Neonicotinoid usage in the United States

Pesticide usage in the United States

The USGS had some great data on pesticide usage in the United States that causes great envy among Australian pest researchers. In this post we have a look at some of this data and present some spatial and temporal trends in an important pesticide group that has been the subject of much controversy in recent history.

Such data allows researchers to better estimate selection pressure for resistance evolution, as well as other off-target effects of pesticide usage.

The USGS provides a low and high estimate of pesticide usage as per the methodology described here. For simplicity we use the lower estimate in the following.

First, we load the county level pesticide usage data and a shape file that allows matching of estimated neonicotinoid usage in space.

#Import libraries
library(tidyverse)
library(sf)
library(tmap)

# Common neonicotinoid compounds
neonics = toupper(
  c("acetamiprid", "clothianidin", "dinotefuran", "imidacloprid", "thiamethoxam"))

# US pesticide usage
us = "data/USA land usage/US_pesticide_usage/EPest.county.estimates.2016.txt" %>% 
  read_delim(delim = "\t") %>%
  mutate(atlas_stco = paste0(STATE_FIPS_CODE, COUNTY_FIPS_CODE)) %>% 
  mutate(usage_kg = EPEST_LOW_KG) %>% 
  filter(COMPOUND %in% neonics) %>% 
  group_by(atlas_stco) %>% 
  summarise(usage_kg = sum(usage_kg, na.rm=T))

# US shape file
shp = st_read("data/USA land usage/US_counties/CoUS17_WGS84WMAS.shp") 
## 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
## projected CRS:  WGS 84 / Pseudo-Mercator
# join usage to shape file for chem and scale for usage intensity
uspest = shp %>% 
  mutate(atlas_ha = atlas_acre * 0.404686) %>% # metric FTW
  left_join(us) %>% 
  mutate(usage_g_ha = 1e3*usage_kg/atlas_ha)

Then after scaling neonicotinoid usage by county area to estimate usage intensity, we can plot this data on a linear scale, and then a log scale to better see variation

# range of values
logrange = uspest %>% 
  filter(! (is.na(usage_g_ha)|usage_g_ha==0)) %>%
  pull(usage_g_ha) %>% 
  log10 %>% 
  range %>%
  round

# plot usage intensity
pal = rev(viridis::magma(10))
mytmtheme = tm_layout(legend.position = c("right", "bottom"), bg.color = "#37314a",
            scale = 0.5,outer.bg.color = "#37314a", frame = F)
# linear
tm_shape(uspest) + 
  tm_fill(col="usage_g_ha", title = "County level 2016\nneonicotinoid usage (g/ha)",
          style = "cont", palette = pal) +
  mytmtheme

#log
tm_shape(uspest) + 
  tm_fill(col="usage_g_ha", title = "County level 2016\nneonicotinoid usage (g/ha)",
          style = "cont", breaks = 10^(logrange[1]:logrange[2]), palette = pal) +
  mytmtheme

The USGS provides historical data through time at a crop level that allows us to look at trends in usage the crop level.

Here we can see that neonicotinoid usage increased rapidly but dropped off precipitously after 2014. Soybean and corn production account for most neonicotinoid usage in the United States.

# get usage by crop type through time
crop_usage = 
  "data/USA land usage/US_pesticide_usage/LowEstimate_AgPestUsebyCropGroup92to16.txt" %>% read_delim(delim = "\t") %>%
  filter(Compound %in% neonics) %>%
  gather(crop, usage, -State_FIPS_code, -State, -Compound, -Year, -Units) %>% 
  group_by(crop, Year) %>%
  summarise(usage_t = sum(usage, na.rm=T)/1e3, .groups="drop")

# plot usage by crop type through time
crop_usage %>% 
  mutate(crop = gsub("_", " ", crop)) %>%
  ggplot(aes(Year, usage_t, color=reorder(crop, desc(usage_t)))) + 
  geom_line(stat="identity", size=1) + 
  # theme_bw() + 
  mydarktheme +
  theme(panel.background = element_blank()) +
  scale_color_manual(values=rainbow(10, s = 0.5)) +
  ylab("Neonicotinoid usage (tonnes)") + 
  guides(color = guide_legend(""))

Total neonicotinoid usage by year is summarised in the following table.

# total usage
crop_usage %>% 
  group_by(Year) %>% 
  summarise(`Neonicotinoid Usage (tonnes)` = sum(usage_t, na.rm=T), 
            .groups="drop") %>% 
  arrange(desc(Year)) %>% 
  knitr::kable()
Year Neonicotinoid Usage (tonnes)
2016 564.4619
2015 673.7456
2014 3302.7119
2013 3012.3767
2012 2671.2835
2011 2162.5249
2010 1652.5687
2009 1381.7818
2008 1125.2659
2007 954.7714
2006 717.1579
2005 658.2359
2004 506.4804
2003 174.2318
2002 136.4577
2001 115.0020
2000 122.1248
1999 119.0795
1998 133.5481
1997 151.2078
1996 84.2564
1995 72.6969
1994 8.0689

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