Phytophtera disease incidence in a pepper field

Format

A data frame with 800 observations on the following 6 variables.

field: field factor, 2 levels
row: x ordinate
quadrat: y ordinate
disease: presence (Y) or absence (N) of disease
water: soil moisture percent
leaf: leaf assay count

Details

Each field is 20 rows by 20 quadrates, with 2 to 3 bell pepper plants per plot. If any plant was wilted, dead, or had lesions, the Phytophthora disease was considered to be present in the plot. The soil pathogen load was assayed as the number of leaf disks colonized out of five. In field 2, the pattern of disease presence appears to follow soil water content. In field 1, no obvious trends were present.

Gumpertz et al. model the presence of disease using soil moisture and leaf assay as covariates, and using disease presence of neighboring plots as covariates in an autologistic model.

Used with permission of Marcia Gumpertz. Research funded by USDA.

Source

Marcia L. Gumpertz; Jonathan M. Graham; Jean B. Ristaino (1997). Autologistic Model of Spatial Pattern of Phytophthora Epidemic in Bell Pepper: Effects of Soil Variables on Disease Presence. Journal of Agricultural, Biological, and Environmental Statistics, Vol. 2, No. 2., pp. 131-156.

Examples

if (FALSE) { # \dontrun{

library(agridat)
data(gumpertz.pepper)
dat <- gumpertz.pepper

# Gumpertz deletes two outliers
dat[ dat$field =="F1" & dat$row==20 & dat$quadrat==10, 'water'] <- NA
dat[ dat$field =="F2" & dat$row==5 & dat$quadrat==4, 'water'] <- NA

# Horizontal flip
dat <- transform(dat, row=21-row)

# Disease presence.  Gumpertz fig 1a, 2a.
libs(desplot)
grays <- colorRampPalette(c("#d9d9d9","#252525"))
desplot(dat, disease ~ row*quadrat|field,
        col.regions=c('white','black'), aspect=1, # uncertain aspect
        main="gumpertz.pepper disease presence", )

# Soil water.  Gumpertz fig 1b, 2b
desplot(dat, water ~ row*quadrat|field,
        col.regions=grays(5), aspect=1, # uncertain aspect
        at=c(5,7.5,10,12.5,15,18),
        main="gumpertz.pepper soil moisture")

# Leaf assay.  Gumpertz fig 1c, 2c
desplot(dat, leaf ~ row*quadrat|field,
        col.regions=grays(6),
        at=c(0,1,2,3,4,5,6)-.5, aspect=1, # uncertain aspect
        main="gumpertz.pepper leaf assay", )


# Use the inner 16x16 grid of plots in field 2
dat2 <- droplevels(subset(dat, field=="F2" & !is.na(water) &
                          row > 2 & row < 19 & quadrat > 2 & quadrat < 19))

m21 <- glm(disease ~ water + leaf, data=dat2, family=binomial)
coef(m21) # These match Gumpertz et al table 4, model 1
## (Intercept)       water        leaf
##  -9.1019623   0.7059993   0.4603931
dat2$res21 <- resid(m21)
if(0){
  libs(desplot)
  desplot(dat2, res21 ~ row*quadrat,
          main="gumpertz.pepper field 2, model 1 residuals")
  # Still shows obvious trends.  Gumpertz et al add spatial covariates for
  # neighboring plots, but with only minor improvement in misclassification
}

} # }