agridat (version 1.23)

mercer.wheat.uniformity: Uniformity trial of wheat

Description

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.

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

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) {

  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")

}