Precompute Biofortification Scenarios

Module 6: Scenarios

Why Precompute Scenarios?

The Shiny dashboard requires real-time response to user interactions. Computing biofortification impacts on-the-fly would involve:

Resolving each farmer’s conversion ratio along the continuous adoption curve for every national production coverage level.
Looking up pre-computed individual profiles (baseline intake, QPM intake, baseline stunting, QPM stunting) for the 4,065 children of the synthetic population or the 4,134 ENCOVI children, depending on the pathway.
Allocating biofortified maize across departments using the gravity-based redistribution from the market baseline.
Aggregating survey-weighted metrics across 22 departments and 11 production coverage levels.

This pipeline takes approximately 3-5 minutes per scenario combination. The framework evaluates a grid of 18 scenario combinations: three seed variants (2022, 2026 and 2028 seed), three subsidy regimes (current, 50% reduction, 75% reduction), and two stunting methodologies (covariate prediction and meta-analysis). Computing the grid on demand would take longer than an interactive session allows, so every scenario is computed in advance and the dashboard reads the result.

This page documents the simulation methodology with the baseline scenario combining the 2022 biofortified seed variety with the current subsidy regime as of April 2026 as the canonical reference. Alternative seed variants and subsidy regimes follow the same methodology with different upstream parameters.

Simulation Architecture

The scenario simulation pipeline generates two complementary datasets for each combination of seed × subsidy × pathway:

Output	Rows	Use case
Departmental scenarios	242 (22 × 11)	Geographic comparisons, departmental filtering
National scenarios	11	National-level indicators, overview metrics

The two share the same simulation logic and differ in the aggregation level: the departmental dataset aggregates metrics by departamento, and the national one computes weighted means directly at the Guatemala level.

Direct Aggregation Principle

National metrics are computed from individual-level data using survey weights, not by averaging departmental summaries. Averaging the summaries would weight each department equally regardless of its population, which is where Simpson’s paradox enters.

Pre-Computed Individual Profiles

A defining feature of this module is the separation of responsibilities with respect to the upstream nutritional and stunting modeling:

Module 6 receives pre-computed individual profiles (baseline intake, QPM intake, baseline stunting, QPM stunting, delta HAZ) from Modules 4 and 5. Module 6’s role is exclusively to (i) determine which children receive biofortified maize at each production coverage level using the hierarchical priority assignment, and (ii) aggregate the resulting individual outcomes to departmental and national level. The nutritional modeling itself (intake calculation, bioavailability adjustment, stunting prediction) is upstream of this module.

This separation is important for two reasons. First, it makes Module 6 fast and reproducible — no re-fitting of models, no recomputation of nutrient intakes, just lookup and aggregation. Second, it isolates the decisions that change across pathways (which stunting methodology is applied) from the decisions that change across production coverage levels (which farmers adopt and which children consume biofortified maize).

The source of the individual profiles depends on the pathway:

Covariate prediction pathway (PCO) — Profiles come from the synthetic population built in Baseline and QPM Profiles. Stunting is predicted with the GAM fitted in Module 4.
Meta-analysis (Gunaratna) pathway (PMA) — Profiles come from the ENCOVI children carrying the hierarchy of Biofortification Priority Assignment. The stunting impact uses the Gunaratna et al. (2010) meta-analysis effect size.

Simulation Pipeline

For each production coverage level (0% to 100% in 10% increments) the pipeline executes five sequential steps:

Step 1: Determine Each Farmer’s Conversion Ratio

Production coverage is implemented as a continuous adoption process driven by the per-farmer parameters calibrated in Module 2. At national production coverage p, every farmer with entry point p_entry ≤ p is in the adoption pool, and contributes a fraction of their potential biofortified production determined by the profile-specific sigmoid:

\[ r_i(p) = \text{sigmoid}(p, p_{entry,i}, \text{floor}_{profile}, k_{profile}) \]

The effective biofortified production of farmer i at production coverage p is:

\[ \text{bio}_i(p) = \text{production\_biofortified}_i \times \text{weight\_calibrated}_i \times r_i(p) \]

This formulation expresses adoption as a smooth conversion intensity per farmer. The conversion curves and their calibration are documented in detail in the Market Baseline Calculation page.

Step 2: Calculate Economic Metrics

For adopting farmers (those with p_entry ≤ p), the script calculates survey-weighted aggregates:

n_farmers_bio — Total adopting farmers (with conversion ratio applied).
land_bio_ha — Biofortified land area (hectares).
production_biofortified_total — Total biofortified production (quintales).
income_increment_total — Additional farmer income (Q).
cost_seeds_increment_total — Change in seed costs (Q).

The seed cost component depends on the active subsidy regime: under the current regime the per-area subsidy is funded entirely by Semilla Nueva, while under the reduced regimes part of the cost becomes producer subsidy absorption and part is passed to the farmer. The decomposition by actor is documented in Module 2.

Step 3: Retrieve Departmental Coverage

Consumption coverage percentages at each production coverage level are retrieved from Market Baseline Calculation, where the gravity-based redistribution has already been applied. Both stages therefore read the same coverage trajectory.

\[ \text{Coverage}_d(p) = \frac{\text{Biofortified production consumed in } d \text{ at } p}{\text{Total maize consumption in } d} \]

Consumption coverage can exceed local production share when departments receive redistributed surplus from saturated neighbours through the gravity model.

Step 4: Assign Biofortified Maize to Children

Children in the target population receive biofortified maize based on departmental consumption coverage and household priority. This is the methodologically complex step of the pipeline.

Hierarchical Assignment Logic

ENCOVI (farmer data) and SIVESNU (child anthropometry) cannot be linked at the household level — there is no common identifier. We cannot directly identify children of adopting farmers who consume biofortified maize from own-production.

The hierarchical assignment uses observable proxies in the target population (head of household occupation, urban/rural area) to approximate the probability that a child’s household would access locally-produced biofortified maize. This is a probabilistic assignment based on plausible consumption patterns, not a deterministic link to specific adopting households.

Priority hierarchy. The two pathways order children differently, because the information available about each child’s household differs between them.

