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

Uniformity trial of wheat at Rothamsted Experiment Station, England, 1910.

Format

A data frame with 500 observations on the following 4 variables.

row: row
col: column
grain: grain yield, pounds
straw: straw yield, pounds

Details

The wheat crop was grown in the summer of 1910 at Rothamsted Experiment Station (Harpenden, Hertfordshire, England). In the Great Knott, a seemingly uniform area of 1 acre was harvested in separate plots, each 1/500th acre in size. The grain and straw from each plot was weighed separately.

McCullagh gives more information about the plot size.

Field width: 25 plots * 8 ft = 200 ft

Field length: 20 plots * 10.82 ft = 216 ft

D. G. Rossiter (2014) uses this data for an extensive data analysis tutorial.

Source

Mercer, WB and Hall, AD, (1911). The experimental error of field trials The Journal of Agricultural Science, 4, 107-132. Table 5. https://doi.org/10.1017/S002185960000160X

References

McCullagh, P. and Clifford, D., (2006). Evidence for conformal invariance of crop yields, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Science, 462, 2119–2143. https://doi.org/10.1098/rspa.2006.1667

Theodor Roemer (1920). Der Feldversuch. Page 65, table 6.

D. G. Rossiter (2014). Tutorial: Using the R Environment for Statistical Computing An example with the Mercer & Hall wheat yield dataset.

G. A. Baker (1941). Fundamental Distribution of Errors for Agricultural Field Trials. National Mathematics Magazine, 16, 7-19. https://doi.org/10.2307/3028105

The 'spdep' package includes the grain yields (only) and spatial positions of plot centres in its example dataset 'wheat'.

Note, checked that all '4.03' values in this data match the original document.

Examples

if (FALSE) { # \dontrun{

  library(agridat)
  data(mercer.wheat.uniformity)
  dat <- mercer.wheat.uniformity

  
  libs(desplot)
  desplot(dat, grain ~ col*row,
          aspect=216/200, # true aspect
          main="mercer.wheat.uniformity - grain yield")

  
  libs(lattice)
  xyplot(straw ~ grain, data=dat, type=c('p','r'),
         main="mercer.wheat.uniformity - regression")

  
  libs(hexbin)
  hexbinplot(straw ~ grain, data=dat)


  libs(sp, gstat)
  plot.wid <- 2.5
  plot.len <- 3.2
  nr <- length(unique(dat$row))
  nc <- length(unique(dat$col))
  
  xy <- expand.grid(x = seq(plot.wid/2, by=plot.wid, length=nc),
                    y = seq(plot.len/2, by=plot.len, length=nr))
  dat.sp <- dat
  coordinates(dat.sp) <- xy
  
  # heatmap
  spplot(dat.sp, zcol = "grain", cuts=8,
         cex = 1.6,
         col.regions =  bpy.colors(8),
         main = "Grain yield", key.space = "right")

  # variogram
  # Need gstat::variogram to get the right method
  vg <- gstat::variogram(grain ~ 1, dat.sp, cutoff = plot.wid * 10, width = plot.wid)
  plot(vg, plot.numbers = TRUE,
       main="mercer.wheat.uniformity - variogram")

} # }