battese.survey: Survey and satellite data for corn and soy areas in Iowa

In agridat: Agricultural Datasets

Survey and satellite data for corn and soy areas in Iowa

Description

Survey and satellite data for corn and soy areas in Iowa

Usage

data("battese.survey")

Format

A data frame with 37 observations on the following 9 variables.

county

county name

segment

sample segment number (within county)

countysegs

number of segments in county

cornhect

hectares of corn in segment

soyhect

hectares of soy

cornpix

pixels of corn in segment

soypix

pixels of soy

cornmean

county mean of corn pixels per segment

soymean

county mean of soy pixels per segment

Details

The data are for 12 counties in north-central Iowa in 1978.

The USDA determined the area of soybeans in 37 area sampling units (called 'segments'). Each segment is about one square mile (about 259 hectares). The number of pixels of that were classified as corn and soybeans came from Landsat images obtained in Aug/Sep 1978. Each pixel represents approximately 0.45 hectares.

Data originally compiled by USDA.

This data is also available in R packages: 'rsae::landsat' and 'JoSAE::landsat'.

Source

Battese, George E and Harter, Rachel M and Fuller, Wayne A. (1988). An error-components model for prediction of county crop areas using survey and satellite data. Journal of the American Statistical Association, 83, 28-36. https://doi.org/10.2307/2288915

Battese (1982) preprint version. https://www.une.edu.au/__data/assets/pdf_file/0017/15542/emetwp15.pdf

References

Pushpal K Mukhopadhyay and Allen McDowell. (2011). Small Area Estimation for Survey Data Analysis Using SAS Software SAS Global Forum 2011.

Examples

## Not run: 

library(agridat)
data(battese.survey)
dat <- battese.survey

# Battese fig 1 & 2.  Corn plot shows outlier in Hardin county
libs(lattice)
dat <- dat[order(dat$cornpix),]
xyplot(cornhect ~ cornpix, data=dat, group=county, type=c('p','l'),
       main="battese.survey", xlab="Pixels of corn", ylab="Hectares of corn",
       auto.key=list(columns=3))

dat <- dat[order(dat$soypix),]
xyplot(soyhect ~ soypix, data=dat, group=county, type=c('p','l'),
       main="battese.survey", xlab="Pixels of soy", ylab="Hectares of soy",
       auto.key=list(columns=3))

libs(lme4, lucid)
  
# Fit the models of Battese 1982, p.18.  Results match
m1 <- lmer(cornhect ~ 1 + cornpix + (1|county), data=dat)
fixef(m1)
## (Intercept)     cornpix 
##   5.4661899   0.3878358 
vc(m1)
##      grp        var1 var2   vcov  sdcor
##   county (Intercept) <NA>  62.83  7.926
## Residual        <NA> <NA> 290.4  17.04 
m2 <- lmer(soyhect ~ 1 + soypix + (1|county), data=dat)
fixef(m2)
## (Intercept)      soypix 
##  -3.8223566   0.4756781 
vc(m2)
##      grp        var1 var2  vcov sdcor
##   county (Intercept) <NA> 239.2 15.47
## Residual        <NA> <NA> 180   13.42
  
# Predict for Humboldt county as in Battese 1982 table 2
5.4662+.3878*290.74
# 118.2152 # mu_i^0
5.4662+.3878*290.74+ -2.8744
# 115.3408 # mu_i^gamma
(185.35+116.43)/2
# 150.89 # y_i bar
  
# Survey regression estimator of Battese 1988
  
# Delete the outlier
dat2 <- subset(dat, !(county=="Hardin" & soyhect < 30))
  
# Results match top-right of Battese 1988, p. 33
m3 <- lmer(cornhect ~ cornpix + soypix + (1|county), data=dat2)
fixef(m3)
## (Intercept)     cornpix      soypix 
##  51.0703979   0.3287217  -0.1345684 
vc(m3)
##      grp        var1 var2  vcov sdcor
##   county (Intercept) <NA> 140   11.83
## Residual        <NA> <NA> 147.3 12.14
m4 <- lmer(soyhect ~ cornpix + soypix + (1|county), data=dat2)
fixef(m4)
##  (Intercept)      cornpix       soypix 
## -15.59027098   0.02717639   0.49439320 
vc(m4)
##      grp        var1 var2  vcov sdcor
##   county (Intercept) <NA> 247.5 15.73
## Residual        <NA> <NA> 190.5 13.8 


## End(Not run)

agridat documentation built on Oct. 27, 2024, 5:07 p.m.