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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"),
  region_fallback = c("member_mix", "none"),
  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).

region_fallback

How to give a Bouwman feed region to a reporting bucket the crosswalk leaves without one. "member_mix" (default) splits Rest of World (area_code 999) across the Bouwman regions of the 62 reporting areas folded into it, weighted by the livestock those members carry. "none" leaves the bucket unmapped, which drops its whole feed demand from the feed-type mix; that was the behaviour before whep#467 and is kept selectable for comparison. Neither value affects any bucket the crosswalk already resolves.

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).

plus the polity columns below.

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. That grain is keyed by territory, not area_code, so it carries no polity columns.

Polity columns

Every area-keyed output carries the polity its area_code resolves to in that row's year:

  • polity_area_code: The numeric key rows are AGGREGATED on, for the matrix workflows. It is a bucket, not an identity: use reporting_polity_code to say which territory a row belongs to.

  • reporting_polity_code: The polity itself, e.g. ESP-1846-1914. It is year-aware, so the same area_code resolves to different polities in different years, which is the point of the crosswalk.

  • reporting_polity_name: Its name. It can differ from the area's own name where the area folds into an aggregate.

  • reporting_polity_has_geometry: Whether the polity has a polygon in the WHEP polity database, for callers that need to map or intersect it. FALSE is a documented gap upstream, not an error.

Rows whose area_code resolves to no polity keep the columns with NA rather than being dropped, so a gap is visible instead of silent.

Rows before the back-cast anchor year resolve to the polity live in that anchor year rather than to the polity live in the row's own year, because WHEP's pre-anchor series are back-cast onto the anchor-year territory. See add_polity_code() for the reasoning. Where that polity is not live in the row's own year – 41.5% of the pre-1961 (area, year) cells – add_polity_code() says so as mapping_status == "backcast_anchor", and polity_coverage_gaps() reports it as gap_kind == "backcast_anchor". These columns do not say so either way.

A row whose year no mapped period covers is resolved to the NEAREST period of the same area instead, so reporting_polity_code can name a polity that did not exist in that row's year – FAOSTAT bucket 206 "Sudan (former)" keeps reporting after SUD-1956-2011 ends, and its post-2011 rows carry that code. These columns do not say so: add_polity_code() reports such a row as mapping_status == "out_of_span", and that column is dropped here so that adding it does not change the schema of every area-keyed output at once. polity_coverage_gaps() reports the stand-in rows of a built table, and options(whep.polity_mapping_status = "flag") (or "status") carries the signal on the outputs themselves. Both are opt-in; the default is no extra column.

Examples

build_feed_demand(example = TRUE)
#> # A tibble: 8 × 9
#>    year area_code polity_area_code reporting_polity_code reporting_polity_name
#>   <int>     <int>            <int> <chr>                 <chr>                
#> 1  2000        79               79 DEU-1990-2025         Germany              
#> 2  2000        79               79 DEU-1990-2025         Germany              
#> 3  2000        79               79 DEU-1990-2025         Germany              
#> 4  2000        79               79 DEU-1990-2025         Germany              
#> 5  2000        79               79 DEU-1990-2025         Germany              
#> 6  2000        79               79 DEU-1990-2025         Germany              
#> 7  2000        79               79 DEU-1990-2025         Germany              
#> 8  2000        79               79 DEU-1990-2025         Germany              
#> # ℹ 4 more variables: reporting_polity_has_geometry <lgl>,
#> #   livestock_category <chr>, demand_dm_t <dbl>, method_demand <chr>
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>