Reparameterizing Litter Decomposition Using a Simplified Monte Carlo Method Improves Litter Decay Simulated by a Microbial Model and Alters Bioenergy Soil Carbon Estimates

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. Decomposition breaks down organic matter like leaves and roots, creating soil organic material and releasing essential nutrients for plant and microbial growth. Soil creation and nutrient release are processes that affect how much carbon is stored in soil. Soil carbon storage in bioenergy agriculture may help create a favorable carbon balance for biofuels, ultimately reducing the rate of climate change. However, environmental decision makers need reliable information about how different bioenergy plants change soil carbon stocks to predict long-term outcomes of present-day decisions. These predictions are generated by computer models that mathematically represent ecological processes using observations from field studies. However, some models that include microbial decomposition lack a robust observational and mathematical basis for their representation of decomposition. We used a large-scale litter decomposition dataset and simplified a statistical simulation that is typically complex and time-consuming to improve the mathematical basis for litter decomposition in a soil carbon model. We used the improved decomposition representation in a different model that calculates soil carbon in bioenergy agriculture, and found the new representation increased predicted soil carbon in bioenergy feedstocks. Our statistical, data-based framework can be adopted to help make model predictions more accurate, and environmental management decisions more effective. Simplified Monte Carlo method uses constrained parameter sets and extensive dataset to parameterize decomposition in a microbial model The new litter decomposition parameters improved carbon and nitrogen decomposition metrics in a process-based microbial model Applying the new parameters to bioenergy systems altered modeled soil carbon with variation by plant traits, management, and litter inputs