agridat (version 1.12)

cullis.earlygen: Early generation variety trial in wheat

Description

Early generation variety trial in wheat

Format

A data frame with 670 observations on the following 5 variables.

gen

genotype factor

row

row ordinate

col

column ordinate

entry

entry (genotype) number

yield

yield of each plot, kg/ha

weed

weed score

Details

The data are from an unreplicated field experiment conducted at Tullibigeal, New South Wales, Australia in 1987-88. In each row, every 6th plot was the variety 'Kite'. Six other standard varieties were randomly interspersed over the trial. Each plot was 15m x 1.8m.

The 'weed' variable is a visual score on a 0 to 10 scale, 0 = no weeds, 10 = 100 percent weeds.

The replicated check variety was numbered 526. A further 6 replicated commercially available varieties (numbered 527 to 532) were also randomly assigned to plots with between 3 to 5 plots of each. The aim of these trials is to identify and retain the top, say 20 percent of lines for further testing. Cullis et al. (1989) presented an analysis of early generation variety trials that included a one-dimensional spatial analysis. Below, a two-dimensional spatial analysis is presented.

Note: The 'row' and 'col' variables are as in the VSN link below (switched compared to the paper by Cullis et al.)

References

http://www.vsni.co.uk/software/asreml/htmlhelp/asreml/xwheat.htm.

Examples

# NOT RUN {
data(cullis.earlygen)
dat <- cullis.earlygen

# Show field layout of checks.  Cullis Table 1.
dat$check <- ifelse(dat$entry < 8, dat$entry, NA)
desplot(yield ~ col*row, dat, main="cullis.earlygen (yield)",
        col="check", cex=1, flip=TRUE)

desplot(weed ~ col*row, dat, main="cullis.earlygen (weed)", flip=TRUE)

require(lattice)
bwplot(yield ~ weed, dat, horizontal=FALSE, main="cullis.earlygen")

# }
# NOT RUN {
require(asreml)
# Start with the standard AR1xAR1 analysis
dat <- transform(dat, xf=factor(col), yf=factor(row))
dat <- dat[order(dat$xf, dat$yf),]
m2 <- asreml(yield ~ weed, data=dat, random= ~gen,
             rcov = ~ ar1(xf):ar1(yf))

# Variogram suggests a polynomial trend
m3 <- update(m2, fixed= yield~weed+pol(col,-1))

# Now add a nugget variance
m4 <- update(m3, random= ~ gen + units)

require(lucid)
vc(m4)
##           effect component std.error z.ratio constr
##      gen!gen.var  73770    10420         7.1    pos
##  units!units.var  30440     8074         3.8    pos
##       R!variance  54720    10630         5.1    pos
##         R!xf.cor      0.38     0.115     3.3  uncon
##         R!yf.cor      0.84     0.045    19    uncon

# Predictions from models m3 and m4 are non-estimable.  Why?
# Use model m2 for predictions
predict(m2)$pred
##         gen predicted.value standard.error est.status
## 1    Banks         2723.534       93.14633  Estimable
## 2    Eno008        2981.057      162.85053  Estimable
## 3    Eno009        2978.009      161.56930  Estimable
## 4    Eno010        2821.399      153.96697  Estimable
## 5    Eno011        2991.610      161.53308  Estimable
## 6    Eno012        2771.148      162.21897  Estimable


# Compare AR1 with Moving Grid
require(mvngGrAd)
shape <- list(c(1),
              c(1),
              c(1:4),
              c(1:4))
# sketchGrid(10,10,20,20,shapeCross=shape, layers=1, excludeCenter=TRUE)
m5 <- movingGrid(rows=dat$row, columns=dat$col, obs=dat$yield,
                 shapeCross=shape, layers=NULL)
dat$mg <- fitted(m5)
dat$ar1 <- fitted(m2)
head(dat[ , c('yield','ar1','mg')])
##    yield      ar1       mg
## 1   2652 2467.980 2531.998
## 11  3394 3071.681 3052.160
## 21  3148 2826.188 2807.031
## 31  3426 3026.985 3183.649
## 41  3555 3070.102 3195.910
## 51  3453 3006.352 3510.511
pairs(dat[ , c('yield','ar1','mg')])

# }
# NOT RUN {
# }