21st‐century modeling of greenhouse gas (GHG) emissions from bioenergy crops is necessary to quantify the extent to which bioenergy production can mitigate climate change. For over 30 years, the Century‐based biogeochemical models have provided the preeminent framework for belowground carbon and nitrogen cycling in ecosystem and earth system models. While monthly Century and the daily time‐step version of Century (DayCent) have advanced our ability to predict the sustainability of bioenergy crop production, new advances in feedstock generation, and our empirical understanding of sources and sinks of GHGs in soils call for a re‐visitation of DayCent's core model structures. Here, we evaluate current challenges with modeling soil carbon dynamics, trace gas fluxes, and drought and age‐related impacts on bioenergy crop productivity. We propose coupling a microbial process‐based soil organic carbon and nitrogen model with DayCent to improve soil carbon dynamics. We describe recent improvements to DayCent for simulating unique plant structural and physiological attributes of perennial bioenergy grasses. Finally, we propose a method for using machine learning to identify key parameters for simulating N₂O emissions. Our efforts are focused on meeting the needs for modeling bioenergy crops; however, many updates reviewed and suggested to DayCent will be broadly applicable to other systems.

In the age of biofuel innovation, bioenergy crop sustainability assessment has determined how candidate systems alter the carbon (C) and nitrogen (N) cycle. These research efforts revealed how perennial crops, such as switchgrass, increase belowground soil organic carbon (SOC) and lose less N than annual crops, like maize. As demand for bioenergy increases, land managers will need to choose whether to invest in food or fuel cropping systems. However, little research has focused on the C and N cycle impacts of reverting purpose‐grown perennial bioenergy crops back to annual cropping systems. We investigated this knowledge gap by measuring C and N pools and fluxes over 2 years following reversion of a mature switchgrass stand to an annual maize rotation. The most striking treatment difference was in ecosystem respiration (ER), with the maize‐converted treatment showing the highest respiration flux of 2,073.63 (± 367.20) g C m⁻² year⁻¹ compared to the switchgrass 1,412.70 (± 28.72) g C m⁻² year⁻¹ and maize‐control treatments 1,699.16 (± 234.79) g C m⁻² year⁻¹. This difference was likely driven by increased heterotrophic respiration of belowground switchgrass necromass in the maize‐converted treatment. Predictions from the DayCent model showed it would take approximately 5 years for SOC dynamics in the converted treatment to return to conditions of the maize‐control treatment. N losses were highest from the maize‐converted treatment when compared to undisturbed switchgrass and maize‐control, particularly during the first conversion year. These results show substantial C and N losses occur within the first 2 years after reversion of switchgrass to maize. Given farmers are likely to rotate between perennial and annual crops in the future to meet market demands, our results indicate that improvements to the land conversion approach are needed to preserve SOC built up by perennial crops to maintain the long‐term ecological sustainability of bioenergy cropping systems.

Advancing our predictive understanding of bioenergy systems is critical to design decision tools that can inform which feedstock to plant, where to plant it, and how to manage its production to provide both energy and ecosystem carbon (C) benefits. Here, we lay the foundation for that advancement by integrating recent developments in the science of belowground processes in shaping the C cycle into a new bioenergy model, FUN‐BioCROP (Fixation and Uptake of Nitrogen‐Bioenergy Carbon, Rhizosphere, Organisms, and Protection). We show that FUN‐BioCROP can approximate the historical trajectory of soil C dynamics as natural ecosystems were successively converted into intensive agriculture and bioenergy systems. This ability relies in part on a novel tillage representation that mechanistically models tillage as a process that increases microbial access to C. Importantly, the impacts of tillage and feedstock choice also influence FUN‐BioCROP simulations of warming responses with no‐till perennial feedstocks, miscanthus, and switchgrass, having more C that is unprotected and susceptible to warming than tilled annual feedstocks like corn–corn–soybean. However, this susceptibility to warming is balanced by a greater potential for increases in belowground C allocation to enhance soil C stocks in perennial systems. Collectively, our model results highlight the importance of belowground processes in evaluating the ecosystem C benefits of bioenergy production.

Western US forests represent a carbon sink that contributes to meeting regional and global greenhouse gas targets. Forest thinning is being implemented as a strategy for reducing forest vulnerability to disturbance, including mortality from fire, insects, and drought, as well as protecting human communities. However, the terrestrial carbon balance impacts of thinning remain uncertain across regions, spatiotemporal scales, and treatment types. Continuous and in situ long‐term measurements of partial harvest impacts to stand‐scale carbon and water cycle dynamics are nonetheless rare. Here, we examine post‐thinning carbon and water flux impacts in a young ponderosa pine forest in Northern Idaho. We examine in situ stock and flux impacts during the 3 years after treatment as well as simulate the forest sector carbon balance through 2050, including on and off‐site net emissions. During the observation period, increases in tree‐scale net primary production (NPP) and water use persistence through summer drought did not overcome the impacts of density reduction, leading to 45% annual reductions of NPP. Growth duration remained constrained by summer drought in control and thinned stands. Ecosystem model and life cycle assessment estimates demonstrated a net forest sector carbon deficit relative to control stands of 27.0 Mg C ha⁻¹ in 2050 due to emissions from dead biomass pools despite increases to net ecosystem production. Our results demonstrate dynamics resulting in carbon losses from forest thinning, providing a baseline with which to inform landscape‐scale modeling and assess tradeoffs between harvest losses and potential gains from management practices.