verbyla.lupin: Multi-environment trial of lupin, multiple varieties and densities

Description

Yield of 9 varieties of lupin at different planting densities across 2 years and multiple locations.

Arguments

Format

gen: genotype, 9 varieties
site: site, 11 levels
rep: rep, 2-3 levels
rate: seeding rate in plants/m^2
row: row
col: column
serp: factor of 4 levels for serpentine seeding effect
linrow: centered row position as a numeric variate (row-8.5)/10
lincol: centered column position as a numeric variate (col-3.5)
linrate: linear effect of seedrate, scaled (seedrate-41.92958)/10
yield: yield in tons/hectare
year: year, 1991-1992
loc: location

Details

Nine varieties of lupin were tested for yield response to plant density at 11 sites. The target density in 1991 was 10, 20, ..., 60 plants per m^2, and in 1992 was 20, 30, ..., 70 plants per m^2.

Plot dimensions are not given.

The variety Myallie was grown only in 1992.

Each site had 2 reps in 1991 and 3 reps in 1992. Each rep was laid out as a factorial RCB design; one randomization was used for all sites in 1991 and one (different) randomization was used for all sites in 1992. (This was confirmed with the principal investigator.)

In 1991 at the Mt. Barker location, the data for columns 5 and 6 was discarded due to problems with weeds.

Variety 'Myallie' was called '84L:439' in Verbyla 1997.

The year of release for the varieties is

Unicrop	1973
Illyarrie	1979
Yandee	1980
Danja	1986
Gungurru	1988
Yorrel	1989
Warrah	1989
Merrit	1991
Myallie	1995

Examples

Run this code

# NOT RUN {
data(verbyla.lupin)
dat <- verbyla.lupin

# The same RCB randomization was used at all sites in each year
if(require(desplot)){
  desplot(gen~col+row|site, data=dat,
          out1=rep, num=rate,
          # aspect unknown
          main="verbyla.lupin - experiment design")
}

# Figure 3 of Verbyla
require(lattice)
foo <- xyplot(yield ~ rate|loc*gen, data=dat, subset=year==92,
              type=c('p','smooth'), cex=.5,
              main="verbyla.lupin: 1992 yield response curves",
              xlab="Seed rate (plants/m^2)",
              ylab="Yield (tons/ha)",
              strip=strip.custom(par.strip.text=list(cex=.7)))

if (require(latticeExtra)) { # for useOuterStrips
  useOuterStrips(foo,
                 strip=strip.custom(par.strip.text=list(cex=.7)),
                 strip.left=strip.custom(par.strip.text=list(cex=.7)))
}

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

# }
# NOT RUN {
  
  # We try to reproduce the analysis of Verbyla 1999.
  # May not be exactly the same, but is pretty close.
  
  # Check nlevels for size of random-coefficient structures
  length(with(dat, table(gen)))      # 9 varieties for RC1
  length(with(dat, table(gen,site))) # 99 site:gen combinations for RC2
  
  # Make row and col into factors
  dat <- transform(dat, colf=factor(col), rowf=factor(row))
  # sort for asreml
  dat <- dat[order(dat$site, dat$rowf, dat$colf),]
  
  # Make site names more useful for plots
  dat <- transform(dat, site=factor(paste0(year,".",substring(loc,1,4))))
  
  # Initial model from top of Verbyla table 9.
#  require(asreml4)
  # FIXME asreml4 This is not working in asreml4
  
##   m0 <- asreml(yield ~ 1
##                + site
##                + linrate
##                + site:linrate,
##                data = dat,
##                random = ~ spl(rate)
##                + dev(rate)
##                + site:spl(rate)
##                + site:dev(rate)
##                + str(~gen+gen:linrate, ~us(2):id(9)) # RC1
##                + gen:spl(rate)
##                + gen:dev(rate)
##                + str(~site:gen+site:gen:linrate, ~us(2):id(99)) # RC2
##                + site:gen:spl(rate)
##                + site:gen:dev(rate),
##                residual = ~ dsum( ~ ar1(rowf):ar1(colf)|site) # Spatial AR1 x AR1
##              )
## m0 <- update(m0)
## m0 <- update(m0) # to get convergence

## # Variograms match Verbyla 1999 figure 7 (scale slightly different)
## plot(varioGram(m0), xlim=c(1:19), zlim=c(0,2),
##      main="verbyla.lupin - variogram by site")
  
##   # Sequence of models in Verbyla 1999 table 10
## m1 <- update(m0, fixed= ~ .
##              + at(site, c(2,5,6,8,9,10)):lincol
##              + at(site, c(3,5,7,8)):linrow
##              + at(site, c(2,3,5,7,8,9,11)):serp
##              , random = ~ .
##              + at(site, c(3,6,7,9)):rowf
##              + at(site, c(1,2,3,9,10)):colf
##              + at(site, c(5,7,8,10)):units)
## m1 <- update(m1)

## m2 <- update(m1,
##              random = ~ .
##              - site:gen:spl(rate) - site:gen:dev(rate))

## m3 <- update(m2,
##              random = ~ .
##              - site:dev(rate) - gen:dev(rate))
             
## m4 <- update(m3,
##              random = ~ .
##              - dev(rate))

## m5 <- update(m4,
##              random = ~ .
##              - at(site, c(5,7,8,10)):units + at(site, c(5,7,8)):units)

## # Variance components are a pretty good match to Verbyla 1997, table 15
## require(lucid)
## vc(m5)
## .001004/sqrt(.005446*.0003662) # .711 correlation for RC1
## .00175/sqrt(.01881*.000167) # .987 correlation for RC2

## # Matches Verbyla 1999 figure 5
## plot(varioGram(m5),
##      main="verbyla.lupin - final model variograms",
##      xlim=c(1:19), zlim=c(0,1.5))

# }
# NOT RUN {
# }

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