Three estimation methods are combined:
1. **IPCC linear model** (IPCC 2019 Refinement, Vol.4, Ch.11, Table 11.1a): `AG_residue_DM = Slope * Yield_DM + Intercept`
2. **Biomass_coefs ratio model** (package default): `Residue_FM = Yield_FM * kg_residue_kg_product_FM`
3. **Modern variety adjustment** (Krausmann et al. 2013; Evenson & Gollin 2003): Historical correction for pre-Green-Revolution varieties that had lower harvest index (more residue per unit product).
The final estimate is a weighted mean of methods 1 and 2, with irrigation and variety-era adjustments applied.
Arguments
- Dataset
Data frame with crop production data. Required columns:
- Name_biomass
Crop name matching Biomass_coefs classification.
- Prod_ygpit_Mg
Production in Mg fresh matter.
- Area_ygpit_ha
Harvested area in hectares.
- Year
Year of production (for modern variety adjustment).
- region_HANPP
Region name matching Modern_variety_adoption.
- Water_regime
One of "Irrigated", "Rainfed", or "Mixed".
- w_ipcc
Numeric weight for the IPCC linear model in the ensemble (0-1). Default 0.5. The Biomass_coefs ratio gets weight `1 - w_ipcc`.
- simple
Logical. If `TRUE`, bypasses all context-dependent corrections (irrigation, modern-variety HI, N-input adjustments), setting all adjustment factors to 1. No `Water_regime`, `region_HANPP`, or `N_input_kgha` columns required. Default `FALSE`.
Value
Data frame with added columns:
- Prod_MgDM
Product dry matter (Mg).
- Residue_MgDM
Estimated above-ground residue dry matter (Mg).
- Yield_DM_Mgha
Yield in Mg DM per hectare (intermediate).
Details
Estimates crop residue (straw, stover, etc.) dry matter from yield data using an ensemble of IPCC 2019 linear models, Biomass_coefs ratios, and context-dependent adjustments for irrigation, N input, and modern variety adoption.
**IPCC linear model**: For each crop mapped via `IPCC_crop_mapping`, the per-hectare residue is estimated as: `Residue_IPCC_Mgha = Slope_AG * Yield_DM_Mgha + Intercept_AG_MgDMha` This captures the empirical finding (Lassaletta et al. 2014) that residue production is not purely proportional to yield — there is a base structural component (intercept).
**Irrigation adjustment**: Irrigated crops typically have higher harvest index (Sadras 2007), meaning less residue per unit product. The adjustment factor (default 0.90 for irrigated) is from the `Irrigation_adj` table.
**Modern variety correction**: Pre-Green-Revolution varieties had lower harvest index. The `Modern_variety_adoption` table provides crop-group- specific regional time-series of adoption rates from Evenson & Gollin (2003), interpolated to annual resolution. Combined with `HI_crop_ranges` (crop-specific HI gap factors), this computes: `HI_correction = 1 + (1 - Modern_share) * (HI_gap_factor - 1)`.
**Ensemble**: The final residue estimate blends both methods: `Residue_MgDM = w_ipcc * IPCC_estimate + (1 - w_ipcc) * ratio_estimate`
Requires these objects from `load_general_data()`: - `Biomass_coefs` (with `Product_kgDM_kgFM`, `Residue_kgDM_kgFM`, `kg_residue_kg_product_FM`) - `IPCC_residue_coefs`, `IPCC_crop_mapping` (with `crop_group` column) - `Irrigation_adj`, `Modern_variety_adoption`, `HI_crop_ranges`
References
IPCC (2019) 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories, Volume 4, Chapter 11, Table 11.1a.
Krausmann et al. (2013) Global human appropriation of net primary production doubled in the 20th century. PNAS 110:10324-10329.
Lassaletta et al. (2014) 50 year trends in nitrogen use efficiency of world cropping systems. Biogeosciences 11:2889-2907.
Sadras (2007) Evolutionary aspects of the trade-off between seed size and number in crops. Field Crops Research 100:125-138.
Examples
if (FALSE) { # \dontrun{
load_general_data()
crop_data |>
calculate_crop_residues(w_ipcc = 0.5)
} # }