Potato scab infection with sulfur treatments

Format

A data frame with 32 observations on the following 5 variables.

inf: infection percent
trt: treatment factor
row: row
col: column

Details

The experiment was conducted to investigate the effect of sulfur on controlling scab disease in potatoes. There were seven treatments. Control, plus spring and fall application of 300, 600, 1200 pounds/acre of sulfur. The response variable was infection as a percent of the surface area covered with scab. A completely randomized design was used with 8 replications of the control and 4 replications of the other treatments.

Although the original analysis did not show significant differences in the sulfur treatments, including a polynomial trend in the model uncovered significant differences (Tamura, 1988).

Source

W.G. Cochran and G. Cox, 1957. Experimental Designs, 2nd ed. John Wiley, New York.

References

Tamura, R.N. and Nelson, L.A. and Naderman, G.C., (1988). An investigation of the validity and usefulness of trend analysis for field plot data. Agronomy Journal, 80, 712-718.

https://doi.org/10.2134/agronj1988.00021962008000050003x

Examples

# \dontrun{

library(agridat)
data(cochran.crd)
dat <- cochran.crd

# Field plan
libs(desplot)
desplot(dat, inf~col*row,
        text=trt, cex=1, # aspect unknown
        main="cochran.crd")

# CRD anova.  Table 6 of Tamura 1988
contrasts(dat$trt) <- cbind(c1=c(1,1,1,-6,1,1,1),   # Control vs Sulf
                            c2=c(-1,-1,-1,0,1,1,1)) # Fall vs Sp
m1 <- aov(inf ~ trt, data=dat)
anova(m1)
#> Analysis of Variance Table
#> 
#> Response: inf
#>           Df  Sum Sq Mean Sq F value  Pr(>F)  
#> trt        6  972.34 162.057  3.6081 0.01026 *
#> Residuals 25 1122.88  44.915                  
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(m1, split=list(trt=list("Control vs Sulf"=1, "Fall vs Spring"=2)))
#>                        Df Sum Sq Mean Sq F value  Pr(>F)   
#> trt                     6  972.3   162.1   3.608 0.01026 * 
#>   trt: Control vs Sulf  1  518.0   518.0  11.533 0.00229 **
#>   trt: Fall vs Spring   1  228.2   228.2   5.080 0.03322 * 
#> Residuals              25 1122.9    44.9                   
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

# Quadratic polynomial for columns...slightly different than Tamura 1988
m2 <- aov(inf ~ trt + poly(col,2), data=dat)
anova(m2)
#> Analysis of Variance Table
#> 
#> Response: inf
#>              Df Sum Sq Mean Sq F value    Pr(>F)    
#> trt           6 972.34 162.057  7.1253 0.0002205 ***
#> poly(col, 2)  2 599.76 299.881 13.1850 0.0001531 ***
#> Residuals    23 523.11  22.744                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(m2, split=list(trt=list("Control vs Sulf"=1, "Fall vs Spring"=2)))
#>                        Df Sum Sq Mean Sq F value   Pr(>F)    
#> trt                     6  972.3   162.1   7.125 0.000221 ***
#>   trt: Control vs Sulf  1  518.0   518.0  22.776 8.21e-05 ***
#>   trt: Fall vs Spring   1  228.2   228.2  10.032 0.004301 ** 
#> poly(col, 2)            2  599.8   299.9  13.185 0.000153 ***
#> Residuals              23  523.1    22.7                     
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

# }