首页> 外文期刊>Soil Biology & Biochemistry >Predicting soil respiration using carbon stock in roots, litter and soil organic matter in forests of Loess Plateau in China.
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Predicting soil respiration using carbon stock in roots, litter and soil organic matter in forests of Loess Plateau in China.

机译:黄土高原森林根系,凋落物和土壤有机质的碳储量预测土壤呼吸

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Understanding the dominant variables driving soil respiration is critically important for predicting soil CO2 emission and assessing the carbon balance of forest ecosystems. In a small catchment of the semiarid Loess Plateau in China, soil respiration and soil biophysical factors were studied on sites of five forest types, comprising plots established in a pure Pinus tabulaeformis plantation, a pure Robinia pseudoacacia plantation, a mixed P. tabulaeformis and R. pseudoacacia plantation, a pure Platycladus orientalis plantation, and a natural Populus davidiana stand. Soil temperature at the 10 cm depth was found to be the most predominant factor controlling the temporal pattern of soil respiration, accounting for 11-40% seasonal variation in the rate of soil CO2 efflux across forest types. By applying an empirical model and the calculated temperature sensitivity of soil respiration (Q10) and the rate of basal soil respiration (R10), annual soil CO2 emission was estimated separately for each forest type using the automatically monitored data of soil temperature at the 10 cm depth. The annual soil CO2 emission varied greatly with forest types and ranged from 647.71 g C m-2 y-1 in the P. orientalis plantation to 1448.50 g C m-2 y-1 in the natural P. davidiana stand. Annual soil CO2 efflux is better predicted by soil organic carbon content and the amount of carbon in roots, litter and top soil than soil temperature when data are pooled for all plots of the five forest types. A first order exponential analysis indicates that about 77% of the variation in annual soil CO2 efflux is explained by root carbon stock, 63% by the combined carbon stock in roots, litter, and top soil, and 48% by the combined carbon stock in litter and top soil. We conclude that annual soil CO2 efflux can be predicted by carbon pools in roots and soils rather than by soil temperature in watersheds where spatial variation in soil temperature is relatively small in the semiarid Loess Plateau of China.
机译:理解驱动土壤呼吸的主要变量对于预测土壤CO 2 的排放和评估森林生态系统的碳平衡至关重要。在中国半干旱黄土高原的一个小流域,研究了五种森林类型的土壤呼吸和土壤生物物理因素,包括在纯松油松人工林,纯刺槐人工林,油松混交林和R麻混交林中建立的样地。 。pseudoacacia人工林,纯侧柏(Platycladus Orientalis)人工林和天然Populus davidiana林分。 10 cm深的土壤温度是控制土壤呼吸时间格局的最主要因素,占森林类型中土壤CO 2 外排速率的季节性变化的11-40%。通过应用经验模型和计算得出的土壤呼吸温度敏感性(Q 10 )和基础土壤呼吸速率(R 10 ),年土壤CO 2 排放是使用自动监测的10厘米深度的土壤温度数据分别估算每种森林类型的排放量。侧柏人工林年土壤CO 2 年排放量随森林类型的不同而有很大差异,范围为647.71 g C m -2 y -1 天然P. davidiana林分中的1448.50 g C m -2 y -1 。当收集五种森林类型的所有样地的数据时,土壤有机碳含量以及根,凋落物和表层土壤中的碳含量比土壤温度更好地预测了年度土壤CO 2 外排量。一阶指数分析表明,每年土壤CO 2 外排量中约77%的变化由根系碳储量解释,63%由根,凋落物和表层土壤中的总碳储量解释,并且占枯枝落叶和表层土壤总碳储量的48%。我们得出的结论是,可以通过根和土壤中的碳库而不是通过分水岭的土壤温度来预测年土壤CO 2 的外流,而在中国半干旱黄土高原,土壤温度的空间变化相对较小。

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