文献类型：期刊文献

英文题名：Modelling site site index of Chinese fir plantations using a random effects model across regional site types in Hunan province, China

作者：Zhu, Guangyu[1,2] Hu, Song[1] Chhin, Sophan[3] Zhang, Xiongqing[4] He, Peng[5]

第一作者：Zhu, Guangyu

通信作者：Zhang, XQ[1]

机构：[1]Cent South Univ Forest & Technol, Coll Forestry, Changsha 410004, Hunan, Peoples R China;[2]Key Lab State Forestry Adm Forest Resources Manag, Changsha 410004, Hunan, Peoples R China;[3]West Virginia Univ, Div Forestry & Nat Resources, 322 Percival Hall,POB 6125, Morgantown, WV 26506 USA;[4]Chinese Acad Forestry, Res Inst Forestry, Beijing 100091, Peoples R China;[5]Natl Forestry & Grassland Adm, Cent South Inventory & Planning Inst, Changsha 410004, Hunan, Peoples R China

年份：2019

卷号：446

起止页码：143-150

外文期刊名：FOREST ECOLOGY AND MANAGEMENT

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000473376700015)】；

基金：The study was supported by the National Natural Science Foundation of China (No. 31670634, 31570631), the Young Elite Scientists Sponsorship Program by CAST of China (No. 2017QNRC001), and the Scientific and Technological Task in China (No. 2016YFD0600302-1).

语种：英文

外文关键词：Chinese fir; Site effect; Site type; Polymorphic site index model; Nonlinear mixed model

摘要：Site index is a popular method used for assessing site productivity. Based on sampling of 418 plots of Chinese fir (Cunninghamia lanceolata) across Hunan province, China, a site index model of this species in relation to site type as a random effect was developed. First, site factors (topography) influencing stand dominant height were screened as main factors through one-way ANOVA analysis. The results showed that elevation was the most significant factor, followed by soil type, aspect, and slope. Second, ten widely base models were developed, and found that these models performed poorly (R-2 ranged from 0.3850 to 0.4977). Although the models performed poorly, the best base model (M4) was selected (R-2 = 0.4977). Considering the influence of site factors on the site index curve, the different site factors and its combination as a random effect were simulated by nonlinear mixed-effect approach. The R-2 values had improved from 0.3850 similar to 0.4977 to 0.5132 similar to 0.9018, and the model with combined site type (110 levels) performed best (R-2 = 0.9018). K-means cluster method was used to cluster the 110 site types into 8 site type groups, and then a mixed-effects model with random effect of site type groups was developed and resulted in improved model performance (R-2 = 0.9268). The results have practical utility and guidelines for regional level forest productivity estimation.