Bridging the gap between microbial ecology and modeling to improve N2O models

Authors: Emmett, Bryan; Del Grosso, Stephen - Steve; Maul, Jude; Venterea, Rodney - Rod; Cavigelli, Michel

Submitted to: ASA-CSSA-SSSA Annual Meeting Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: 10/26/2021
Publication Date: 11/9/2021
Citation: Emmett, B.D., Del Grosso, S.J., Maul, J.E., Venterea, R.T., Cavigelli, M.A. 2021. Bridging the gap between microbial ecology and modeling to improve N2O models [abstract]. ASA-CSSA-SSSA. Paper No. 135591.

Interpretive Summary:

Technical Abstract: Process-based ecosystem models provide a powerful tool to simulate nitrogen transformations, including nitrous oxide emissions from soil. Improving these models to accurately predict emissions at the field and landscape scales has been an ongoing challenge. Models--mathematical descriptions of chemical, physical, and microbial dynamics--vary in their degrees of simplification depending on trade-offs between input availability, desired accuracy, and scale of application. The basic pathways of nitrous oxide production via microbial denitrification and nitrification have been recognized for decades but models vary in the degree to which they incorporate process-level understanding. Meanwhile, our knowledge of the microbial communities responsible for N2O production and consumption, their regulation and their impact on process rates has continued to advance. However, a substantial portion of this understanding has not been incorporated into widely used models. This talk is developed as a conversation between modelers, microbial ecologists and soil scientists about 1) the gaps between model representations and the underlying biology and kinetics of N2O and N2 production and consumption in soil, 2) assumptions built into models, and 3) data or experimentation that microbiologists can provide to inform and improve models. We concentrate on microbial activity but acknowledge simplified representation of other key variables (e.g. soil structural, chemical, and physical processes) influencing N2O emissions also contribute to model uncertainty. We further focus on two widely used models, DAYCENT and DNDC, because they vary in how completely microbial dynamics are represented but share virtually identical data requirements so the issue of optimal model complexity can be addressed. Our hope is that by discussing these challenges in a cross-disciplinary setting, we can guide efforts in both subfields and ultimately improve our understanding of the processes contributing to N2O emissions and our ability to predict those emissions across spatial and temporal scales.