In applications of branching processes, usually it is hard to obtain samples of a large size. Therefore, a bootstrap procedure allowing inference based on a small sample size is very useful. Unfortunately, in the critical branching process with stationary immigration the standard parametric bootstrap is invalid. In this paper, we consider a process with non-stationary immigration, whose mean and variance vary regularly with nonnegative exponents α and β , respectively. We prove that 1 + 2 α is the threshold for the validity of the bootstrap in this model. If β < 1 + 2 α , the standard bootstrap is valid and if β > 1 + 2 α it is invalid. In the case β = 1 + 2 α , the validity of the bootstrap depends on the slowly varying parts of the immigration mean and variance. These results allow us to develop statistical inferences about the parameters of the process in its early stages.