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

### 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. http://doi.org/10.1007/s10681-006-9248-7

Used with permission of Rodomiro Ortiz.

### Examples

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

    if(require(pls) & require(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)

[Package agridat version 1.16 Index]