Uniformity trial of cotton — williams.cotton.uniformity • agridat

Uniformity trial of cotton at Narrabri, New South Wales, 1984.

Format

A data frame with 288 observations on the following 3 variables.

row: row
col: column
yield: lint yield, kg/ha divided by 10

Details

Cotton uniformity trial grown at Narrabri, New South Wales, 1984-1985. Plots were 12m long, 1m apart, 12 rows by 24 columns, with an irrigation furrow between columns.

Field width: 24 plots * 1 m = 24 m

Field length: 12 plots * 12 m = 144 m

Source

Williams, ER and Luckett, DJ. 1988. The use of uniformity data in the design and analysis of cotton and barley variety trials. Australian Journal of Agricultural Research, 39, 339-350. https://doi.org/10.1071/AR9880339

Examples

# \dontrun{

library(agridat)
data(williams.cotton.uniformity)
dat <- williams.cotton.uniformity

libs(desplot)
desplot(dat, yield ~ col*row,
        aspect=144/24, # true aspect
        main="williams.cotton.uniformity")


# Smoothed contour/persp plot like Williams 1988 Fig 1a, 2a
dat$fit <- fitted(loess(yield~col*row, dat, span=.5))
libs("lattice")
contourplot(fit~col*row, data=dat,
            aspect=144/24,
            region=TRUE, cuts=6, col.regions=RedGrayBlue,
            main="williams.cotton.uniformity")
# wireframe(fit~col*row, data=dat, zlim=c(100, 250),
#           main="williams.cotton.uniformity")

# Williams table 1
anova(aov(yield ~ factor(row) + factor(col), dat))
#> Analysis of Variance Table
#> 
#> Response: yield
#>              Df Sum Sq Mean Sq F value    Pr(>F)    
#> factor(row)  11  30707 2791.58  14.450 < 2.2e-16 ***
#> factor(col)  23  44762 1946.16  10.074 < 2.2e-16 ***
#> Residuals   253  48875  193.18                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# }