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Get type and amount of residue produced for each crop production item.

Usage

get_primary_residues(version = NULL)

Arguments

version

File version to use as input. See whep_inputs for details.

Value

A tibble with the crop residue data. It contains the following columns:

  • year: The year in which the recorded event occurred.

  • area_code: The code of the country where the data is from. For code details see e.g. add_area_name().

  • item_cbs_code_crop: FAOSTAT internal code for each commodity balance sheet item. This is the crop that is generating the residue.

  • item_cbs_code_residue: FAOSTAT internal code for each commodity balance sheet item. This is the obtained residue. In the commodity balance sheet, this can be three different items right now:

    • 2105: Straw

    • 2106: Other crop residues

    • 2107: Firewood

    These are actually not FAOSTAT defined items, but custom defined by us. When necessary, FAOSTAT codes are extended for our needs.

  • value: The amount of residue produced, measured in tonnes.

Examples

# Note: These are smaller samples to show outputs, not the real data.
# For all data, call the function with default version (i.e. no arguments).
get_primary_residues(version = "20250721T150132Z-dfd94")
#>  Fetching files for crop_residues...
#> # A tibble: 4,504 × 5
#>     year area_code item_cbs_code_crop item_cbs_code_residue    value
#>    <dbl>     <dbl>              <dbl>                 <dbl>    <dbl>
#>  1  2003        53               2570                  2107  19903. 
#>  2  2008        NA               2551                  2107 222849. 
#>  3  2001        NA               2605                  2106   2309. 
#>  4  1981       136               2605                  2106    421. 
#>  5  2019       121               2605                  2106  19703. 
#>  6  2005        40               2625                  2107   5904. 
#>  7  1978       166               2605                  2106    828. 
#>  8  1996       158               2549                  2105     92.9
#>  9  1976        10               2612                  2107   5401. 
#> 10  2012         3               2605                  2106  12086. 
#> # ℹ 4,494 more rows