Multi-environment trial of tomato in Latin America, weight/yield and environmental covariates

data("ortiz.tomato.covs")
  data("ortiz.tomato.yield")

Format

The ortiz.tomato.covs data frame has 18 observations on the following 18 variables.

env: environment
Day: degree days (base 10)
Dha: days to harvest
Driv: drivings (0/1)
ExK: extra potassium (kg / ha)
ExN: extra nitrogen (kg / ha)
ExP: extra phosphorous (kg / ha)
Irr: irrigation (0/1)
K: potassium (me/100 g)
Lat: latitude
Long: longitude
MeT: mean temperature (C)
MnT: min temperature (C)
MxT: max temperature (C)
OM: organic matter (percent)
P: phosphorous (ppm)
pH: soil pH
Prec: precipitation (mm)
Tri: trimming (0/1)

The ortiz.tomato.yield data frame has 270 observations on the following 4 variables.

env: environment
gen: genotype
yield: marketable fruit yield t/ha
weight: fruit weight, g

Details

The environment locations are:

E04	Estanzuela, Guatemala
E05	Baja Verapaz, Guatemala
E06	Cogutepeque, El Salvador
E07	San Andres, El Salvador
E11	Comayagua, Honduras
E14	Valle de Sabaco, Nicaragua
E15	San Antonio de Belen, Costa Rica
E20	San Cristobal, Dominican Republic
E21	Constanza, Dominican Republic
E27	Palmira, Colombia
E40	La Molina, Peru
E41	Santiago, Chile
E42	Chillan, Chile
E43	Curacavi, Chile
E44	Colina, Chile
E50	Belem, Brazil
E51	Caacupe, Paraguay
E53	Centeno, Trinidad Tobago

Used with permission of Rodomiro Ortiz.

Source

Rodomiro Ortiz and Jose Crossa and Mateo Vargas and Juan Izquierdo, 2007. Studying the Effect of Environmental Variables On the Genotype x Environment Interaction of Tomato. Euphytica, 153, 119--134. https://doi.org/10.1007/s10681-006-9248-7

Examples

# \dontrun{

library(agridat)
data(ortiz.tomato.covs)
data(ortiz.tomato.yield)

libs(pls, reshape2)
# Double-centered yield matrix
Y <- acast(ortiz.tomato.yield, env ~ gen, value.var='yield')
Y <- sweep(Y, 1, rowMeans(Y, na.rm=TRUE))
Y <- sweep(Y, 2, colMeans(Y, na.rm=TRUE))

# Standardized covariates
X <- ortiz.tomato.covs
rownames(X) <- X$env
X <- X[,c("MxT", "MnT", "MeT", "Prec", "Day", "pH", "OM", "P", "K",
          "ExN", "ExP", "ExK", "Trim", "Driv", "Irr", "Dha")]
X <- scale(X)

# Now, PLS relating the two matrices.
# Note: plsr deletes observations with missing values

m1 <- plsr(Y~X)
# Inner-product relationships similar to Ortiz figure 1.
biplot(m1, which="x", var.axes=TRUE, main="ortiz.tomato - env*cov biplot")
#biplot(m1, which="y", var.axes=TRUE)
# }