In matrix population models, the process of population “projection” through a number of time steps is fundamentally multiplicative, hence the arithmetic mean of the consecutive matrices is of doubtful meaning, while the geometric mean quite corresponds to the multiplication principle. The geometric mean of positive numbers does not bear any problem, but that of matrices does. The “population projection matrices” (PPMs) are rather nonnegative than positive, with the allocation of non-zeros that is predetermined by the life cycle graph (LCG) reflecting the development biology of a given species, and this graph is principally incomplete. The average matrix A should logically have the same fixed pattern of zeros as those to be averaged, and this causes the averaging matrix equation to be overdetermined as a system of element-wise algebraic equations for the unknown positive elements. Therefore, the exact solution to the problem of pattern-geometric averaging does generally not exist, while the classical (least-squares) approximation leads to a significant error. My heuristic approach to finding a better approximate A for the PPMs in the form of L = T(transition) + F(fertility) is to solve the problem in a combined way: the pattern-geometric approximation for the T part and the exact arithmetic mean for F (as population recruitment is an additive process). As a result, the approximation error decreases drastically due to matrix T being always substochastic, while the combined, TF-averaging, method turns out efficient even under ‘reproductive uncertainty’ in data, i.e., for the whole families of feasible matrices F in the sum L = T + {F}. I illustrate the method of TF-averaging with 5 matrices L(t) calibrated for each pair of consecutive years from a 6-year period of observation in a case study of Eritrichium caucasicum, a short-lived perennial herbaceous species. The approximate TF-average enables gaining the ‘age-specific traits from stage-specific models’ (Caswell, 2001, p. 116) that are characteristic of the entire period, and I discuss other motivations/advantages for/of pattern-geometric means in matrix population models.