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Estimate direct soil N2O emissions from applied nitrogen.

Source: R/n_balance_losses.R
calculate_soil_n2o.Rd

Three emission-factor regimes for direct nitrous oxide from nitrogen applied to soil. "ipcc2019" (the default) is the IPCC 2019 Tier 1 climate-disaggregated EF1, needing only climate: it reuses the n2o_efs_disaggregated table's two climate-level rows (irrig_type == "Tier_1" for ATL, irrig_type == "Med_average" for MED, 0.010 wet / 0.005 dry) with no mf multiplier; the ATL value (0.010) is the same value documented as EF1 in build_crop_soil_n2o_extension(), pulled from one shared source of truth rather than hardcoded a second time. It is the default because it is the internationally standard, globally applicable Tier 1 method. "aguilera" (n_fun.r:906-912) is a finer Mediterranean-calibrated disaggregation (Cayuela et al. 2017), selectable where its irrig_type/fert_type granularity is available and its regional emission factors apply: n2o_direct_n_t = n_input_t * ef * mf, ef from n2o_efs_disaggregated on (irrig_type, climate), mf from fertiliser_n2o_modifiers on (fert_type, climate). "ipcc2006" uses the n2o_efs_ipcc2006 table (IPCC 2006 Tier 1 defaults, flat 0.010 except flooded rice 0.003), keyed like "aguilera" on (irrig_type, climate) with no mf multiplier.

Usage

calculate_soil_n2o(
  x,
  method = c("ipcc2019", "aguilera", "ipcc2006"),
  example = FALSE
)

Arguments

x

A tibble with n_input_t and climate. method = "aguilera" or "ipcc2006" additionally require irrig_type and (aguilera only) fert_type.

method

"ipcc2019" (default, IPCC 2019 Tier 1, climate-only), "aguilera" (Mediterranean-calibrated, needs irrig_type/fert_type) or "ipcc2006" (IPCC 2006 Tier 1, needs irrig_type).

example

If TRUE, return a small fixture instead of computing from x. Defaults to FALSE.

Value

x with n2o_direct_n_t and method_soil_n2o appended.

Examples

calculate_soil_n2o(example = TRUE)
#> # A tibble: 1 × 5
#>   n_input_t climate irrig_type  n2o_direct_n_t method_soil_n2o
#>       <dbl> <chr>   <chr>                <dbl> <chr>          
#> 1        10 MED     Med_average           0.05 ipcc2019