You have two dummy variables, and your model is fit in the logit space. The intercept is the value of your outcome (logit-transformed) when both independent dummy variables are at the reference value. The targetVoronezh estimate of 0.974 is the change in the outcome compared to the intercept when target is Voronezh as compared to the reference category that was absorbed by the Intercept. Importantly, this is the estimate for the difference only for when the subtreatment is at its reference level.
The exact same thing for subtreatmentFIC. This is the change in the outcome in the logit space when subtreatment equals FIC as compared to the reference category for subtreatment for when target is at its reference level only (i.e., not Voronezh).
The interaction then represents how the effects of the two variables change when the other variable is not at the reference level. To get the estimate of the outcome for each combination, you have to add the estimates together.
When both dummy variables are at reference level, the estimate for value = 2.96.
When subtreatment is at the reference level, but target is Voronezh, the estimate for value can be calculated by adding the Intercept to the effect targetVoronezh, like so:
2.96+0.974 [1] 3.934
When target is at the reference level, but subtreatment equals FIC, you get the estimate of the outcome value by adding together the Intercept and the effect subtreatmentFIC, like this:
> 2.96+0.737 [1] 3.697
Now, the interaction is a little bit trickier. To get the effect you have to add up the relevant effects, as the interaction is reporting the difference in the effect. So, for example, to get the difference between the reference level of target and when Voronezh when subtreatment equals FIC, you need to add the Intercept, the effect for targetVoronezh, and the interaction, like this:
2.96+0.974+1.336 [1] 5.27
