构建湖南省马尾松和杉木林分断面积生长模型,为森林可持续经营提供基础生长数据。利用湖南省838块马尾松和1 484块杉木样地数据,从9个具有生物学意义的备选模型中选出一个最优基础模型。以树种、立地等级和起源作为哑变量,构建统一的林分断面积生长模型。结果显示:马尾松和杉木林分断面积最优生长模型都是Richards形式的模型,决定系数分别为0.985和0.987;引入树种、立地等级和起源作为哑变量后,与基础模型相比,断面积生长模型拟合精度都有提高,决定系数提高到0.992,均方根误差下降到0.682。研究表明:带树种、立地等级和起源的哑变量模型能同时反映马尾松和杉木林分断面积生长规律,这样既减少了建模工作量,又提供了不同林分合并建模的方法;湖南省杉木林分断面积生长上限值高于马尾松;相同林分密度条件下,马尾松和杉木的生长速率均随着立地质量的下降而降低。

Developing the stand basal area growth models for Pinus massoniana and Cunninghamia lanceolata in Hunan Province was a data support measure for forest sustainable management.Based on the data of 838 plots of Pinus massoniana and 1 484 plots of Cunninghamia lanceolata in Hunan province,an optimal basic model was selected from 9 candidate models with biological significance.Taking species,site class and stand origin as dummy variable,the stand basal area growth model was constructed.The results showed that the optimal fitting models of Pinus massoniana and Cunninghamia lanceolata were in the form of Richards,with corresponding coefficients of determination at 0.985 and 0.987,respectively.Compared with the basic model,the fitting accuracy of the stand basal area growth model was improved after introducing dummy variable such as species,site class and stand origin,with a R² at 0.992 and a RMSE dropping to 0.682.Conclusions:The growth pattern of stand basal for Pinus massoniana and Cunninghamia lanceolata in Hunan province can be reflected by the model with species and site class and stand origin as dummy variable,which reduced the workload of computation and provided a method for integrating different forest stands.The limit value of stand basal area for Cunninghamia lanceolata was higher than that for Pinus massoniana.Furthermore,in the same stand density,the growth rate of stand basal area for Pinus massoniana and Cunninghamia lanceolata both decreased with the decrease of site quality.