Sugar beet trial with competition effects — kempton.competition • agridat

Yield of sugar beets for 36 varieties in a 3-rep RCB experiment. Competition effects are present.

Format

A data frame with 108 observations on the following 5 variables.

gen: genotype, 36 levels
rep: rep, 3 levels
row: row
col: column
yield: yield, kg/plot

Details

Entries are grown in 12m rows, 0.5m apart. Guard rows were grown alongside replicate boundaries, but yields of these plots are not included.

Source

R Kempton, 1982. Adjustment for competition between varieties in plant breeding trials, Journal of Agricultural Science, 98, 599-611. https://doi.org/10.1017/S0021859600054381

Examples

# \dontrun{
  
library(agridat)

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

# Raw means in Kempton table 2
round(tapply(dat$yield, dat$gen, mean),2)
#>   G01   G02   G03   G04   G05   G06   G07   G08   G09   G10   G11   G12   G13 
#>  6.57 14.70  3.73 18.70  6.17  8.80 10.20  4.57  8.67 16.63  7.77  6.07 14.07 
#>   G14   G15   G16   G17   G18   G19   G20   G21   G22   G23   G24   G25   G26 
#> 12.17 15.67 11.93 19.60 11.63 13.50  6.43  8.47 15.97 18.07 15.33  6.03  6.13 
#>   G27   G28   G29   G30   G31   G32   G33   G34   G35   G36 
#> 11.87 12.20 22.03 15.07 13.60 14.70 13.67 16.27  5.40  8.43 

# Fixed genotype effects, random rep effects,
# Autocorrelation of neighboring plots within the same rep, phi = -0.22
libs(nlme)
m1 <- lme(yield ~ -1+gen, random=~1|rep, data=dat,
          corr=corAR1(form=~col|rep))
# Lag 1 autocorrelation is negative--evidence of competition
plot(ACF(m1), alpha=.05, grid=TRUE, main="kempton.competition",
     ylab="Autocorrelation between neighborning plots")

# Genotype effects
round(fixef(m1),2)
#> genG01 genG02 genG03 genG04 genG05 genG06 genG07 genG08 genG09 genG10 genG11 
#>   7.29  14.69   4.44  17.67   5.88   8.69  10.11   4.20   8.66  17.23   8.30 
#> genG12 genG13 genG14 genG15 genG16 genG17 genG18 genG19 genG20 genG21 genG22 
#>   6.49  13.94  11.97  15.60  12.07  19.50  12.15  13.38   6.69   8.55  15.87 
#> genG23 genG24 genG25 genG26 genG27 genG28 genG29 genG30 genG31 genG32 genG33 
#>  17.86  15.00   6.70   7.08  11.68  12.52  21.71  14.11  12.81  14.93  12.92 
#> genG34 genG35 genG36 
#>  16.60   5.68   8.79 

# Variance of yield increases with yield
plot(m1, main="kempton.competition")

# }