hildebrand.systems | R Documentation |
Maize yields for four cropping systems at 14 on-farm trials.
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
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.
P. E. Hildebrand, 1984. Modified Stability Analysis of Farmer Managed, On-Farm Trials. Agronomy Journal, 76, 271–274. https://doi.org/10.2134/agronj1984.00021962007600020023x
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
## Not run:
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
# ----------
if(require("asreml", quietly=TRUE)){
libs(asreml,lucid)
# 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")$pvals
## system pred.value std.error est.stat
## CCA 1.164 0.2816 Estimable
## CCAF 2.657 0.3747 Estimable
## LM 1.35 0.1463 Estimable
## LMF 2.7 0.2561 Estimable
# Variances, table 5
# lucid::vc(m1)[c(2,4,7,11),]
## effect component std.error z.ratio constr
## R!system.CCA:CCA 1.11 0.4354 2.5 pos
## R!system.CCAF:CCAF 1.966 0.771 2.5 pos
## R!system.LM:LM 0.2996 0.1175 2.5 pos
## R!system.LMF:LMF 0.9185 0.3603 2.5 pos
# Stability variance model
m2 <- asreml(yield ~ system, data=dat,
random = ~ farm,
resid = ~ dsum( ~ units|system))
m2 <- update(m2)
# predict(m2, data=dat, classify="system")$pvals
## # Variances, table 6
# lucid::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
}
## End(Not run)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.