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.
Where are climatically similar locations?
This is not a straightforward question because a range of conditions contribute to climate including rainfall, temperature, and the seasonal and daily variation in these properties.
A key set of spatial data that contains useful information on these ecoclimatic properties are the BIOCLIM variables frequently used in species distributinon models.
We can download the data which contain the following variables.
library(tidyverse) "data/bioclim/bioclim_var_description.csv" %>% read_csv() %>% knitr::kable() var desc bio01 Annual Mean Temperature bio02 Mean Diurnal Range (Mean of monthly (max temp - min temp)) bio03 Isothermality (BIO2/BIO7) (×100) bio04 Temperature Seasonality (standard deviation ×100) bio05 Max Temperature of Warmest Month bio06 Min Temperature of Coldest Month bio07 Temperature Annual Range (BIO5-BIO6) bio08 Mean Temperature of Wettest Quarter bio09 Mean Temperature of Driest Quarter bio10 Mean Temperature of Warmest Quarter bio11 Mean Temperature of Coldest Quarter bio12 Annual Precipitation bio13 Precipitation of Wettest Month bio14 Precipitation of Driest Month bio15 Precipitation Seasonality (Coefficient of Variation) bio16 Precipitation of Wettest Quarter bio17 Precipitation of Driest Quarter bio18 Precipitation of Warmest Quarter bio19 Precipitation of Coldest Quarter We can load this data into R and plot them for Australia.
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).