首页> 外文期刊>Arid Land Research and Management >Allometric models for estimating shrub biomass in desert grassland in northern China
【24h】

Allometric models for estimating shrub biomass in desert grassland in northern China

机译:北方沙漠草地灌木生物量估算灌木生物量的同传模型

获取原文
获取原文并翻译 | 示例
           

摘要

The development of shrub allometric models is crucial for accurate biomass assessment, as well as for scientific studies of carbon storage and carbon cycling of desert ecosystems. The aim of the present study was to construct allometric models to predict biomass using easily measured variables for xerophytic shrubs. The 12 most widespread shrub species of northern China were selected and a total of 385 individuals were harvested to obtain the weight of its components (leaves, twigs, branches, and roots), the crown area (CA) and plant height (H). Based on a high coefficient of determination (R2), a low standard error of estimate (SEE), and low Akaike information criterion (AIC) values, 72 species-specific and 24 multispecies models with CA and H as independent variables were developed. The function lnW (biomass of different components) = a + b x lnX (predictor variable) was selected as optimal model. CA was revealed as the best independent variable for the biomass of leaves and twigs, and V (CA x H) was the best predictor variable for branches, aboveground, belowground, and total biomass. In conclusion, for the first time species-specific and multispecies models were constructed with a high goodness of fit of leaves, twigs, branches, aboveground, belowground, and total biomass for 12 shrub species in northern China. Compared to multispecies models, species-specific models had improved accuracy. Since biomass quantification is the basis of carbon stocks estimation, the models presented here can be considered as alternative tool for assessing carbon storage and carbon cycling of desert ecosystems.
机译:灌木等因素模型的发展对于准确的生物量评估至关重要,以及对沙漠生态系统的碳储存和碳循环的科学研究。本研究的目的是构建各种模型以预测使用易测量的杂草灌木的变量来预测生物质。选择北方12种最普遍的灌木种类,并收获了385个个体,以获得其组分(叶子,树枝,分支和根)的重量,冠部(CA)和植物高度(H)。基于高系数(R2),开发了具有CA和H作为独立变量的低标准误差(参见)估计(参见)和低akaike信息标准(AKAike信息标准(AKAike信息标准(AIC)值,72种MultiSpecies模型。功能LNW(不同组件的生物量)= A + B X LNX(预测器变量)被选择为最佳模型。 CA被揭示为叶片和树枝生物量的最佳独立变量,V(Ca x H)是分支机构,地上,地下的最佳预测因子变量和总生物质。总之,对于第一次特异性和多数模型构建,具有高质量的叶子,树枝,分支机构,地下,地下,下面的12个灌木种类的总生物量。与多数模型相比,物种特定模型提高了准确性。由于生物质量化是碳储备估计的基础,因此这里提出的模型可被视为用于评估沙漠生态系统的碳储存和碳循环的替代工具。

著录项

  • 来源
    《Arid Land Research and Management》 |2017年第3期|共18页
  • 作者单位

    Chinese Acad Sci Northwest Inst Ecoenvironm &

    Resources Shapotou Desert Res &

    Expt Stn Donggang West Rd 320 Lanzhou 730000 Gansu Peoples R China;

    Chinese Acad Sci Northwest Inst Ecoenvironm &

    Resources Shapotou Desert Res &

    Expt Stn Donggang West Rd 320 Lanzhou 730000 Gansu Peoples R China;

    Chinese Acad Sci Northwest Inst Ecoenvironm &

    Resources Shapotou Desert Res &

    Expt Stn Donggang West Rd 320 Lanzhou 730000 Gansu Peoples R China;

    Chinese Acad Sci Northwest Inst Ecoenvironm &

    Resources Shapotou Desert Res &

    Expt Stn Donggang West Rd 320 Lanzhou 730000 Gansu Peoples R China;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 农业基础科学;
  • 关键词

    Biomass estimation; desert ecosystems; multispecies; species-specific; xerophytic shrub;

    机译:生物量估计;沙漠生态系统;多数;特异性物种;杂草灌木;

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号