agridat (version 1.16)

gilmour.serpentine: Wheat yield in South Australia with serpentine row/col effects

Description

An RCB experiment of wheat in South Australia, with strong spatial variation and serpentine row/column effects.

Format

A data frame with 330 observations on the following 5 variables.

col

column

row

row

rep

replicate factor, 3 levels

gen

wheat variety, 108 levels

yield

yield

Details

A randomized complete block experiment. There are 108 varieties in 3 reps. Plots are 6 meters long, 0.75 meters wide, trimmed to 4.2 meters lengths before harvest. Trimming was done by spraying the wheat with herbicide. The sprayer travelled in a serpentine pattern up and down columns. The trial was sown in a serpentine manner with a planter that seeds three rows at a time (Left, Middle, Right).

Field width 15 columns * 6 m = 90 m

Field length 22 plots * .75 m = 16.5 m

References

N. W. Galwey. 2014. Introduction to Mixed Modelling: Beyond Regression and Analysis of Variance. Table 10.9

Examples

# NOT RUN {
data(gilmour.serpentine)
dat <- gilmour.serpentine

if(require(desplot)){
  desplot(yield~ col*row, data=dat,
          num=gen, show.key=FALSE, out1=rep,
          aspect = 16.5/90, # true aspect
          main="gilmour.serpentine")
}

# Extreme field trend.  Blocking insufficient--needs a spline/smoother
# xyplot(yield~col, data=dat, main="gilmour.serpentine")

# ----------------------------------------------------------------------------

# }
# NOT RUN {
  # asreml3
  require(asreml)
  dat <- transform(dat, rowf=factor(row), colf=factor(10*(col-8)))
  dat <- dat[order(dat$rowf, dat$colf), ] # Sort order needed by asreml
  
  # RCB
  m0 <- asreml(yield ~ gen, data=dat, random=~rep)
  
  # Add AR1 x AR1
  m1 <- asreml(yield ~ gen, data=dat, rcov = ~ar1(rowf):ar1(colf))
  
  # Add spline
  m2 <- asreml(yield ~ gen + col, data=dat,
               random= ~ spl(col) + colf,
               rcov = ~ar1(rowf):ar1(colf))
  
  # Figure 4 shows serpentine spraying
  p2 <- predict(m2, data=dat, classify="colf")$predictions$pvals
  plot(p2$predicted, type='b', xlab="column number", ylab="BLUP")
  
  # Define column code (due to serpentine spraying)
  # Rhelp doesn't like double-percent modulus symbol, so compute by hand
  dat <- transform(dat, colcode = factor(dat$col-floor((dat$col-1)/4)*4 -1))
  
  m3 <- asreml(yield ~ gen + lin(colf) + colcode, data=dat,
               random= ~ colf + rowf + spl(colf),
               rcov = ~ar1(rowf):ar1(colf))
  
  # Figure 6 shows serpentine row effects
  p3 <- predict(m3, data=dat, classify="rowf")$predictions$pvals
  plot(p3$predicted, type='l', xlab="row number", ylab="BLUP")
  text(1:22, p3$predicted, c('L','L','M','R','R','M','L','L',
                             'M','R','R','M','L','L','M','R','R','M','L','L','M','R'))
  
  # Define row code (due to serpentine planting). 1=middle, 2=left/right
  dat <- transform(dat, rowcode = factor(row))
  levels(dat$rowcode) <- c('2','2','1','2','2','1','2','2','1',
                           '2','2','1','2','2','1','2','2','1','2','2','1','2')
  
  m6 <- asreml(yield ~ gen + lin(colf) + colcode +rowcode, data=dat,
               random= ~ colf + rowf + spl(col),
               rcov = ~ar1(rowf):ar1(colf))
  plot(variogram(m6), xlim=c(0:17), ylim=c(0,11), zlim=c(0,4000),
       main="gilmour.serpentine")
# }
# NOT RUN {
# ----------------------------------------------------------------------------

# }
# NOT RUN {
  ## require(asreml4)
  ## dat <- transform(dat, rowf=factor(row), colf=factor(10*(col-8)))
  ## dat <- dat[order(dat$rowf, dat$colf), ] # Sort order needed by asreml

  ## # RCB
  ## m0 <- asreml(yield ~ gen, data=dat, random=~rep)

  ## # Add AR1 x AR1
  ## m1 <- asreml(yield ~ gen, data=dat, resid = ~ar1(rowf):ar1(colf))
  
  ## # Add spline
  ## m2 <- asreml(yield ~ gen + col, data=dat,
  ##              random= ~ spl(col) + colf,
  ##              resid = ~ar1(rowf):ar1(colf))
  
  ## # Figure 4 shows serpentine spraying
  ## p2 <- predict(m2, data=dat, classify="colf")$pvals
  ## plot(p2$predicted, type='b', xlab="column number", ylab="BLUP")
  
  ## # Define column code (due to serpentine spraying)
  ## # Rhelp doesn't like double-percent modulus symbol, so compute by hand
  ## dat <- transform(dat, colcode = factor(dat$col-floor((dat$col-1)/4)*4 -1))
  
  ## m3 <- asreml(yield ~ gen + lin(colf) + colcode, data=dat,
  ##              random= ~ colf + rowf + spl(colf),
  ##              resid = ~ar1(rowf):ar1(colf))
  
  ## # Figure 6 shows serpentine row effects
  ## p3 <- predict(m3, data=dat, classify="rowf")$pvals
  ## plot(p3$predicted, type='l', xlab="row number", ylab="BLUP")
  ## text(1:22, p3$predicted, c('L','L','M','R','R','M','L','L',
  ##                            'M','R','R','M','L','L','M','R','R','M','L','L','M','R'))
  
  ## # Define row code (due to serpentine planting). 1=middle, 2=left/right
  ## dat <- transform(dat, rowcode = factor(row))
  ## levels(dat$rowcode) <- c('2','2','1','2','2','1','2','2','1',
  ##                          '2','2','1','2','2','1','2','2','1','2','2','1','2')
  
  ## m6 <- asreml(yield ~ gen + lin(colf) + colcode +rowcode, data=dat,
  ##              random= ~ colf + rowf + spl(col),
  ##              resid = ~ar1(rowf):ar1(colf))
  ## plot(varioGram(m6), xlim=c(0:17), ylim=c(0,11), zlim=c(0,4000),
  ##      main="gilmour.serpentine")
# }
# NOT RUN {
# }