wiebe.wheat.uniformity: Uniformity trial of wheat

Description Format Details Source References Examples

Description

Uniformity trial of wheat at Aberdeen, Idaho, 1927.

Format

A data frame with 1500 observations on the following 3 variables.

row

row

col

column (series)

yield

yield in grams per plot

Details

Yield trial conducted in 1927 near Aberdeen, Idaho. The crop was Federation wheat (C.I. no 4734). Plots were seeded on April 18 with a drill that sowed eight rows at a time. Individual rows were harvested in August and threshed with a small nursery thresher. Some authors recommend analyzing the square root of the yields.

Rows were 15 feet long, 1 foot apart.

Field width: 12 cols * 15 feet = 180 feet wide.

Field length: 125 rows * 12 in = 125 feet

Source

Wiebe, G.A. 1935. Variation and Correlation in Grain Yield among 1,500 Wheat Nursery Plots, Journal of Agricultural Research, 50, 331-357. http://naldc.nal.usda.gov/download/IND43968632/PDF

References

D.A. Preece, 1981, Distributions of final digits in data, The Statistician, 30, 31–60. http://doi.org/10.2307/2987702

Wilkinson et al. (1983). Nearest Neighbour (NN) Analysis of Field Experiments. J. R. Statist. Soc. B, 45, 151-211.

Examples

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data(wiebe.wheat.uniformity)
dat <- wiebe.wheat.uniformity

if(require(desplot)){
  desplot(yield~col+row, dat,
          aspect=125/180, flip=TRUE, # true aspect
          main="wiebe.wheat.uniformity: yield") # row 1 is at south
}


# Preece (1981) found the last digits have an interesting distribution
# with 0 and 5 much more common than other digits.
dig <- substring(dat$yield, nchar(dat$yield))
dig <- as.numeric(dig)
hist(dig, breaks=0:10-.5, xlab="Last digit",
     main="wiebe.wheat.uniformity - histogram of last digit")
table(dat$col, dig) # Table 3 of Preece

# Loess
# m3 <- loess(yield~row+col, dat)
# plot(dat$row, resid(m3), ylim=c(-300,300),
#      main="wiebe.wheat.uniformity")

# Wilkinson (1983, p. 152) noted that an 8-row planter was used which
# produced a recurring pattern of row effects on yield.  This can be seen
# in the high autocorrelations of row means at lag 8 and lag 16
rowm <- tapply(dat$yield, dat$row, mean)
acf(rowm, main="wiebe.wheat.uniformity row means")
# Plot the row mean against the planter row unit 1-8
require("lattice")
xyplot(rowm~rep(1:8, length=125),
       main="wiebe.wheat.uniformity",
       xlab="Planter row unit", ylab="Row mean yield")

agridat documentation built on May 2, 2019, 4:01 p.m.