The covariate prediction pathway works on the synthetic population, where the household is characterised by the occupation of its head and by area of residence:

Priority	Group	Rationale
1	Cereal farmers (`Granjero cereales`)	Direct access to biofortified production
2	Other farmers (`Granjero otros`)	Agricultural households with market access
3	Agricultural workers (`Trabajador del campo`)	Rural employment in the farming sector
4	Rural, other occupations	Rural households without a direct farming connection
5	Urban households	Dependent on commercial distribution

The Meta-analysis (Gunaratna) pathway works on the ENCOVI children and reuses the household-level hierarchy built in Biofortification Priority Assignment, which is based on observed agricultural production rather than on occupation: maize producers with self-consumption first, then maize producers without, then other agricultural households, then rural and urban.

Assignment algorithm. For each department at each production coverage level, children are sorted by priority and, within a priority level, by a random tie-breaker. The tie-breaker is drawn per child in the covariate pathway and per household in the meta-analysis pathway, so siblings there enter together. The cumulative survey weight is then accumulated down that order and compared against the departmental target:

\[ W_{\text{target},d} = \text{coverage}_d \times \sum_{i \in d} w_i \qquad \text{flag}_i = \mathbb{1}\left[\sum_{j \preceq i} w_j \le W_{\text{target},d}\right] \]

The quota is therefore expressed in represented children rather than in sampled records, which keeps the assigned share consistent with the departmental consumption coverage under the survey design. Farmer households enter first, urban households only once rural demand is met, and the ordering inside each priority level carries no further structure.

Step 5: Lookup Pre-Computed Profiles and Aggregate

For each child, the script retrieves the pre-computed individual profile produced upstream:

If flag_biofortified = 1, use the QPM profile (intake under biofortified maize consumption, stunting outcome under QPM).
If flag_biofortified = 0, use the baseline profile (intake under conventional maize, baseline stunting outcome).

Individual-level outcomes are then aggregated using survey weights to produce departmental and national metrics: mean intakes, inadequacy prevalence, stunting prevalence, mean HAZ, and cases averted.

Heterogeneous Effects

Heterogeneity in the response to biofortification enters upstream, at the level of the individual profiles. In the covariate prediction pathway the GAM admits a non-linear relationship between the nutrient index and HAZ; over the fitted range the estimated smooth turns out close to linear, so each child’s change in HAZ follows mainly from the size of their own change in the index. In the Meta-analysis (Gunaratna) pathway the effect size is applied to height growth velocity, modulated per child by their protein adequacy. Both routes produce per-child outcomes, which is what allows the aggregation at this stage.

Simulation Results

The following sections summarise the pre-computed national scenarios, organised in three parallel blocks. The first two blocks document the stunting and nutrient outcomes under the two pathways (covariate prediction and meta-analysis), with the current subsidy regime held fixed and the three seed variants compared. The third block documents the economic outcomes (which are invariant to the pathway), with the full grid of three seed variants and three subsidy regimes.

Scenarios Under Covariate Prediction Pathway

The covariate prediction pathway estimates per-child stunting outcomes using a GAM model fitted in Module 4 on the synthetic population generated in Module 4. For each child, the model captures the non-linear relationship between the nutritional index and the height-for-age z-score (HAZ), and the HAZ change is the difference between its fitted values at the baseline and QPM index positions. Aggregating individual outcomes across all children yields the national progression below.

This block compares the three seed variants under the current subsidy regime. The seed variant primarily affects yield change factors per segment, and therefore the volume of biofortified production available at each production coverage level; subsidy regimes (held constant here) modulate the order in which farmers enter the adoption pool but converge at the endpoints. See the Economic Progression section for the full subsidy × seed grid on the economic side.

2022 Seed — Covariate Prediction

Table 1: National scenario progression — 2022 seed, covariate prediction pathway, current subsidy

National Metrics by Production Coverage Level — Seed 2022. Pathway: Covariate prediction | Subsidy: current regime
Production Coverage	Farmers Bio	Coverage	Stunting (%)	Mean HAZ	Zinc Inad. (%)	Iron Inad. (%)
National Metrics by Production Coverage Level — Seed 2022
Pathway: Covariate prediction \| Subsidy: current regime
0%	0	0.0%	48.6	−2.026	52.8	53.8
10%	82,481	4.5%	48.1	−2.018	52.5	53.6
20%	175,567	9.7%	47.8	−2.009	51.8	53.2
30%	255,593	14.7%	47.4	−2.000	51.5	53.1
40%	379,845	21.5%	46.9	−1.986	51.0	52.7
50%	460,401	27.8%	46.6	−1.975	50.6	52.4
60%	530,771	35.2%	46.0	−1.961	49.9	52.3
70%	587,764	43.8%	45.4	−1.945	48.8	51.6
80%	625,229	53.5%	44.9	−1.926	47.6	51.2
90%	672,430	66.4%	44.1	−1.904	47.0	50.5
100%	783,474	85.5%	43.2	−1.877	45.5	49.6

Table 2: National nutrient intake progression — 2022 seed, covariate prediction pathway, current subsidy

Nutrient Intake by Production Coverage Level — Seed 2022. Weighted mean bioavailable nutrients — Pathway: Covariate prediction | Subsidy: current regime
Production Coverage	Coverage	Absorbed Intake			Change vs Baseline
Nutrient Intake by Production Coverage Level — Seed 2022
Weighted mean bioavailable nutrients — Pathway: Covariate prediction \| Subsidy: current regime
Production Coverage	Coverage	Zinc (mg)	Iron (mg)	Protein (g)	Zn Δ%	Fe Δ%	Prot Δ%
0%	0.0%	1.121	0.513	15.44	0.0%	0.0%	0.0%
10%	4.5%	1.125	0.515	15.77	+0.3%	+0.4%	+2.2%
20%	9.7%	1.128	0.517	16.14	+0.7%	+0.9%	+4.5%
30%	14.7%	1.133	0.520	16.52	+1.0%	+1.4%	+7.0%
40%	21.5%	1.138	0.523	17.07	+1.5%	+2.1%	+10.6%
50%	27.8%	1.143	0.526	17.55	+1.9%	+2.7%	+13.7%
60%	35.2%	1.148	0.530	18.10	+2.5%	+3.3%	+17.2%
70%	43.8%	1.155	0.534	18.77	+3.0%	+4.2%	+21.6%
80%	53.5%	1.162	0.539	19.55	+3.7%	+5.2%	+26.6%
90%	66.4%	1.171	0.545	20.45	+4.5%	+6.3%	+32.4%
100%	85.5%	1.183	0.552	21.57	+5.5%	+7.7%	+39.7%
Zinc and iron values are absorbed (TAZ via Miller equation, Fe at 5% absorption). Protein is PDCAAS-adjusted.

