Split-plot experiment of barley with fungicide treatments

Format

A data frame with 560 observations on the following 6 variables.

yield: yield, tonnes/ha
block: block, 4 levels
gen: genotype, 70 levels
fung: fungicide, 2 levels
row: row
bed: bed (column)

Details

Grown in 1995-1996 at the Scottish Crop Research Institute. Split-plot design with 4 blocks, 2 whole-plot fungicide treatments, and 70 barley varieties or variety mixes. Total area was 10 rows (north/south) by 56 beds (east/west).

Used with permission of Maria Durban.

Source

Durban, Maria and Hackett, Christine and McNicol, James and Newton, Adrian and Thomas, William and Currie, Iain. 2003. The practical use of semiparametric models in field trials, Journal of Agric Biological and Envir Stats, 8, 48-66. https://doi.org/10.1198/1085711031265.

Examples

if (FALSE) { # \dontrun{

  library(agridat)
  data(durban.splitplot)
  dat <- durban.splitplot

  libs(desplot)
  desplot(dat, yield~bed*row,
          out1=block, out2=fung, num=gen, # aspect unknown
          main="durban.splitplot")


  # Durban 2003, Figure 2
  m20 <- lm(yield~gen + fung + gen:fung, data=dat)
  dat$resid <- m20$resid
  ## libs(lattice)
  ## xyplot(resid~row, dat, type=c('p','smooth'), main="durban.splitplot")
  ## xyplot(resid~bed, dat, type=c('p','smooth'), main="durban.splitplot")

  # Figure 4 doesn't quite match due to different break points
  libs(lattice)
  xyplot(resid ~ bed|factor(row), data=dat,
         main="durban.splitplot",
         type=c('p','smooth'))


  # Figure 6 - field trend
  # note, Durban used gam package like this
  # m2lo <- gam(yield ~ gen*fung + lo(row, bed, span=.082), data=dat)
  libs(mgcv)
  m2lo <- gam(yield ~ gen*fung + s(row, bed,k=45), data=dat)
  new2 <- expand.grid(row=unique(dat$row), bed=unique(dat$bed))
  new2 <- cbind(new2, gen="G01", fung="F1")
  p2lo <- predict(m2lo, newdata=new2)
  libs(lattice)
  wireframe(p2lo~row+bed, new2, aspect=c(1,.5),
            main="durban.splitplot - Field trend")

  if(require("asreml", quietly=TRUE)) {
    libs(asreml,lucid)
    
    # Table 5, variance components.  Table 6, F tests
    dat <- transform(dat, rowf=factor(row), bedf=factor(bed))
    dat <- dat[order(dat$rowf, dat$bedf),]
    m2a2 <- asreml(yield ~ gen*fung, random=~block/fung+units, data=dat,
                   resid =~ar1v(rowf):ar1(bedf))
    m2a2 <- update(m2a2)
    
    lucid::vc(m2a2)
    ##             effect component std.error z.ratio bound 
    ##              block   0              NA      NA     B  NA
    ##         block:fung   0.01206  0.01512      0.8     P   0
    ##              units   0.02463  0.002465    10       P   0
    ##       rowf:bedf(R)   1              NA      NA     F   0
    ## rowf:bedf!rowf!cor   0.8836   0.03646     24       U   0
    ## rowf:bedf!rowf!var   0.1261   0.04434      2.8     P   0
    ## rowf:bedf!bedf!cor   0.9202   0.02846     32       U   0
    
    wald(m2a2)
  }
  
} # }