U.S. historical crop yields by state — nass.corn • agridat

Yields and acres harvested in each state for the major agricultural crops in the United States, from approximately 1900 to 2011. Crops include: barley, corn, cotton, hay, rice, sorghum, soybeans, wheat.

nass.barley
  nass.corn
  nass.cotton
  nass.hay
  nass.sorghum
  nass.wheat
  nass.rice
  nass.soybean

Format

year: year
state: state factor
acres: acres harvested
yield: average yield

Details

Be cautious with yield values for states with small acres harvested.

Yields are in bushels/acre, except: cotton pounds/acre, hay tons/acre, rice pounds/acre.

Each crop is in a separate dataset: nass.barley, nass.corn, nass.cotton, nass.hay, nass.sorghum, nass.wheat, nass.rice, nass.soybean.

Source

United States Department of Agriculture, National Agricultural Statistics Service. https://quickstats.nass.usda.gov/

Examples


library(agridat)

data(nass.corn)
dat <- nass.corn

# Use only states that grew at least 100K acres of corn in 2011
keep <- droplevels(subset(dat, year == 2011 & acres > 100000))$state
dat <- droplevels(subset(dat, is.element(state, keep)))

# Acres of corn grown each year
libs(lattice)
xyplot(acres ~ year|state, dat, type='l', as.table=TRUE,
       main="nass.corn: state trends in corn acreage")

## Plain levelplot, using only states
## libs(reshape2)
## datm <- acast(dat, year~state, value.var='yield')
## redblue <- colorRampPalette(c("firebrick", "lightgray", "#375997"))
## levelplot(datm, aspect=.7, col.regions=redblue,
##           main="nass.corn",
##           scales=list(x=list(rot=90, cex=.7)))

# Model the rate of genetic gain in Illinois as a piecewise regression
# Breakpoints define periods of open-pollinated varieties, double-cross,
# single-cross, and transgenic hybrids.

dil <- subset(nass.corn, state=="Illinois" & year >= 1900)
m1 <- lm(yield ~ pmin(year,1932) + pmax(1932, pmin(year, 1959)) +
         pmax(1959, pmin(year, 1995)) + pmax(1995, year), dil)
signif(coef(m1)[-1],3) # Rate of gain for each segment
#>             pmin(year, 1932) pmax(1932, pmin(year, 1959)) 
#>                        -0.19                         1.48 
#> pmax(1959, pmin(year, 1995))             pmax(1995, year) 
#>                         1.75                         2.30 
plot(yield ~ year, dil, main="nass.corn: piecewise linear model of Illinois corn yields")
lines(dil$year, fitted(m1))
abline(v=c(1932,1959,1995), col="wheat")