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• W2120733110 abstract "Many studies have shown that elevated atmospheric CO2 concentrations result in increased plant carbon inputs to soil that can accelerate the decomposition of native soil organic matter, an effect known as priming. Consequently, it is important to understand and quantify the priming effect for future predictions of carbon-climate feedbacks. There are potential pitfalls, however, when representing this complex system with a simple, first-order model. Here, we show that a multi-pool soil carbon model can match the change in bulk turnover time calculated from overall respiration and carbon stocks (a one-pool approach) at elevated CO2 , without a change in decomposition rate constants of individual pools (i.e., without priming). Therefore, the priming effect cannot be quantified using a one-pool model alone, and even a two-pool model may be inadequate, depending on the effect size as well as the distribution of soil organic carbon and turnover times. In addition to standard measurements of carbon stocks and CO2 fluxes, we argue that quantifying the fate of new plant inputs requires isotopic tracers and microbial measurements. Our results offer insights into modeling and interpreting priming from observations." @default.
• W2120733110 created "2016-06-24" @default.
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• W2120733110 date "2015-11-02" @default.
• W2120733110 modified "2023-10-16" @default.
• W2120733110 title "Toward improved model structures for analyzing priming: potential pitfalls of using bulk turnover time" @default.
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• W2120733110 doi "https://doi.org/10.1111/gcb.13039" @default.
• W2120733110 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/26182905" @default.
• W2120733110 hasPublicationYear "2015" @default.
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