Nutrition¶

Overview¶

The nutrition module ensures that the optimized food system meets population dietary requirements. This includes:

Macronutrient constraints: Carbohydrates, protein, fat, and calories per capita
Food group constraints: Consumption of whole grains, fruits, vegetables, etc.
Population scaling: Aggregating per-capita needs to regional/national totals

Macronutrients¶

Configuration¶

Macronutrient constraints are specified in config/default.yaml:

macronutrients: {}
  # For each of "carb", "protein", "fat" and "kcal" we support "min",
  # "max" and "equal" keywords, which are given in g/person/day; see
  # example below.
  # carb:
  #   min: 250     # g/person/day
  # protein:
  #   min: 50      # g/person/day
  # fat:
  #   min: 50      # g/person/day
  # cal:
  #   min: 2000    # kcal/person/day

# --- section: byproducts ---
# Foods that are not for direct human consumption (excluded from food group tracking)
byproducts:
- wheat-bran
- wheat-germ
- rice-bran
- barley-bran
- oat-bran
- buckwheat-hulls
- sunflower-meal
- rapeseed-meal

Constraint types:

min: Lower bound (≥)
max: Upper bound (≤)
equal: Exact requirement (=)

Food Groups¶

Beyond macronutrients, the model can also constrains consumption of food groups. Moreover, food groups are used to assess dietary risk factors (see Health Impacts).

Configuration¶

food_groups:
  included:
  - whole_grains
  - grain
  - fruits
  - vegetables
  - legumes
  - nuts_seeds
  - starchy_vegetable
  - oil
  - red_meat
  - poultry
  - dairy
  - eggs
  - sugar
  # Optional per-group constraints with "min", "max" or "equal" in g/person/day
  constraints: {}
  equal_by_country_source: null

List the active groups under food_groups.included and only specify constraints for the ones that need limits (min, max, or equal in g/person/day). Leaving constraints empty allows the optimizer to choose any mix of foods that satisfies macronutrient and other requirements.

Foods are assigned to groups in data/food_groups.csv. Example:

Population Data¶

Population projections come from the UN World Population Prospects (WPP) 2024 revision.

Data Processing¶

The prepare_population rule (workflow/scripts/prepare_population.py):

Load WPP data: data/downloads/WPP_population.csv.gz
Filter:
- Countries in config['countries']
- Planning horizon year (config['planning_horizon'], e.g., 2030)
- Medium variant projection
Aggregate: Sum population by country (converts thousands → persons)
Output:
- processing/{name}/population.csv: Total population by country
- processing/{name}/population_age.csv: Age-structured population for health module

Age Structure¶

Age-structured population is used in the health module to weight dietary risk factors by demographic composition (children vs. adults vs. elderly have different disease burdens).

Nutritional Content Data¶

The file data/nutrition.csv contains nutritional composition for each food product, sourced from the USDA FoodData Central database. This data is retrieved from the SR Legacy (Standard Reference) database, which provides laboratory-analyzed nutrient data for foods.

Data source: U.S. Department of Agriculture, Agricultural Research Service. FoodData Central, 2019. https://fdc.nal.usda.gov/

Content: Macronutrient values (protein, carbohydrates, fat) and energy (kcal) per 100g of food product.

License: Public domain under CC0 1.0 Universal. See Data Sources for full details.

The FAO Nutrient Conversion Table for Supply Utilization Accounts (2024 edition) is also stored locally in data/downloads/fao_nutrient_conversion_table_for_sua_2024.xlsx via the download_fao_nutrient_conversion_table workflow rule, providing FAO-authored nutrient factors for cross-checking FAOSTAT supply data (subject to FAO’s non-commercial use guidance). workflow/scripts/prepare_fao_edible_portion.py distils the edible portion coefficients from sheet 03 of that workbook for all configured crops, materialising them in processing/{name}/fao_edible_portion.csv for downstream use.

When the model assembles crop→food conversion links it rescales dry-matter crop production to fresh edible food mass using these coefficients together with moisture fractions from data/crop_moisture_content.csv: dry harvests are uplifted by edible_portion_coefficient / (1 - moisture_fraction) before applying the pathway-specific processing factors from data/foods.csv. Each processing pathway can produce multiple food products with factors that maintain mass balance (sum ≤ 1.0). Crops flagged in data/yield_unit_conversions.csv are the few cases where GAEZ reports processed outputs (sugar or oil); those entries handle the unit conversion back to dry matter so that downstream processing can proceed uniformly.

Retrieval:

The repository includes pre-fetched nutritional data from USDA
To update with fresh data, enable data.usda.retrieve_nutrition: true in the config
Run: snakemake -- data/nutrition.csv (requires network access and API key)
Food-to-USDA mappings are maintained in data/usda_food_mapping.csv
A shared API key is included in the repository; users can optionally obtain their own free API key at https://fdc.nal.usda.gov/api-key-signup

Per-Capita vs. Total Consumption¶

The model works with total annual flows (Mt/year) but nutritional requirements are per-capita per-day. Conversion:

\[\text{Total requirement (Mt/year)} = \frac{\text{per capita (g/day)} \times \text{population} \times 365}{10^{12}}\]

This is handled internally by _per_capita_to_bus_units() in workflow/scripts/build_model.py.

From the model’s perspective:

Food buses carry total food availability (Mt)
Nutrient buses carry total nutrient availability (Mt for mass, PJ for energy)
Constraints compare these totals to population-scaled requirements

Dietary Patterns¶

The model does not currently prescribe specific dietary patterns (e.g., Mediterranean, vegetarian, EAT-Lancet) but rather:

Lower / upper bounds: Ensure minimum nutritional adequacy
Cost minimization: Subject to those bounds, minimize environmental + health costs

Workflow Integration¶

Nutritional constraints are incorporated in the build_model rule:

Load population: processing/{name}/population.csv
Load nutrition data: data/nutrition.csv
Create nutrient buses: Per-country buses for each nutrient
Create food → nutrient links: Based on nutritional content
Add global constraints: Population × requirement bounds

No separate rule needed—nutrition is integrated into the model structure.