leaf class, 'sun' or 'shade'.

layer: layer in the canopy, 1 to 8.

hour: hour of the day, (0–23).

Rad: direct light.

Itot: total radiation.

Temp: air temperature, (Celsius).

RH: relative humidity, (0–1).

WindSpeed: wind speed, (m $s^{-1}$).

LAI: leaf area index.

Source

simulated

Description

Simulates dry biomass growth during an entire growing season. It represents an integration of the photosynthesis function c4photo, canopy evapo/transpiration CanA, the multilayer canopy model sunML and a dry biomass partitioning calendar and senescence. It also considers, carbon and nitrogen cycles and water and nitrogen limitations.

Usage

CropGro(WetDat, day1 = NULL, dayn = NULL, timestep = 1, lat = 40,
  iRhizome = 7, irtl = 1e-04, canopyControl = list(),
  seneControl = list(), photoControl = list(), phenoControl = list(),
  soilControl = list(), nitroControl = list(),
  SOMpoolsControl = list(), SOMAssignParmsControl = list(),
  GetCropCentStateVarParmsControl = list(),
  GetSoilTextureParmsControl = list(),
  GetBioCroToCropcentParmsControl = list(),
  GetErosionParmsControl = list(), GetC13ParmsControl = list(),
  GetLeachingParmsControl = list(),
  GetSymbNFixationParmsControl = list(), centuryControl = list())

Arguments

Value

a list structure with components

• DayofYear Day of the year

• Hour Hour for each day

• CanopyAssim Hourly canopy assimilation, (Mg $ha^-1$ ground $hr^-1$).

• CanopyTrans Hourly canopy transpiration, (Mg $ha^-1$ ground $hr^-1$).

• Leaf leaf dry biomass (Mg $ha^-1$).

• Stem stem dry biomass(Mg $ha^-1$).

• Root root dry biomass (Mg $ha^-1$).

• Rhizome rhizome dry biomass (Mg $ha^-1$).

• LAI leaf area index ($m^2$ $m^-2$).

• ThermalT thermal time (Celsius $day^-1$).

• StomatalCondCoefs Coefficeint which determines the effect of water stress on stomatal conductance and photosynthesis.

• LeafReductionCoefs Coefficient which determines the effect of water stress on leaf expansion reduction.

• LeafNitrogen Leaf nitrogen.

• AboveLitter Above ground biomass litter (Leaf + Stem).

• BelowLitter Below ground biomass litter (Root + Rhizome).

• VmaxVec Value of Vmax during the growing season.

• AlphaVec Value of alpha during the growing season.

• SpVec Value of the specific leaf area.

• MinNitroVec Nitrogen in the mineral pool.

• RespVec Soil respiration.

• SoilEvaporation Soil Evaporation.

Examples

## Not run: 
data(weather05)

res0 <- BioGro(weather05)

plot(res0)

## Looking at the soil model

res1 <- BioGro(weather05, soilControl = soilParms(soilLayers = 6))
plot(res1, plot.kind='SW') ## Without hydraulic distribution
res2 <- BioGro(weather05, soilControl = soilParms(soilLayers = 6, hydrDist=TRUE))
plot(res2, plot.kind='SW') ## With hydraulic distribution


## Example of user defined soil parameters.
## The effect of phi2 on yield and soil water content

ll.0 <- soilParms(FieldC=0.37,WiltP=0.2,phi2=1)
ll.1 <- soilParms(FieldC=0.37,WiltP=0.2,phi2=2)
ll.2 <- soilParms(FieldC=0.37,WiltP=0.2,phi2=3)
ll.3 <- soilParms(FieldC=0.37,WiltP=0.2,phi2=4)

ans.0 <- BioGro(weather05,soilControl=ll.0)
ans.1 <- BioGro(weather05,soilControl=ll.1)
ans.2 <- BioGro(weather05,soilControl=ll.2)
ans.3 <-BioGro(weather05,soilControl=ll.3)

lattice::xyplot(ans.0$SoilWatCont +
       ans.1$SoilWatCont +
       ans.2$SoilWatCont +
       ans.3$SoilWatCont ~ ans.0$DayofYear,
       type='l',
       ylab='Soil water Content (fraction)',
       xlab='DOY')