2026 Seed — Covariate Prediction

Table 3: National scenario progression — 2026 seed, covariate prediction pathway, current subsidy

National Metrics by Production Coverage Level — Seed 2026. Pathway: Covariate prediction | Subsidy: current regime
Production Coverage	Farmers Bio	Coverage	Stunting (%)	Mean HAZ	Zinc Inad. (%)	Iron Inad. (%)
National Metrics by Production Coverage Level — Seed 2026
Pathway: Covariate prediction \| Subsidy: current regime
0%	0	0.0%	48.6	−2.026	52.8	53.8
10%	87,083	5.1%	48.1	−2.017	52.5	53.6
20%	177,804	10.8%	47.8	−2.008	51.7	53.2
30%	242,606	16.0%	47.3	−1.998	51.4	53.1
40%	362,746	23.3%	46.9	−1.983	50.8	52.7
50%	451,653	30.4%	46.4	−1.971	50.2	52.4
60%	527,816	38.4%	45.9	−1.956	49.5	52.0
70%	593,208	48.0%	45.1	−1.937	48.3	51.4
80%	635,743	58.7%	44.6	−1.917	47.2	50.7
90%	685,018	72.9%	43.8	−1.894	46.5	50.2
100%	783,474	93.9%	43.2	−1.869	44.9	49.5

Table 4: National nutrient intake progression — 2026 seed, covariate prediction pathway, current subsidy

Nutrient Intake by Production Coverage Level — Seed 2026. Weighted mean bioavailable nutrients — Pathway: Covariate prediction | Subsidy: current regime
Production Coverage	Coverage	Absorbed Intake			Change vs Baseline
Nutrient Intake by Production Coverage Level — Seed 2026
Weighted mean bioavailable nutrients — Pathway: Covariate prediction \| Subsidy: current regime
Production Coverage	Coverage	Zinc (mg)	Iron (mg)	Protein (g)	Zn Δ%	Fe Δ%	Prot Δ%
0%	0.0%	1.121	0.513	15.44	0.0%	0.0%	0.0%
10%	5.1%	1.125	0.515	15.81	+0.4%	+0.5%	+2.4%
20%	10.8%	1.129	0.518	16.20	+0.7%	+1.0%	+4.9%
30%	16.0%	1.133	0.520	16.61	+1.1%	+1.5%	+7.6%
40%	23.3%	1.139	0.524	17.20	+1.6%	+2.2%	+11.4%
50%	30.4%	1.144	0.527	17.71	+2.1%	+2.9%	+14.7%
60%	38.4%	1.151	0.531	18.32	+2.7%	+3.6%	+18.7%
70%	48.0%	1.158	0.536	19.10	+3.3%	+4.6%	+23.7%
80%	58.7%	1.166	0.542	19.91	+4.0%	+5.6%	+29.0%
90%	72.9%	1.175	0.548	20.86	+4.8%	+6.8%	+35.1%
100%	93.9%	1.187	0.554	21.89	+5.9%	+8.1%	+41.8%
Zinc and iron values are absorbed (TAZ via Miller equation, Fe at 5% absorption). Protein is PDCAAS-adjusted.

2028 Seed — Covariate Prediction

Table 5: National scenario progression — 2028 seed, covariate prediction pathway, current subsidy

National Metrics by Production Coverage Level — Seed 2028. Pathway: Covariate prediction | Subsidy: current regime
Production Coverage	Farmers Bio	Coverage	Stunting (%)	Mean HAZ	Zinc Inad. (%)	Iron Inad. (%)
National Metrics by Production Coverage Level — Seed 2028
Pathway: Covariate prediction \| Subsidy: current regime
0%	0	0.0%	48.6	−2.026	52.8	53.8
10%	91,141	5.5%	48.1	−2.016	52.3	53.6
20%	177,439	11.2%	47.7	−2.007	51.6	53.2
30%	226,816	16.2%	47.3	−1.998	51.4	53.0
40%	337,434	24.0%	46.8	−1.983	50.7	52.7
50%	437,536	32.2%	46.2	−1.968	50.1	52.4
60%	511,035	40.5%	45.9	−1.952	49.1	51.9
70%	584,655	50.9%	45.0	−1.931	48.1	51.2
80%	631,769	62.4%	44.3	−1.910	47.0	50.6
90%	689,777	77.9%	43.5	−1.886	46.0	50.0
100%	783,474	100.0%	43.0	−1.863	44.5	49.3

Table 6: National nutrient intake progression — 2028 seed, covariate prediction pathway, current subsidy

Nutrient Intake by Production Coverage Level — Seed 2028. Weighted mean bioavailable nutrients — Pathway: Covariate prediction | Subsidy: current regime
Production Coverage	Coverage	Absorbed Intake			Change vs Baseline
Nutrient Intake by Production Coverage Level — Seed 2028
Weighted mean bioavailable nutrients — Pathway: Covariate prediction \| Subsidy: current regime
Production Coverage	Coverage	Zinc (mg)	Iron (mg)	Protein (g)	Zn Δ%	Fe Δ%	Prot Δ%
0%	0.0%	1.121	0.513	15.44	0.0%	0.0%	0.0%
10%	5.5%	1.125	0.515	15.85	+0.4%	+0.5%	+2.6%
20%	11.2%	1.130	0.518	16.25	+0.8%	+1.0%	+5.2%
30%	16.2%	1.133	0.520	16.58	+1.1%	+1.4%	+7.4%
40%	24.0%	1.139	0.524	17.22	+1.7%	+2.2%	+11.5%
50%	32.2%	1.146	0.528	17.84	+2.2%	+3.0%	+15.5%
60%	40.5%	1.152	0.532	18.48	+2.8%	+3.8%	+19.7%
70%	50.9%	1.160	0.538	19.32	+3.5%	+4.9%	+25.1%
80%	62.4%	1.169	0.543	20.18	+4.2%	+6.0%	+30.7%
90%	77.9%	1.179	0.550	21.19	+5.1%	+7.2%	+37.2%
100%	100.0%	1.190	0.556	22.15	+6.2%	+8.5%	+43.5%
Zinc and iron values are absorbed (TAZ via Miller equation, Fe at 5% absorption). Protein is PDCAAS-adjusted.

