Straw length and ear emergence for wheat genotypes.

Straw length and ear emergence for wheat genotypes. Data are unbalanced with respect to experiment year and genotype.

Usage

data("giles.wheat")

Format

A data frame with 247 observations on the following 4 variables.

gen: genotype. Note, this is numeric!
env: environment
straw: straw length
emergence: ear emergence, Julian date

Details

Highly unbalanced data of straw length and ear emergence date for wheat genotypes.

The 'genotype' column is called 'Accession number' in original data. The genotypes were chosen to represent the range of variation in the trait.

The Julian date was found to be preferable to other methods (such as days from sowing).

Piepho (2003) fit a bilinear model to the straw emergence data. This is similar to Finlay-Wilkinson regression.

Source

R. Giles (1990). Utilization of unreplicated observations of agronomic characters in a wheat germplasm collection. In: Wheat Genetic Resources. Meeting Diverse Needs. Wiley, Chichester, U.K., pp.113-130.

References

Piepho, HP (2003). Model-based mean adjustment in quantitative germplasm evaluation data. Genetic Resources and Crop Evolution, 50, 281-290. https://doi.org/10.1023/A:1023503900759

Examples

if (FALSE) { # \dontrun{

library(agridat)
data(giles.wheat)
dat <- giles.wheat
dat <- transform(dat, gen=factor(gen))
dat_straw <- droplevels( subset(dat, !is.na(straw)) )
dat_emerg <- droplevels( subset(dat, !is.na(emergence)) )

# Traits are not related
# with(dat, plot(straw~emergence))

# Show unbalancedness of data
libs(lattice, reshape2)
redblue <- colorRampPalette(c("firebrick", "lightgray", "#375997"))
levelplot(acast(dat_straw, env ~ gen, value.var='straw'),
          col.regions=redblue,
          scales=list(x=list(rot=90)),
          xlab="year", ylab="genotype",
          main="giles.wheat - straw length")



# ----- Analysis of straw length -----

libs(emmeans)

# Mean across years. Matches Piepho Table 7 'Simple'
m1 = lm(straw ~ gen, data=dat_straw)
emmeans(m1, 'gen')

# Simple two-way model.  NOT the bi-additive model of Piepho.
m2 = lm(straw ~ gen + env, data=dat_straw)
emmeans(m2, 'gen')

# Bi-additive model. Matches Piepho Table 6, rows (c)
libs(gnm)
m3 <- gnm(straw ~ env + Mult(gen,env), data=dat_straw)
cbind(adjusted=round(fitted(m3),0), dat_straw) 

# ----- Analysis of Ear emergence -----

# Simple two-way model. 
m4 = lm(emergence ~ 1 + gen + env, data=dat_emerg)
emmeans(m4, c('gen','env')) # Matches Piepho Table 9. rpws (c)
emmeans(m4, 'gen') # Match Piepho table 10, Least Squares column

} # }