lonnquist.maize {agridat} | R Documentation |

##
Multi-environment trial of maize, half diallel

### Description

Half diallel of maize

### Usage

data("lonnquist.maize")

### Format

A data frame with 78 observations on the following 3 variables.

`p1`

parent 1 factor

`p2`

parent 2 factor

`yield`

yield

### Details

Twelve hybrids were selfed/crossed in a half-diallel design.
The data here are means adjusted for block effects.
Original experiment was 3 reps at 2 locations in 2 years.

### Source

J. H. Lonnquist, C. O. Gardner. (1961)
Heterosis in Intervarietal Crosses in Maize and Its Implication in
Breeding Procedures.
Crop Science, 1, 179-183. Table 1.

### References

Mohring, Melchinger, Piepho. (2011).
REML-Based Diallel Analysis.
*Crop Science*, 51, 470-478.
https://doi.org/10.2135/cropsci2010.05.0272

C. O. Gardner and S. A. Eberhart. 1966.
Analysis and Interpretation of the Variety Cross Diallel and Related
Populations. *Biometrics*, 22, 439-452.
https://doi.org/10.2307/2528181

### Examples

## Not run:
library(agridat)
data(lonnquist.maize)
dat <- lonnquist.maize
dat <- transform(dat,
p1=factor(p1,
levels=c("C","L","M","H","G","P","B","RM","N","K","R2","K2")),
p2=factor(p2,
levels=c("C","L","M","H","G","P","B","RM","N","K","R2","K2")))
libs(lattice)
redblue <- colorRampPalette(c("firebrick", "lightgray", "#375997"))
levelplot(yield ~ p1*p2, dat, col.regions=redblue,
main="lonnquist.maize - yield of diallel cross")
# Calculate the F1 means in Lonnquist, table 1
# libs(reshape2)
# mat <- acast(dat, p1~p2)
# mat[upper.tri(mat)] <- t(mat)[upper.tri(mat)] # make symmetric
# diag(mat) <- NA
# round(rowMeans(mat, na.rm=TRUE),1)
## C L M H G P B RM N K R2 K2
## 94.8 89.2 95.0 96.4 95.3 95.2 97.3 93.7 95.0 94.0 98.9 102.4
# ----------
# asreml4
# Mohring 2011 used 6 varieties to calculate GCA & SCA
# Matches Table 3, column 2
d2 <- subset(dat, is.element(p1, c("M","H","G","B","K","K2")) &
is.element(p2, c("M","H","G","B","K","K2")))
d2 <- droplevels(d2)
libs(asreml,lucid)
m2 <- asreml(yield~ 1, data=d2, random = ~ p1 + and(p2))
# vc(m2)
## effect component std.error z.ratio con
## p1!p1.var 3.865 3.774 1 Positive
## R!variance 15.93 5.817 2.7 Positive
# Calculate GCA effects
m3 <- asreml(yield~ p1 + and(p2), data=d2)
coef(m3)$fixed-1.462
# Matches Gardner 1966, Table 5, Griffing method
## End(Not run)

[Package

*agridat* version 1.18

Index]