agridat (version 1.12)

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, numeric

row

Row, numeric

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).

Examples

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

desplot(yield~ col*row, data=dat, num=gen, out1=rep,
        #aspect = 6/.75,
        main="gilmour.serpentine")

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

# }
# NOT RUN {
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, 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, 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")

# }