筛网孔径与取样量对蒙古栎树木组织部分碳质量分数及其变异的影响

Effects of Mesh Size and Sampling Amount on Carbon Content and Its Variation of Quercus mongolica Oak Components

利用不同孔径筛网(1 700、830、380、180、96μm)处理蒙古栎不同树木组织部分的粉碎样品,并利用德国元素总有机碳分析仪实测不同树木组织部分的碳质量分数,旨在确定合适的取样量和筛网孔径,探索蒙古栎不同树木组织部分碳质量分数测定过程中数据的变异及其可能的影响因素,比较不同树木组织部分碳质量分数的差异,为蒙古栎林碳储量的客观评估提供依据。结果表明:筛网孔径对细根、中根、细枝、中枝碳质量分数影响较大,40目筛网处理的样品碳质量分数低估和变异程度比较小;测定蒙古栎不同树木组织部分的合适取样量为14.93mg;蒙古栎不同树木组织部分的碳质量分数从大到小依次为中根、老枝、细枝、中枝、老叶、细根、粗根、树皮、树干、新叶,不同树木组织部分碳质量分数总体上差异显著。可确定,合适的取样量和筛网孔径能够保证蒙古栎各树木组织部分测量结果的准确度,在利用森林清查数据估算森林碳储量与碳封存能力时,蒙古栎的不同树木组织部分碳质量分数的差异是不容忽视的变量。

This study was conducted to determine the appropriate sampling quantity and the pore size of screen,and explore the data of determination of carbon content in the different components of Quercus mongolica. Different bore diameter of screen mesh（ 1 700,830,380,180 and 96 μm） was used for sample preparation,and the Vario Toc analyzer made in German was adopt for carbon content determination. The variation of carbon content of different components and their possible influencing factors were compared,providing the basis for objective assessment of Q. mongolica forest carbon reserves. The pore size of the screen had great influence on the carbon content of fine root,middle root,branch and middle branch. The carbon content of the samples treated by the 40 mesh screen was smaller in underestimation and variation. The appropriate sampling amount for different components of Q. mongolica was 14.93 mg,and the carbon content of the different components from large to small were middle root,old branch,branch,middle branch,old leaf,fine root,coarse root,bark,trunk and new leaf. The carbon content of different components was significantly different. It is determined that the appropriate sampling quantity and sieve pore diameter can ensure the accuracy of the measurement results of the Q. mongolica components,and the difference in carbon content for different components cannot be ignored when estimating carbon storage and carbon sequestration capacity based on forest inventory data.