Simulated biomass, climate change impacts, and nitrogen management to achieve switchgrass biofuel production at diverse sites in U.S.

Authors: KIM, SUMIN - Oak Ridge Institute For Science And Education (ORISE); KIM, SOJUNG - Texas A&M University; CHO, JAEPIL - Apec Climate Center (APCC); PARK, SEONGGYU - Texas Agrilife Research; PEREZ, FERNANDO - Texas A&M University; Kiniry, James

Submitted to: Agronomy
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/30/2020
Publication Date: 4/2/2020
Citation: Kim, S., Kim, S., Cho, J., Park, S., Perez, F., Kiniry, J.R. 2020. Simulated biomass, climate change impacts, and nitrogen management to achieve switchgrass biofuel production at diverse sites in U.S. Agronomy. 10(4):503-521. https://doi.org/10.3390/agronomy10040503.
DOI: https://doi.org/10.3390/agronomy10040503

Interpretive Summary: Switchgrass is a warm season perennial native grass and an ideal biofuel feedstock. Predicting switchgrass yield across a geographically diverse region and under future climate conditions is important for determining a realistic future ethanol production. This study compiled switchgrass data from field trials across U.S. The data was used for two simulation models, statistical model (ARM) and a process-based model (ALMANAC). The ARM model showed relationships among climate, location, and management, and switchgrass yield. Switchgrass yields were highly related to longitude. ALMANAC showed that crop management had more effect on yield than location. Since ALMANAC describes functional relationships, it provides better understanding of interactions between plant physiological processes and environmental factors (water, soil, climate, and nutrient). ALMANAC quantifed the impacts of climate change on switchgrass yields. Simulated yields of lowland switchgrass decreased more by the year 2050 with low fertilizer input than high fertilizer input. Simulated upland yields are stable under high fertilizer input. This study result showed that N fertilization is a key factor controling switchgrass yields under future climate conditions.

Technical Abstract: Switchgrass (Panicum virgatum L.) is a C4 warm season perennial native grass and has been strongly recommended as an ideal biofuel feedstock. Accurate forecasting of switchgrass yield across a geographically diverse region and under future climate conditions is essential for determining a realistic future ethanol production from switchgrass. This study compiled a switchgrass database through reviewing the existing literature from field trials across U.S. Using observed switchgrass data, two models, an additive regression model (ARM) in machine learning and a process-based model (ALMANAC), were developed. Through ARM simulation, the relationships among few yield determinant parameters (e.g. climate, location, and management) and switchgrass yield were determined. According to results of ARM simulation, switchgrass yields were highly related to longitude of planting location. The ALMANAC simulation results, in contrast, showed that crop management had more effect on yield than planting location. Since the ALMANAC model consists of functional relationships that provide better understating of interactions among plant physiological processes and environmental factors (water, soil, climate, and nutrient) giving realistic predictions in different climate condition, it was used to quantify the impacts of climate change on switchgrass yields. Simulated lowland switchgrass would have more yield decreases in 2050 with low fertilizer input than high fertilizer input. Simulated upland yields are stable under high fertilizer input. This study result showed that N fertilization is a key factor in controlling switchgrass yields under future climate conditions.