Uniformity trial of wheat — kalamkar.wheat.uniformity • agridat

Uniformity trial of wheat at Rothamsted, UK in 1931.

data("kalamkar.wheat.uniformity")

Format

A data frame with 1280 observations on the following 4 variables.

row: row
col: column
yield: yield, grams/half-meter
ears: ears per half-meter

Details

Kalamkar's paper published in 1932. Estimated crop year 1931.

Plot 18 of the Four Course Rotation Experiment, Great Hoos, at Rothamsted, UK was used. Sown with Yeoman II wheat.

Field width = 16 segments * 0.5 meters = 8 meters.

Field length: 80 rows * 6 inches apart = 40 feet.

The grain yield and number of ears for each half-meter length were recorded. This is quite a small field, only 1/40 acre in size.

Edge rows have higher yields. Row-end units have higher yields than interior units. These border effects are significant. Variation between rows is greater than variation within rows. Negative correlation between rows may indicate competition effects.

For ears, Kalamkar discarded 4 rows from each side and 3 half-meter lengths at each end.

Kalamkar suggested using four parallel half-meter rows as a sampling unit.

Note, the Rothamsted report for 1931, page 57, says: During the year three workers (F. R. Immer, S. H. Justensen and R. J. Kalamkar) have taken up the question of the most efficient use of land in experiments in which an edge row must be discarded...

Source

Kalamkar, R. J (1932). A Study in Sampling Technique with Wheat. The Journal of Agricultural Science, Vol.22(4), pp.783-796. https://doi.org/10.1017/S0021859600054599

References

None.

Examples

# \dontrun{

  library(agridat)
  data(kalamkar.wheat.uniformity)
  dat <- kalamkar.wheat.uniformity
  
  plot(yield ~ ears, dat, main="kalamkar.wheat.uniformity")
  
  # totals match Kalamkar
  # sum(dat$yield) # 24112.5
  # sum(dat$ears) # 25850
  
  libs(desplot)
  desplot(dat, ears ~ col*row,
          flip=TRUE, aspect=(80*0.5)/(16*1.64042), # true aspect
          main="kalamkar.wheat.uniformity - ears")
  desplot(dat, yield ~ col*row,
          flip=TRUE, aspect=(80*0.5)/(16*1.64042), # true aspect
          main="kalamkar.wheat.uniformity - yield")
  
  # ----------
  
  libs(asreml,lucid) # asreml4
    
  # Show the negative correlation between rows
  dat <- transform(dat,
                   rowf=factor(row), colf=factor(col))
  dat <- dat[order(dat$rowf, dat$colf),]
  m1 = asreml(yield ~ 1, data=dat, resid= ~ ar1(rowf):ar1(colf))
#> Model fitted using the gamma parameterization.
#> ASReml 4.1.0 Mon Jan 11 17:08:47 2021
#>           LogLik        Sigma2     DF     wall    cpu
#>  1     -3434.313       80.1883   1279 17:08:47    0.0
#>  2     -3416.532       78.3967   1279 17:08:47    0.0
#>  3     -3401.703       78.3835   1279 17:08:47    0.0
#>  4     -3396.808       79.9191   1279 17:08:47    0.0
#>  5     -3396.034       81.2062   1279 17:08:47    0.0
#>  6     -3396.011       81.4745   1279 17:08:47    0.0
#>  7     -3396.010       81.5251   1279 17:08:47    0.0
  vc(m1)
#>              effect component std.error z.ratio bound %ch
#>         rowf:colf!R  81.53      3.525      23       P 0  
#>  rowf:colf!rowf!cor  -0.09464   0.0277     -3.4     U 0.1
#>  rowf:colf!colf!cor   0.2976    0.02629    11       U 0.1
  ##             effect component std.error z.ratio bound pctch
  ##        rowf:colf!R  81.53      3.525      23       P 0  
  ## rowf:colf!rowf!cor  -0.09464   0.0277     -3.4     U 0.1
  ## rowf:colf!colf!cor   0.2976    0.02629    11       U 0.1

# }