Counts of eelworms before and after fumigant treatments

Format

A data frame with 48 observations on the following 7 variables.

block: block factor, 4 levels
row: row
col: column
fumigant: fumigant factor
dose: dose, Numeric 0,1,2. Maybe should be a factor?
initial: count of eelworms pre-treatment
final: count of eelworms post-treatment
grain: grain yield in pounds
straw: straw yield in pounds
weeds: ratio of weeds to total oats

Details

A soil fumigation experiment on Spring Oats, conducted in 1935.

Each plot is 30 links x 41.7 links, but it is not clear which side of the plot has a specific length.

Treatment codes: Con = Control, Chl = Chlorodinitrobenzen, Cym = Cymag, Car = Carbon Disulphide jelly, See = Seekay.

Experiment was conducted in 1935 at Rothamsted Experiment Station. In early March 400 grams of soil (4 x 100g) were sampled and the number of eelworm cysts were counted. Fumigants were added to the soil, oats were sown and later harvested. In October, the plots were again sampled and the final count of cysts recorded.

The Rothamsted report concludes that "Car" and "Cym" produced higher yields, due partly to the nitrogen in the fumigant, while "Chl" decreased the yield. All fumigants reduced weeds. The crop was 'unusually weedy'. "Car" and "See" decreased the number of eelworm cysts in the soil.

The original data can be found in the Rothamsted Report. The report notes the position of the blocks in the field were slightly different than shown.

The experiment plan shown in Bailey (2008, p. 73), shows columns 9-11 shifted slightly upward. It is not clear why.

Thanks to U.Genschel for identifying a typo.

Source

Cochran and Cox, 1950. Experimental Designs. Table 3.1.

References

R. A. Bailey (2008). Design of Comparative Experiments. Cambridge.

Other Experiments at Rothamsted (1936). Report For 1935, Rothamsted Research. pp 174 - 193. https://doi.org/10.23637/ERADOC-1-67

Examples

if (FALSE) { # \dontrun{

  library(agridat)
  data(cochran.eelworms)
  dat <- cochran.eelworms

  libs(lattice)
  splom(dat[ , 5:10],
        group=dat$fumigant, auto.key=TRUE,
        main="cochran.eelworms")
  
  libs(desplot)
  desplot(dat, fumigant~col*row, text=dose, flip=TRUE, cex=2)
  
  # Very strong spatial trends
  desplot(dat, initial ~ col*row,
          flip=TRUE, # aspect unknown
          main="cochran.eelworms")


  # final counts are strongly related to initial counts
  libs(lattice)
  xyplot(final~initial|factor(dose), data=dat, group=fumigant,
         main="cochran.eelworms - by dose (panel) & fumigant",
         xlab="Initial worm count",
         ylab="Final worm count", auto.key=list(columns=5))
  
  # One approach...log transform, use 'initial' as covariate, create 9 treatments
  dat <- transform(dat, trt=factor(paste0(fumigant, dose)))
  m1 <- aov(log(final) ~ block + trt + log(initial), data=dat)
  anova(m1)

} # }