Investment modeling for scalable agricultural learning.

Area of Science:

• Agricultural Extension
• Information and Communication Technology for Development (ICTD)
• Economic Impact Assessment

Background:

• Localized farmer training faces scalability challenges.
• The economic value of scalable agricultural learning initiatives is underexplored.
• Digital technologies offer opportunities to transform training delivery.

Purpose of the Study:

• To develop a general framework for evaluating the economic impact of scalable agricultural learning.
• To identify key drivers influencing the returns of such initiatives.
• To assess the economic viability of using multilingual animations and YouTube for farmer education.

Main Methods:

• Systems modeling was employed to simulate potential economic returns.
• Sensitivity analysis was conducted to identify key drivers of impact.
• The study estimated the number of farmers needed for economic viability.

Main Results:

• Economic returns are most sensitive to the cost of informing farmers, adoption rates, and income gains.
• Adapting existing content and extending its lifespan can achieve economic viability with fewer farmers.
• Linguistic adaptation for minority languages becomes economically feasible under these conditions.

Conclusions:

• Scalable agricultural learning initiatives can be economically viable, particularly when using adaptable and durable digital content.
• Systems modeling is a valuable tool for prioritizing high-impact agricultural solutions in research-for-development.
• Tailoring economic models to specific contexts is crucial for accurate impact estimation.