SOME BACKGROUND
I am working on a training Random Forest regressor, for predicting yield in crops. Some of my predictor variables apply only to some cases, e.g. I have a variable denoting the number of rows, which only applies to crops grown in a polytunnel. If the crops are grown in a glasshouse, the number of rows does not apply, so it is left as a null value. I also have another variable which denotes whether the crop is grown under a polytunnel or glasshouse.
THE PROBLEM
As Random Forest does not handle missing values, is there a strategy that could deal with cases where variables take null values for cases where they do not apply? Tutorials and papers on the topic suggest imputing the values, but under the scenarios they consider these variables still apply, and are missing because of some external factor (e.g. rich people don't generally want to reveal their salaries).