Unifying soil organic matter formation and persistence frameworks: the MEMS model

Robertson, Andy D.; Paustian, Keith; Ogle, Stephen; Wallenstein, Matthew D.; Lugato, Emanuele; Cotrufo, M. Francesca

doi:10.5194/bg-16-1225-2019

Title: Unifying soil organic matter formation and persistence frameworks: the MEMS model

Journal Article · Mon Mar 25 00:00:00 EDT 2019 · Biogeosciences (Online)

DOI:https://doi.org/10.5194/bg-16-1225-2019· OSTI ID:1505777

Robertson, Andy D. ^[1]; Paustian, Keith ^[1]; Ogle, Stephen ^[1]; Wallenstein, Matthew D. ^[1]; Lugato, Emanuele ^[2]; Cotrufo, M. Francesca ^[1]

Colorado State Univ., Fort Collins, CO (United States)
European Commission, Ispra (Italy). Joint Research Centre

Soil organic matter (SOM) dynamics in ecosystem-scale biogeochemical models have traditionally been simulated as immeasurable fluxes between conceptually defined pools. This greatly limits how empirical data can be used to improve model performance and reduce the uncertainty associated with their predictions of carbon (C) cycling. Recent advances in our understanding of the biogeochemical processes that govern SOM formation and persistence demand a new mathematical model with a structure built around key mechanisms and biogeochemically relevant pools. Here, we present one approach that aims to address this need. Our new model (MEMS v1.0) is developed from the Microbial Efficiency-Matrix Stabilization framework, which emphasizes the importance of linking the chemistry of organic matter inputs with efficiency of microbial processing and ultimately with the soil mineral matrix, when studying SOM formation and stabilization. Building on this framework, MEMS v1.0 is also capable of simulating the concept of C saturation and represents decomposition processes and mechanisms of physico-chemical stabilization to define SOM formation into four primary fractions. After describing the model in detail, we optimize four key parameters identified through a variance-based sensitivity analysis. Optimization employed soil fractionation data from 154 sites with diverse environmental conditions, directly equating mineral-associated organic matter and particulate organic matter fractions with corresponding model pools. Finally, model performance was evaluated using total topsoil (0–20 cm) C data from 8192 forest and grassland sites across Europe. Despite the relative simplicity of the model, it was able to accurately capture general trends in soil C stocks across extensive gradients of temperature, precipitation, annual C inputs and soil texture. The novel approach that MEMS v1.0 takes to simulate SOM dynamics has the potential to improve our forecasts of how soils respond to management and environmental perturbation. Ensuring these forecasts are accurate is key to effectively informing policy that can address the sustainability of ecosystem services and help mitigate climate change.

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Research Organization:: Colorado State Univ., Fort Collins, CO (United States)

Sponsoring Organization:: USDOE Advanced Research Projects Agency - Energy (ARPA-E)

Grant/Contract Number:: AR0000826

OSTI ID:: 1505777

Journal Information:: Biogeosciences (Online), Vol. 16, Issue 6; ISSN 1726-4189

Publisher:: European Geosciences UnionCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 69 works

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Microbial and abiotic controls on mineral-associated organic matter in soil profiles along an ecosystem gradient Mikutta, Robert; Turner, Stephanie; Schippers, Axel Scientific Reports, Vol. 9, Issue 1 https://doi.org/10.1038/s41598-019-46501-4	journal	July 2019
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Microbial and abiotic controls on mineral-associated organic matter in soil profiles along an ecosystem gradient Mikutta, Robert; Turner, Stephanie; Schippers, Axel Berlin : Springer https://doi.org/10.15488/8622	other	January 2019
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Enhancing natural cycles in agro-ecosystems to boost plant carbon capture and soil storage Buss, Wolfram; Yeates, Kirsty; Rohling, Eelco J. Oxford Open Climate Change, Vol. 1, Issue 1 https://doi.org/10.1093/oxfclm/kgab006	journal	May 2021
Stoichiometrically coupled carbon and nitrogen cycling in the MIcrobial-MIneral Carbon Stabilization model version 1.0 (MIMICS-CN v1.0) Kyker-Snowman, Emily; Wieder, William R.; Frey, Serita D. Geoscientific Model Development, Vol. 13, Issue 9 https://doi.org/10.5194/gmd-13-4413-2020	journal	January 2020

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