Production Costs

Overview

The model incorporates production costs for both crop and livestock systems to represent the economic considerations of agricultural production. Costs are applied as marginal costs on production links in the PyPSA network, ensuring that the optimization accounts for both physical and economic constraints.

This page provides an overview of how production costs are sourced, processed, and applied throughout the model.

Cost Categories

The model distinguishes between three main categories of production costs:

Crop Production Costs

Costs associated with growing crops, including labor, machinery, energy, and other inputs (excluding fertilizer, which is modeled endogenously).

Livestock Production Costs

Costs associated with raising animals for meat, milk, and eggs, including labor, veterinary services, housing, and energy (excluding feed and land, which are modeled endogenously).

Grazing Costs

Costs specifically associated with pasture-based livestock production, representing the management and maintenance of grassland feed systems.

Land Conversion Costs

Investment costs for expanding agriculture onto new land, covering physical clearing and soil preparation. Differentiated by forest vs. non-forest cover type, annualized using a capital recovery factor over a configurable investment horizon. Applied as marginal costs on land_conversion and new_to_pasture links. See Land Use & Resource Classes for details.

What Costs Include and Exclude

Included Costs

Production costs in the model capture the following expense categories:

  • Labor: Both hired labor and the opportunity cost of unpaid/family labor

  • Veterinary services: Animal health care and preventive treatments (livestock only)

  • Energy: Electricity and fuel for farm operations

  • Machinery and equipment: Depreciation and maintenance

  • Housing and facilities: Depreciation of buildings and infrastructure (livestock only)

  • Interest on operating capital: Financial costs of production

  • Other variable inputs: Seeds, pesticides, and other operational expenses (crops only)

Excluded Costs (Modeled Endogenously)

The following cost categories are excluded from the production cost data because they are represented explicitly in the optimization model:

  • Feed costs: Crop and residue feed inputs are modeled as network flows with their own costs

  • Fertilizer costs: Synthetic fertilizer is a constrained resource in the model

  • Land costs and rent: Land opportunity cost is implicit in the land allocation decisions

  • Grazing feed costs (for livestock production costs): Grassland feed is modeled separately with its own grazing costs

This separation ensures that costs are not double-counted while maintaining accurate economic representation.

Data Sources

Crop Costs: FAOSTAT Producer Prices

Crop production costs are derived from FAOSTAT data, using producer prices as a revenue proxy scaled by a configurable cost share.

Coverage:

  • Spatial: Per-(crop, country) for all modeled countries

  • Temporal: Configurable averaging period (default 2015-2022)

  • Crops: All crops with FAOSTAT price and yield data; unmapped crops use proxy mappings from data/curated/faostat_cost_proxies.yaml

Data characteristics:

  • Prices from the FAOSTAT Prices (PP) domain (element 5532: Producer Price USD/tonne)

  • Yields from the FAOSTAT Production (QCL) domain (element 5412: Yield hg/ha)

  • CPI-deflated to the configured base year before averaging

Source:

Workflow script: workflow/scripts/prepare_faostat_crop_costs.py

Livestock Costs: USDA and FADN

Livestock production costs are sourced from two agricultural accounting systems.

USDA Economic Research Service (United States):

  • Coverage: Dairy (milk), beef cattle (cow-calf), hogs (pork)

  • Time period: 2015-2024 (averaged across years)

  • Units: USD per acre or USD per head

  • Workflow script: workflow/scripts/retrieve_usda_animal_costs.py

FADN - Farm Accountancy Data Network (European Union):

  • Coverage: All major animal production systems

  • Time period: 2004-2020 (averaged across years)

  • Units: EUR per farm (allocated to livestock categories)

  • Workflow script: workflow/scripts/retrieve_fadn_animal_costs.py

Cost Processing Methodology

The cost data undergoes several processing steps to ensure consistency and accuracy across different sources and production systems.

Crop Costs

Crop production costs are derived from FAOSTAT producer prices and yields, providing per-(crop, country) cost estimates.

Script: workflow/scripts/prepare_faostat_crop_costs.py

The cost model uses revenue per hectare as a proxy for total production cost, scaled by a configurable share:

\[C_{\mathrm{ha}} = P \times Y \times f_{\mathrm{non\text{-}endog}}\]

where \(P\) is the producer price (USD/t), \(Y\) is yield (t/ha), and \(f_{\mathrm{non\text{-}endog}}\) is the non-endogenous cost share (default 0.7).