## Compare LAI

lattice::xyplot(ans.0$LAI +
       ans.1$LAI +
       ans.2$LAI +
       ans.3$LAI ~ ans.0$DayofYear,
       type='l',
       ylab='Leaf Area Index',
       xlab='DOY')




## End(Not run)

Description

Example for a given day of the year to illustrate the CanA function.

Format

data frame of dimensions 24 by 8.

Details

LAI: leaf area index.

year: year.

doy: 124 in this case.

hour: hour of the day, (0–23).

solarR: direct solar radiation.

DailyTemp.C: hourly air temperature (Celsius).

RH: relative humidity, (0–1).

WindSpeed: 4.1 m $s^{-1}$ average daily value in this case.

Source

simulated

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 = 380, oa = 210, vcmax = 60,
  vpmax = 120, vpr = 80, jmax = 400)

Arguments

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

results of call to C function eC4photo_sym

a list structure with components

• Assim net assimilation rate ($\mu$ mol $m^{-2}$ $s^{-1}$).

• Gs stomatal conductance rate ($\mu$ mol $m^{-2}$ $s^{-1}$).

• Ci CO2 concentration in the bundle-sheath ($\mu$bar).

• 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
lattice::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))
lattice::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)

Description

It represents an integration of the photosynthesis function eC4photo, canopy evapo/transpiration and the multilayer canopy model sunML.

Usage

eCanA(LAI, doy, hour, solarR, AirTemp, RH, WindS, Vcmax, Vpmax, Vpr, Jmax,
  Ca = 380, Oa = 210, StomataWS = 1)

Arguments

Value

numeric

returns a single value which is hourly canopy assimilation (mol $m^{-2}$ ground $hr^{-1}$)

Examples

## Not run: 
data(doy124)
tmp1 <- numeric(24)
for(i in 1:24){
  lai <- doy124[i,1]
  doy <- doy124[i,3]
  hr  <- doy124[i,4]
  solar <- doy124[i,5]
  temp <- doy124[i,6]
  rh <- doy124[i,7]/100
  ws <- doy124[i,8]

  tmp1[i] <- CanA(lai,doy,hr,solar,temp,rh,ws)
}

plot(c(0:23),tmp1,
     type='l',lwd=2,
     xlab='Hour',
     ylab=expression(paste('Canopy assimilation (mol  ',
     m^-2,' ',s^-1,')')))

## End(Not run)

Description

Weather data with the precipitation column giving precipitation plus irrigation.

Format

A data frame with 8760 observations on the following 8 variables.

list('year')

a numeric vector

list('doy')

a numeric vector

list('hour')

a numeric vector

list('solarR')

a numeric vector

list('DailyTemp.C')

a numeric vector

list('RH')

a numeric vector

list('WindSpeed')

a numeric vector

list('precip')

a numeric vector

Details

~~ If necessary, more details than the above ~~

Source

~~ reference to a publication or URL from which the data were obtained ~~

References

~~ possibly secondary sources and usages ~~

Examples

data(EngWea94i)
## maybe str(EngWea94i) ; plot(EngWea94i) ...

Description

Weather data with the precipitation column giving precipitation without irrigation.

Format

A data frame with 8760 observations on the following 8 variables.

list('year')

a numeric vector

list('doy')

a numeric vector

list('hour')

a numeric vector

list('solarR')

a numeric vector

list('DailyTemp.C')

a numeric vector

list('RH')

a numeric vector

list('WindSpeed')

a numeric vector

list('precip')

a numeric vector

Details

~~ If necessary, more details than the description above ~~

Source

~~ reference to a publication or URL from which the data were obtained ~~

References

~~ possibly secondary sources and usages ~~

Examples

data(EngWea94rf)
## maybe str(EngWea94rf) ; plot(EngWea94rf) ...

