shaw.oats {agridat} | R Documentation |

##
Multi-environment trial of oats in India

### Description

Multi-environment trial of oats in India, 13 genotypes, 3 year, 2 loc,
5 reps

### Usage

data("shaw.oats")

### Format

A data frame with 390 observations on the following 5 variables.

`env`

environment, 2 levels

`year`

year, 3 levels

`block`

block, 5 levels

`gen`

genotype variety, 13 levels

`yield`

yield of oats, pounds per plot

### Details

An oat trial in India of 11 hybrid oats compared to 2 established
high-yielding varieties, labeled L and M. The trail was conducted at 2
locations. The size and exact locations of the plots varied from year
to year.

At Pusa, the crop was grown without irrigation. At Karnal the crop was
given 2-3 irrigations. Five blocks were used, each plot 1000 square
feet. In 1932, variety L was high-yielding at Pusa, but low-yielding
at Karnal.

Shaw used this data to illustrate ANOVA for a multi-environment trial.

### Source

F.J.F. Shaw (1936).
A Handbook of Statistics For Use In Plant Breeding and Agricultural Problems.
The Imperial Council of Agricultural Research, India.
https://archive.org/details/HandbookStatistics1936/page/n12
P. 126

### References

None

### Examples

library(agridat)
data(shaw.oats)
dat <- shaw.oats
# sum(dat$yield) # 16309 matches Shaw p. 125
# sum( (dat$yield-mean(dat$yield)) ^2) # total SS matches Shaw p. 141
dat$year <- factor(dat$year)
libs(lattice)
dotplot(yield ~ gen|env, data=dat, groups=year,
main="shaw.oats",
par.settings=list(superpose.symbol=list(pch=c('2','3','4'))),
panel=function(x,y,...){
panel.dotplot(x,y,...)
panel.superpose(x,y,..., panel.groups=function(x,y,col.line,...) {
dd<-aggregate(y~x,data.frame(x,y),mean)
panel.xyplot(x=dd$x, y=dd$y, col=col.line, type="l")
})},
auto.key=TRUE)
# Shaw & Bose meticulously calculate the ANOVA table, p. 141
m1 <- aov(yield ~ year*env*block*gen - year:env:block:gen, dat)
anova(m1)

[Package

*agridat* version 1.18

Index]