# Investment modeling for scalable agricultural learning

## TL;DR

This paper explores how scalable agricultural learning initiatives can be economically viable by using multilingual animations and YouTube, showing how factors like cost and adoption rates affect returns.

## Contribution

The study introduces a systems modeling framework to evaluate the economic impact of scalable agricultural learning initiatives.

## Key findings

- Returns are most influenced by cost per individual, adoption rates, and income gains.
- Adapting existing educational content can make learning initiatives economically viable with few farmers.
- Tailoring models to specific contexts is crucial for accurate economic impact estimates.

## Abstract

With the rise of information and communication technologies, localized farmer training can be transformed into scalable strategies applicable across diverse communities, cultures, and languages. However, the economic value of these approaches and the factors shaping their returns remain underexplored. This study presents a general framework for evaluating the economic impact of scalable agricultural learning initiatives, using multilingual instructional animations and YouTube dissemination as a case study. Systems modeling was used to simulate potential returns, assess key drivers of impact, and estimate the number of farmers required for economic viability. Sensitivity analysis shows that returns are most influenced by the cost to inform an individual, adoption rates, and income gains, and to a lesser degree, technique-sharing rates and adoption costs. When existing educational content is adapted and its lifespan extended, learning initiatives can be economically viable with few targeted farmers, making the linguistic adaption into minority or rarer languages an economically viable option. The wide variation in returns across scenarios highlights the importance of tailoring models to specific contexts to obtain more precise estimates of economic impact. These findings underscore the value of adaptable and durable learning materials and suggest that future research-for-development (R4D) investments could benefit from systems modeling to identify and prioritize high-impact agricultural solutions.