durban.rowcol {agridat}R Documentation

Row-column experiment of spring barley, many varieties

Description

Row-column experiment of spring barley, many varieties

Format

A data frame with 544 observations on the following 5 variables.

row

row

bed

bed (column)

rep

rep, 2 levels

gen

genotype, 272 levels

yield

yield, tonnes/ha

Details

Spring barley variety trial of 272 entries (260 new varieties, 12 control). Grown at the Scottish Crop Research Institute in 1998. Row-column design with 2 reps, 16 rows (north/south) by 34 beds (east/west). The land sloped downward from row 16 to row 1. Plot yields were converted to tonnes per hectare.

Plot dimensions are not given.

Retrieved from: ftp://ftp.bioss.sari.ac.uk/pub/maria

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

References

Edmondson, Rodney (2020). Multi-level Block Designs for Comparative Experiments. J of Agric, Biol, and Env Stats. https://doi.org/10.1007/s13253-020-00416-0

Examples

## 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)

[Package agridat version 1.18 Index]