agridat (version 1.23)

miguez.biomass: Biomass of 3 crops in Greece

Description

Biomass of 3 crops in Greece

Usage

data("miguez.biomass")

Format

A data frame with 212 observations on the following 5 variables.

doy

day of year

block

block, 1-4

input

management input, Lo/Hi

crop

crop type

yield

yield tons/ha

Details

Experiment was conducted in Greece in 2009. Yield values are destructive Measurements of above-ground biomass for fiber sorghum, maize, sweet sorghum.

Hi management refers to weekly irrigation and high nitrogen applications. Lo management refers to bi-weekly irrigation and low nitrogen.

The experiment had 4 blocks.

Crops were planted on DOY 141 with 0 yield.

References

Sotirios V. Archontoulis and Fernando E. Miguez (2013). Nonlinear Regression Models and Applications in Agricultural Research. Agron. Journal, 105:1-13. https://doi.org/10.2134/agronj2012.0506

Hamze Dokoohaki. https://www.rpubs.com/Para2x/100378 https://rstudio-pubs-static.s3.amazonaws.com/100440_26eb9108524c4cc99071b0db8e648e7d.html

Examples

if (FALSE) {

  library(agridat)
  data(miguez.biomass)
  dat <- miguez.biomass
  dat <- subset(dat, doy > 141)

  libs(lattice)
  xyplot(yield ~ doy | crop*input,  data = dat,
         main="miguez.biomass",
         groups = crop,
         type=c('p','smooth'),
         auto.key=TRUE)


  # ----------
  # Archontoulis et al fit some nonlinear models.
  # Here is a simple example which does NOT account for crop/input
  # Slow, so dont run
  if(0){
    dat2 <- transform(dat, eu = paste(block, input, crop))
    dat2 <- groupedData(yield ~ doy | eu, data = dat2)
    fit.lis <- nlsList(yield ~ SSfpl(doy, A, B, xmid, scal),
                       data = dat2,
                       control=nls.control(maxiter=100))
    print(plot(intervals(fit.lis)))
    
    libs(nlme)
    # use all data to get initial values
    inits <- getInitial(yield ~ SSfpl(doy, A, B, xmid, scal), data = dat2)
    inits
    xvals <- 150:325
    y1 <- with(as.list(inits), SSfpl(xvals, A, B, xmid, scal))
    plot(yield ~ doy, dat2)
    lines(xvals,y1)
    
    # must have groupedData object to use augPred
    dat2 <- groupedData(yield ~ doy|eu, data=dat2)
    plot(dat2)
    
    # without 'random', all effects are included in 'random'
    m1 <- nlme(yield ~ SSfpl(doy, A, B, xmid,scale),
               data= dat2,
               fixed= A + B + xmid + scale ~ 1,
               # random = B ~ 1|eu, # to make only B random
               random = A + B + xmid + scale ~ 1|eu,
               start=inits)
    fixef(m1)
    summary(m1)
    plot(augPred(m1, level=0:1),
         main="miguez.biomass - observed/predicted data") # only works with groupedData object
  }
  
}