Long term rotation experiment at Rothamsted

Usage

data("payne.wheat")

Format

A data frame with 480 observations on the following 4 variables.

rotation: rotation treatment
nitro: nitrogen rate kg/ha
year: year
yield: metric tons per hectare

Details

The rotation treatments are:

AB = arable rotation with spring barley. AF = arable rotation with bare fallow. Ln3 = 3-year grass lay between crops. Ln8 = 8-year grass lay between crops. Lc3 = 3-year grass-clover lay between crops. Lc8 = 8-year grass-clover lay between crops.

The full data are available via CC-BY 4.0 license at: Margaret Glendining, Paul Poulton, Andrew Macdonald, Chloe MacLaren, Suzanne Clark (2022). Dataset: Woburn Ley-arable experiment: yields of wheat as first test crop, 1976-2018 Electronic Rothamsted Archive, Rothamsted Research. https://doi.org/10.23637/wrn3-wheat7618-01

The data used here are a subset as appearing in the paper by Payne.

Source

Payne, R. (2013) Design and analysis of long-term rotation experiments. Agronomy Journal, 107, 772-785. https://doi.org/10.2134/agronj2012.0411

References

None

Examples

if (FALSE) { # \dontrun{

  library(agridat)
  data(payne.wheat)
  dat <- payne.wheat

  # make factors
  dat <- transform(dat,
                   rotf = factor(rotation),
                   yrf = factor(year),
                   nitrof = factor(nitro))
    
  # visualize the response to nitrogen
  libs(lattice)
  # Why does Payne use nitrogen factor, when it is an obvious polynomial term?
  # Probably doesn't matter too much.
  xyplot(yield ~ nitro|yrf, dat,
         groups=rotf, type='b',
         auto.key=list(columns=6),
         main="payne.wheat")
  
  # What are the long-term trends?  Yields are decreasing
  xyplot(yield ~ year | rotf, data=dat, groups=nitrof,
         type='l', auto.key=list(columns=4))

  if(require("asreml", quietly=TRUE)){
    libs(asreml)
# Model 5: drop 3-way interaction and return to pol function (easier prediction)
    m5 <- asreml(yield ~ rotf * nitrof * pol(year,2) -
                   (rotf:nitrof:pol(year,2)),
                 data=dat,
                 random = ~yrf,
                 residual = ~ dsum( ~ units|yrf))
    summary(m5)$varcomp # Table 7 of Payne
    # lucid::vc(m5)

    # Table 8 of Payne
    wald(m5, denDF="default") 
    
    # Predictions of three-way interactions from final model
    p5 <- predict(m5, classify="rotf:nitrof:year")
    p5 <- p5$pvals # Matches Payne table 8
    head(p5)
    
    # Plot the predictions.  Matches Payne figure 1
    xyplot(predicted.value ~ year | rotf, data=p5,
           groups=nitrof,
           ylab="yield t/ha", type='l', auto.key=list(columns=5))
  }
  
} # }