Most teams don’t have a data shortage. They have a data integration problem.

Joining field-trial, SoilGrids and CHIRPS rainfall data on location and date — and what that teaches M&E teams with data in disconnected systems.
Data Integration
Monitoring and Evaluation
GIS
Case Study
Author

Victor Mandela

Published

16 September 2026

Keywords

agricultural analytics, R Shiny dashboard, maize yield, Rwanda, data integration, monitoring and evaluation, gis

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Victor Mandela, M&E and data science consultant
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For the maize yield analysis, I worked with three sources:

Most teams don't have a data shortage. They have a data integration problem. — infographic

Each source alone is thin

  • Trial data tells you a treatment worked, not why
  • Soil data describes an environment with no outcome attached
  • Rainfall alone is just weather

Joined on location and date, they answer the real question — yield emerges from the interaction of management + soil + weather + location.

The unglamorous part

Trusting coordinates, reconciling resolutions, aligning dates to the growing season, and handling missingness honestly.

This applies far beyond agriculture. Many M&E teams have programme data, routine data and geographic data sitting in three systems that never talk. The value usually isn’t collecting more. It’s connecting what you already have.

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

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