ortiz.tomato {agridat}R Documentation

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

Description

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

Usage

  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

## Not run: 

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)

## End(Not run)

[Package agridat version 1.18 Index]