Factorial experiment of rice, 3x5x3x3 — chakravertti.factorial • agridat

Factorial experiment of rice, 3x5x3x3.

Usage

data("chakravertti.factorial")

Format

A data frame with 405 observations on the following 7 variables.

block: block/field
yield: yield
date: planting date, 5 levels
gen: genotype/variety, 3 levels
treat: treatment combination, 135 levels
seeds: number of seeds per hole, 3 levels
spacing: spacing, inches, 3 levels

Details

There were 4 treatment factors:

3 Genotypes (varieties): Nehara, Bhasamanik, Bhasakalma

5 Planting dates: Jul 16, Aug 1, Aug 16, Sep 1, Sep 16

3 Spacings: 6 in, 9 in, 12 inches

3 Seedlings per hole: 1, 2, local method

There were 3x5x3x3=135 treatment combinations. The experiment was divided in 3 blocks (fields). Total 405 plots.

"The plots of the same sowing date within each block were grouped together, and the position occupied by the sowing date groups within Within the blocks were determined at random. This grouping together of plots of the same sewing date was adopted to facilitate cultural operations. For the same reason, the three varieties were also laid out in compact rows. The nine combinations of spacings and seedling numbers were then thrown at random within each combination of date of planting and variety as shown in the diagram."

Note: The diagram appears to show the treatment combinations, NOT the physical layout.

Basically, date is a whole-plot effect, genotype is a sub-plot effect, and the 9 treatments (spacings * seedlings) are completely randomized withing the sub-plot effect.

Source

Chakravertti, S. C. and S. S. Bose and P. C. Mahalanobis (1936). A complex experiment on rice at the Chinsurah farm, Bengal, 1933-34. The Indian Journal of Agricultural Science, 6, 34-51. https://archive.org/details/in.ernet.dli.2015.271737/page/n83/mode/2up

References

None

Examples

if (FALSE) { # \dontrun{

  libs(agridat)
  data(chakravertti.factorial)
  dat <- chakravertti.factorial
  
  # Simple means for each factor. Same as Chakravertti Table 3
  group_by(dat, gen) 
  group_by(dat, date) 
  group_by(dat, spacing) 
  group_by(dat, seeds) 

  libs(HH)
  interaction2wt(yield ~ gen + date + spacing + seeds, data=dat, main="chakravertti.factorial")

  # ANOVA matches Chakravertti table 2
  # This has a very interesting error structure.
  # block:date is error term for date
  # block:date:gen is error term for gen and date:gen
  # Residual is error term for all other tests (not needed inside Error())
  dat <- transform(dat,spacing=factor(spacing))
  m2 <- aov(yield ~ block + date + 
              gen + date:gen + 
              spacing + seeds +
              seeds:spacing + date:seeds + date:spacing + gen:seeds + gen:spacing +
              date:gen:seeds + date:gen:spacing + date:seeds:spacing + gen:seeds:spacing +
              date:gen:seeds:spacing + Error(block/(date + date:gen)),
            data=dat)
  summary(m2)
  
} # }