Fig 1.
Basic systems diagram illustrating the economic impact assessment of scalable agricultural learning initiatives.
The blue boxes represent generic processes that convert subsystem inputs (e.g., costs) into outputs. Note that some outputs serve as inputs for subsequent subsystems. The green box represents the process to estimate the internal rate of return (IRR), which also serves as the overall system output.
Fig 2.
Simulation of information dissemination.
The Susceptibility-Informed (SI) model captures the change in the number of informed famers over time as a result of sharing of knowledge. In this example, 10% of a population of 100,000 farmers learn the presented material.
Table 1.
Key model parameters based on values in agricultural learning literature.
Fig 3.
Simulation using the base model.
The top panel shows the projected increase over time in the number of farmers expected to adopt the taught technique. The dotted vertical line represents the period when 10% of population is informed. The bottom panel illustrates the corresponding increase in revenue resulting from farmers’ adoption of the technique.
Fig 4.
Heatmap visualizing the influence of base model changes on returns.
Relative changes of 1/4× = 25%, 1/2× = 50%, 1× = 100% (base model), 2× = 200%, and 4× = 400% were simulated. Higher returns are represented in blue and lower returns in red, with a neutral reference point set at the base model return of 208%.
Table 2.
Influence of production costs and impact period on the number of farmers needing to be targeted to breakeven and the max IRR using a target population of 10 million farmers.
Fig 5.
The effect of target population size on internal rate of returns (IRR).
Increasing the target population size (S(0)), which represents the number of farmers who would benefit from the taught technique, led to higher returns. The left plot shows how reducing production costs affects returns slightly. The center plot reveals that lower deployment costs (i.e., more cost-effective campaigns) can substantially enhance returns for larger target populations. The right plot indicates that reductions in adoption costs have a minimal impact on returns.
Fig 6.
Impact period influence on internal rate of return (IRR) and breakeven point.
Breakeven points represent the number of farmers needing to be targeted to cross the 0% IRR threshold. All impact periods show similar maximum returns (IRR) but varied in terms of breakeven points. Longer impact periods (10, 15, 30 years) significantly shift breakeven points to smaller target populations, indicating that short-term projects (3 and 5 years) require targeting larger farmer groups to remain economically viable.
Fig 7.
Number of farmers required to breakeven.
The number of farmers required to breakeven under different scenarios for both new animation creation (left) and a new language variant (right) creation of an existing animation at 30 years or 3 years.