Suppose a model is right. It processes soil moisture, weather, and crop stage, and it produces a number: water this much, on this day. Now suppose a farmer sees it and does something else entirely. The model has failed at the only thing that matters.
Trust is built by explanation
A bare number invites suspicion. The same number with a short reason attached is much easier to accept, and it lets an experienced grower catch situations the model cannot see. Explanations also make disagreement productive, because a farmer can point to which input is wrong.
Trust is built by admitting uncertainty
Systems that always sound certain lose credibility the first time they are visibly wrong. A model that communicates confidence, and says plainly when data is stale or missing, survives its mistakes. Overconfidence is a durability problem, not just an honesty problem.
Trust is built by small stakes first
Nobody hands their whole operation to new software. Adoption tends to start on one plot, over one season, alongside existing practice. Designing for that comparison period, rather than fighting it, is how a tool earns a larger role.
The engineering lesson is uncomfortable for engineers. Improving accuracy from good to slightly better often matters less than making the same output legible, humble, and easy to test.