## Not run: 

  library(agridat)
  data(durban.rowcol)
  dat <- durban.rowcol
  
  libs(desplot)
  desplot(dat, yield~bed*row,
          out1=rep, num=gen, # aspect unknown
          main="durban.rowcol")
  

  # Durban 2003 Figure 1
  m10 <- lm(yield~gen, data=dat)
  dat$resid <- m10$resid
  ## libs(lattice)
  ## xyplot(resid~row, dat, type=c('p','smooth'), main="durban.rowcol")
  ## xyplot(resid~bed, dat, type=c('p','smooth'), main="durban.rowcol")
  
  # Figure 3
  libs(lattice)
  xyplot(resid ~ bed|factor(row), data=dat,
         main="durban.rowcol",
         type=c('p','smooth'))
  
  

  # Figure 5 - field trend
  # note, Durban used gam package like this
  # m1lo <- gam(yield ~ gen + lo(row, span=10/16) + lo(bed, span=9/34), data=dat)
  libs(mgcv)
  m1lo <- gam(yield ~ gen + s(row) + s(bed, k=5), data=dat)
  new1 <- expand.grid(row=unique(dat$row),bed=unique(dat$bed))
  new1 <- cbind(new1, gen="G001")
  p1lo <- predict(m1lo, newdata=new1)
  libs(lattice)
  wireframe(p1lo~row+bed, new1, aspect=c(1,.5), main="Field trend")


  libs(asreml) # asreml4

  dat <- transform(dat, rowf=factor(row), bedf=factor(bed))
  dat <- dat[order(dat$rowf, dat$bedf),]

  m1a1 <- asreml(yield~gen + lin(rowf) + lin(bedf), data=dat,
                 random=~spl(rowf) + spl(bedf) + units,
                 family=asr_gaussian(dispersion=1))
  m1a2 <- asreml(yield~gen + lin(rowf) + lin(bedf), data=dat,
                 random=~spl(rowf) + spl(bedf) + units,
                 resid = ~ar1(rowf):ar1(bedf))
  m1a2 <- update(m1a2)
  m1a3 <- asreml(yield~gen, data=dat, random=~units,
                 resid = ~ar1(rowf):ar1(bedf))

  # Figure 7
  libs(lattice)
  v7a <- asr_varioGram(x=dat$bedf, y=dat$rowf, z=m1a3$residuals)
  wireframe(gamma ~ x*y, v7a, aspect=c(1,.5)) # Fig 7a
  
  v7b <- asr_varioGram(x=dat$bedf, y=dat$rowf, z=m1a2$residuals)
  wireframe(gamma ~ x*y, v7b, aspect=c(1,.5)) # Fig 7b
  
  v7c <- asr_varioGram(x=dat$bedf, y=dat$rowf, z=m1lo$residuals)
  wireframe(gamma ~ x*y, v7c, aspect=c(1,.5)) # Fig 7c


## End(Not run)