What if agricultural research became the decision-support tool itself?

Turning maize trial research into an interactive R Shiny decision-support application built around the questions extension officers actually ask.
Decision Support
R Shiny
Monitoring and Evaluation
Case Study
Author

Victor Mandela

Published

9 September 2026

Keywords

agricultural analytics, R Shiny dashboard, maize yield, Rwanda, decision support, r shiny, monitoring and evaluation

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Victor Mandela, M&E and data science consultant
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Portfolio Edition ••• Monitoring & Evaluation · Impact Evaluation · R Shiny Decision Support ••• Nairobi, KenyaUPDATED NOV 2026

That was one of the objectives of the maize yield project I recently worked on.

The goal wasn’t to produce statistical tables. It was an interactive Shiny application built around what an extension officer would actually ask.

What if agricultural research became the decision-support tool itself? — infographic

What it brings together

  • Urea performance analysis
  • Yield-driver analysis
  • Location-specific insights
  • Yield prediction
  • Interactive trial-field maps

Report vs. decision tool

A research report tells you what happened. A decision-support application lets someone ask:

  • Where did it happen?
  • Under what conditions?
  • What factors mattered?
  • What does the model predict for my district?

That’s the direction I see for M&E and agricultural analytics: moving from reporting data to building tools people decide with.

▶ Try it yourself — live R Shiny demo. Open the Rwanda Maize Yield Analyzer · Username: test · Password: test

Part 3 of 10 — The Maize Yield Case Study. Read the full case study and launch the dashboard › · Work with me on a similar project ›