Livestock feed intake

Get amount of items used for feeding livestock.

Usage

get_feed_intake(example = FALSE)

Arguments

example

If TRUE, return a small example output without downloading remote data. Default is FALSE.

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

get_feed_intake(example = TRUE)
#> # A tibble: 10 × 10
#>     year area_code live_anim_code item_cbs_code feed_type    supply     intake
#>    <dbl>     <dbl>          <dbl>         <dbl> <chr>         <dbl>      <dbl>
#>  1  1990        51           1096          2515 crops        429       386    
#>  2  2007         3            976          2570 crops          5.51      4.96 
#>  3  1996        54            960          2531 crops       5451      4906    
#>  4  2011        NA           1052          2532 crops          0.84      0.756
#>  5  1996       110           1052          2549 crops         17.1      15.4  
#>  6  1986         4           1053          2514 crops     297464    267717    
#>  7  2010       150           1068          2595 crops       2645      2380    
#>  8  1978        NA           1096          2536 crops        159       143    
#>  9  2021        23           1053          2511 crops        627       347    
#> 10  1977       114            976          2517 crops         32.5      29.2  
#> # ℹ 3 more variables: intake_dry_matter <dbl>, loss <dbl>, loss_share <dbl>