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 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

### Examples

    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)

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

[Package agridat version 1.16 Index]