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Dynamic growth models for Caragana korshinskii shrub biomass in China

文献类型： 外文期刊

作者： Xu, Hao 1 ; Wang, Zhanjun 2 ; Li, Ying 3 ; He, Jianlong 2 ; Wu, Xudong 2 ;

作者机构： 1.Ningxia Univ, Sch Econ & Management, Yinchuan, Ningxia, Peoples R China

2.Ningxia Acad Agr & Forestry Sci, Res Inst Desertificat Control, Yinchuan, Ningxia, Peoples R China

3.Ningxia Vocat Tech Coll Ind & Commerce, Yinchuan, Ningxia, Peoples R China

关键词： Dynamic growth models; Fixed and mixed effects; Heteroscedasticity; Caragana korshinskii shrub

期刊名称：JOURNAL OF ENVIRONMENTAL MANAGEMENT （ 影响因子：6.789； 五年影响因子：6.914 ）

ISSN： 0301-4797

年卷期： 2020 年 269 卷

页码：

收录情况： SCI

摘要： The Caragana korshinskii shrub is a widely distributed plant found in arid regions and plays an important role in ecological environment protection. Accurate estimations of shrub biomass are particularly important for natural resource management decision making. 114 individual C. korshinskii shrubs from three regions were collected in northwest China in this study. With regions as fixed (dummy variables) and random effects, the nonlinear least square (NLS) regression approach, nonlinear fixed effects (NLFE) approach and nonlinear mixed effects (NLME) approach were developed to predict dynamic growth of total, aboveground, stem, foliage, and root biomass values of C. korshinskii shrub based on logistic function. Results revealed that both NLFE and NLME models performed better than NLS, which indicated that regions were important factors influenced shrub biomass dynamic growth. Additionally, NLME models had a smaller Bayesian information criterion (BIC) than NLFE models. For NLME models, the random effects of regions mainly influenced the growth rate and asymptotic value of the dynamic growth curve, and there was no significant influence on the values associated with the curve shape. Moreover, the modified NLME models with hetemscedasticity exhibited extremely significant differences (p < 0.0001) when compared to NLME models by the likelihood ratio (LR) test. The NLME models were proved to be an efficient approach for considering the random effects on shrub biomass dynamic growth and accounted for the hetemscedasticity of shrub biomass data.

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