Field experiment with unreplicated genotypes plus one repeated check.

data("burgueno.unreplicated")

Format

A data frame with 434 observations on the following 4 variables.

gen: genotype, 281 levels
col: column
row: row
yield: yield, tons/ha

Details

A field experiment with 280 new genotypes. A check genotype is planted in every 4th column.

The plot size is not given.

Electronic version of the data obtained from CropStat software.

Used with permission of Juan Burgueno.

Source

J Burgueno, A Cadena, J Crossa, M Banziger, A Gilmour, B Cullis (2000). User's guide for spatial analysis of field variety trials using ASREML. CIMMYT.

Examples

# \dontrun{

  library(agridat)
  data(burgueno.unreplicated)
  dat <- burgueno.unreplicated

  # Define a 'check' variable for colors
  dat$check <- ifelse(dat$gen=="G000", 2, 1)
  # Every fourth column is the 'check' genotype
  libs(desplot)
  desplot(dat, yield ~ col*row,
          col=check, num=gen, #text=gen, cex=.3, # aspect unknown
          main="burgueno.unreplicated")

  libs(asreml,lucid) # asreml4

  # AR1 x AR1 with random genotypes
  dat <- transform(dat, xf=factor(col), yf=factor(row))
  dat <- dat[order(dat$xf,dat$yf),]
  m2 <- asreml(yield ~ 1, data=dat, random = ~ gen,
               resid = ~ ar1(xf):ar1(yf))
#> Model fitted using the gamma parameterization.
#> ASReml 4.1.0 Mon Jan 11 17:08:01 2021
#>           LogLik        Sigma2     DF     wall    cpu
#>  1      -237.568       1.03480    433 17:08:01    0.0
#>  2      -229.545       0.89061    433 17:08:01    0.0
#>  3      -217.471       0.65204    433 17:08:01    0.0
#>  4      -212.727       0.53716    433 17:08:01    0.0
#>  5      -211.786       0.50071    433 17:08:01    0.0
#>  6      -211.729       0.49978    433 17:08:01    0.0
#>  7      -211.725       0.49941    433 17:08:01    0.0
#>  8      -211.724       0.49931    433 17:08:01    0.0
  vc(m2)
#>        effect component std.error z.ratio bound %ch
#>           gen    0.9122   0.127       7.2     P 0  
#>       xf:yf!R    0.4993   0.05601     8.9     P 0  
#>  xf:yf!xf!cor   -0.243    0.09154    -2.7     U 0.1
#>  xf:yf!yf!cor    0.1254   0.07057     1.8     U 0.3
  ##       effect component std.error z.ratio bound 
  ##          gen    0.9122   0.127       7.2     P 0  
  ##     xf:yf(R)    0.4993   0.05601     8.9     P 0  
  ## xf:yf!xf!cor   -0.2431   0.09156    -2.7     U 0  
  ## xf:yf!yf!cor    0.1255   0.07057     1.8     U 0.1
  
  # Note the strong saw-tooth pattern in the variogram.  Seems to
  # be column effects.
  plot(varioGram(m2), xlim=c(0,15), ylim=c(0,9), zlim=c(0,0.5),
       main="burgueno.unreplicated - AR1xAR1")
  # libs(lattice) # Show how odd columns are high
  # bwplot(resid(m2) ~ col, data=dat, horizontal=FALSE)
  
  # Define an even/odd column factor as fixed effect
  # dat$oddcol <- factor(dat$col 
  # The modulus operator throws a bug, so do it the hard way.
  dat$oddcol <- factor(dat$col - floor(dat$col / 2) *2 )
  
  m3 <- update(m2, yield ~ 1 + oddcol)
#> Model fitted using the gamma parameterization.
#> ASReml 4.1.0 Mon Jan 11 17:08:01 2021
#>           LogLik        Sigma2     DF     wall    cpu
#>  1      -202.465      0.474601    432 17:08:01    0.0
#>  2      -200.994      0.451132    432 17:08:01    0.0
#>  3      -199.872      0.429339    432 17:08:01    0.0 (1 restrained)
#>  4      -199.582      0.423518    432 17:08:01    0.0
#>  5      -199.519      0.420714    432 17:08:01    0.0
#>  6      -199.518      0.421386    432 17:08:01    0.0
#> Warning: Some components changed by more than 1% on the last iteration.
  m3$loglik # Matches Burgueno table 3, line 3
#> [1] -199.5175
  
  plot(varioGram(m3), xlim=c(0,15), ylim=c(0,9), zlim=c(0,0.5),
       main="burgueno.unreplicated - AR1xAR1 + Even/Odd")
  # Much better-looking variogram
  
# }