Uniformity trial of wheat
kalamkar.wheat.uniformity.Rd
Uniformity trial of wheat at Rothamsted, UK in 1931.
Usage
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
Examples
if (FALSE) { # \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")
# ----------
if(require("asreml", quietly=TRUE)){
libs(asreml,lucid)
# 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))
lucid::vc(m1)
## 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
}
} # }