agridat (version 1.23)

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. The data here are means adjusted for block effects. Original experiment was 3 reps at 2 locations in 2 years.

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

if (FALSE) {

  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


  # Griffings method
  # https://www.statforbiology.com/2021/stat_met_diallel_griffing/
  # libs(lmDiallel)
  # dat2 <- lonnquist.maize
  # dat2 <- subset(dat2,
  #                is.element(p1, c("M","H","G","B","K","K2")) &
  #                is.element(p2, c("M","H","G","B","K","K2")))
  # dat2 <- droplevels(dat2)
  # dmod1 <- lm(yield ~ GCA(p1, p2) + tSCA(p1, p2),
  #             data = dat2)
  # dmod2 <- lm.diallel(yield ~ p1 + p2, 
  #                     data = dat2, fct = "GRIFFING2")
  # anova.diallel(dmod1, MSE=7.1, dfr=60)
  ## Response: yield
  ##              Df Sum Sq Mean Sq F value    Pr(>F)    
  ## GCA(p1, p2)   5 234.23  46.846  6.5980 5.923e-05 ***
  ## tSCA(p1, p2) 15 238.94  15.929  2.2436   0.01411 *  
  ## Residuals    60          7.100                      

  
  # ----------

  if(require("asreml", quietly=TRUE)){
    # 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))
    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
  }
  
}