Skip to main content
ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #403600

Research Project: From Field to Watershed: Enhancing Water Quality and Management in Agroecosystems through Remote Sensing, Ground Measurements, and Integrative Modeling

Location: Hydrology and Remote Sensing Laboratory

Title: Nitrous oxide emissions from multiple agroecosystems in the U.S. Corn Belt simulated using the modified SWAT-C model

Author
item LIANG, K. - University Of Maryland
item Zhang, Xuesong
item QI, JUNYU - University Of Maryland
item Emmett, Bryan
item Johnson, Jane
item Malone, Robert - Rob
item Moglen, Glenn
item Venterea, Rodney - Rod

Submitted to: Environmental Pollution
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/8/2023
Publication Date: 9/12/2023
Citation: Liang, K., Zhang, X., Qi, J., Emmett, B.D., Johnson, J.M., Malone, R.W., Moglen, G.E., Venterea, R.T. 2023. Nitrous oxide emissions from multiple agroecosystems in the U.S. Corn Belt simulated using the modified SWAT-C model . Environmental Pollution. 337(2023). https://doi.org/10.1016/j.envpol.2023.122537.
DOI: https://doi.org/10.1016/j.envpol.2023.122537

Interpretive Summary: Agroecosystems are a major source of emissions of nitrous oxide (N2O), a potent greenhouse gas. Using numerical models to assess and optimize crop management practices holds promise to reduce N2O emissions. Here, we integrated multiple N2O algorithms into the Soil and Water Assessment Tool – Carbon (SWAT-C) model and compared their performance using field observations of N2O from 14 experimental treatments at five experimental sites across the U.S. Midwest. Different N2O algorithms exhibited a wide range of performance, with those algorithms that consider soil pH and respiration effects performing better. The best performing algorithms can reasonably represent how different agricultural conservation practices (e.g., fertilization, tillage, and crop rotation) impact N2O emissions. The improved SWAT-C model, with its flexible configurations and robust modeling and assessment capabilities, will be a valuable tool for studying and managing N2O emissions from agroecosystems.

Technical Abstract: Agriculture is a major source of nitrous oxide (N2O) emissions into the atmosphere. However, assessing the impacts of agricultural conservation practices, land use change, and climate adaptation measures on N2O emissions at a large scale is a challenge for process based model applications. Here, we integrated six N2O emission algorithms for the nitrification processes and seven N2O emission algorithms for the denitrification process into the SWAT-Carbon (SWAT-C). We evaluated the different combinations of methods in simulating N2O emissions against measurements from 14 experimental treatments at five experimental sites across the U.S. Midwest. SWAT-C exhibited wide variability in simulating daily average N2O emissions across treatment-years with different method configurations, as indicated by the ranges of R2, NSE, and BIAS (0.04 - 0.68, -1.78 - 0.60, and -0.94 - 0.001, respectively). Our results indicate that the denitrification process has a stronger impact on N2O emissions than the nitrification process. The best performing N2O emission algorithms are those rooted in the CENTURY model, which considers soil pH and respiration effects that were overlooked by other algorithms. The best performing N2O emission algorithm explained about 63% of the variability of annual average N2O emissions for the 83 treatment-year of data, with NSE and BIAS of 0.60 and -0.033, respectively. The best performing model configuration can reasonably represent the impacts of agricultural conservation practices, such as fertilization, tillage, and crop rotation on N2O emissions. We anticipate that the improved SWAT C model, with its flexible configurations and robust modeling and assessment capabilities, will provide a valuable tool for studying and managing N2O emissions from agroecosystems.