agridat (version 1.23)

vargas.txe: Treatment x environment interaction in agronomy trials

Description

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.

Examples

if (FALSE) {
  
  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)

}