vold.longterm: Long-term barley yields at different fertilizer levels

Description Usage Format Details Source References Examples

Description

Long-term barley yields at different fertilizer levels

Usage

1
data("vold.longterm")

Format

A data frame with 76 observations on the following 3 variables.

year

year

nitro

nitrogen fertilizer, grams/m^2

yield

yield, grams/m^2

Details

Trials conducted at Osaker, Norway. Nitrogen fertilizer amounts were increased by twenty percent in 1978.

Vold (1998) fit a Michaelis-Menten type equation with a different maximum in each year and a decreasing covariate for non-fertilizer nitrogen.

Miguez used a non-linear mixed effects model with asymptotic curve.

Source

Arild Vold (1998). A generalization of ordinary yield response functions. Ecological modelling, 108, 227-236. http://doi.org/10.1016/S0304-3800(98)00031-3

References

Fernando E. Miguez (2008). Using Non-Linear Mixed Models for Agricultural Data.

Examples

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data(vold.longterm)
dat <- vold.longterm

require(lattice)
foo1 <- xyplot(yield ~ nitro | factor(year), data = dat,
       as.table=TRUE, type = "o",
       main=list("vold.longterm", cex=1.5),
       xlab = list("N fertilizer",cex=1.5,font=4),
       ylab = list("Yield", cex=1.5))
# print(foo1)

## Not run: 
# Long term trend shows decreasing yields
xyplot(yield ~ year , data = dat, group=nitro, type='o',
       main="vold.longterm - yield level by nitrogen",
       auto.key=list(columns=4))

## End(Not run)


## Not run: 
  # Global model
  m1.nls <- nls(yield ~ SSasymp(nitro, max, int, lograte), data=dat)
  summary(m1.nls)
  require(MASS) # for 'confint'
  confint(m1.nls)
  
  # Raw data plus global model.  Year variation not modeled.
  pdat <- data.frame(nitro=seq(0,14,0.5))
  pdat$pred <- predict(m1.nls, newdata=pdat)
  if(require(latticeExtra)) { # for layers
    foo1 + xyplot(pred ~ nitro , data = pdat,
                  as.table=TRUE, type='l', col='red', lwd=2)
  }

  # Separate fit for each year.  Overfitting with 3x19=57 params.
  require(nlme)
  m2.lis <- nlsList(yield ~ SSasymp(nitro,max,int,lograte) | year, data=dat)
  plot(intervals(m2.lis),layout = c(3,1)) # lograte might be same for each year

## End(Not run)


# Fixed overall asymptotic model, plus random deviations for each year
# Simpler code, but less clear about what model is fit: m3.lme <- nlme(m2.lis)
require(nlme)
m3.lme <- nlme(yield ~ SSasymp(nitro, max, int, lograte), data=dat,
               groups = ~ year,
               fixed = list(max~1, int~1, lograte~1),
               random= max + int + lograte ~ 1,
               start= c(max=300, int=100, rate=-2))

## # Fixed effects are similar for the nls/lme models
## coef(m1.nls)
## fixef(m3.lme)
## # Random effects are normally distributed
## qqnorm(m3.lme, ~ ranef(.),col="black")
## # Note the trend in intercept effects over time
## plot(ranef(m3.lme),layout=c(3,1))

## # Correlation between int,lograte int,max may not be needed
## intervals(m3.lme,which="var-cov")
## pairs(m3.lme,pch=19,col="black")

## # Model with int uncorrelated with max,lograte.  AIC is worse.
## # fit4.lm3 <- update(m3.lme, random=pdBlocked(list(max+lograte~1,int ~ 1)))
## # intervals(fit4.lm3, which="var-cov")
## # anova(m3.lme, fit4.lm3)

# Plot the random-effect model.  Excellent fit with few parameters.
pdat2 <- expand.grid(year=1970:1988, nitro=seq(0,15,length=50))
pdat2$pred <- predict(m3.lme, new=pdat2)
pdat2$predf <- predict(m3.lme, new=pdat2, level=0)
foo1 <- update(foo1, type='p', key=simpleKey(c("Observed","Fixed","Random"),
                                 col=c("blue","red","darkgreen"), points=FALSE, columns=3))
if(require(latticeExtra)){
  foo2 <- xyplot(pred~nitro|year, data=pdat2, type='l', col="darkgreen", lwd=2)
  foo3 <- xyplot(predf~nitro|year, data=pdat2, type='l', col="red",lwd=1)
  foo1 + foo2 + foo3
}

## # Income is maximized at about 15
## pdat2 <- transform(pdat2, income = predf*2 - 7*nitro)
## with(pdat2, xyplot(income~nitro))

agridat documentation built on May 2, 2019, 4:01 p.m.