agridat (version 1.21)

hildebrand.systems: Multi-environment trial of maize for four cropping systems

Description

Maize yields for four cropping systems at 14 on-farm trials.

Format

A data frame with 56 observations on the following 4 variables.

village

village, 2 levels

farm

farm, 14 levels

system

cropping system

yield

yield, t/ha

Details

Yields from 14 on-farm trials in Phalombe Project region of south-eastern Malawi. The farms were located near two different villages.

On each farm, four different cropping systems were tested. The systems were: LM = Local Maize, LMF = Local Maize with Fertilizer, CCA = Improved Composite, CCAF = Improved Composite with Fertilizer.

References

H. P. Piepho, 1998. Methods for Comparing the Yield Stability of Cropping Systems. Journal of Agronomy and Crop Science, 180, 193--213. https://doi.org/10.1111/j.1439-037X.1998.tb00526.x

Examples

if (FALSE) {
  
  library(agridat)
  data(hildebrand.systems)
  dat <- hildebrand.systems

  # Piepho 1998 Fig 1
  libs(lattice)
  dotplot(yield ~ system, dat, groups=village, auto.key=TRUE,
          main="hildebrand.systems", xlab="cropping system by village")


  # Plot of risk of 'failure' of System 2 vs System 1
  s11 = .30;  s22 <- .92; s12 = .34
  mu1 = 1.35; mu2 = 2.70
  lambda <- seq(from=0, to=5, length=20)
  system1 <- pnorm((lambda-mu1)/sqrt(s11))
  system2 <- pnorm((lambda-mu2)/sqrt(s22))

  # A simpler view
  plot(lambda, system1, type="l", xlim=c(0,5), ylim=c(0,1),
       xlab="Yield level", ylab="Prob(yield < level)",
       main="hildebrand.systems - risk of failure for each system")
  lines(lambda, system2, col="red")
  
  # Prob of system 1 outperforming system 2. Table 8
  pnorm((mu1-mu2)/sqrt(s11+s22-2*s12))
  # .0331

  # ----------

  libs(asreml,lucid) # asreml4

  # Environmental variance model, unstructured correlations
  
  dat <- dat[order(dat$system, dat$farm),]
  m1 <- asreml(yield ~ system, data=dat,
               resid = ~us(system):farm)
  
  # Means, table 5
  ## predict(m1, data=dat, classify="system")$pvals
  ##  system pred.value std.error  est.stat
  ##     CCA      1.164    0.2816 Estimable
  ##    CCAF      2.657    0.3747 Estimable
  ##      LM      1.35     0.1463 Estimable
  ##     LMF      2.7      0.2561 Estimable
  
  # Variances, table 5
  # vc(m1)[c(2,4,7,11),]
  ##              effect component std.error z.ratio constr
  ##    R!system.CCA:CCA    1.11      0.4354     2.5    pos
  ##  R!system.CCAF:CCAF    1.966     0.771      2.5    pos
  ##      R!system.LM:LM    0.2996    0.1175     2.5    pos
  ##    R!system.LMF:LMF    0.9185    0.3603     2.5    pos
  
  # Stability variance model
  m2 <- asreml(yield ~ system, data=dat,
               random = ~ farm,
               resid = ~ dsum( ~ units|system))
  m2 <- update(m2)
  # predict(m2, data=dat, classify="system")$pvals
  
  ## # Variances, table 6
  # vc(m2)
  ##        effect component std.error z.ratio bound 
  ##          farm 0.2998      0.1187   2.5        P 0  
  ##  system_CCA!R 0.4133      0.1699   2.4        P 0  
  ## system_CCAF!R 1.265       0.5152   2.5        P 0  
  ##   system_LM!R 0.0003805   0.05538  0.0069     P 1.5
  ##  system_LMF!R 0.5294      0.2295   2.3        P 0  

}