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Build livestock feed demand.

Source: R/feed_intake_redistribute.R
build_feed_demand.Rd

Estimate the dry-matter feed demand of each livestock category: the first stage of get_feed_intake(), exposed on its own. Demand is national, per (year, area_code, livestock_category), and is computed before any matching against feed supply, so it can be audited or reused (for example in land or nitrogen footprints) independently of the allocation.

Usage

build_feed_demand(
  demand_tier = c("ipcc", "fcr"),
  by = c("category", "feed_type"),
  example = FALSE
)

Arguments

demand_tier

Demand-estimation tier. "ipcc" (default) uses the IPCC Tier-2 energy model for the ruminant species, Bouwman feed-conversion ratios for pigs and poultry, and Krausmann per-head intake for draft and other species. "fcr" uses the Bouwman / Krausmann magnitude for every species. The method actually used for each row is recorded in method_demand.

by

Output grain. "category" (default) returns the per-livestock category demand. "feed_type" splits it across feed types and returns the feed_demand table that redistribute_feed() consumes, so the two compose: build_feed_demand(by = "feed_type") |> redistribute_feed(feed_avail).

example

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

Value

With by = "category", a tibble with one row per (year, area_code, livestock_category):

  • year: The year of the demand.

  • area_code: The country code. For code details see e.g. add_area_name().

  • livestock_category: The feed-demand grouping of livestock (e.g. Cattle_milk, Cattle_meat, Pigs, Poultry).

  • demand_dm_t: Dry-matter feed demand in tonnes.

  • method_demand: The demand method(s) used, e.g. ipcc_tier2_energy, bouwman_fcr or krausmann_per_head (a +-joined set for a mixed category whose animals used different methods).

With by = "feed_type", the demand split across feed types as the redistribute_feed() feed_demand contract: year, territory, sub_territory, livestock_category, item_cbs_code, feed_group, feed_quality, demand_dm_t, fixed_demand.

Examples

build_feed_demand(example = TRUE)
#> # A tibble: 8 × 5
#>    year area_code livestock_category demand_dm_t method_demand     
#>   <int>     <int> <chr>                    <dbl> <chr>             
#> 1  2000        79 Cattle_milk            5800000 ipcc_tier2_energy 
#> 2  2000        79 Cattle_meat            9400000 ipcc_tier2_energy 
#> 3  2000        79 Sheep                  1100000 ipcc_tier2_energy 
#> 4  2000        79 Goats                   200000 ipcc_tier2_energy 
#> 5  2000        79 Pigs                   8700000 bouwman_fcr       
#> 6  2000        79 Poultry                3900000 bouwman_fcr       
#> 7  2000        79 Horses                  150000 krausmann_per_head
#> 8  2000        79 Other                    30000 krausmann_per_head
build_feed_demand(example = TRUE, by = "feed_type")
#> # A tibble: 5 × 9
#>    year territory sub_territory livestock_category item_cbs_code feed_group
#>   <int> <chr>     <chr>         <chr>                      <int> <chr>     
#> 1  2000 79        NA            Cattle_milk                   NA NA        
#> 2  2000 79        NA            Cattle_milk                   NA NA        
#> 3  2000 79        NA            Cattle_milk                   NA NA        
#> 4  2000 79        NA            Pigs                          NA NA        
#> 5  2000 79        NA            Pigs                          NA NA        
#> # ℹ 3 more variables: feed_quality <chr>, demand_dm_t <dbl>, fixed_demand <lgl>