Scenarios Under Meta-Analysis (Gunaratna) Pathway

The Meta-analysis (Gunaratna) pathway estimates per-child stunting outcomes using the effect size from Gunaratna et al. (2010), which reports a 9% increase in height growth velocity for children consuming Quality Protein Maize relative to conventional maize. This effect is the common reference effect, modulated for each child by the protein-adequacy factor defined in Module 5, and applied to the ENCOVI children population produced by 05_06, with stunting outcomes derived from the resulting HAZ trajectories.

The Meta-analysis (Gunaratna) pathway functions as a methodological sensitivity check for the covariate prediction pathway. Both pathways express child growth on the same height-for-age z-score (HAZ) scale, but reach it by different routes: they use different populations (an ENCOVI-based synthetic population enriched with SIVESNU anthropometric donors, against the ENCOVI child population directly) and different stunting models (GAM-based vs effect-size-based), while sharing the same upstream farmer adoption mechanics, the same hierarchical priority assignment, and the same gravity-based redistribution. Comparing the two pathways across the same seed variants provides a range of plausible impact estimates under different methodological assumptions.

2022 Seed — Meta-Analysis

Table 7: National scenario progression — 2022 seed, Meta-analysis (Gunaratna) pathway, current subsidy

National Metrics by Production Coverage Level — Seed 2022. Pathway: Meta-analysis (Gunaratna) | Subsidy: current regime
Production Coverage	Farmers Bio	Coverage	Stunting (%)	Mean HAZ	Zinc Inad. (%)	Iron Inad. (%)
National Metrics by Production Coverage Level — Seed 2022
Pathway: Meta-analysis (Gunaratna) \| Subsidy: current regime
0%	0	0.0%	48.6	−1.976	53.6	54.2
10%	82,481	4.5%	48.3	−1.969	53.2	54.0
20%	175,567	9.7%	47.8	−1.961	52.4	53.6
30%	255,593	14.7%	47.5	−1.952	51.8	53.2
40%	379,845	21.5%	46.8	−1.941	50.9	52.9
50%	460,401	27.8%	46.2	−1.932	50.1	52.6
60%	530,771	35.2%	45.8	−1.921	49.4	52.3
70%	587,764	43.8%	45.3	−1.908	48.7	51.9
80%	625,229	53.5%	44.6	−1.891	48.1	51.5
90%	672,430	66.4%	43.7	−1.871	47.0	51.1
100%	783,474	85.5%	43.0	−1.845	45.9	50.3

Table 8: National nutrient intake progression — 2022 seed, Meta-analysis (Gunaratna) pathway, current subsidy

Nutrient Intake by Production Coverage Level — Seed 2022. Weighted mean bioavailable nutrients — Pathway: Meta-analysis (Gunaratna) | Subsidy: current regime
Production Coverage	Coverage	Absorbed Intake			Change vs Baseline
Nutrient Intake by Production Coverage Level — Seed 2022
Weighted mean bioavailable nutrients — Pathway: Meta-analysis (Gunaratna) \| Subsidy: current regime
Production Coverage	Coverage	Zinc (mg)	Iron (mg)	Protein (g)	Zn Δ%	Fe Δ%	Prot Δ%
0%	0.0%	1.120	0.511	15.43	0.0%	0.0%	0.0%
10%	4.5%	1.124	0.513	15.81	+0.3%	+0.5%	+2.5%
20%	9.7%	1.129	0.516	16.27	+0.7%	+1.1%	+5.4%
30%	14.7%	1.133	0.519	16.65	+1.1%	+1.6%	+8.0%
40%	21.5%	1.139	0.523	17.23	+1.6%	+2.3%	+11.7%
50%	27.8%	1.144	0.526	17.78	+2.1%	+3.0%	+15.3%
60%	35.2%	1.150	0.530	18.43	+2.6%	+3.8%	+19.5%
70%	43.8%	1.156	0.535	19.19	+3.2%	+4.8%	+24.4%
80%	53.5%	1.163	0.540	19.91	+3.8%	+5.7%	+29.1%
90%	66.4%	1.172	0.545	20.70	+4.6%	+6.7%	+34.2%
100%	85.5%	1.182	0.551	21.62	+5.5%	+7.8%	+40.2%
Zinc and iron values are absorbed (TAZ via Miller equation, Fe at 5% absorption). Protein is PDCAAS-adjusted.

2026 Seed — Meta-Analysis

Table 9: National scenario progression — 2026 seed, Meta-analysis (Gunaratna) pathway, current subsidy

National Metrics by Production Coverage Level — Seed 2026. Pathway: Meta-analysis (Gunaratna) | Subsidy: current regime
Production Coverage	Farmers Bio	Coverage	Stunting (%)	Mean HAZ	Zinc Inad. (%)	Iron Inad. (%)
National Metrics by Production Coverage Level — Seed 2026
Pathway: Meta-analysis (Gunaratna) \| Subsidy: current regime
0%	0	0.0%	48.6	−1.976	53.6	54.2
10%	87,083	5.1%	48.1	−1.968	53.1	54.0
20%	177,804	10.8%	47.8	−1.959	52.4	53.6
30%	242,606	16.0%	47.3	−1.950	51.5	53.2
40%	362,746	23.3%	46.6	−1.939	50.7	52.7
50%	451,653	30.4%	46.2	−1.928	49.9	52.4
60%	527,816	38.4%	45.6	−1.916	49.1	52.2
70%	593,208	48.0%	45.0	−1.901	48.4	51.8
80%	635,743	58.7%	44.2	−1.882	47.8	51.4
90%	685,018	72.9%	43.5	−1.862	46.5	50.8
100%	783,474	93.9%	42.9	−1.834	45.6	50.1

