Stand density management strategies under risk: effects of stochastic prices

A method is described for determining optimal economic strategies for density management in loblolly pine (Pinus taeda L.) stands in the southern United States. A stochastic dynamic programming model employs a price state transition matrix constructed using a first-order conditional cumulative density function for price based on time-series data for national forest pine stumpage in the South. The model also incorporates WITHIN, a pine growth and yield simulator widely used in the South to support analyses of alternative stand management strategies. Results indicate that at current average prices for pulpwood, on average sites, optimal planting density decisions recommended by the stochastic model are very similar to those obtained with a deterministic price equivalent. However, there are many instances where the thinning decisions recommended by the two models are different, especially when they are used to evaluate better sites or when the price of pulpwood is substantially increased above current region-wide averages.