Biofortified Maize Impact Analysis

An ex-ante model of biofortification in Guatemala

Guatemala has one of the highest stunting rates in the world. Biofortified maize varieties are bred to contain higher levels of iron, zinc, and quality protein (lysine and tryptophan) so that an everyday staple food can deliver more nutrition to households.

This site documents the methodology used to estimate the potential impact of biofortified maize for both the children who eat the maize and for the farmers who grow it.

Model description

This framework is an ex-ante model: it projects the impacts of biofortified maize across the country in advance of large-scale rollout.

The ex-ante model is built as a sequence of distinct analytical problems, each solved using real Guatemalan data and chained together so that the output of one stage becomes the input of the next. The chain starts from reported household food consumption and ends in a nationwide simulation of impact. Every stage is documented in full on this site; this page shows how those stages connect.

The model addresses two questions:

  • The nutritional dimension — how biofortified maize changes children’s nutrient intake, and how that change translates into improved growth and reduced stunting.
  • The economic dimension — which farmers are likely to adopt the improved seed, and how adoption affects household income.

Both dimensions are combined in a two-dimensional simulation across 22 departments and a range of production coverage levels (the share of maize-growing area planted with biofortified seed).

From seed to national simulation

The intervention follows a short logical chain: biofortified seed increases the nutrient content of the maize that households consume, which raises children’s nutrient intake, which is linked to improved growth and lower stunting rates. Estimating the magnitude of each link from Guatemalan data is what the model does, and the diagram below shows the stages it is organised into. A parallel branch follows the agronomic consequences of the same intervention for farmers.

Biofortified maize seed Ex-ante starting point NUTRITIONAL ECONOMIC Individual nutrient intakes Household consumption split per person Intake estimated for surveyed children Transfer models, adjusted for bioavailability Child growth and stunting Two independent methods, one impact calculation Farmer adoption of the seed Modeled by farmer segment Change in household income Yield change and seed cost, by segment Biofortified maize supply Determines coverage reaching households Two-dimensional simulation By department and coverage level

The two dimensions begin from the same point — the decision to plant biofortified seed — and run in parallel. The economic branch determines how biofortified maize adoption impacts farmer incomes and how much biofortified maize reaches households at each coverage level; the nutritional branch determines what that maize does for child growth. The national simulation combines both.

Two dimensions, modeled separately, simulated together

Nutritional dimension

Children’s daily food consumption is estimated from household survey data, the maize contribution is separated from the rest of the diet, and the effect of biofortification on nutrient intake is estimated.

Estimating the resulting change in child growth and stunting rates is the most demanding step in the model: growth depends on many factors beyond diet, and the link between nutrient intake and growth is indirect. For this reason, child growth and stunting reduction are estimated through two independent pathways:

  • Covariate prediction — a statistical model relating each child’s nutrient profile to height-for-age, applied to a simulated child population.
  • Meta-analysis (Gunaratna) — a published effect size for quality protein maize, applied to the Guatemalan child population.

Economic dimension

Guatemalan maize farmers are characterised from national survey data, calibrated against the official agricultural census.

The model then estimates which farmers are likely to adopt the biofortified seed and how adoption changes household income. Adoption determines how much biofortified maize is produced, and therefore how much reaches households at each coverage level — the link between an agricultural intervention and a nutritional outcome.

Why two pathways for stunting. The two pathways rest on different data sources and different modelling assumptions. The covariate pathway derives the relationship between nutrient intake and growth from the Guatemalan anthropometric survey; the meta-analysis pathway applies an externally published effect size for quality protein maize. Estimating the same outcome by two routes with independent stunting-estimation machinery, applied to the same upstream adoption and distribution scenario, allows the convergence of their results to be examined directly. With the 2028 seed at full production coverage, the two pathways converge on a reduction of 5.6 to 5.8 percentage points in child stunting prevalence.