Table 10: National nutrient intake progression — 2026 seed, Meta-analysis (Gunaratna) pathway, current subsidy

Nutrient Intake by Production Coverage Level — Seed 2026. Weighted mean bioavailable nutrients — Pathway: Meta-analysis (Gunaratna) | Subsidy: current regime
Production Coverage	Coverage	Absorbed Intake			Change vs Baseline
Nutrient Intake by Production Coverage Level — Seed 2026
Weighted mean bioavailable nutrients — Pathway: Meta-analysis (Gunaratna) \| Subsidy: current regime
Production Coverage	Coverage	Zinc (mg)	Iron (mg)	Protein (g)	Zn Δ%	Fe Δ%	Prot Δ%
0%	0.0%	1.120	0.511	15.43	0.0%	0.0%	0.0%
10%	5.1%	1.125	0.514	15.85	+0.4%	+0.5%	+2.7%
20%	10.8%	1.129	0.517	16.32	+0.8%	+1.1%	+5.8%
30%	16.0%	1.134	0.519	16.74	+1.2%	+1.7%	+8.5%
40%	23.3%	1.140	0.524	17.40	+1.8%	+2.5%	+12.8%
50%	30.4%	1.146	0.528	18.02	+2.3%	+3.3%	+16.8%
60%	38.4%	1.152	0.532	18.68	+2.8%	+4.1%	+21.1%
70%	48.0%	1.159	0.537	19.48	+3.4%	+5.1%	+26.3%
80%	58.7%	1.167	0.542	20.20	+4.1%	+6.0%	+31.0%
90%	72.9%	1.176	0.547	21.07	+4.9%	+7.1%	+36.6%
100%	93.9%	1.186	0.553	21.91	+5.9%	+8.2%	+42.0%
Zinc and iron values are absorbed (TAZ via Miller equation, Fe at 5% absorption). Protein is PDCAAS-adjusted.

2028 Seed — Meta-Analysis

Table 11: National scenario progression — 2028 seed, Meta-analysis (Gunaratna) pathway, current subsidy

National Metrics by Production Coverage Level — Seed 2028. Pathway: Meta-analysis (Gunaratna) | Subsidy: current regime
Production Coverage	Farmers Bio	Coverage	Stunting (%)	Mean HAZ	Zinc Inad. (%)	Iron Inad. (%)
National Metrics by Production Coverage Level — Seed 2028
Pathway: Meta-analysis (Gunaratna) \| Subsidy: current regime
0%	0	0.0%	48.6	−1.976	53.6	54.2
10%	91,141	5.5%	48.1	−1.967	53.1	54.0
20%	177,439	11.2%	47.7	−1.958	52.3	53.6
30%	226,816	16.2%	47.3	−1.950	51.5	53.1
40%	337,434	24.0%	46.6	−1.938	50.6	52.7
50%	437,536	32.2%	46.0	−1.925	49.8	52.3
60%	511,035	40.5%	45.5	−1.913	49.0	52.1
70%	584,655	50.9%	44.7	−1.896	48.2	51.6
80%	631,769	62.4%	44.1	−1.877	47.4	51.2
90%	689,777	77.9%	43.3	−1.855	46.3	50.6
100%	783,474	100.0%	42.8	−1.825	45.2	49.9

Table 12: National nutrient intake progression — 2028 seed, Meta-analysis (Gunaratna) pathway, current subsidy

Nutrient Intake by Production Coverage Level — Seed 2028. Weighted mean bioavailable nutrients — Pathway: Meta-analysis (Gunaratna) | Subsidy: current regime
Production Coverage	Coverage	Absorbed Intake			Change vs Baseline
Nutrient Intake by Production Coverage Level — Seed 2028
Weighted mean bioavailable nutrients — Pathway: Meta-analysis (Gunaratna) \| Subsidy: current regime
Production Coverage	Coverage	Zinc (mg)	Iron (mg)	Protein (g)	Zn Δ%	Fe Δ%	Prot Δ%
0%	0.0%	1.120	0.511	15.43	0.0%	0.0%	0.0%
10%	5.5%	1.125	0.514	15.91	+0.4%	+0.6%	+3.1%
20%	11.2%	1.130	0.517	16.41	+0.8%	+1.2%	+6.4%
30%	16.2%	1.134	0.520	16.77	+1.2%	+1.7%	+8.7%
40%	24.0%	1.141	0.524	17.42	+1.8%	+2.5%	+12.9%
50%	32.2%	1.147	0.529	18.17	+2.4%	+3.5%	+17.8%
60%	40.5%	1.154	0.533	18.88	+3.0%	+4.4%	+22.4%
70%	50.9%	1.161	0.538	19.68	+3.6%	+5.4%	+27.6%
80%	62.4%	1.169	0.543	20.44	+4.4%	+6.3%	+32.5%
90%	77.9%	1.178	0.549	21.29	+5.2%	+7.4%	+38.0%
100%	100.0%	1.189	0.554	22.09	+6.1%	+8.4%	+43.2%
Zinc and iron values are absorbed (TAZ via Miller equation, Fe at 5% absorption). Protein is PDCAAS-adjusted.

Economic Progression

The economic dimension is invariant to the stunting pathway: the same farmers adopt the same land under the same conversion curves regardless of whether the downstream stunting impact is estimated via covariate prediction or meta-analysis. The economic block therefore consolidates the full grid of nine combinations — three seed variants (2022, 2026 and 2028 seed) and three subsidy regimes (current, 50% reduction, 75% reduction) — drawing from the covariate prediction pathway outputs.

The subsidy regime affects the seed cost structure faced by farmers. Under the current regime the per-area subsidy is funded entirely by Semilla Nueva, at 242 Q/mz of direct commitment, with no producer subsidy absorption and no extra cost to the farmer. Under the reduced regimes the reduction is split equally between producer subsidy absorption, through improved producibility, and the farmer, through a higher biofortified seed price. The complete decomposition by actor is documented in Module 2.

