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Related Experiment Video

Updated: Mar 12, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Investment modeling for scalable agricultural learning.

Norman Peter Reeves1, Rebecca Pietrelli2, Ian Brooks3

  • 1Sumaq Life LLC, Lansing, Michigan, United States of America.

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Summary
This summary is machine-generated.

Scalable agricultural training using digital tools can be economically viable, even for minority languages. Key factors for success include cost per farmer, adoption rates, and income improvements.

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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.