If you use synthetic data (from the crop simulation models), the model will kind of reverse-engineer it (it will learn what the simulation models are doing).

Using a mix of it with real word data, is like regularizing your model (adding a prior) to the simulation rules.

This is something that makes sense, and mixing data often is used. But "making sense" doesn't necessarily means it helps ... that depends a lot on your application. Also the next question is how much synthetic data you may want to mix ... fundamentally you'll have to figure it out by trial&error and having some way of measuring if things are getting better for whatever your extrinsic goal is (your business objective).