Mapping agricultural land usage in the US from census data

Where do we grow things? This is an important question that can be answered in a few ways, but a more direct way is to just ask growers. This is what they did at a national scale in the 2017 United States’ Agriculture Census and here I am going to use R to load it, filter it, and plot the result.

To start we need to download the data and the shape file of the USA counties (or states). I am using counties here. You will need to extract the compressed files using a program like 7zip.

Then we load the shapefile and census data into R.


# county shape file
usa_county = st_read("data/USA land usage/US_counties/CoUS17_WGS84WMAS.shp") %>% 
  mutate(county = atlas_caps)
## Reading layer `CoUS17_WGS84WMAS' from data source `C:\Users\james\Documents\git\jmaino_website\content\post\data\USA land usage\US_counties\CoUS17_WGS84WMAS.shp' using driver `ESRI Shapefile'
## Simple feature collection with 3070 features and 8 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: -13884420 ymin: 2820030 xmax: -7452828 ymax: 6340332
## epsg (SRID):    3857
## proj4string:    +proj=merc +a=6378137 +b=6378137 +lat_ts=0.0 +lon_0=0.0 +x_0=0.0 +y_0=0 +k=1.0 +units=m +nadgrids=@null +wktext +no_defs
# load 2017 ag census data
col_names = c('y1','y2','y3','y4','commodity','data_item','y5','region_level','state_no', 'state_code', 'state',
d = read_delim("data/USA land usage/2017-census-data.txt", delim = "\t", col_names = col_names)

A little bit of filtering is needed as there is other data besides land usage in the census.

# make shapefile with state level commodity acreage
commods = d %>% 
  filter(commodity == "CROPS") %>% 
  # distinct(data_item) %>% View
  filter(data_item %in% paste0(c("BLUEBERRIES", "GRAPES",   "CHERRIES, SWEET", 
                                "STRAWBERRIES", "RASPBERRIES, RED", "RASPBERRIES, BLACK"), " - ACRES BEARING")) %>% 
  filter(region_level == "COUNTY") %>% 
  filter(!is.na(value)) %>% 
  filter(is.na(domain_category)) %>%  
  mutate(atlas_stco = paste0(state_no, cntya)) %>% 
  distinct(data_item, atlas_stco, value) %>% 
  spread(key = data_item, value = value)

I had to create a variable from state code and county code which is then used to match each county to its respective polygon in the shapefile.

county_commods = usa_county %>% 
  left_join(commods, by = "atlas_stco")

At the counties consist of different land areas, it makes sense to scale the acres bearing of each commodities by the total acreage of the state.

So here are where the strawberries are.

county_commods %>% 
  mutate(`Proportion of land strawberries` = `STRAWBERRIES - ACRES BEARING`/atlas_acre) %>%
tm_shape() + 
  tm_fill(col = "Proportion of land strawberries", palette = "Reds", style = "cont", 
          breaks = 10^seq(-8,-1), showNA = F, colorNA = "white") + 
  tm_borders(col = "grey")

And here are where the blueberries are.

county_commods %>% 
  mutate(`Proportion of land blueberries` = `BLUEBERRIES - ACRES BEARING`/atlas_acre) %>%
tm_shape() + 
  tm_fill(col = "Proportion of land blueberries", palette = "Blues", style = "cont", 
          breaks = 10^seq(-7,-2), showNA = F, colorNA = "white") + 
  tm_borders(col = "grey")


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