Description

Atempts to guess good initial vales for dry biomass coefficients that can be passed to BioGro, OpBioGro, constrOpBioGro, or MCMCBioGro. It is very fragile.

Usage

idbp(data, phenoControl = list())

Arguments

Details

This function will not accept missing values. It can be quite fragile and it is rather inflexible in what it expects in terms of data.

Value

It returns a vector of length 25 suitable for BioGro, OpBioGro, constrOpBioGro, or MCMCBioGro.

Note

It is highly recommended that the results of this function are tested with valid_dbp.

Author(s)

Fernando E. Miguez

See Also

valid_dbp

Examples

## See ?OpBioGro

Description

This estimates initial dry biomass partitioning coefficients based on data for an annual grass

Usage

idbpm(data, MaizePhenoControl = list())

Arguments

Value

vector of biomass pools

Author(s)

Fernando E. Miguez

Description

Layer data for evapo/transpiration. Simulated data to show the result of the EvapoTrans function.

Format

data frame of dimensions 384 by 9.

Details

lfClass: leaf class, 'sun' or 'shade'.

layer: layer in the canopy, 1 to 8.

hour: hour of the day, (0–23).

Rad: direct light.

Itot: total radiation.

Temp: air temperature, (Celsius).

RH: relative humidity, (0–1).

WindSpeed: wind speed, (m $s^{-1}$).

LAI: leaf area index.

Source

simulated

Description

Simulates light macro environment based on latitude, day of the year. Other coefficients can be adjusted.

Usage

lightME(lat = 40, DOY = 190, t.d = 12, t.sn = 12, atm.P = 1e+05,
  alpha = 0.85)

Arguments

Details

The equations used here can be found in http://www.life.illinois.edu/plantbio/wimovac/newpage9.htm The original source is Monteith, 1991

Value

a list structure with components:

• "I.dir"Direct radiation ($\mu$ mol $m^{-2}s^{-1}$

• "I.diff"Indirect (diffuse) radiation ($\mu$ mol$m^{-2}s^{-1}$

• "cos.th"cosine of $\theta$, solar zenith angle.

• "propIdir"proportion of direct radiation.

• "propIdiff"proportion of indirect (diffuse) radiation.

Examples

## Not run: 
## Direct and diffuse radiation for DOY 190 and hours 0 to 23

res <- lightME(t.d=0:23)

lattice::xyplot(I.dir + I.diff ~ 0:23 , data = res,
type='o',xlab='hour',ylab='Irradiance')

lattice::xyplot(propIdir + propIdiff ~ 0:23 , data = res,
type='o',xlab='hour',ylab='Irradiance proportion')

plot(acos(lightME(lat = 42, t.d = 0:23)$cos.th) * (1/dtr))

## End(Not run)

Description

It takes weather data as input (hourly timesteps) and several parameters and it produces phenology, photosynthesis, LAI, etc.

Usage

MaizeGro(WetDat, plant.day = NULL, emerge.day = NULL,
  harvest.day = NULL, plant.density = 7, timestep = 1, lat = 40,
  canopyControl = list(), MaizeSeneControl = list(),
  photoControl = list(), MaizePhenoControl = list(),
  MaizeCAllocControl = list(), laiControl = list(),
  soilControl = list(), MaizeNitroControl = list(),
  centuryControl = list())

Arguments

Details

The phenology follows the 'Corn Growth and Development' Iowa State Publication.