How the model is built: six modules

The ex-ante model is implemented as six modules. Each module solves one problem in the chain and passes its result to the next. Modules 1, 3, 4 and 5 build and apply the nutritional dimension, module 2 covers the economic dimension, and module 6 combines both into the national simulation. Each module links to its full technical documentation.

Module 1 · NutritionalNutrient IntakesHousehold surveys record food consumption at the level of the whole household, not the individual. This module reconstructs individual daily nutrient intake from household food consumption (ENCOVI 2023) combined with INCAP food composition tables, distributing household consumption to each member through the Adult Male Equivalent method. The maize contribution is kept separate from the rest of the diet, so that the effect of replacing conventional maize with biofortified maize can be isolated later. Module 2 · EconomicEconomic ImpactThis module characterises maize farmers from the agricultural section of ENCOVI 2023, calibrated to the Guatemalan Ministry of Agriculture (MAGA) census so that the sample represents the national farming population. Farmers are grouped into segments based on the type of maize seed they typically plant, the likelihood of adopting biofortified seed is modeled for each, and the income consequences of adoption — through changes in yield and seed cost — are estimated by segment. The resulting production determines how much biofortified maize is available in the food system at each coverage level. Module 3 · NutritionalTransfer ModelsNutrient intake is observed in the consumption survey (ENCOVI), while child growth is measured in a separate anthropometric survey (SIVESNU). The two surveys share no common identifier that would link a child to their diet. This module bridges that gap: survey-weighted regression models relate sociodemographic characteristics — available in both surveys — to nutrient intake, so that intake can be estimated for the children in the anthropometric survey. Separate models for maize and non-maize sources allow biofortification to be simulated directly. Module 4 · NutritionalCovariate PredictionThis module estimates the change in child growth and associated stunting reduction attributable to biofortified maize. It accounts for nutrient bioavailability — the proportion of an ingested nutrient the body can absorb and utilize, which varies with the food matrix, the chemical form of the nutrient, and the composition of the rest of the diet — and incorporates micronutrient supplementation (Chispitas) already reaching part of the population. A model then relates each child's nutrient profile to height-for-age. Because the surveys do not cover every combination of characteristics needed for nationwide simulation, a simulated child population representative of the country is generated. This is the first of the two stunting pathways. Module 5 · NutritionalMeta-analysisThis module estimates the stunting reduction attributable to quality protein maize using a published meta-analysis effect size (Gunaratna et al., 2010), applied to the Guatemalan child population. Resting on different data and assumptions from module 4, it provides a second, independent estimate of the same outcome. Module 6 · SimulationScenariosThe final module combines the nutritional and economic dimensions into the national ex-ante simulation, across 22 departments and a range of production coverage levels. It accounts for the geographic distribution of supply, including redistribution of biofortified maize between departments, to estimate impact under each scenario.

Where the numbers come from

The model is built on national data sources.

Dataset Description Year Provider
ENCOVI National Survey of Living Conditions 2023 INE Guatemala
SIVESNU National Anthropometric Surveillance System 2018 SESAN / INCAP
ENSMI National Maternal and Child Health Survey 2014–2015 MSPAS / INE
INE Vital statistics: annual births 2019–2023 INE Guatemala
INCAP Food composition tables 2014 INCAP Guatemala
WHO Child growth standards (height-for-age) 2006 World Health Organization
MAGA Agricultural production statistics 2023 Ministry of Agriculture
OSRM / OpenStreetMap Departmental travel-time matrix (22×22) 2026 Open routing project

Citation

@software{newseed_biofortification_guatemala,
  title  = {Biofortified Maize Impact Assessment Framework for Guatemala},
  author = {Sánchez Tormo, Julia María and Palomo Llinares, Rubén and
            Soto Méndez, María José and Adams, Katherine P. and Bowen, Curt},
  year    = {2026},
  version = {4.0.0},
  license = {AGPL-3.0},
  url     = {https://github.com/Azahar-Data-Insights/newseed-biofortification-guatemala-public}
}

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