Stumpage price processes are estimated via regression analysis (with alternative autoregressive models) with data from the Iranian Caspian forests. The parameter estimates indicate that the stumpage price may be regarded as a stationary stochastic process. The optimal harvest decisions were calculated via stochastic dynamic programming. The harvest decisions that maximize the expected present value of all profits over time are made adaptively, conditional on the latest available price and stock level information. The results show that it is possible to determine the optimal harvesting level for different price and stock states. We may increase the expected present value by more than 26% when we let optimal adaptive decisions replace optimal deterministic planning decisions. Dynamic game theory is applied to analyze the timber market in northern Iran as a duopsony. The Nash equilibrium and the dynamic properties of the system based on marginal adjustments are determined. When timber is sold, the different mills use mixed strategies to give sealed bids. It is found that the decision probability combination of the different mills follow a special form of attractor and that centers should be expected to appear in unconstrained games. Since the probabilities of different strategies are always found in the interval [0,1], the boundaries of the feasible set are sometimes binding constraints. Then, the attractor becomes a constrained probability orbit. In the studied game, the probability that the Nash equilibrium will be reached is almost zero. The dynamic properties of timber prices derived via the duopsony game model are found also in the real empirical price series from the north of Iran. Dynamic duopoly game theory was also used to analyze the sawnwood and pulpwood markets in northern Iran. The differential equation system governing the simultaneous optimal adjustments of the decision frequencies of the two players gives cyclical solutions.