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      Spatial distribution and influencing factors of urban soil organic carbon stocks in Xi'an City, China

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          An extraction method for measuring soil microbial biomass C

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            The Microbial Efficiency-Matrix Stabilization (MEMS) framework integrates plant litter decomposition with soil organic matter stabilization: do labile plant inputs form stable soil organic matter?

            The decomposition and transformation of above- and below-ground plant detritus (litter) is the main process by which soil organic matter (SOM) is formed. Yet, research on litter decay and SOM formation has been largely uncoupled, failing to provide an effective nexus between these two fundamental processes for carbon (C) and nitrogen (N) cycling and storage. We present the current understanding of the importance of microbial substrate use efficiency and C and N allocation in controlling the proportion of plant-derived C and N that is incorporated into SOM, and of soil matrix interactions in controlling SOM stabilization. We synthesize this understanding into the Microbial Efficiency-Matrix Stabilization (MEMS) framework. This framework leads to the hypothesis that labile plant constituents are the dominant source of microbial products, relative to input rates, because they are utilized more efficiently by microbes. These microbial products of decomposition would thus become the main precursors of stable SOM by promoting aggregation and through strong chemical bonding to the mineral soil matrix. © 2012 Blackwell Publishing Ltd.
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              Global soil carbon: understanding and managing the largest terrestrial carbon pool

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                Author and article information

                Journal
                Urban Ecosystems
                Urban Ecosyst
                1083-8155
                1573-1642
                June 2023
                December 15 2022
                June 2023
                : 26
                : 3
                : 677-688
                Article
                10.1007/s11252-022-01316-6
                f26d21ae-cb77-4337-b890-42b59d751ec9
                © 2023

                https://www.springernature.com/gp/researchers/text-and-data-mining

                https://www.springernature.com/gp/researchers/text-and-data-mining

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