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:
  carb:
    min: 250     # g/person/day
  protein:
    min: 50      # g/person/day
  fat:
    min: 50      # g/person/day
  kcal:
    equal: 2400  # kcal/person/day (source: EAT/Lancet 2025)

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:
  whole_grains:
    min: 0
  grain:
    min: 0
  fruits:
    min: 0
  vegetables:
    min: 0
  legumes:
    min: 0
  nuts_seeds:
    min: 0
  starchy_vegetable:
    min: 0
  oil:
    min: 0
  red_meat:
    min: 0
  dairy:
    min: 0

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):

1. Load WPP data: data/downloads/WPP_population.csv.gz

2. Filter:

   • Countries in config['countries']

   • Planning horizon year (config['planning_horizon'], e.g., 2030)

   • Medium variant projection

3. Aggregate: Sum population by country (converts thousands → persons)

4. 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 and water content (g/100g) 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: dry harvests are uplifted by edible_portion_coefficient / (1 - water_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 retain their reported units and skip this rescaling, as their GAEZ yields already describe the processed output.

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, Mcal 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:

1. Lower / upper bounds: Ensure minimum nutritional adequacy

2. Cost minimization: Subject to those bounds, minimize environmental + health costs

Workflow Integration

Nutritional constraints are incorporated in the build_model rule:

1. Load population: processing/{name}/population.csv

2. Load nutrition data: data/nutrition.csv

3. Create nutrient buses: Per-country buses for each nutrient

4. Create food → nutrient links: Based on nutritional content

5. Add global constraints: Population × requirement bounds

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