By Aymen Sawassi

In East Africa, land and life are deeply intertwined—and both depend on increasingly scarce and unreliable water. Across Kenya, Ethiopia, Sudan, and Egypt, farmers have long drawn on deep experience and ingenuity to adapt: reusing water, planting drought-resistant crops, finding creative ways to survive. But these traditional practices, while often effective, are fragmented, isolated, and not universally applicable. What works in one place may fail in another. That’s where science comes in.

"In East Africa, traditional practices, while often effective, are fragmented and isolated."

When we arrived in these communities, we didn’t come with ready-made answers. We listened. Under trees and in fields, farmers spoke not just of dry wells and lost harvests, but of strategies passed down and hard-won wisdom. What they lacked wasn’t knowledge—but a system to connect that knowledge, scale it, and tailor it to changing conditions.
So we began translating stories into structured data. We mapped not only challenges but possibilities. We identified Best Management Practices (BMPs): tested ways to retain soil moisture, optimize irrigation, and protect crops. But the real breakthrough came in how we evaluated them—through modeling.
Every region is different. A solution in northern Kenya may not work in eastern Sudan. So we developed a digital decision-support toolbox: a modeling system that simulates how a farming technique will perform in specific conditions—climate, soil, water access, even local governance. It’s not a tool to replace farmers. It’s a tool to collaborate with them.

"We began translating stories into structured data and modeling."

And that collaboration is essential. The models weren’t imposed—they were co-created. Farmers helped define the problems and test solutions. They challenged assumptions, improving the science itself. Because of this, the results weren’t just effective—they were grounded, trusted, and usable.