Can I predict values from a multiple regression model with fewer predictors than there are in the model? I have built a linear regression model with three predictor variables: the model predicts forest growth (y = stand volume) with stand age, stand basal area and site type (x1, x2 and x3 respectively). However, my goal is to predict the forest volume so that I only know the forest age and site type. Is there any way to make the prediction with the model when the stand basal area is unknown? I have also tried to build the model with only two predictor variables but it doesn't produce good values if the stand basal area is removed.
Do you have any tips? I couldn't find anything by googling except this: https://stackoverflow.com/questions/28528703/ols-predict-using-only-a-subset-of-explanatory-variables. It suggests setting the value of unused variable as 0.
EDIT:
So I have already trained my model and then tested it with a testing dataset with the predict() function. BOTH, the training AND the testing data sets indeed contained the value for area. But I'm now confused when I would have to find the volume for a certain-aged forest (in a future) as I don't know the area of the forest for example after 50 years. Thus, I can not add it to the model as a predictor variable. I added this in case someone thought that I haven't done the model predictions with a testing data set yet.

That's very unusual, I can't see how one could be expected to estimate a volume without an area. I would suggest running predictions with a number of example values of area. For example, choose three values of area that you think are reasonable given your context, then run the model three times - each with a fixed area value (and variable x1 and x3 values). Then present prediction results as a range of volumes that could reasonably be expected from stand ages and site types.