Value

It currently returns a list with the following components

Author(s)

Fernando E Miguez

See Also

BioGro

Examples

data(weather05)
res <- MaizeGro(weather05, plant.day = 110, emerge.day = 120, harvest.day=300,
                  MaizePhenoControl = MaizePhenoParms(R6 = 2000))

Description

Lists values for phenology parameters

Usage

MaizePhenoParms(base.temp = 10, max.leaves = 20, plant.emerg = 100,
  phyllochron1 = 46.7, phyllochron2 = 31.1, R1 = 747, R2 = 858,
  R3 = 969, R4 = 1080, R5 = 1136, R6 = 1452)

Arguments

Description

Lists values for senecence parameters

Usage

MaizeSeneParms(senStem = 3000, senLeaf = 3500, senRoot = 4000)

Arguments

Description

Simulated Annealing and Markov chain Monte Carlo for estimating parameters for Biomass Growth

Usage

MCMCBioGro(niter = 10, niter2 = 10, phen = 6, iCoef = NULL,
  saTemp = 5, coolSamp = 20, scale = 0.5, WetDat, data,
  day1 = NULL, dayn = NULL, timestep = 1, lat = 40, iRhizome = 7,
  iLeaf = iRhizome * 1e-04, iStem = iRhizome * 0.001,
  iRoot = iRhizome * 0.001, canopyControl = list(),
  seneControl = list(), photoControl = list(), phenoControl = list(),
  soilControl = list(), nitroControl = list(),
  centuryControl = list(), sd = c(0.02, 1e-06))

Arguments

Details

This function atempts to implement the simulated annealing method for estimating parameters of a generic C4 crop growth.

This function implements a hybrid algorithm where the first portion is simulated annealing and the second portion is a Markov chain Monte Carlo. The user controls the number of iterations in each portion of the chain with niter and niter2.

Value

An object of class MCMCBioGro consisting of a list with 23 components. The easiest way of accessing the information is with the print and plot methods.

Note

The automatic method for guessing the last day of the growing season differs slightly from that in BioGro. To prevent error due to a shorter simulated growing season than the observed one the method in MCMCBioGro adds one day to the last day of the growing season. Although the upper limit is fixed at 330.

Author(s)

Fernando E. Miguez

Fernando E. Miguez

See Also

See Also as BioGro, OpBioGro and constrOpBioGro.

Examples

## Not run: 

data(weather05)

## Some coefficients
pheno.ll <- phenoParms(kLeaf1=0.48,kStem1=0.47,kRoot1=0.05,kRhizome1=-1e-4,
                       kLeaf2=0.14,kStem2=0.65,kRoot2=0.21, kRhizome2=-1e-4,
                       kLeaf3=0.01, kStem3=0.56, kRoot3=0.13, kRhizome3=0.3,
                       kLeaf4=0.01, kStem4=0.56, kRoot4=0.13, kRhizome4=0.3,
                       kLeaf5=0.01, kStem5=0.56, kRoot5=0.13, kRhizome5=0.3,
                       kLeaf6=0.01, kStem6=0.56, kRoot6=0.13, kRhizome6=0.3)

system.time(ans <- BioGro(weather05, phenoControl = pheno.ll))

ans.dat <- as.data.frame(unclass(ans)[1:11])
sel.rows <- seq(1,nrow(ans.dat),400)
simDat <- ans.dat[sel.rows,c('ThermalT','Stem','Leaf','Root','Rhizome','Grain','LAI')]
plot(ans,simDat)

## Residual sum of squares before the optimization

ans0 <- BioGro(weather05)
RssBioGro(simDat,ans0)


op1.mc <- MCMCBioGro(phen=1, niter=200,niter2=200,
                     WetDat=weather05,
                     data=simDat)


plot(op1.mc)

plot(op1.mc, plot.kind='trace', subset = nams %in%
\t\t\t\tc('kLeaf_1','kStem_1','kRoot_1'))


## End(Not run)

Description

This function implement Markov chain Monte Carlo methods for the C4 photosynthesis model of Collatz et al. The chain is constructed using a Gaussian random walk. This is definitely a beta version of this function and more testing and improvements are needed. The value of this function is in the possibility of examining the empirical posterior distribution (i.e. vectors) of the vmax and alpha parameters. Notice that you will get different results each time you run it.

