Dynamic Programming to Determine Optimum Levels of Growing Stock

An important investment problem is to determine optimal levels of growing stock in even-aged stands during a rotation. Various models of marginal analysis have been used to obtain solutions. An alternative model, using dynamic programming, seems appropriate where the mensurational relationships or economic data indicate interdependence between growing stock levels. We developed a deterministic, one-dimensional, discrete dynamic program and tested it by using data from a publication in which marginal analysis was applied to loblolly pine timber stocking. We found dynamic programming to have analytic and computational advantages as a method of solving for optimal levels of growing stock.