The seed variant affects yield change factors per farmer segment. The 2022 variety reflects current field performance; the 2026 and 2028 varieties reflect expected improvements from ongoing breeding pipelines. Higher yield change factors translate into higher per-farmer income increments and larger production volumes at the same production coverage level.

2022 Seed

Table 13: Economic impact progression — 2022 seed, current subsidy regime

Economic Impact by Production Coverage Level — Seed 2022. Farmer adoption, land use, and economic outcomes — Subsidy: Current
Production Coverage	Adopting Farmers			Ha	Prod Bio (qq)	Net Income Change (M GTQ)
Economic Impact by Production Coverage Level — Seed 2022
Farmer adoption, land use, and economic outcomes — Subsidy: Current
Production Coverage	Total	Hybrid	OPV	Ha	Prod Bio (qq)	Net Income Change (M GTQ)
0%	0	0	0	0	0	0.00
10%	82,481	75,821	6,660	43,256	2,982,717	117.40
20%	175,567	98,001	77,566	91,735	6,410,123	265.57
30%	255,593	104,381	151,213	140,826	9,729,805	408.58
40%	379,845	111,676	268,170	222,139	14,204,918	589.78
50%	460,401	117,306	343,095	294,526	18,410,713	749.11
60%	530,771	125,480	405,291	378,238	23,260,121	952.41
70%	587,764	132,621	455,142	474,938	28,964,662	1,181.81
80%	625,229	147,178	478,051	582,585	35,350,737	1,433.00
90%	672,430	166,059	506,371	737,807	43,926,380	1,773.88
100%	783,474	188,662	594,812	1,156,213	56,504,051	1,544.18
Net Income Change is the change in net income, with the seed cost difference included. Land converted at 0.7 ha/mz.

Table 14: Economic impact progression — 2022 seed, 50% subsidy reduction

Economic Impact by Production Coverage Level — Seed 2022. Farmer adoption, land use, and economic outcomes — Subsidy: 50% reduction
Production Coverage	Adopting Farmers			Ha	Prod Bio (qq)	Net Income Change (M GTQ)
Economic Impact by Production Coverage Level — Seed 2022
Farmer adoption, land use, and economic outcomes — Subsidy: 50% reduction
Production Coverage	Total	Hybrid	OPV	Ha	Prod Bio (qq)	Net Income Change (M GTQ)
0%	0	0	0	0	0	0.00
10%	82,480	75,379	7,102	42,818	2,967,463	114.29
20%	183,224	99,354	83,870	93,476	6,561,867	268.40
30%	259,763	105,636	154,126	140,938	9,742,970	398.54
40%	383,441	112,589	270,853	221,542	14,235,959	573.17
50%	464,488	119,451	345,036	293,389	18,448,894	727.80
60%	529,334	126,993	402,342	373,471	23,206,714	918.50
70%	582,082	134,595	447,487	466,450	28,852,241	1,137.37
80%	618,261	149,182	469,080	568,234	35,329,490	1,388.83
90%	667,164	171,178	495,987	714,539	44,021,203	1,709.37
100%	783,474	188,662	594,812	1,156,213	56,504,051	1,443.42
Net Income Change is the change in net income, with the seed cost difference included. Land converted at 0.7 ha/mz.

Table 15: Economic impact progression — 2022 seed, 75% subsidy reduction

Economic Impact by Production Coverage Level — Seed 2022. Farmer adoption, land use, and economic outcomes — Subsidy: 75% reduction
Production Coverage	Adopting Farmers			Ha	Prod Bio (qq)	Net Income Change (M GTQ)
Economic Impact by Production Coverage Level — Seed 2022
Farmer adoption, land use, and economic outcomes — Subsidy: 75% reduction
Production Coverage	Total	Hybrid	OPV	Ha	Prod Bio (qq)	Net Income Change (M GTQ)
0%	0	0	0	0	0	0.00
10%	83,558	76,456	7,102	42,747	2,970,329	112.36
20%	188,173	102,273	85,900	94,339	6,594,312	264.32
30%	262,246	108,561	153,685	140,635	9,736,407	389.50
40%	385,013	114,635	270,378	220,934	14,206,334	559.78
50%	463,948	120,984	342,965	291,765	18,434,068	713.58
60%	527,808	127,718	400,090	370,786	23,180,343	902.59
70%	579,854	135,687	444,167	463,242	28,858,464	1,118.65
80%	615,700	150,155	465,545	562,891	35,289,700	1,365.13
90%	663,131	172,893	490,237	703,875	43,968,543	1,675.89
100%	783,474	188,662	594,812	1,156,213	56,504,051	1,393.87
Net Income Change is the change in net income, with the seed cost difference included. Land converted at 0.7 ha/mz.

2026 Seed

Table 16: Economic impact progression — 2026 seed, current subsidy regime

Economic Impact by Production Coverage Level — Seed 2026. Farmer adoption, land use, and economic outcomes — Subsidy: Current
Production Coverage	Adopting Farmers			Ha	Prod Bio (qq)	Net Income Change (M GTQ)
Economic Impact by Production Coverage Level — Seed 2026
Farmer adoption, land use, and economic outcomes — Subsidy: Current
Production Coverage	Total	Hybrid	OPV	Ha	Prod Bio (qq)	Net Income Change (M GTQ)
0%	0	0	0	0	0	0.00
10%	87,083	85,365	1,718	42,371	3,353,793	159.24
20%	177,804	114,643	63,162	93,108	7,135,312	358.41
30%	242,606	118,950	123,656	136,323	10,547,967	551.37
40%	362,746	126,122	236,624	215,831	15,421,034	814.56
50%	451,653	130,131	321,521	292,531	20,105,501	1,051.67
60%	527,816	136,849	390,968	376,417	25,386,970	1,345.12
70%	593,208	142,423	450,785	476,126	31,770,027	1,697.25
80%	635,743	151,157	484,585	591,162	38,786,027	2,075.88
90%	685,018	166,891	518,127	752,019	48,223,293	2,584.22
100%	783,474	188,662	594,812	1,156,212	62,103,890	2,623.58
Net Income Change is the change in net income, with the seed cost difference included. Land converted at 0.7 ha/mz.

