stroup.nin: Nebraska Intrastate Nursery field experiment

Description

The yield data from an advanced Nebraska Intrastate Nursery (NIN) breeding trial conducted at Alliance, Nebraska, in 1988/89.

Arguments

source

Stroup, Walter W., P Stephen Baenziger, Dieter K Mulitze (1994) Removing Spatial Variation from Wheat Yield Trials: A Comparison of Methods. Crop Science, 86:62--66.

Details

Four replicates of 19 released cultivars, 35 experimental wheat lines and 2 additional triticale lines were laid out in a 22 row by 11 column rectangular array of plots; the varieties were allocated to the plots using a randomised complete block (RCB) design. The blocks are not rectangular but partially overlap columns. All plots with missing data are coded as being gen = "Lancer". (For ASREML, missing plots need to be included for spatial analysis and the level of 'gen' needs to be one that is already in the data.) The complete experiment was grown at four locations (Lincoln, Clay Center, North Platte, and Alliance). Only data for the Alliance location is included here. These data were first analyzed by Stroup et al (1994) and subsequently by Littell et al (1996, page 321), Pinheiro and Bates (2000, page 260), and Butler et al (2004). This version of the data expresses the yield in bushels per acre. The results published in Stroup et al (1994) are expressed in kg/ha. For wheat, 1 bu/ac = 67.25 kg/ha. Some of the gen names are different in Stroup et al (1994). (Sometimes an experimental genotype is given a new name when it is released for commercial use.) At a minimum, the following differences in gen names should be noted: ll{ stroup.nin Stroup et al NE83498 Rawhide KS831374 Karl } Some published versions of the data use long/lat instead of x/y. To obtain the correct value of 'long', multiply 'x' by 1.2. To obtain the correct value of 'lat', multiply 'y' by 4.3.

References

Littell, R.C. and Milliken, G.A. and Stroup, W.W. and Wolfinger, R.D. 1996. SAS system for mixed models, SAS Institute, Cary, NC. Jose Pinheiro and Douglas Bates, 2000, Mixed Effects Models in S and S-Plus, Springer. Butler, D., B R Cullis, A R Gilmour, B J Goegel. (2004) Spatial Analysis Mixed Models for S language environments

Examples

Run this code

dat <- stroup.nin
dat <- transform(dat, xf=factor(x), yf=factor(y))

# Show experiment layout
# Note: all "Buckskin" plots are near left side
desplot(yield~x*y, dat, out1="rep", num=gen, cex=1, main="stroup.nin")

require(nlme)
# Random block model
lme1 <- lme(yield ~ 0 + gen, random=~1|rep, data=dat, na.action=na.omit)

# Linear (Manhattan distance) correlation model
lme2 = gls(yield ~ 0 + gen, correlation = corLin(form = ~ x+y,nugget=TRUE), data=dat,
  na.action=na.omit)

# Compare the estimates from the two methods
eff = data.frame(ranblock=fixef(lme1), spat = coef(lme2))
rownames(eff) <- gsub("gen", "", rownames(eff))
plot(eff$ranblock, eff$spat, xlim=c(13,37), ylim=c(13,37),
  xlab="RCB (random block)", ylab="corLin",type='n')
text(eff$ranblock, eff$spat, rownames(eff), cex=0.5)
abline(0,1)

require("asreml")

# RCB analysis
dat.rcb <- asreml(yield ~ gen, random = ~ rep, data=dat,
            na.method.X="omit")
pred.rcb <- predict(dat.rcb,classify="gen")$predictions

# Two-dimensional AR1xAR1 spatial model
dat <- dat[order(dat$xf, dat$yf),]
dat.sp <- asreml(yield~gen, rcov=~ar1(xf):ar1(yf),data=dat)
pred.sp <- predict(dat.sp,classify="gen")$predictions

# Compare the estimates from the two methods
plot(pred.rcb$pvals[,2],pred.sp$pvals[,2], xlim=c(16,37), ylim=c(16,37),
  xlab="RCB",ylab="AR1xAR1",type='n')
text(pred.rcb$pvals[,2],pred.sp$pvals[,2],
     as.character(pred.rcb$pvals[,1]),cex=0.5)
abline(0,1)