Calculates all decision variables for the WHEP typology using only production and consumption data (no import/export).
Usage
create_typologies_whep(
prod_destiny = create_n_prov_destiny(),
prod_n = dplyr::rename(create_n_production(), production_n = prod),
years = 2020
)Arguments
- prod_destiny
Tibble with N flows from
create_n_prov_destiny().- prod_n
Tibble with per-item N production from
create_n_production()(columnprod, renamed here toproduction_n).- years
Numeric vector of years to include (default = 2020).
Examples
# Minimal stand-ins for the two real inputs, carrying only the columns the
# decision variables read. Lugo feeds its livestock mostly on local grass;
# Barcelona feeds them mostly on imported N.
prod_destiny <- tibble::tribble(
~year, ~province_name, ~box, ~item, ~origin, ~destiny, ~mg_n,
2020, "Lugo", "Cropland", "Wheat and products",
"Cropland", "population_food", 100,
2020, "Lugo", "semi_natural_agroecosystems", "Grassland",
"semi_natural_agroecosystems", "livestock_rum", 800,
2020, "Lugo", "Cropland", "Maize and products",
"Cropland", "livestock_rum", 200,
2020, "Lugo", "Cropland", "Soyabean cake",
"Outside", "livestock_mono", 50,
2020, "Barcelona", "Cropland", "Wheat and products",
"Cropland", "population_food", 100,
2020, "Barcelona", "Cropland", "Soyabean cake",
"Outside", "livestock_mono", 900,
2020, "Barcelona", "Cropland", "Maize and products",
"Cropland", "livestock_mono", 100
)
prod_n <- tibble::tribble(
~year, ~province_name, ~box, ~production_n,
2020, "Lugo", "Cropland", 300,
2020, "Barcelona", "Cropland", 200
)
create_typologies_whep(
prod_destiny = prod_destiny,
prod_n = prod_n,
years = 2020
) |>
dplyr::select(year, province_name, human_share, import_share, Category)
#> # A tibble: 2 × 5
#> year province_name human_share import_share Category
#> <dbl> <chr> <dbl> <dbl> <chr>
#> 1 2020 Barcelona 0.5 0.9 Imported feed-based system
#> 2 2020 Lugo 0.333 0.0476 Local grass-based livestock syst…
