Sugarcane fractional factorial 1/3 3^5

Sugarcane fractional factorial 1/3 3^5.

data("chinloy.fractionalfactorial")

Format

A data frame with 81 observations on the following 10 variables.

yield

yield

block

block

row

row position

col

column position

trt

treatment code

N

nitrogen treatment: 0, 1, 2

P

phosphorous treatment: 0, 1, 2

K

potassium treatment: 0, 1, 2

B

bagasse treatment: 0, 1, 2

F

filter press mud treatment: 0, 1, 2

Details

An experiment grown in 1949 at the Worthy Park Estate in Jamaica.

Nitrogen was applied as sulphate of ammonia at 0, 3, 6 hundred-weight per acre.

Phosphorous was applied as superphosphate at 0, 4, 8 hundred-weight per acre.

Potassium was applied as muriate of potash at 0, 1, 2 hundred-weight per acre.

Bagasse applied pre-plant at 0, 20, 40 tons per acre.

Filter press mud applied pre-plant at 0, 10, 20 tons per acre.

Each plot was 18 yards long by 6 yards (3 rows) wide. Plots were arranged in nine columns of nine, a 2-yard space separating plots along the rows and two guard rows separating plots across the rows.

Field width: 6 yards * 9 plots + 4 yards * 8 gaps = 86 yards

Field length: 18 yards * 9 plots + 2 yards * 8 gaps = 178 yards

Source

T. Chinloy, R. F. Innes and D. J. Finney. (1953). An example of fractional replication in an experiment on sugar cane manuring. Journ Agricultural Science, 43, 1-11. https://doi.org/10.1017/S0021859600044567

References

None

Examples

# \dontrun{

library(agridat)
data(chinloy.fractionalfactorial)
dat <- chinloy.fractionalfactorial

# Treatments are coded with levels 0,1,2. Make sure they are factors
dat <- transform(dat,
                 N=factor(N), P=factor(P), K=factor(K), B=factor(B), F=factor(F))

# Experiment layout
libs(desplot)
desplot(dat, yield ~ col*row,
        out1=block, text=trt, shorten="no", cex=0.6,
        aspect=178/86,
        main="chinloy.fractionalfactorial")

# Main effect and some two-way interactions. These match Chinloy table 6.
# Not sure how to code terms like P^2K=B^2F
m1 <- aov(yield ~ block + N + P + K + B + F + N:P + N:K + N:B + N:F, dat)
anova(m1)
#> Analysis of Variance Table
#> 
#> Response: yield
#>           Df  Sum Sq Mean Sq F value    Pr(>F)    
#> block      8 10.6223  1.3278  2.9797  0.008942 ** 
#> N          2  4.5399  2.2699  5.0940  0.010037 *  
#> P          2 11.9864  5.9932 13.4496 2.517e-05 ***
#> K          2  2.5091  1.2546  2.8154  0.070230 .  
#> B          2  5.2851  2.6426  5.9303  0.005112 ** 
#> F          2 13.9405  6.9702 15.6422 6.566e-06 ***
#> N:P        4  5.0740  1.2685  2.8467  0.034366 *  
#> N:K        4  1.2432  0.3108  0.6975  0.597636    
#> N:B        4  1.9243  0.4811  1.0796  0.377618    
#> N:F        4  2.1110  0.5277  1.1843  0.330265    
#> Residuals 46 20.4979  0.4456                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

# }