lonnquist.maize: Multi-environment trial of maize, half diallel

Description Usage Format Details Source References Examples

Description

Half diallel of maize

Usage

1
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.

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. 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

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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

## End(Not run)

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

## Not run: 
  # Mohring 2011 used 6 varieties to calculate GCA & SCA
  # Matches Table 3, column 2
  ## d2 <- subset(dat, p1 
  ##                     p2 
  ## 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

## End(Not run)

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

agridat documentation built on May 2, 2019, 4:01 p.m.