Treatment x environment interaction in agronomy trials

Usage

data("vargas.txe.covs")
data("vargas.txe.yield")

Format

The 'vargas.txe.covs' data has 10 years of measurements on 28 environmental covariates:

year: year
MTD: mean maximum temperature in December
MTJ: mean maximum temperature in January
MTF: mean maximum temperature in February
MTM: mean maximum temperature in March
MTA: mean maximum temperature in April
mTD: mean minimum temperature in December
mTJ: mean minimum temperature in January
mTF: mean minimum temperature in February
mTM: mean minimum temperature in March
mTA: mean minimum temperature in April
mTUD: mean minimum temperature in December
mTUJ: mean minimum temperature in January
mTUF: mean minimum temperature in February
mTUM: mean minimum temperature in March
mTUA: mean minimum temperature in April
PRD: total monthly precipitation in December
PRJ: total monthly precipitation in Jan
PRF: total monthly precipitation in Feb
PRM: total monthly precipitation in Mar
SHD: sun hours per day in Dec
SHJ: sun hours per day in Jan
SHF: sun hours per day in Feb
EVD: total monthly evaporation in Dec
EVJ: total monthly evaporation in Jan
EVF: total monthly evaporation in Feb
EVM: total monthly evaporation in Mar
EVA: total monthly evaporation in Apr

The 'vargas.txe.yield' dataframe contains 240 observations on three variables

year: Year
trt: Treatment. See details section
yield: Grain yield, kg/ha

Details

The treatment names indicate:

T	deep knife
t	no deep knife
S	sesbania
s	soybean
M	chicken manure
m	no chicken manure
0	no nitrogen
n	100 kg/ha nitrogen
N	200 kg/ha nitrogen

Used with permission of Jose Crossa.

Source

Vargas, Mateo and Crossa, Jose and van Eeuwijk, Fred and Sayre, Kenneth D. and Reynolds, Matthew P. (2001). Interpreting Treatment x Environment Interaction in Agronomy Trials. Agron. J., 93, 949-960. Table A1, A3. https://doi.org/10.2134/agronj2001.934949x

Examples

if (FALSE) { # \dontrun{
  
  library(agridat)
  data(vargas.txe.covs)
  data(vargas.txe.yield)
  
  libs(reshape2)
  libs(lattice)
  redblue <- colorRampPalette(c("firebrick", "lightgray", "#375997"))
  Z <- vargas.txe.yield
  Z <- acast(Z, year ~ trt, value.var='yield')
  levelplot(Z, col.regions=redblue,
            main="vargas.txe.yield", xlab="year", ylab="treatment",
            scales=list(x=list(rot=90)))
  
  # Double-centered like AMMI
  Z <- sweep(Z, 1, rowMeans(Z))
  Z <- sweep(Z, 2, colMeans(Z))
  # Vargas figure 1
  biplot(prcomp(Z, scale.=FALSE), main="vargas.txe.yield")

  # Now, PLS relating the two matrices
  U <- vargas.txe.covs
  U <- scale(U) # Standardized covariates
  libs(pls)
  m1 <- plsr(Z~U)
  # Vargas Fig 2, flipped vertical/horizontal
  biplot(m1, which="x", var.axes=TRUE)

} # }