文献类型：期刊文献

英文题名：Site Index Model for Southern Subtropical Masson Pine Forests Using Stand Dominant Height

作者：Zou, Kailun[1] Duan, Guangshuang[2] Wu, You[1] Wang, Zhanyin[3] Liu, Xianzhao[1] Gusti, Mykola[]

第一作者：Zou, Kailun

通信作者：Liu, XZ[1]

机构：[1]Chinese Acad Forestry, State Key Lab Efficient Prod Forest Resources, Key Lab Forest Management & Growth Modelling, Inst Forest Resource Informat Tech,Natl Forestry &, Beijing 100091, Peoples R China;[2]Xinyang Normal Univ, Fac Math & Stat, Xinyang 464000, Peoples R China;[3]Forest Resources Protect Ctr Jiangxi, Nanchang 330038, Peoples R China

年份：2024

卷号：15

期号：1

外文期刊名：FORESTS

收录：;EI(收录号：20240515458107);Scopus(收录号：2-s2.0-85183320984);WOS:【SCI-EXPANDED(收录号:WOS:001148953300001)】；

基金：We would like to express our gratitude to Zeng Ji, who works at the Tropical Forest Experimental Center of the Chinese Academy of Forestry. He participated in the field survey and data collection for this study.

语种：英文

外文关键词：GADA; site index model; mixed-effect model; dominant height; terrain factor; Bayesian approach

摘要：Stand dominant height has a close relationship with stand productivity and is not much affected by stand density and thinning within a reasonable density range, making it an excellent indicator for estimating stand site quality. Topographic factors (altitude, aspect, slope, etc.) have a significant influence on the growth process of stand level, and the combination of different site factors increases the randomness of the evaluation of forest productivity. In this paper, with one-way ANOVA, it was determined that the effects of density and management mode on the Masson pine stand dominant height were not significant. The data on the Masson pine stand dominant height in the southern subtropics in Guangxi, China, were analyzed, and the GADA model was established using the nonlinear least squares method, the Bayesian approach, and the one-level nonlinear mixed-effects model with the topographic factor as the random effect, respectively. The results indicated that the nonlinear mixed-effects model had the best fitting performance and the highest prediction accuracy for stand site quality (a 0.27% improvement in R-2 compared to the least squares method and a 1.30% improvement in R-2 compared to the Bayesian approach), while the model obtained by the Bayesian approach had more elasticity and biological significance. In summary, when the data distribution is uniform and comprehensive, introducing terrain factors into the establishment of site index models can provide a more scientific basis for estimating the productivity of southern subtropical Masson pine stands under different site conditions. When the data distribution is uneven, applying the Bayesian approach can make the site index model more biologically meaningful. The stand site quality model can predict the potential production capacity of forests, which is an important basis and can support forest management and harvest prediction. The results of this study provide a theoretical and practical basis for the establishment of a reasonable site index model for the Masson pine stand.