马尾松是我国南方主要造林树种,在人工林中占有很大比例,但其林分经营过程的密度控制问题一直是未能很好解决的问题。在动态规划方法建立的马尾松人工林密度控制模型的基础上,以密度二次效应模型为基础、林分立木密度为目标函数,提出以净现值最大为标准,应用遗传算法优化马尾松人工林经营过程的间伐时间及其保留密度和主伐时间的最优组合方案。马尾松人工林经营过程的密度决策方案优化表明,遗传算法与经济评价相结合优化林分间伐时间及其保留密度和主伐时间决策组合效果很理想。马尾松人工林在地位指数10～18之间时,均以间伐两次为最优,第1次在11～12a ,第2次在16～17a ,而主伐年龄以22～25a为最优。每一地位指数均优化得到其最优的营林措施组合方案,此时净现值最大且内部收益率大于12%。研究结果为马尾松人工林密度管理决策提供了理论参考
Pinus massoniana is a main has planting species in south of China which accounts for a large proportion among plantations, but its problem of density control has always not been solved very well. In this paper based on density control model of P. massoniana plantation established by dynamic programming, Genetic Algorithm was applied to select optimal decision in determining thinning age, optimal density and cutting age during stand management, which was on the basis of the density effect model, took the stand density as objective function and maximal present net worth (PNW) as standard. The density optimal projection of P. massoniana plantation during its management showed that combination of Genetic Algorithm and method of economic evaluation obtained satisfactory results. The optimized results showed that two times were optimal for thinning of P. massoniana plantation during its management where the site index was 10～18. The first time for thinning was in 11～12 years, the second time was in 16～17 years, the optimal time for final cutting was in 22～25 years. And P. massoniana plantation in each site index had its own optimizing management projection by which the highest PNW could be achieved, and IRR was more than 12%. So the results of this study will provide a theoretical reference for density management of P. massoniana plantation.
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