Usage

MCMCc4photo(data, niter = 20000, ivmax = 39, ialpha = 0.04,
  ikparm = 0.7, itheta = 0.83, ibeta = 0.93, iRd = 0.8,
  Catm = 380, b0 = 0.08, b1 = 3, StomWS = 1, ws = c("gs",
  "vmax"), scale = 1, sds = c(1, 0.005), prior = c(39, 10, 0.04,
  0.02), uppertemp = 37.5, lowertemp = 3)

Arguments

Value

an object of class MCMCc4photo with components

• accept number of accepted moves in the chain.

• resuMC matrix of dimensions niter by 3 containing the values for Vmax and alpha and the RSS in each iteration of the chain.

References

Brooks, Stephen. (1998). Markov chain Monte Carlo and its application. The Statistician. 47, Part 1, pp. 69-100.

Examples

## Not run: 
## Using Beale, Bint and Long (1996)
data(obsBea)

## Different starting values
resB1 <- MCMCc4photo(obsBea, 100000, scale=1.5)
resB2 <- MCMCc4photo(obsBea, 100000, ivmax=25, ialpha=0.1, scale=1.5)
resB3 <- MCMCc4photo(obsBea, 100000, ivmax=45, ialpha=0.02, scale=1.5)

## Use the plot function to examine results
plot(resB1,resB2,resB3)
plot(resB1,resB2,resB3,plot.kind='density',burnin=1e4)


## End(Not run)

Description

This function attempts to implement Markov chain Monte Carlo methods for models with no likelihoods. In this case it is done for the von Caemmerer C4 photosynthesis model. The method implemented is based on a paper by Marjoram et al. (2003). The method is described in Miguez (2007). The chain is constructed using a Gaussian random walk. This is definitely a beta version of this function and more testing and improvements are needed. The value of this function is in the possibility of examining the empirical posterior distribution (i.e. vectors) of the Vcmax and alpha parameters. Notice that you will get different results each time you run it.

Usage

MCMCEc4photo(obsDat, niter = 30000, iCa = 380, iOa = 210,
  iVcmax = 60, iVpmax = 120, iVpr = 80, iJmax = 400,
  thresh = 0.01, scale = 1)

Arguments

Value

a list structure with components

• RsqBI This is the $R^2$ for the “burn-in” period. This value becomes the cut off value for the acceptance in the chain.

• CoefBI parameter estimates after the burn-in period. These are not optimal as in the case of the optimization routine but are starting values for the chain.

• accept1 number of iterations for the initial burn-in period.

• resuBI matrix of dimensions 5 by accept1 containing the values for Vcmax and alpha and the $R^2$ in each iteration of the burn-in period.

• resuMC matrix of dimensions 5 by accept2 containing the values for Vcmax and alpha and the $R^2$ in each iteration of the chain period.

• accept2 number of accepted samples or length of the chain.

• accept3 number of accepted moves in the chain.

References

P. Marjoram, J. Molitor, V. Plagnol, S. Tavare, Markov chain monte carlo without likelihoods, PNAS 100 (26) (2003) 15324–15328.

Miguez (2007) Miscanthus x giganteus production: meta-analysis, field study and mathematical modeling. PhD Thesis. University of Illinois at Urbana-Champaign.

Examples

## Not run: 
## This is an example for the MCMCEc4photo
## evaluating the convergence of the chain
## Notice that if a parameter does not seem
## to converge this does not mean that the method
## doesn't work. Careful examination is needed
## in order to evaluate the validity of the results
data(obsNaid)
res1 <- MCMCEc4photo(obsNaid,100000,thresh=0.007)
res1

## Run it a few more times
## and test the stability of the method
res2 <- MCMCEc4photo(obsNaid,100000,thresh=0.007)
res3 <- MCMCEc4photo(obsNaid,100000,thresh=0.007)

## Now plot it
plot(res1,res2,res3)
plot(res1,res2,res3,type='density')

## End(Not run)

Description

It is a wrapper for Opc3photo which allows for optimization of multiple runs of curves (A/Q or A/Ci).

