Separating soil CO 2 efflux into C-pool-specific decay rates via inverse analysis of soil incubation data
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¨ RNER SPECIAL TOPIC: IN HONOR OF CHRISTIAN KO
Separating soil CO2 efflux into C-pool-specific decay rates via inverse analysis of soil incubation data Christina Scha¨del • Yiqi Luo • R. David Evans Shenfeng Fei • Sean M. Schaeffer
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Received: 28 February 2012 / Accepted: 18 December 2012 / Published online: 22 January 2013 Springer-Verlag Berlin Heidelberg 2013
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. Keywords SOC Labile C Recalcitrant C Data assimilation Parameter estimation
Communicated by Russell Monson. C. Scha¨del (&) Department of Biology, University of Florida, Gainesville, FL 32611, USA e-mail: [email protected] C. Scha¨del Y. Luo S. Fei Department of Microbiology and Plant Biology, University of Oklahoma, Norman, OK 73019, USA R. David Evans School of Biological Sciences, Washington State University, Pullman, WA 99164, USA S. Fei Department of Computer Science and Engineering, Texas A&M University, College Station, TX 77840, USA S. M. Schaeffer Department of Biosystems Engineering and Soil Science, University of Tennessee, Knoxville, TN 37996, USA
Introduction Soils contain about two-thirds of all organic carbon (3,000 Pg C) that is stored in terrestrial ecosystems (Jobba´gy and Jackson 2000) and yearly release 98 ± 11 Pg C to the atmosphere (Bond-Lamberty and Thomson 2010). Total soil CO2 efflux yearly exceeds the current rate of anthropogenic CO2 emissions from deforestation and burning of fossil fuels
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