Rice yield in wet & dry seasons with nitrogen fertilizer treatments

Format

A data frame with 96 observations on the following 4 variables.

season: season = wet/dry
nitrogen: nitrogen fertilizer kg/ha
rep: replicate
yield: grain yield, t/ha

Details

Five nitrogen fertilizer treatments were tested in 2 seasons using 3 reps.

Used with permission of Kwanchai Gomez.

Source

Gomez, K.A. and Gomez, A.A.. 1984, Statistical Procedures for Agricultural Research. Wiley-Interscience. Page 318.

References

Rong-Cai Yang, Patricia Juskiw. (2011). Analysis of covariance in agronomy and crop research. Canadian Journal of Plant Science, 91:621-641. https://doi.org/10.4141/cjps2010-032

Examples

if (FALSE) { # \dontrun{

library(agridat)
data(gomez.wetdry)
dat <- gomez.wetdry

libs(lattice)
foo1 <- xyplot(yield ~ nitrogen|season, data=dat, 
               group=rep,type='l',auto.key=list(columns=3),
               ylab="yield in each season",
               main="gomez.wetdry raw data & model")

# Yang & Juskiw fit a quadratic model with linear and quadratic
# contrasts using non-equal intervals of nitrogen levels.
# This example below omits the tedious contrasts

libs(latticeExtra, lme4)
m1 <-lmer(yield ~ season*poly(nitrogen, 2) + (1|season:rep), data=dat)
pdat <- expand.grid(season=c('dry','wet'),
                    nitrogen=seq(from=0,to=150,by=5))
pdat$pred <- predict(m1, newdata=pdat, re.form= ~ 0)
foo1 + 
  xyplot(pred ~ nitrogen|season, data=pdat, type='l',lwd=2,col="black")


# m2 <-lmer(yield ~ poly(nitrogen, 2) + (1|season:rep), data=dat)
# anova(m1,m2)
## m2: yield ~ poly(nitrogen, 2) + (1 | season:rep)
## m1: yield ~ season * poly(nitrogen, 2) + (1 | season:rep)
##    Df    AIC    BIC  logLik deviance  Chisq Chi Df Pr(>Chisq)    
## m2  5 86.418 93.424 -38.209   76.418                             
## m1  8 64.216 75.425 -24.108   48.216 28.202      3  3.295e-06 ***

} # }