Processing steps:

  1. Load FAOSTAT bulk data: Producer prices from the PP domain (element 5532) and yields from the QCL domain (element 5412)

  2. Map crops to FAOSTAT items: Using data/curated/faostat_crop_item_map.csv; crops without a direct mapping use proxy crops defined in data/curated/faostat_cost_proxies.yaml (e.g., alfalfa uses soybean prices, biomass-sorghum uses sorghum)

  3. CPI deflation: Prices are deflated to the configured base year using US CPI-U data

  4. Merge price and yield: For each (crop, country, year), compute revenue per hectare = price × yield

  5. Temporal averaging: Average revenue per hectare across the configured period (default 2015-2022)

  6. Apply cost share: Multiply by non_endogenous_cost_share (default 0.5) to obtain the cost estimate

  7. Fill gaps: Missing (crop, country) pairs receive the global median cost for that crop as fallback

  8. Output: processing/{name}/faostat_crop_costs.csv with columns:

    • crop: Crop name

    • country: ISO3 country code

    • cost_usd_{base_year}_per_ha: Production cost estimate (USD/ha)

    • n_years: Number of years with valid price-yield data

    • is_fallback: Whether the value is a global median fallback

The resulting cost estimates vary substantially across crops and countries, reflecting differences in local prices, yields, and agricultural productivity. The map below shows the median cost per country (across all crops), while the distribution plot reveals the cross-country spread for each crop individually.

World map of median crop production costs per country

Median crop production cost across all crops per country (USD/ha, log scale). Higher costs in Europe and North America reflect higher input prices and labour costs; lower costs in Sub-Saharan Africa and South Asia reflect lower price levels.

Distribution of crop production costs across countries for each crop

Cross-country cost distributions per crop (USD/ha, log scale). Boxen (letter-value) plots show the full distributional shape; crops are grouped by category. Vegetables and fruits have the highest and most variable costs, while cereals and legumes cluster at lower levels.

Calibration Correction

An optional additive calibration correction adjusts production costs for crops, grassland, and animals based on shadow prices from a model solved with tight production stability constraints. When the model is forced to reproduce observed production levels (±1%), the dual variables on those constraints reveal how much each production link’s cost would need to change to make the observed allocation cost-optimal. These duals are extracted as additive corrections and applied at build time.

  • Correction files: data/curated/calibration/crop_cost.csv, grassland_cost.csv, animal_cost.csv

  • Application: Additive corrections to marginal costs; clipped to zero (no negative costs)

  • Configuration: Controlled by top-level cost_calibration section (disabled by default)

  • Generation: tools/smk --configfile config/cost_calibration.yaml -- data/curated/calibration/crop_cost.csv data/curated/calibration/grassland_cost.csv data/curated/calibration/animal_cost.csv

Livestock Costs

Livestock production costs follow a similar two-source approach with additional complexity due to the need to convert per-head costs to per-tonne costs.

USDA Livestock Cost Processing

Script: workflow/scripts/retrieve_usda_animal_costs.py

  1. Download Data: Fetch cost and returns data from USDA ERS for each animal product

  2. Filter and Aggregate Costs:

    • Included: Operating costs, allocated overhead, labor (including opportunity cost)

    • Excluded: Feed costs (endogenous), land rent

    • Grazing costs: Separately extracted using grazing_cost_items parameter (e.g., “Grazed feed” line item)

  3. Per-Head Calculation: Sum relevant cost line items to get total cost per animal per year

  4. Physical Yield Data: Use USDA production statistics to get output per head:

    • Milk: Pounds per cow per year → tonnes per head

    • Meat: Live weight → carcass weight → retail meat weight (using USDA conversion factors)

  5. Convert to Per-Tonne Costs:

    \[\text{Cost per tonne product} = \frac{\text{Cost per head (USD/year)}}{\text{Yield (tonnes product/head/year)}}\]

  6. Separate Grazing Costs: Maintain separate column for grazing-specific costs

  7. Inflation Adjustment: Convert to base year USD using CPI

  8. Output: processing/{name}/usda_animal_costs.csv

FADN Livestock Cost Processing

Script: workflow/scripts/retrieve_fadn_animal_costs.py

FADN livestock costs use a sophisticated allocation methodology:

  1. Farm-Level Cost Extraction:

    • Read FADN farm accounting data for livestock-specialized farms

    • Extract total livestock costs (labor, veterinary, energy, depreciation, etc.)

