Generalizing Microbial Parameters in Soil Biogeochemical Models: Insights From a Multi‐Site Incubation Experiment

Author:

Jian Siyang12ORCID,Li Jianwei2ORCID,Wang Gangsheng3,Zhou Jizhong145,Schadt Christopher W.6ORCID,Mayes Melanie A.7ORCID

Affiliation:

1. Institute for Environmental Genomics and Department of Microbiology & Plant Biology University of Oklahoma Norman OK USA

2. Department of Agricultural and Environmental Sciences Tennessee State University Nashville TN USA

3. State Key Laboratory of Water Resources Engineering and Management Institute for Water‐Carbon Cycles and Carbon Neutrality Wuhan University Wuhan China

4. School of Civil Engineering and Environmental Sciences University of Oklahoma Norman OK USA

5. Earth and Environmental Sciences Lawrence Berkeley National Laboratory Berkeley CA USA

6. Oak Ridge National Laboratory Biosciences Division & Climate Change Science Institute Oak Ridge TN USA

7. Oak Ridge National Laboratory Environmental Sciences Division & Climate Change Science Institute Oak Ridge TN USA

Abstract

AbstractIncorporating microbial processes into soil biogeochemical models has received growing interest. However, determining the parameters that govern microbially driven biogeochemical processes typically requires case‐specific model calibration in various soil and ecosystem types. Here each case refers to an independent and individual experimental unit subjected to repeated measurements. Using the Microbial‐ENzyme Decomposition model, this study aimed to test whether a common set of microbially‐relevant parameters (i.e., generalized parameters) could be obtained across multiple cases based on a two‐year incubation experiment in which soil samples of four distinct soil series (i.e., Coland, Kesswick, Westmoreland, and Etowah) collected from forest and grassland were subjected to cellulose or no cellulose amendment. Results showed that a common set of parameters controlling microbial growth and maintenance as well as extracellular enzyme production and turnover could be generalized at the soil series level but not land cover type. This indicates that microbial model developments need to prioritize soil series type over plant functional types when implemented across various sites. This study also suggests that, in addition to heterotrophic respiration and microbial biomass data, extracellular enzyme data sets are needed to achieve reliable microbial‐relevant parameters for large‐scale soil model projections.

Funder

National Science Foundation

U.S. Department of Energy

U.S. Department of Agriculture

National Institute of Food and Agriculture

Office of Science

Wuhan University

National Natural Science Foundation of China

Publisher

American Geophysical Union (AGU)

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