Investment modeling for scalable agricultural learning
Norman Peter Reeves, Rebecca Pietrelli, Ian Brooks, Victor G. Sal y Rosas Celi, Kumpati Narendra, Jean C. Ngabitsinze, Maximo Torero Cullen, Anne N. Lutomia, John W. Medendorp, Julia M. Bello-Bravo, Barry R. Pittendrigh, Yang (Jack) Lu, Yang (Jack) Lu, Yang (Jack) Lu
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.
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…
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Taxonomy
TopicsICT in Developing Communities · Diverse Educational Innovations Studies · Agricultural Innovations and Practices