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Litter decomposition determines soil organic matter (SOM) formation and plant‐available nutrient cycles. Therefore, accurate model representation of litter decomposition is critical to improving soil carbon (C) projections of bioenergy feedstocks. Soil C models that simulate microbial physiology (i.e., microbial models) are new to bioenergy agriculture, and their parameterization is often based on small datasets or manual calibration to reach benchmarks. Here, we reparameterized litter decomposition in a microbial soil C model (CORPSE ‐ Carbon, Organisms, Rhizosphere, and Protection in the Soil Environment) using the continental‐scale Long‐term Inter‐site Decomposition Experiment Team (LIDET) dataset which documents decomposition across a range of litter qualities over a decade. We conducted a simplified Monte Carlo simulation that constrained parameter values to reduce computational costs. The LIDET‐derived parameters improved modeled C and nitrogen (N) remaining, decomposition rates, and litter mean residence times as compared to Baseline parameters. We applied the LIDET litter decomposition parameters to a microbial bioenergy model (Fixation and Uptake of Nitrogen – Bioenergy Carbon, Rhizosphere, Organisms, and Protection) to examine soil C estimates generated by Baseline and LIDET parameters. LIDET parameters increased estimated soil C in bioenergy feedstocks, with even greater increases under elevated plant inputs (i.e., by increasing residue, N fertilization). This was due to the integrated effects of plant litter quantity, quality, and agricultural practices (tillage, fertilization). Collectively, we developed a simple framework for using large‐scale datasets to inform the parameterization of microbial models that impacts projections of soil C for bioenergy feedstocks.
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Microbial Models for Simulating Soil Carbon Dynamics: A Review
Key Points More than 70 microbial models have recently been developed to simulate soil carbon dynamics Diversity in model structures and parameters indicates uncertainty in translating current knowledge of microbial processes into models Data‐driven model development and parameterization are highly recommended to improve the prediction of microbial models
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- PAR ID:
- 10446663
- Date Published:
- Journal Name:
- Journal of Geophysical Research: Biogeosciences
- Volume:
- 128
- Issue:
- 8
- ISSN:
- 2169-8953
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1029/2023JG007436
