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通过对土壤培养数据的反分析,将土壤 CO2 释放分离为 C 库特异性衰减速率。

Separating soil CO2 efflux into C-pool-specific decay rates via inverse analysis of soil incubation data.

机构信息

Department of Biology, University of Florida, Gainesville, FL 32611, USA.

出版信息

Oecologia. 2013 Mar;171(3):721-32. doi: 10.1007/s00442-012-2577-4. Epub 2013 Jan 22.

Abstract

Soil organic matter (SOM) is heterogeneous in structure and has been considered to consist of various pools with different intrinsic turnover rates. Although those pools have been conceptually expressed in models and analyzed according to soil physical and chemical properties, separation of SOM into component pools is still challenging. In this study, we conducted inverse analyses with data from a long-term (385 days) incubation experiment with two types of soil (from plant interspace and from underneath plants) to deconvolute soil carbon (C) efflux into different source pools. We analyzed the two datasets with one-, two- and three-pool models and used probability density functions as a criterion to judge the best model to fit the datasets. Our results indicated that soil C release trajectories over the 385 days of the incubation study were best modeled with a two-pool C model. For both soil types, released C within the first 10 days of the incubation study originated from the labile pool. Decomposition of C in the recalcitrant pool was modeled to contribute to the total CO2 efflux by 9-11 % at the beginning of the incubation. At the end of the experiment, 75-85 % of the initial soil organic carbon (SOC) was modeled to be released over the incubation period. Our modeling analysis also indicated that the labile C-pool in the soil underneath plants was larger than that in soil from interspace. This deconvolution analysis was based on information contained in incubation data to separate carbon pools and can facilitate integration of results from incubation experiments into ecosystem models with improved parameterization.

摘要

土壤有机质(SOM)在结构上具有异质性,被认为由具有不同固有周转率的各种库组成。尽管这些库在模型中被概念性地表达,并根据土壤物理和化学性质进行了分析,但将 SOM 分离成组成库仍然具有挑战性。在这项研究中,我们对两种土壤(植物间和植物下)的长期(385 天)孵化实验数据进行了逆分析,以将土壤碳(C)流出分解为不同的源库。我们使用一个、两个和三个库模型分析了这两个数据集,并使用概率密度函数作为判断最佳模型拟合数据集的标准。我们的结果表明,在 385 天的孵化研究中,土壤 C 释放轨迹最好用双库 C 模型来模拟。对于两种土壤类型,孵化研究的前 10 天释放的 C 来自于易降解库。难降解库中 C 的分解被模拟为在孵化初期对总 CO2 通量的贡献为 9-11%。在实验结束时,75-85%的初始土壤有机碳(SOC)被模拟为在孵化期间释放。我们的建模分析还表明,植物下土壤中的易降解 C 库大于植物间土壤中的易降解 C 库。这种解卷积分析是基于孵化数据中包含的信息来分离碳库,可以促进将孵化实验结果整合到具有改进参数化的生态系统模型中。

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