agridat (version 1.16)

durban.competition: Sugar beet yields with competition effects

Description

Sugar beet yields with competition effects

Format

A data frame with 114 observations on the following 5 variables.

gen

Genotype factor, 36 levels plus Border

col

Column

block

Row/Block

wheel

Position relative to wheel tracks

yield

Root yields, kg/plot

Details

This sugar-beet trial was conducted in 1979.

Single-row plots, 12 m long, 0.5 m between rows. Each block is made up of all 36 genotypes laid out side by side. Guard/border plots are at each end. Root yields were collected.

Wheel tracks are located between columns 1 and 2, and between columns 5 and 6, for each set of six plots. Each genotype was randomly allocated once to each pair of plots (1,6), (2,5), (3,4) across the three reps. Wheel effect were not significant in _this_ trial.

Field width: 18m + 1m guard rows = 19m

Field length: 3 blocks * 12m + 2*0.5m spacing = 37m

Examples

# NOT RUN {
data(durban.competition)
dat <- durban.competition

# Check that genotypes were balanced across wheel tracks.
with(dat, table(gen,wheel))

if(require(desplot)){
  desplot(yield ~ col*block, dat,
          out1=block, text=gen, col=wheel, aspect=37/19, # true aspect
          main="durban.competition")
}


# Calculate residual after removing block/genotype effects
m1 <- lm(yield ~ gen + block, data=dat)
dat$res <- resid(m1)

## desplot(res ~ col*block, dat, out1=block, text=gen, col=wheel,
##         main="durban.competition - residuals")

# Calculate mean of neighboring plots
dat$comp <- NA
dat$comp[3:36] <- ( dat$yield[2:35] + dat$yield[4:37] ) / 2
dat$comp[41:74] <- ( dat$yield[40:73] + dat$yield[42:75] ) / 2
dat$comp[79:112] <- ( dat$yield[78:111] + dat$yield[80:113] ) / 2

# Demonstrate the competition effect
# Competitor plots have low/high yield -> residuals are negative/positive
require(lattice)
xyplot(res~comp, dat, type=c('p','r'), main="durban.competition",
       xlab="Average yield of neighboring plots", ylab="Residual")

# }

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