Multi-environment trial of maize for four cropping systems
hildebrand.systems.RdMaize yields for four cropping systems at 14 on-farm trials.
Format
A data frame with 56 observations on the following 4 variables.
villagevillage, 2 levels
farmfarm, 14 levels
systemcropping system
yieldyield, 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.
Source
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
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
# \dontrun{ 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")#> [1] 0.03309629# .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)#> Model fitted using the sigma parameterization. #> ASReml 4.1.0 Mon Jan 11 17:08:45 2021 #> LogLik Sigma2 DF wall cpu #> 1 -10.2291 1.0 52 17:08:45 0.0 #> 2 -6.9482 1.0 52 17:08:45 0.0 #> 3 -3.8775 1.0 52 17:08:45 0.0 #> 4 -2.3455 1.0 52 17:08:45 0.0 #> 5 -2.2515 1.0 52 17:08:45 0.0 #> 6 -2.2510 1.0 52 17:08:45 0.0# Means, table 5 ## predict(m1, data=dat, classify="system")$pvals ## system pred.value std.error est.stat ## CCA 1.164 0.2816 Estimable ## CCAF 2.657 0.3747 Estimable ## LM 1.35 0.1463 Estimable ## LMF 2.7 0.2561 Estimable # Variances, table 5 # vc(m1)[c(2,4,7,11),] ## effect component std.error z.ratio constr ## R!system.CCA:CCA 1.11 0.4354 2.5 pos ## R!system.CCAF:CCAF 1.966 0.771 2.5 pos ## R!system.LM:LM 0.2996 0.1175 2.5 pos ## R!system.LMF:LMF 0.9185 0.3603 2.5 pos # Stability variance model m2 <- asreml(yield ~ system, data=dat, random = ~ farm, resid = ~ dsum( ~ units|system))#> Multi-section model using the sigma parameterization. #> ASReml 4.1.0 Mon Jan 11 17:08:46 2021 #> LogLik Sigma2 DF wall cpu #> 1 -22.5014 1.0 52 17:08:46 0.0 #> 2 -18.0904 1.0 52 17:08:46 0.0 #> 3 -15.9713 1.0 52 17:08:46 0.0 (1 restrained) #> 4 -15.2537 1.0 52 17:08:46 0.0 (1 restrained) #> 5 -15.1232 1.0 52 17:08:46 0.0 (1 restrained) #> 6 -15.1214 1.0 52 17:08:46 0.0 (1 restrained) #> 7 -15.1214 1.0 52 17:08:46 0.0#> Warning: Some components changed by more than 1% on the last iteration.#> Multi-section model using the sigma parameterization. #> ASReml 4.1.0 Mon Jan 11 17:08:46 2021 #> LogLik Sigma2 DF wall cpu #> 1 -15.1214 1.0 52 17:08:46 0.0 #> 2 -15.1214 1.0 52 17:08:46 0.0#> Warning: Some components changed by more than 1% on the last iteration.# predict(m2, data=dat, classify="system")$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 # }