    • Excluded: Feed costs, land rent

    • Grazing costs: Separately identified using grazing_cost_items (SE codes for pasture/grazing)

  2. Allocation to Livestock Categories:

    • Specific costs (veterinary, animal-specific inputs): Allocated by livestock output value share

    • Shared overhead (buildings, energy, general labor): Allocated by livestock sector’s share of total farm output to avoid over-allocation in mixed farms

  3. Normalize to Livestock Units (LU):

    • Convert farm-level costs to per-LU using standard coefficients:

      • 1 Dairy Cow = 1.0 LU

      • 1 Beef Cow = 0.8 LU

      • 1 Pig = 0.3 LU

      • 1 Sheep = 0.1 LU

    • This produces cost per head for each animal category

  4. Physical Yield Calculation:

    • Use FAOSTAT country-level data: Total Production ÷ Total Stocks = Yield per head

    • This captures regional differences in:

      • Slaughter weights and cycles per year (meat)

      • Dairy productivity (milk yield per cow)

      • Herd structure (breeding vs. production animals)

  5. Convert to Per-Tonne Costs (same formula as USDA)

  6. Currency and Inflation: Inflate to base year EUR (HICP), convert to international USD (PPP)

  7. Separate Grazing Costs: Maintain separate column for pasture/grazing-related costs

  8. Output: processing/{name}/fadn_animal_costs.csv

Merging Livestock Costs

Script: workflow/scripts/merge_animal_costs.py

  1. Load Multiple Sources: Combine USDA and FADN livestock cost estimates

  2. Average Across Sources: For products with multiple data sources, compute mean

  3. Apply Fallback Mappings: For products without direct data:

    • Chicken → Pork (similar intensive housed systems)

    • Eggs → Pork (intensive production)

    • Defined in configuration under animal_cost_fallbacks

  4. Maintain Separate Grazing Costs: Keep grazing cost column distinct from general production costs

  5. Output: processing/{name}/animal_costs.csv with columns:

    • product: Animal product name

    • cost_per_mt_usd_{base_year}: Production cost excluding grazing (USD/tonne product)

    • grazing_cost_per_mt_usd_{base_year}: Grazing-specific cost (USD/tonne product)

Grazing Costs

Grazing costs are extracted as a separate component during livestock cost processing and then converted to feed-basis costs for application in the model.

Extraction from Source Data

During USDA and FADN livestock cost processing, grazing costs are identified using configured line items:

USDA: Line items labeled “Grazed feed” or similar in the cost and returns spreadsheets

FADN: SE codes corresponding to pasture management, grassland maintenance

These costs are:

  • Allocated to livestock products by output value share (same methodology as other costs)

  • Stored in separate grazing_cost_per_mt_usd_{base_year} column

  • Expressed per tonne of animal product (not per tonne of feed)

Conversion to Feed-Basis Costs

Script: workflow/scripts/build_model/grassland.py (function calculate_grazing_cost_per_tonne_dm)

Since grazing costs in the source data are per tonne of animal product, they must be converted to per tonne of dry matter (DM) feed for application to grassland feed links:

  1. Load Data:

    • Grazing costs per tonne product from animal_costs.csv

    • Feed conversion efficiencies from feed_to_products.csv (tonnes product per tonne feed DM)

  2. Calculate Implied Feed Cost:

    For each ruminant product, the grazing cost per tonne of feed is:

    \[\text{Feed Cost (USD/tonne DM)} = \text{Product Cost (USD/tonne)} \times \text{Efficiency (tonne product/tonne DM)}\]

  3. Global Averaging: Average the implied feed costs across all ruminant products to get a single grazing cost rate

  4. Result: A single global grazing cost in USD per tonne dry matter feed

This approach ensures that grazing costs are:

  • Properly allocated across different ruminant products

  • Consistent with the feed conversion efficiencies used in the model

  • Applied at the correct point in the production chain (grassland feed production)

Application in the Optimization Model

Production costs are applied as marginal costs on PyPSA network links, affecting the objective function during optimization.

Crop Production Costs

Implementation: workflow/scripts/build_model/crops.py

Crop costs are applied to production links that convert land into crop output:

Link structure:

  • Input (bus0): Land pool (Mha) by region, resource class, water supply

  • Output (bus1): Crop commodity bus (Mt) by country

  • Efficiency: Crop yield (Mt/Mha)

Cost calculation:

For single-season crops:

# Look up per-(crop, country) cost from FAOSTAT-derived data
cost_per_ha = crop_costs.get((crop, country), global_median_cost.get(crop, 0.0))

# Convert USD/ha to bnUSD/Mha (PyPSA units)
marginal_cost = cost_per_ha * 1e6 * USD_TO_BNUSD

# Optional: add calibration correction (bnUSD/Mha, additive)
marginal_cost += cost_calibration.get((crop, country), 0.0)

For multi-cropping systems (multiple crops per year on the same land):

# Sum per-(crop, country) costs across all crops in the combination
total_cost = sum(crop_costs.get((c, country), median) for c in crops_in_cycle)

# Convert to bnUSD/Mha
marginal_cost = total_cost * 1e6 * USD_TO_BNUSD
Interpretation:

  • The marginal cost represents the economic cost of using one Mha of land for crop production

  • Costs vary by country, reflecting local price and yield conditions

  • The optimization balances production costs against other objectives (nutrition, emissions, etc.)

