agridat (version 1.16)

lonnquist.maize: 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 planted in 3 reps at 2 locations in 2 years. The data here are means adjusted for block effects.

References

Mohring, Melchinger, Piepho. (2011). REML-Based Diallel Analysis. Crop Science, 51, 470-478. http://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. http://doi.org/10.2307/2528181

Examples

# NOT RUN {
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")))
require(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
if(require(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
}

# ----------------------------------------------------------------------------

# }
# NOT RUN {
  # 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)
  # asreml4
  require(asreml)
  m2 <- asreml(yield~ 1, data=d2, random = ~ p1 + and(p2))
  require(lucid)
  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
# }
# NOT RUN {
# ----------------------------------------------------------------------------

# }
# NOT RUN {
  # Mohring 2011 used 6 varieties to calculate GCA & SCA
  # Matches Table 3, column 2
  ## d2 <- subset(dat, p1 <!-- %in% c("M","H","G","B","K","K2") & -->
  ##                     p2 <!-- %in% c("M","H","G","B","K","K2")) -->
  ## d2 <- droplevels(d2)
  ## require(asreml4)
  ## m2 <- asreml(yield~ 1, data=d2, random = ~ p1 + and(p2))
  ## require(lucid)
  ## 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
# }
# NOT RUN {
# ----------------------------------------------------------------------------

# }

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