durban.competition {agridat} | R Documentation

## Sugar beet yields with competition effects

### Description

Sugar beet yields with competition effects

### Format

A data frame with 114 observations on the following 5 variables.

`gen`

  Genotype factor, 36 levels plus Border

`col`

  Column

`block`

  Row/Block

`wheel`

  Position relative to wheel tracks

`yield`

  Root yields, kg/plot

### Details

This sugar-beet trial was conducted in 1979.

Single-row plots, 12 m long, 0.5 m between rows. Each block is made up of all 36 genotypes laid out side by side. Guard/border plots are at each end. Root yields were collected.

Wheel tracks are located between columns 1 and 2, and between columns 5 and 6, for each set of six plots. Each genotype was randomly allocated once to each pair of plots (1,6), (2,5), (3,4) across the three reps. Wheel effect were not significant in _this_ trial.

Field width: 18m + 1m guard rows = 19m

Field length: 3 blocks * 12m + 2*0.5m spacing = 37m

### Source

Durban, M., Currie, I. and R. Kempton, 2001. Adjusting for fertility and competition in variety trials. J. of Agricultural Science, 136, 129–140.

Retrieved from http://www.ma.hw.ac.uk/~iain/research/JAgSciData/data/Trial1.dat

Used with permission of Iain Currie.

### Examples

    data(durban.competition)
    dat <- durban.competition

    # Check that genotypes were balanced across wheel tracks.
    with(dat, table(gen,wheel))

    if(require(desplot)){
      desplot(yield ~ col*block, dat,
              out1=block, text=gen, col=wheel, aspect=37/19, # true aspect
              main="durban.competition")
    }

    # Calculate residual after removing block/genotype effects
    m1 <- lm(yield ~ gen + block, data=dat)
    dat$res <- resid(m1)

    ## desplot(res ~ col*block, dat, out1=block, text=gen, col=wheel,
    ##         main="durban.competition - residuals")

    # Calculate mean of neighboring plots
    dat$comp <- NA
    dat$comp[3:36] <- ( dat$yield[2:35] + dat$yield[4:37] ) / 2
    dat$comp[41:74] <- ( dat$yield[40:73] + dat$yield[42:75] ) / 2
    dat$comp[79:112] <- ( dat$yield[78:111] + dat$yield[80:113] ) / 2

    # Demonstrate the competition effect
    # Competitor plots have low/high yield -> residuals are negative/positive
    require(lattice)
    xyplot(res~comp, dat, type=c('p','r'), main="durban.competition",
           xlab="Average yield of neighboring plots", ylab="Residual")

[Package agridat version 1.16 Index]