Switchgrass (SG; Panicum virgatum L.) is a perennial C4 grass native to the tallgrass prairies and a most promising feedstock in the U.S. for bioenergy production. SG has been shown to input carbon into the soil and thus has the potential to increase C sequestration. However, to fully evaluate the sustainability of SG-based biofuel production, it is crucial to understand the impacts of SG establishment on biotic/abiotic characteristics of nutrient-poor soils. Here, we aim to characterize the ecosystem-scale consequences of SG cultivation in comparison with native annual grassland fallows (FL) at two field sites (designated Red River-RR and 3rd Street-3S) in Oklahoma that are low N and P nutrient availability. We hypothesize that SG sustainability relates to improvements of soil quality mediated by its influence on the soil microbial communities and the activation of beneficial plant-microbe interactions. This will ultimately result in measurable effects on key ecosystem functions like C sequestration and greenhouse gas (GHG) mitigation. During the first two growing seasons, these four plots were monitored monthly. We measured topsoil chemistry, GHG fluxes (CO2 and CH4), and characterized microbial communities using 16S rRNA high-throughput sequencing. GHG concentrations were measured using cavity ring down spectrometry (i.e. Picarro G2508 analyzer).
Results/Conclusions
We found that SG significantly increased plant-available P levels, SOM content, and soil C/N at the RR site when compared to the FL. No significant changes in soil chemistry were observed between SG and FL plots at the 3S site. Similar seasonal microbial successional patterns were observed for all plots, but community structures differed between sites and plot types. SG cultivation did not influence annual CO2 fluxes compared to the FL plots suggesting potential soil carbon accrual is not directly lost to enhanced soil respiration rates under SG influence. However, SG significantly reduced the annual CH4 consumption, implying carbon balance considerations may need to be accounted for to sustainably cultivate SG. A machine learning approach was used to identify important factors for predicting soil trace gas dynamics and we found that soil temperature, soil moisture, and month of the year were the most influential variables. Further investigations are underway to elucidate the link between the microbial communities and the GHG emissions to understand specific changes in ecosystem function during SG conversion along with incorporating all this data into long-term ecosystem and soil carbon modeling.