Competition experiment in beans with height measurements

Usage

data("besag.beans")

Format

A data frame with 152 observations on the following 6 variables.

gen: genotype / variety
height: plot height, cm
yield: plot yield, g
row: row / block
rep: replicate factor
col: column

Details

Field beans of regular height were grown beside shorter varieties. In each block, each variety occurred once as a left-side neighbor and once as a right-side neighbor of every variety (including itself). Border plots were placed at the ends of each block. Each block with 38 adjacent plots. Each plot was one row, 3 meters long with 50 cm spacing between rows. No gaps between plots. Spacing between plants was 6.7 cm. Four blocks (rows) were used, each with six replicates.

Plot yield and height was recorded.

Kempton and Lockwood used models that adjusted yield according to the difference in height of neighboring plots.

Field length: 4 plots * 3m = 12m

Field width: 38 plots * 0.5 m = 19m

Source

Julian Besag and Rob Kempton (1986). Statistical Analysis of Field Experiments Using Neighbouring Plots. Biometrics, 42, 231-251. Table 6. https://doi.org/10.2307/2531047

References

Kempton, RA and Lockwood, G. (1984). Inter-plot competition in variety trials of field beans (Vicia faba L.). The Journal of Agricultural Science, 103, 293–302.

Examples

if (FALSE) { # \dontrun{

library(agridat)

data(besag.beans)
dat = besag.beans

libs(desplot)
desplot(dat, yield ~ col*row,
        aspect=12/19, out1=row, out2=rep, num=gen, cex=1, # true aspect
        main="besag.beans")


libs(reshape2)
# Add a covariate = excess height of neighbors
mat <- acast(dat, row~col, value.var='height')
mat2 <- matrix(NA, nrow=4, ncol=38)
mat2[,2:37] <- (mat[,1:36] + mat[,3:38] - 2*mat[,2:37])
dat2 <- melt(mat2)
colnames(dat2) <- c('row','col','cov')
dat <- merge(dat, dat2)
  
# Drop border plots
dat <- subset(dat, rep != 'R0')
  
libs(lattice)
# Plot yield vs neighbors height advantage
xyplot(yield~cov, data=dat, group=gen,
       main="besag.beans",
       xlab="Mean excess heights of neighbor plots",
       auto.key=list(columns=3))
  
# Trial mean.
mean(dat$yield) # 391 matches Kempton table 3
  
# Mean excess height of neighbors for each genotype
# tapply(dat$cov, dat$gen, mean)/2 # Matches Kempton table 4

# Variety means, matches Kempton table 4 mean yield
m1 <- lm(yield ~ -1 + gen, dat)
coef(m1)

# Full model used by Kempton, eqn 5.  Not perfectly clear.
# Appears to include rep term, perhaps within block
dat$blk <- factor(dat$row)
dat$blkrep <- factor(paste(dat$blk, dat$rep))
m2 <- lm(yield ~ -1 + gen + blkrep + cov, data=dat)
coef(m2) # slope 'cov' = -.72, while Kempton says -.79

} # }