agridat (version 1.16)

connolly.potato: Potato yields in single-drill plots

Description

Potato yields in single-drill plots

Usage

data("connolly.potato")

Format

A data frame with 80 observations on the following 6 variables.

rep

block

gen

variety

row

row

col

column

yield

yield, kg/ha

matur

maturity group

Details

Connolly et el use this data to illustrate how yield can be affected by competition from neighboring plots.

This data uses M1, M2, M3 for maturity, while Connolly et al use FE (first early), SE (second early) and M (maincrop).

The trial was 20 sections, each of which was an independent row of 20 drills. The data here are four reps of single-drill plots from sections 1, 6, 11, and 16.

The neighbor covariate for a plot is defined as the average of the plots to the left and right. For drills at the edge of the trial, the covariate was the average of the one neighboring plot yield and the section (i.e. rep) mean.

It would be interesting to fit a model that uses differences in maturity between a plot and its neighbor as the actual covariate. Anyone...?

Examples

# NOT RUN {
data(connolly.potato)
dat <- connolly.potato

# Field plan
if(require(desplot)){
  desplot(yield~col*row, data=dat,
          out1=rep, # aspect unknown
          main="connolly.potato yields (reps not contiguous)")
}


# Later maturities are higher yielding
require(lattice)
bwplot(yield~matur, dat, main="connolly.potato yield by maturity")

# Observed raw means. Matches Connolly table 2.
mn <- aggregate(yield~gen, data=dat, FUN=mean)
mn[rev(order(mn$yield)),]

# Create a covariate which is the average of neighboring plot yields
if(require(reshape2)){
mat <- acast(dat, row~col, value.var='yield')
mat2 <- matrix(NA, nrow=4, ncol=20)
mat2[,2:19] <- (mat[ , 1:18] + mat[ , 3:20])/2
mat2[ , 1] <- (mat[ , 1] + apply(mat, 1, mean))/2
mat2[ , 20] <- (mat[ , 20] + apply(mat, 1, mean))/2
dat2 <- melt(mat2)
colnames(dat2) <- c('row','col','cov')
dat <- merge(dat, dat2)
# xyplot(yield ~ cov, data=dat, type=c('p','r'))

# Connolly et al fit a model with avg neighbor yield as a covariate
m1 <- lm(yield ~ 0 + gen + rep + cov, data=dat)
coef(m1)['cov'] # = -.303  (Connolly obtained -.31)

# Block names and effects
bnm <- c("R1","R2","R3","R4")
beff <- c(0, coef(m1)[c('repR2','repR3','repR4')])
# Variety names and effects
vnm <- paste0("V", formatC(1:20, width=2, flag='0'))
veff <- coef(m1)[1:20]

# Adjust yield for variety and block effects
dat <- transform(dat, yadj = yield - beff[match(rep,bnm)]
                - veff[match(gen,vnm)])

# Similar to Connolly Fig 1.  Point pattern doesn't quite match
xyplot(yadj~cov, data=dat, type=c('p','r'),
       main="connolly.potato",
       xlab="Avg yield of nearest neighbors",
       ylab="Yield, adjusted for variety and block effects")
}


# }