Split-split-plot experiment of rice

Grain yield of three varieties of rice grown in a split-split plot arrangement with 3 reps, nitrogen level as the main plot, management practice as the sub-plot, and rice variety as the sub-sub plot.

Format

A data frame with 135 observations on the following 7 variables.

rep

block, 3 levels

nitro

nitrogen fertilizer, in kilograms/hectare

management

plot management

gen

genotype/variety of rice

yield

yield

col

column position in the field

row

row position in the field

Used with permission of Kwanchai Gomez.

Source

Gomez, K.A. and Gomez, A.A.. 1984, Statistical Procedures for Agricultural Research. Wiley-Interscience. Page 143.

References

H. P. Piepho, R. N. Edmondson. (2018). A tutorial on the statistical analysis of factorial experiments with qualitative and quantitative treatment factor levels. Jour Agronomy and Crop Science, 8, 1-27. https://doi.org/10.1111/jac.12267

Examples

# \dontrun{

library(agridat)

data(gomez.splitsplit)
dat <- gomez.splitsplit
dat$nf <- factor(dat$nitro)

libs(desplot)
desplot(dat, nf ~ col*row,
        # aspect unknown
        out1=rep, col=management, num=gen, cex=1,
        main="gomez.splitsplit")
desplot(dat, yield ~ col*row,
        # aspect unknown
        out1=rep, main="gomez.splitsplit")


libs(HH)
position(dat$nf) <- c(0,50,80,110,140)
interaction2wt(yield~rep+nf+management+gen, data=dat,
               main="gomez.splitsplit",
               x.between=0, y.between=0,
               relation=list(x="free", y="same"),
               rot=c(90,0), xlab="",
               par.strip.text.input=list(cex=.7))


# AOV.  Gomez page 144-153
m0 <- aov(yield~ nf * management * gen + Error(rep/nf/management),
         data=dat)
summary(m0) # Similar to Gomez, p. 153.
#> 
#> Error: rep
#>           Df Sum Sq Mean Sq F value Pr(>F)
#> Residuals  2  0.732   0.366               
#> 
#> Error: rep:nf
#>           Df Sum Sq Mean Sq F value   Pr(>F)    
#> nf         4  61.64  15.410    27.7 9.73e-05 ***
#> Residuals  8   4.45   0.556                     
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Error: rep:nf:management
#>               Df Sum Sq Mean Sq F value  Pr(>F)    
#> management     2  42.94  21.468  81.996 2.3e-10 ***
#> nf:management  8   1.10   0.138   0.527   0.823    
#> Residuals     20   5.24   0.262                    
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Error: Within
#>                   Df Sum Sq Mean Sq F value  Pr(>F)    
#> gen                2 206.01  103.01 207.867 < 2e-16 ***
#> nf:gen             8  14.14    1.77   3.568 0.00192 ** 
#> management:gen     4   3.85    0.96   1.943 0.11490    
#> nf:management:gen 16   3.70    0.23   0.467 0.95376    
#> Residuals         60  29.73    0.50                    
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

# }