Building With Estimates

When I built the forage calendar for the Rwanda Bee Systems Lab, I used a combination of ICRAF agroforestry data, a FAO report on Rwandan highland flowering seasons, and my own reasoning about altitude variance and microclimate patterns. I did not have a live sensor network. What exists is patchwork. So I used what existed, reasoned forward from it, and labeled it clearly.

The first thing estimates force you to do is be explicit about your assumptions. When you pull a number from a live database, you tend to trust it and move on. When you derive a number from a regional study published in 2017 adjusted for altitude, you have to know exactly what you assumed and why. This makes the estimate-builder more careful, not less.

The second thing estimates do is expose the structure of the problem. When I was building the cooperative readiness scoring, the absence of real compliance data forced me to ask: what are the actual dimensions of readiness? Which ones matter most? With real data, you might skip this and let the data tell you. Working from estimates means you have to define what success looks like before you can quantify it.

There is an objection worth addressing directly: estimated dashboards can give false confidence. Someone might look at a projected output figure and make a resource allocation based on it without understanding the source. This is a real risk. But the answer is not to refuse to build until perfect data arrives. In contexts like rural Rwandan apiculture, that could mean waiting indefinitely. The answer is labeling. Every figure in the lab carries a source indicator.

Working in East African development contexts, I think the fetishization of real-time data is itself an obstacle. It creates a hierarchy where the only legitimate work follows a data collection phase. Meanwhile, the best available synthesis of regional research, expert knowledge, and reasoned inference is a serious contribution, if done with discipline.

The forage calendar will be wrong in specific ways I cannot anticipate. When a beekeeper in Nyamasheke says the Calliandra peak is two weeks later than what I marked, that correction begins something better than what I built. The estimate was the invitation for that conversation.