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 #407375

Research Project: Enhancing Agricultural Management and Conservation Practices by Advancing Measurement Techniques and Improving Modeling Across Scales

Location: Hydrology and Remote Sensing Laboratory

Title: Advancing the SWAT model to simulate perennial bioenergy crops: A case study on switchgrass growth

Author
item DANGOL, S - University Of Maryland
item Zhang, Xuesong
item LIANG, X - University Of Maryland
item BLANC-BETES, E - University Of Illinois

Submitted to: Environmental Modelling & Software
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/25/2023
Publication Date: N/A
Citation: N/A

Interpretive Summary: Growing perennial grasses for cellulosic biofuel production can have significant environmental impacts on water quality and carbon sequestration. However, the widely used Soil and Water Assessment Tool (SWAT) model's utility is limited as it does not explicitly account for shoot and root biomass development. Here, we enhanced the SWAT model by integrating the DAYCENT model's grass growth algorithms. Our test of the new SWAT–GRASS demonstrated improved performance of estimating switchgrass biomass yield and root development. These improvements are key for credibly assessing agronomic and environmental impacts of growing perennial grasses for biomass production The new model is a public domain tool supporting future sustainable bioenergy production efforts.

Technical Abstract: Growing perennial grasses for cellulosic biofuel production may cause substantial environmental impacts, such as water quality and carbon sequestration. Although the Soil and Water Assessment Tool (SWAT) model has been widely used to assess the environmental impacts of growing perennial grasses for bioenergy production, its utility is limited by not explicitly accounting for shoot and root biomass development. In this study, we integrated the DAYCENT model's grass growth algorithms into SWAT (SWAT–GRASSD) and further modified it by considering the impact of leaf area index (LAI) on potential biomass production (SWAT–GRASSM). Based on testing at eight sites in the US Midwest, we found that SWAT–GRASSM generally outperformed SWAT and SWAT–GRASSD in simulating switchgrass biomass yield, particularly in the variability of yield at individual sites, and the seasonal development of LAI. Additionally, SWAT–GRASSM can more realistically represent root development, which is key for the allocation of accumulated biomass and nutrients between aboveground and belowground biomass pools, as influenced by variations in precipitation, temperature, and nutrient availability. These results demonstrate the potential of the new model for providing a credible assessment of agronomic and environmental impacts of growing perennial grasses for biomass production. The new integrated SWAT–GRASSM model is a public domain tool to support future efforts in sustainable bioenergy production.