Livestock feed intake

Get amount of items used for feeding livestock.

Usage

get_feed_intake(version = NULL)

Arguments

version

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

Value

A tibble with the feed intake 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().

• live_anim_code: Commodity balance sheet code for the type of livestock that is fed. For code details see e.g. add_item_cbs_name().

• item_cbs_code: The code of the item that is used for feeding the animal. For code details see e.g. add_item_cbs_name().

• feed_type: The type of item that is being fed. It can be one of:

  • animals: Livestock product, e.g. Bovine Meat, Butter, Ghee, etc.

  • crops: Crop product, e.g. Vegetables, Other, Oats, etc.

  • residues: Crop residue, e.g. Straw, Fodder legumes, etc.

  • grass: Grass, e.g. Grassland, Temporary grassland, etc.

  • scavenging: Other residues. Single Scavenging item.

• supply: The computed amount in tonnes of this item that should be fed to this animal, when sharing the total item feed use from the Commodity Balance Sheet among all livestock.

• intake: The actual amount in tonnes that the animal needs, which can be less than the theoretical used amount from supply.

• intake_dry_matter: The amount specified by intake but only considering dry matter, so it should be less than intake.

• loss: The amount that is not used for feed. This is supply - intake.

• loss_share: The percent that is lost. This is loss / supply.

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_feed_intake(version = "example")
#> ℹ Fetching files for feed_intake...
#> # A tibble: 10,000 × 10
#>     year area_code live_anim_code item_cbs_code feed_type   supply   intake
#>    <dbl>     <dbl>          <dbl>         <dbl> <chr>        <dbl>    <dbl>
#>  1  1983        15           1096          2102 crops         21.1     18.9
#>  2  1985       251           1016          3000 grass     269616.  269616. 
#>  3  2021       222           1052          2781 animals       35.9     32.3
#>  4  2017       105           1079          2598 crops       1727.    1554. 
#>  5  2000        39           1053          2106 residues   12662.   11396. 
#>  6  1968        84           1016          2002 residues    3015.    2714. 
#>  7  2008       170           1053          2595 crops       4294.    3864. 
#>  8  2015         3            976          2101 crops         40.1     36.1
#>  9  2002        79           1052          2558 crops       3788.    3409. 
#> 10  2014        41           1068          2518 crops     148505.  133654. 
#> # ℹ 9,990 more rows
#> # ℹ 3 more variables: intake_dry_matter <dbl>, loss <dbl>, loss_share <dbl>