Forest management planning faces uncertainties regarding future timber prices, tree growth, and survival. Future seed production is an additional source of uncertainty in Korean pine stands managed for the joint production of timber and edible seeds. Modern forest planning uses optimisation to determine the best possible cutting schedule. Optimisation can accommodate uncertainty by using decision rules for adaptive forest management instead of optimising cutting years and intensities. In this study, we optimised two adaptive decision rules for managing Korean pine plantations for the joint production of timber and pinecones when timber prices, tree growth, and seed production are stochastic. The first rule indicated the minimum price to sell timber, i.e., the reservation price, as a function of the mean tree diameter and stand basal area. The second adaptive rule expressed the mean tree diameter at which cutting is optimal as a function of timber price and stand basal area. Both decision rules resulted in nearly the same mean net present value when the optimised rule was applied to 100 stochastic scenarios for future timber prices, tree growth, and seed production. The net present values were over 20% higher than those for the deterministically optimised cutting schedules under the same scenarios. Therefore, the expected economic gain from switching from deterministic to adaptive stochastic optimisation was at least 20%. The cutting years of the adaptive optima were frequently later than those indicated by the deterministic optima, and optimal adaptive harvesting often involved waiting for high timber prices. The minimum price or minimum mean diameter to sell timber was higher when the income from seeds was considered in the optimisation. The cuttings were later, and the rotations were longer in the joint production of timber and pinecones than in timber production alone.

Branch size is a crucial characteristic, closely linked to both tree growth and wood quality. A review of existing branch size models reveals various approaches, but the ability to estimate branch diameter and length within the same whorl remains underexplored. In this study, a total of 77 trees were sampled from Northeast China to model the vertical distribution of branch diameter and length within each whorl along the crown. Several commonly used functions were taken as the alternative model forms, and the quantile regression method was employed and compared with the classical two-step modeling approach. The analysis incorporated stand, tree, and competition factors, with a particular focus on how these factors influence branches of varying sizes. The modified Weibull function was chosen as the optimal model, due to its excellent performance across all quantiles. Eight quantile regression curves (ranging from 0.20 to 0.85) were combined to predict branch diameter, while seven curves (ranging from 0.20 to 0.80) were used for branch length. The results showed that the quantile regression method outperformed the classical approach at model fitting and validation, likely due to its ability to estimate different rates of change across the entire branch size distribution. Lager branches in each whorl were more sensitive to changes in DBH, crown length (CL), crown ratio (CR) and dominant tree height (H_dom), while slenderness (HDR) more effectively influenced small and medium-sized branches. The effect of stand basal area (BAS) was relatively consistent across different branch sizes. The findings indicate that quantile regression is a good way not only a more accurate method for predicting branch size but also a valuable tool for understanding how branch growth responds to stand and tree factors. The models developed in this study are prepared to be further integrated into tree growth and yield simulation system, contributing to the assessment and promotion of wood quality.