Multi-environment trial of maize for four cropping systems
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 # ---------- 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 ## End(Not run)
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