Improving a biogeochemical model to simulate microbial-mediated carbon dynamics in agricultural ecosystems

Authors: DENG, JIA - University Of New Hampshire; FROLKING, STEVE - University Of New Hampshire; BAJGAIN, RAJEN - University Of Oklahoma; CORNELL, CAROLYN - University Of Oklahoma; Wagle, Pradeep; XIAO, XIANGMING - University Of Oklahoma; ZHOU, JIZHONG - University Of Oklahoma; BASARA, JEFFREY - University Of Oklahoma; STEINER, JEAN - Retired ARS Employee; CHANGSHENG, LI - University Of New Hampshire

Submitted to: Journal of Advances in Modeling Earth Systems
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/28/2021
Publication Date: 11/2/2021
Citation: Deng, J., Frolking, S., Bajgain, R., Cornell, C., Wagle, P., Xiao, X., Zhou, J., Basara, J., Steiner, J.L., Changsheng, L. 2021. Improving a biogeochemical model to simulate microbial-mediated carbon dynamics in agricultural ecosystems. Journal of Advances in Modeling Earth Systems. 13. Article e2021MS002752.

Interpretive Summary: Process-based models have been used to quantify changes in soil organic carbon (SOC) and carbon dioxide (CO2) fluxes in agroecosystems. Soil microbes drive decomposition of soil organic matter (SOM) and regulate soil carbon (C) dynamics. Lack of adequate representation of microbial processes that are important to SOC decomposition and their associated microbial communities in process-based biogeochemical models limits the model's capacity of predicting SOC responses to changes in microbial activities. This study developed a microbial-mediated decomposition model based on DeNitrification-DeComposition (DNDC) to simulate C dynamics in agroecosystems. This new model simulates decomposition of SOM and external organic matter, soil respiration, and SOC formation by explicitly simulating aggregate microbial and enzyme dynamics, and their controls on SOM decomposition. Additionally, the model considers impacts of climate, soil, crop, and common farming management practices (FMPs) on C dynamics. We evaluated the model’s performance to predict both CO2 fluxes (i.e., net ecosystem CO2 exchange, NEE) and SOC changes using field observations of NEE and SOC change in two winter-wheat systems. The model successfully simulated the NEE and SOC changes under different FMPs. Inclusion of microbial processes improved the model's performance in simulating peak CO2 fluxes induced by the wheat residue return, primarily by capturing priming effects of wheat residue inputs. Results illustrated that the C input through residue or manure drove microbial activity and predominantly regulated the CO2 fluxes, and manure amendment largely regulated a long-term SOC change. Microbial physiology had considerable impacts on the microbial activities and soil C dynamics, highlighting the necessity of considering microbial physiology and activities when assessing soil C dynamics in agroecosystems where FMPs control soil C.

Technical Abstract: Soil microbes drive decomposition of soil organic matter (SOM) and regulate soil carbon (C) dynamics. Process-based models have been developed to quantify changes in soil organic carbon (SOC) and carbon dioxide (CO2) fluxes in agricultural ecosystems. However, microbial processes related to SOM decomposition often have not been, or are inadequately, represented in these models, limiting the model's capacity of predicting SOC responses to changes in microbial processes and activities. In this study, we developed a microbial-mediated decomposition model based on a widely used biogeochemical model, DeNitrification-DeComposition (DNDC), to simulate C dynamics in agricultural ecosystems. The new model simulates decomposition of SOM and external organic matter, soil respiration, and SOC formation by explicitly simulating aggregate microbial and enzyme dynamics and their controls on SOM decomposition, and considering impacts of climate, soil, crop, and common farming management practices (FMPs) on C dynamics. The model was evaluated against field observations of net ecosystem CO2 exchange (NEE) and SOC change in two winter-wheat systems to assess its performance in predicting CO2 fluxes and SOC changes. The model successfully captured both NEE and SOC changes under different FMPs. Inclusion of microbial processes improved the model's performance in simulating peak CO2 fluxes induced by the wheat residue return, primarily by capturing priming effects of wheat residue inputs. We also applied the model to investigate impacts of microbial physiology, SOM, and FMPs on soil microbial activities, SOC change, and CO2 fluxes. Our results demonstrated that the C input through residue or manure drove microbial activity and predominantly regulated the CO2 fluxes, and manure amendment largely regulated long-term SOC change. The microbial physiology had considerable impacts on the microbial activities and soil C dynamics, emphasizing the necessity of considering microbial physiology and activities when assessing soil C dynamics in agricultural ecosystems where FMPs usually play a major role in controlling soil C.