The agERA5 data is downloaded from the Copernicus Climate Data Store
as NetCDF files format, with a file extension
The data is provided as daily observations and each file correspond to a day. For instance, if you want precipitation data for year 2010, you will have 365 files (or 366 in the case of a leap year).
In case you want to use the precipitation data as model covariates, you have to seek for the specific dates and extract the data corresponding to the locations where the trial was established. Instead, you can use the ag5_extract function as demostrated in the next section.
Let’s say you have observations from field trials of a trait of interest (e.g., yield) and want to link that with rainfall data to explore the effect of rainfall on that trait. You know the the plating and harvest dates for each trial plot. Then you can extract the time series of daily rainfall starting at plating date and finishing at harvest date.
Our synthetic example data shows the dates for random locations in Arusha, Tanzania.
data("arusha_df", package = "ag5Tools") head(arusha_df) #> lon lat start_date end_date #> 1 35.72636 -2.197162 1991-04-22 1991-08-20 #> 2 36.10249 -2.850983 1990-01-24 1990-05-24 #> 3 35.46292 -3.602582 1991-03-06 1991-07-04 #> 4 36.29166 -3.855945 1990-10-10 1991-02-07 #> 5 35.45254 -3.616361 1990-01-22 1990-05-22 #> 6 35.40131 -3.216106 1990-10-19 1991-02-16
ag5_extract() function you can extract the required
data, as long as you have downloaded it already.
library(ag5Tools) <- ag5_extract(coords = arusha_df, arusha_rainfall variable = "Precipitation-Flux", path = "D:/agera5_data/")
Notice that the
data.frame arusha_df already has column
names that match the default arguments in the function. If they do not
match, you should indicate the column names as arguments of the function
For example, if your
data.frame has location columns
y and dates named as
harvest_date, the call to
ag5_extract() will look like:
<- ag5_extract(coords = example_df, arusha_rainfall lon = "x", lat = "y", start_date = "planting_date", end_date = "harvest_date", variable = "Precipitation-Flux", path = "D:/agera5_data/")
Notice that you do not need to worry about providing the specific
folder where the files are located, but only the root folder where you
know the files are. For instance, if you stored all the AgERA5 data
files in folder
D:/agera5_data/, but you also have
sub-folders for precipitation and temperature data, it is not required
to specify that in the
path argument. In this case, only
providing the path
D:/agera5_data/ will suffice.