Table 17: Economic impact progression — 2026 seed, 50% subsidy reduction

Economic Impact by Production Coverage Level — Seed 2026. Farmer adoption, land use, and economic outcomes — Subsidy: 50% reduction
Production Coverage	Adopting Farmers			Ha	Prod Bio (qq)	Net Income Change (M GTQ)
Economic Impact by Production Coverage Level — Seed 2026
Farmer adoption, land use, and economic outcomes — Subsidy: 50% reduction
Production Coverage	Total	Hybrid	OPV	Ha	Prod Bio (qq)	Net Income Change (M GTQ)
0%	0	0	0	0	0	0.00
10%	87,214	85,121	2,092	42,454	3,363,570	157.11
20%	180,250	112,816	67,434	94,013	7,248,879	360.05
30%	242,321	116,361	125,960	135,938	10,541,251	541.94
40%	367,165	125,374	241,790	215,427	15,425,795	799.00
50%	458,721	129,760	328,961	292,694	20,177,785	1,035.01
60%	528,636	136,281	392,355	373,468	25,435,091	1,320.07
70%	594,647	143,990	450,657	471,886	31,761,882	1,659.35
80%	630,995	151,828	479,167	579,915	38,731,639	2,024.28
90%	677,111	167,597	509,515	733,316	48,187,729	2,523.78
100%	783,474	188,662	594,812	1,156,213	62,103,890	2,522.82
Net Income Change is the change in net income, with the seed cost difference included. Land converted at 0.7 ha/mz.

Table 18: Economic impact progression — 2026 seed, 75% subsidy reduction

Economic Impact by Production Coverage Level — Seed 2026. Farmer adoption, land use, and economic outcomes — Subsidy: 75% reduction
Production Coverage	Adopting Farmers			Ha	Prod Bio (qq)	Net Income Change (M GTQ)
Economic Impact by Production Coverage Level — Seed 2026
Farmer adoption, land use, and economic outcomes — Subsidy: 75% reduction
Production Coverage	Total	Hybrid	OPV	Ha	Prod Bio (qq)	Net Income Change (M GTQ)
0%	0	0	0	0	0	0.00
10%	86,375	84,378	1,996	42,319	3,351,685	154.97
20%	181,341	111,394	69,947	93,712	7,242,049	358.98
30%	241,753	115,330	126,423	135,008	10,511,893	536.68
40%	369,468	124,134	245,334	215,554	15,452,687	793.78
50%	459,100	128,542	330,558	292,283	20,224,305	1,028.01
60%	528,521	135,025	393,497	372,600	25,441,376	1,307.79
70%	594,412	144,166	450,246	470,412	31,790,843	1,642.69
80%	629,108	152,443	476,666	574,322	38,742,836	2,002.71
90%	676,356	167,977	508,379	724,186	48,157,378	2,495.44
100%	783,474	188,662	594,812	1,156,213	62,103,890	2,473.27
Net Income Change is the change in net income, with the seed cost difference included. Land converted at 0.7 ha/mz.

2028 Seed

Table 19: Economic impact progression — 2028 seed, current subsidy regime

Economic Impact by Production Coverage Level — Seed 2028. Farmer adoption, land use, and economic outcomes — Subsidy: Current
Production Coverage	Adopting Farmers			Ha	Prod Bio (qq)	Net Income Change (M GTQ)
Economic Impact by Production Coverage Level — Seed 2028
Farmer adoption, land use, and economic outcomes — Subsidy: Current
Production Coverage	Total	Hybrid	OPV	Ha	Prod Bio (qq)	Net Income Change (M GTQ)
0%	0	0	0	0	0	0.00
10%	91,141	91,141	0	40,793	3,655,969	200.51
20%	177,439	143,881	33,558	88,484	7,416,899	371.64
30%	226,816	148,081	78,736	123,257	10,701,955	589.74
40%	337,434	151,138	186,296	196,171	15,859,012	924.93
50%	437,536	152,885	284,651	278,405	21,313,276	1,261.10
60%	511,035	155,229	355,806	358,427	26,768,406	1,601.44
70%	584,655	156,912	427,743	459,326	33,665,118	2,048.04
80%	631,769	159,612	472,157	574,003	41,266,171	2,561.75
90%	689,777	169,615	520,162	751,075	51,477,633	3,220.60
100%	783,474	188,662	594,812	1,156,212	66,544,562	3,479.42
Net Income Change is the change in net income, with the seed cost difference included. Land converted at 0.7 ha/mz.

Table 20: Economic impact progression — 2028 seed, 50% subsidy reduction

Economic Impact by Production Coverage Level — Seed 2028. Farmer adoption, land use, and economic outcomes — Subsidy: 50% reduction
Production Coverage	Adopting Farmers			Ha	Prod Bio (qq)	Net Income Change (M GTQ)
Economic Impact by Production Coverage Level — Seed 2028
Farmer adoption, land use, and economic outcomes — Subsidy: 50% reduction
Production Coverage	Total	Hybrid	OPV	Ha	Prod Bio (qq)	Net Income Change (M GTQ)
0%	0	0	0	0	0	0.00
10%	91,138	91,138	0	41,034	3,657,431	197.68
20%	175,362	140,236	35,127	88,365	7,431,050	369.25
30%	225,568	144,265	81,303	123,303	10,733,618	585.81
40%	339,552	147,627	191,925	197,075	15,908,367	915.98
50%	443,304	149,788	293,515	281,199	21,451,008	1,248.47
60%	512,342	152,493	359,850	358,551	26,803,143	1,576.76
70%	585,163	155,496	429,667	458,631	33,727,663	2,014.74
80%	629,680	159,272	470,408	568,422	41,265,916	2,516.17
90%	683,574	169,161	514,413	737,135	51,430,239	3,160.03
100%	783,474	188,662	594,812	1,156,212	66,544,562	3,378.67
Net Income Change is the change in net income, with the seed cost difference included. Land converted at 0.7 ha/mz.