Livestock Production Costs

Implementation: workflow/scripts/build_model/animals.py (lines 230-243)

Livestock costs are applied to links that convert feed into animal products:

Link structure:

  • Input (bus0): Feed bus (Mt DM) by feed category and country

  • Output (bus1): Animal product bus (Mt fresh weight) by country

  • Efficiency: Feed conversion efficiency (Mt product / Mt feed DM)

Cost calculation:

# Cost from animal_costs.csv (USD per Mt product)
cost_per_mt_product = animal_costs.loc[product]

# Efficiency (Mt product per Mt feed DM)
efficiency = feed_requirements.loc[product, feed_category, 'efficiency']

# Convert to cost per Mt feed input
cost_per_mt_feed = cost_per_mt_product / efficiency

# Convert to bnUSD per Mt (PyPSA units)
marginal_cost = cost_per_mt_feed * USD_TO_BNUSD
Interpretation:

  • The marginal cost represents the economic cost of converting feed into animal product

  • More efficient production systems (higher efficiency) have lower costs per unit feed input

  • The optimization accounts for both feed conversion efficiency and production costs

Grazing Costs

Implementation: workflow/scripts/build_model/grassland.py (lines 235-236)

Grazing costs are applied to links that produce grassland feed from land:

Link structure:

  • Input (bus0): Land pool (Mha) by region and resource class (rainfed only)

  • Output (bus1): Ruminant grassland feed bus (Mt DM) by country

  • Efficiency: Grassland yield (Mt DM / Mha)

Cost calculation:

# Grazing cost (USD per tonne DM)
grazing_cost_per_tonne_dm = calculate_grazing_cost_per_tonne_dm(...)

# Grassland yield (Mt DM per Mha) — already effective feed yield
efficiency = grassland_yield

# Convert to cost per Mha (bnUSD/Mha)
marginal_cost = (grazing_cost_per_tonne_dm * efficiency *
                 MEGATONNE_TO_TONNE * USD_TO_BNUSD)
Interpretation:

  • The marginal cost represents the economic cost of producing grassland feed from one Mha of pasture

  • Higher-yielding grassland has higher costs per Mha (but the cost per tonne DM is constant)

  • Grassland yields are already corrected for utilization in the merge step (see Livestock & Grazing)

Model Units and Conversions

Mass Units

  • Input data: Often in tonnes (t) or kilograms (kg)

  • Model buses: Megatonnes (Mt) for all commodity flows

  • Conversion: 1 Mt = 1,000,000 t = 1e6 t

Area Units

  • Input data: Usually hectares (ha) or acres

  • Model land buses: Mega-hectares (Mha)

  • Conversions:

    • 1 Mha = 1,000,000 ha = 1e6 ha

    • 1 acre = 0.404686 ha

    • 1 ha = 2.47105 acres

Currency Units

  • Input data: USD or EUR (various base years)

  • Model objective: Billion USD (bnUSD) in configured base year

  • Conversion: 1 bnUSD = 1,000,000,000 USD = 1e9 USD

  • Constant: USD_TO_BNUSD = 1e-9

Configuration

Cost-related configuration parameters are specified in config/default.yaml:

Crop costs:

crop_costs:
  non_endogenous_cost_share: 0.5  # Fraction of revenue attributed to non-endogenous costs
  faostat:
    price_element_code: 5532  # Producer Price (USD/tonne)
    yield_element_code: 5412  # Yield (hg/ha)
cost_calibration:
  enabled: false       # Apply calibration corrections
  generate: false      # Generate calibration from solved model
  scenario: "calibration"
  crop_correction_csv: "data/curated/calibration/crop_cost.csv"
  grassland_correction_csv: "data/curated/calibration/grassland_cost.csv"
  animal_correction_csv: "data/curated/calibration/animal_cost.csv"

Animal cost fallback mappings:

animal_cost_fallbacks:
  chicken: pork
  eggs: pork

Grazing cost items (USDA):

grazing_cost_items:
  - "Grazed feed"

Grazing cost items (FADN, SE codes):

fadn_grazing_cost_items:
  SE105: "Forage crops"
  SE110: "Pasture"

See Also