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.

Cover cropping is a promising strategy to improve soil health, but it may also trigger greenhouse gas emissions, especially nitrous oxide (N₂O). Beyond nitrogen (N) availability, cover crop residue decomposition may accelerate heterotrophic respiration to limit soil O₂ availability, hence promote N₂O emissions from denitrification under sub-optimal water-filled pore space (WFPS) conditions that are typically not conducive to large N₂O production. We conducted a 21-day incubation experiment to examine the effects of contrasting cover crop residue (grass vs legume) decomposition on soil O₂ and biogeochemical changes to influence N₂O and CO₂ emissions from ¹⁵N labeled fertilized soils under 50% and 80% WFPS levels. Irrespective of cover crop type, mixing cover crop residue with N fertilizer resulted in high cumulative N₂O emissions under both WFPS conditions. In the absence of cover crop residues, the N fertilizer effect of N₂O was only realized under 80% WFPS, whereas it was comparable to the control under 50% WFPS. The N₂O peaks under 50% WFPS coincided with soil O₂ depletion and concomitant high CO₂ emissions when cover crop residues were mixed with N fertilizer. While N fertilizer largely contributed to the total N₂O emissions from the cover crop treatments, soil organic matter and/or cover crop residue derived N₂O had a greater contribution under 50% than 80% WFPS. Our results underscore the importance of N₂O emissions from cover crop-based fertilized systems under relatively lower WFPS via a mechanism of respiration-induced anoxia and highlight potential risks of underestimating N₂O emissions under sole reliance on WFPS.

Future liquid fuel demand from renewable sources may, in part, be met by converting the seasonally wet portions of the landscape currently managed for soil and water conservation to perennial energy crops. However, this shift may increase nitrous oxide (N₂O) emissions, thus limiting the carbon (C) benefits of energy crops. Particularly high emissions may occur during the transition period when the soil is disturbed, plants are establishing, and nitrate and water accumulation may favor emissions. We measured N₂O emissions and associated environmental drivers during the transition of perennial grassland in a Conservation Reserve Program (CRP) to switchgrass (Panicum virgatum L.) and Miscanthus x giganteus in the bottom 3‐ha of a watershed in the Ridge and Valley ecoregion of the northeastern United States. Replicated treatments of CRP (unconverted), unfertilized switchgrass (switchgrass), nitrogen (N) fertilized switchgrass (switchgrass‐N), and Miscanthus were randomized in four blocks. Each plot was divided into shoulder, backslope, and footslope positions based on the slope and moisture gradient. Soil N₂O flux, soil moisture, and soil mineral nitrogen availability were monitored during the growing season of 2013, the year after the land conversion. Growing season N₂O flux showed a significant vegetation‐by‐landscape position interaction (P < 0.009). Switchgrass‐N and Miscanthus treatments had 3 and 6‐times higher cumulative flux respectively than the CRP in the footslope, but at other landscape positions fluxes were similar among land uses. A peak N₂O emission event, contributing 26% of the cumulative flux, occurred after a 10.8‐cm of rain during early June. Prolonged subsoil saturation coinciding with high mineral N concentration fueled N₂O emission hot spots in the footslopes under energy crops. Our results suggest that mitigating N₂O emissions during the transition of CRP to energy crops would mostly require a site‐specific management of the footslopes.

Identifying hot spots and hot moments of nitrous oxide (N₂O) emissions in the landscape is critical for monitoring and mitigating the emission of this potent greenhouse gas. We propose a novel use of the Lorenz curve and Gini coefficient (G) to improve the estimation of the mean as well as the spatial and temporal variation of N₂O emissions from a bioenergy landscape. The analyses indicate that the G was better correlated (R² = 0.72, P < 0.001) with daily N₂O emissions than the coefficient of variation and skewness. A hot moment for N₂O emissions occurred after a storm event, with a heterogeneous spatial distribution of N₂O emissions (G = 0.65); in contrast, CO₂ emissions remained spatially uniform throughout the same period (G = 0.36). Volumetric soil air content below 0.03 m³ m⁻³ occurred more frequently in the wetter footslope positions and created N₂O hot spots, with a high temporal inequality during the growing season (G = 0.75). In contrast, well‐drained shoulder positions were cold spots, with uniformly distributed and low N₂O emissions (G = 0.44). The spatial N₂O inequality mirrored the landscape wetness generated by rain events, while biogeochemical equality prevailed in the landscape. The Lorenz curve and G are tools to standardize the spatial and temporal variation of N₂O emissions across diverse landscapes and management scenarios. These two inequality indicators, in association with spatial maps, can help delineate the critical spatial mosaics and temporal windows of N₂O emissions and guide landscape‐scale monitoring and mitigation strategies to reduce N₂O emissions.

Soil nitrous oxide (N₂O) emissions exhibit high variability in intensively managed cropping systems, which challenges our ability to understand their complex interactions with controlling factors. We leveraged 17 years (2003–2019) of measurements at the Kellogg Biological Station Long‐Term Ecological Research (LTER)/Long‐Term Agroecosystem Research (LTAR) site to better understand the controls of N₂O emissions in four corn–soybean–winter wheat rotations employing conventional, no‐till, reduced input, and biologically based/organic inputs. We used a random forest machine learning model to predict daily N₂O fluxes, trained separately for each system with 70% of observations, using variables such as crop species, daily air temperature, cumulative 2‐day precipitation, water‐filled pore space, and soil nitrate and ammonium concentrations. The model explained 29%–42% of daily N₂O flux variability in the test data, with greater predictability for the corn phase in each system. The long‐term rotations showed different controlling factors and threshold conditions influencing N₂O emissions. In the conventional system, the model identified ammonium (>15 kg N ha⁻¹) and daily air temperature (>23°C) as the most influential variables; in the no‐till system, climate variables such as precipitation and air temperature were important variables. In low‐input and organic systems, where red clover (Trifolium repens L.; before corn) and cereal rye (Secale cereale L.; before soybean) cover crops were integrated, nitrate was the predominant predictor of N₂O emissions, followed by precipitation and air temperature. In low‐input and biologically based systems, red clover residues increased soil nitrogen availability to influence N₂O emissions. Long‐term data facilitated machine learning for predicting N₂O emissions in response to differential controls and threshold responses to management, environmental, and biogeochemical drivers.