Table 21: Economic impact progression — 2028 seed, 75% subsidy reduction

Economic Impact by Production Coverage Level — Seed 2028. Farmer adoption, land use, and economic outcomes — Subsidy: 75% reduction
Production Coverage	Adopting Farmers			Ha	Prod Bio (qq)	Net Income Change (M GTQ)
Economic Impact by Production Coverage Level — Seed 2028
Farmer adoption, land use, and economic outcomes — Subsidy: 75% reduction
Production Coverage	Total	Hybrid	OPV	Ha	Prod Bio (qq)	Net Income Change (M GTQ)
0%	0	0	0	0	0	0.00
10%	91,400	91,400	0	41,198	3,665,978	196.23
20%	175,261	140,033	35,228	88,358	7,431,726	366.49
30%	224,031	143,689	80,342	122,585	10,713,179	579.04
40%	339,149	146,957	192,193	197,040	15,927,048	909.85
50%	446,382	149,419	296,963	281,742	21,488,675	1,240.36
60%	513,795	152,593	361,203	358,326	26,786,857	1,559.20
70%	586,300	155,800	430,500	457,634	33,738,832	1,996.46
80%	629,995	159,585	470,409	567,059	41,267,972	2,491.34
90%	680,450	169,082	511,368	730,261	51,401,013	3,129.10
100%	783,474	188,662	594,812	1,156,213	66,544,562	3,329.11
Net Income Change is the change in net income, with the seed cost difference included. Land converted at 0.7 ha/mz.

Key Outputs

The pre-computed scenario datasets carry three families of variables.

Economic Variables

Variable	Description	Unit
`n_farmers_bio`	Adopting farmers (survey-weighted, conversion ratio applied)	count
`land_bio_ha`	Biofortified land area	hectares
`production_biofortified_total`	Total biofortified production	quintales
`income_increment_total`	Additional farmer income	Q
`cost_seeds_increment_total`	Change in seed costs	Q
`income_increment_mgtq`	Net income change, seed cost difference included	M GTQ

Nutritional Variables

Variable	Description	Unit
`mean_zinc`	Mean absorbed zinc (TAZ via Miller equation)	mg/day
`mean_iron`	Mean absorbed iron (5% absorption)	mg/day
`mean_protein`	Mean PDCAAS-adjusted protein	g/day
`*_inad_pct`	Nutrient inadequacy prevalence	%
`*_change_pct`	Change vs baseline	%

Stunting Variables

Variable	Description	Unit
`stunting_pct`	Stunting prevalence	%
`mean_zlen`	Mean height-for-age z-score (HAZ)	SD units

Departmental vs National: Key Differences

Aspect	06_02 Departmental	06_03 National
Rows	242 (22 depts × 11 levels)	11 (1 region × 11 levels)
Identifier	`departamento`	`region = "Guatemala Total"`
Quantiles	Included (distribution boxplots)	Excluded (require individual data)
Use	Geographic filtering, departmental comparisons	Overview tab, national indicators
Aggregation	`.by = departamento`	No grouping (national weighted mean)

Both datasets share identical variable schemas for compatibility with downstream Shiny processing.

Methodological Assumptions

Adoption Mechanism

The continuous adoption mechanism assumes that farmers with higher adoption scores (better technology fit, larger scale, prior hybrid experience) enter the adoption pool at lower national production coverage levels. Within each farmer, the conversion intensity follows the profile-specific sigmoid calibrated against empirical adoption anchors in Module 2.

Consumption Coverage Assignment

The hierarchical assignment assumes that farmer households prioritise self-consumption of their own production, that local markets receive biofortified maize before commercial distribution, that urban access depends on formal supply chain reach, and that within each priority group access is random (no further stratification).

Stunting Response

The covariate prediction pathway assumes that the GAM-based relationship between nutritional index and HAZ fitted on the synthetic population generalises to the scenario simulation, and that estimated effects are heterogeneous (stronger marginal benefits for nutritionally deficient children). The Meta-analysis (Gunaratna) pathway assumes that the Gunaratna et al. (2010) effect size on height growth velocity is transportable to the Guatemalan ENCOVI children population. Both scenario applications are model-based ex-ante projections and report point estimates without propagated uncertainty. The covariate prediction pathway derives its response from observational survey data; the meta-analysis pathway imports its reference effect from randomized trial evidence.

Limitations

Key Limitations

No household-level linkage — Children cannot be directly linked to specific adopting farmer households; assignment is based on occupational proxies.
Uniform biofortification factors — All biofortified maize is assumed to have identical nutrient increases regardless of variety, soil conditions, or processing.
No uncertainty quantification — Point estimates only; confidence intervals would require bootstrap or Monte Carlo simulation.
No supply chain modelling — Distribution is assumed to follow the priority hierarchy and the gravity model without modelling transport costs, market access, or price differentials.
Cross-sectional stunting model — Predictions represent point-in-time associations, not longitudinal growth trajectories.

Summary

The scenario precomputation step generates the analytical backbone for the Shiny dashboard. For each of the 18 combinations in the seed × subsidy × pathway grid, it produces departmental (242 rows) and national (11 rows) scenarios with consistent variable schemas.

Key methodological elements

Continuous adoption — Per-farmer conversion intensity along profile-specific sigmoid curves produces smooth saturation trajectories.
Pre-computed individual profiles — Module 6 receives baseline and QPM profiles from Modules 4 and 5 and is responsible only for assignment and aggregation, not for the underlying nutritional and stunting modelling.
Hierarchical priority assignment — Realistic biofortification distribution based on household occupation and geography, calibrated to departmental consumption coverage from the gravity-based redistribution.
Dual pathway — Two parallel stunting methodologies (covariate prediction and meta-analysis) provide a range of plausible impact estimates under different assumptions.
Survey-weighted aggregation — All metrics use calibrated weights for population-representative estimates.

These pre-computed scenarios enable instant dashboard response while maintaining full methodological transparency. The 18 combinations are exported as parquet files for direct consumption by the Shiny dashboard, which is the product of results layered on top of this methodological backbone.