christidis.competition {agridat} | R Documentation |

##
Competition between varieties in cotton

### Description

Competition between varieties in cotton, measurements taken for each row.

### Usage

data("christidis.competition")

### Format

A data frame with 270 observations on the following 8 variables.

`plot`

plot

`plotrow`

row within plot

`block`

block

`row`

row, only 1 row

`col`

column

`gen`

genotype

`yield`

yield, kg

`height`

height, cm

### Details

Nine genotypes/varieties of cotton were used in a variety test.
The plots were 100 meters long and 2.40 meters wide, each plot having
3 rows 0.80 meters apart.

The layout was an RCB of 5 blocks, each block having 2 replicates of
every variety (with the original intention of trying 2 seed
treatments). Each row was harvested/weighed separately.
After the leaves of the plants had dried up and fallen, the mean
height of each row was measured.

Christidis found significant competition between varieties, but not
due to height differences. Crude analysis.

TODO: Find a better analysis of this data which incorporates field
trends AND competition effects, maybe including a random effect for
border rows of all genotype pairs (as neighbors)?

### Source

Christidis, Basil G (1935).
Intervarietal competition in yield trials with cotton.
*The Journal of Agricultural Science*, 25, 231-237. Table 1.
https://doi.org/10.1017/S0021859600009710

### References

None

### Examples

## Not run:
library(agridat)
data(christidis.competition)
dat <- christidis.competition
# Match Christidis Table 2 means
# aggregate(yield ~ gen, aggregate(yield ~ gen+plot, dat, sum), mean)
# Each RCB block has 2 replicates of each genotype
# with(dat, table(block,gen))
libs(lattice)
# Tall plants yield more
# xyplot(yield ~ height|gen, data=dat)
# Huge yield variation across field. Also heterogeneous variance.
xyplot(yield ~ col, dat, group=gen, auto.key=list(columns=5),
main="christidis.competition")
libs(mgcv)
if(is.element("package:gam", search())) detach("package:gam")
# Simple non-competition model to remove main effects
m1 <- gam(yield ~ gen + s(col), data=dat)
p1 <- as.data.frame(predict(m1, type="terms"))
names(p1) <- c('geneff','coleff')
dat2 <- cbind(dat, p1)
dat2 <- transform(dat2, res=yield-geneff-coleff)
libs(lattice)
xyplot(res ~ col, data=dat2, group=gen,
main="christidis.competition - residuals")
## End(Not run)

[Package

*agridat* version 1.18

Index]