Usage

mOpc3photo(data, ID = NULL, iVcmax = 100, iJmax = 180, iRd = 1.1,
  op.level = 1, curve.kind = c("Ci", "Q"), verbose = FALSE, ...)

Arguments

Details

Include more details about the data.

Value

an object of class 'mOpc3photo'

if op.level equals 1 best Vcmax, Jmax and convergence

if op.level equals 2 best Vcmax, Jmax, Rd and convergence

Author(s)

Fernando E. Miguez

See Also

See also Opc3photo

Examples

data(simAssim)
simAssim <- cbind(simAssim[,1:6],Catm=simAssim[,10])
simAssim <- simAssim[simAssim[,1] < 11,]

plotAC(simAssim, trt.col=1)

op.all <- mOpc3photo(simAssim, op.level=1,
                      verbose=TRUE)

plot(op.all)
plot(op.all, parm='jmax')

Description

Wrapper function that allows for optimization of multiple A/Ci or A/Q curves.

Usage

mOpc4photo(data, ID = NULL, ivmax = 39, ialpha = 0.04, iRd = 0.8,
  iupperT = 37.5, ilowerT = 3, ikparm = 0.7, itheta = 0.83,
  ibeta = 0.93, curve.kind = c("Q", "Ci"), op.level = 1,
  op.ci = FALSE, verbose = FALSE, ...)

Arguments

Details

There are printing and plotting methods for the object created by this function. The plotting function has an argument that it is used to dsiplay either vmax or alpha (i.e. parm=c('vmax','alpha')). In both cases the optimized value plus confidence intervals will be displayed for each run.

Value

An object of class mOpc4photo returned

• mat Matrix with optimized parameters.

• op.level Optimization level..

• ciVmax confidence intervals for vmax.

• ciAlpha confidence intervals for alpha.

• curve.kind Whether A/Ci or A/Q curves were optimized.

Author(s)

Fernando E. miguez

See Also

Opc4photo c4photo optim

Examples

data(simAssim)

Description

assimilation as measured in Beale, Bint and Long (1996) in Miscanthus. The first column is the observed net assimilation rate ($\mu$ mol $m^{-2}$ $s^{-1}$). The second column is the observed quantum flux ($\mu$ mol $m^{-2}$ $s^{-1}$). The third column is the temperature (Celsius).Relative humidity was not reported and thus was assumed to be 0.7.

Format

data frame of dimensions 27 by 4.

Source

C. V. Beale, D. A. Bint, S. P. Long, Leaf photosynthesis in the C4-grass miscanthus x giganteus, growing in the cool temperate climate of southern England, J. Exp. Bot. 47 (2) (1996) 267–273.

Description

Assimilation and stomatal conductance as measured in Beale, Bint and Long (1996) in Miscanthus (missing data are also included). The first column is the date, the second the hour. Columns 3 and 4 are assimilation and stomatal conductance respectively.

Format

data frame of dimensions 35 by 6.

Details

The third column is the observed net assimilation rate ($\mu$ mol $m^{-2}$ $s^{-1}$).

The fifth column is the observed quantum flux ($\mu$ mol $m^{-2}$ $s^{-1}$).

The sixth column is the temperature (Celsius).

Source

C. V. Beale, D. A. Bint, S. P. Long, Leaf photosynthesis in the C4-grass miscanthus x giganteus, growing in the cool temperate climate of southern England, J. Exp. Bot. 47 (2) (1996) 267–273.

Description

assimilation as measured in Naidu et al. (2003) in Miscanthus. The first column is the observed net assimilation rate ($\mu$ mol $m^{-2}$ $s^{-1}$) The second column is the observed quantum flux ($\mu$ mol $m^{-2}$ $s^{-1}$) The third column is the temperature (Celsius). The fourth column is the observed relative humidity in proportion (e.g. 0.7).