Graphite is a commonly used raw material across many industries and the demand for high‐quality graphite has been increasing in recent years, especially as a primary component for lithium‐ion batteries. However, graphite production is currently limited by production shortages, uneven geographical distribution, and significant environmental impacts incurred from conventional processing. Here, an efficient method of synthesizing biomass‐derived graphite from biochar is presented as a sustainable alternative to natural and synthetic graphite. The resulting bio‐graphite equals or exceeds quantitative quality metrics of spheroidized natural graphite, achieving a Raman I _D / I _G ratio of 0.051 and crystallite size parallel to the graphene layers ( L _a ) of 2.08 µm. This bio‐graphite is directly applied as a raw input to liquid‐phase exfoliation of graphene for the scalable production of conductive inks. The spin‐coated films from the bio‐graphene ink exhibit the highest conductivity among all biomass‐derived graphene or carbon materials, reaching 3.58 ± 0.16 × 10 ⁴ S m ⁻¹ . Life cycle assessment demonstrates that this bio‐graphite requires less fossil fuel and produces reduced greenhouse gas emissions compared to incumbent methods for natural, synthesized, and other bio‐derived graphitic materials. This work thus offers a sustainable, locally adaptable solution for producing state‐of‐the‐art graphite that is suitable for bio‐graphene and other high‐value products.

Nitrogen (N) fertilizer supports global food production, but its use and overuse drive emissions of nitrous oxide (N ₂ O), a potent and long-lived greenhouse gas. Understanding the drivers of N ₂ O fluxes remains elusive, making it difficult to predict emissions in time and space and to develop and evaluate ways to lower emissions through management. Major scientific uncertainties underlying the understanding of the drivers of N ₂ O fluxes identified in a workshop of N ₂ O emissions experts include poor process-based understanding of controls on soil N ₂ O emissions in the field; insufficient data to reduce uncertainty in N ₂ O budgets from the field to regional scales, including N ₂ O emission measurements and importantly, field-scale N balances; and high uncertainty in model predictions of soil N ₂ O emissions across environmental and management conditions. To reduce these uncertainties, we present the concept of N ₂ Onet, a global collaborative initiative to accelerate advances in N ₂ O measurement, analyses, and mitigation. N ₂ Onet will serve as an observational network of supersites with multi-scale measurements; a database hub for N ₂ O flux and ancillary data; and a catalyst for community building, information sharing, and training. By coalescing and coordinating the global community of researchers, N ₂ Onet will provide a roadmap for reducing N ₂ O emissions from agriculture worldwide.

Innovative methods for estimating commercial-scale switchgrass yields and feedstock quality are essential to optimize harvest logistics and biorefinery efficiency for sustainable aviation fuel production. This study utilized vegetation indices (VIs) derived from multispectral images to predict biomass yield and lignocellulose concentrations of advanced bioenergy-type switchgrass cultivars (“Liberty” and “Independence”) under two N rates (28 and 56 kg N ha⁻¹). Field-scale plots were arranged in a randomized complete block design (RCBD) and replicated three times at Urbana, IL. Multispectral images captured during the 2021–2023 growing seasons were used to extract VIs. The results show that linear and exponential models outperformed partial least square and random forest models, with mid-August imagery providing the best predictions for biomass, cellulose, and hemicellulose. The green normalized difference vegetation index (GNDVI) was the best univariate predictor for biomass yield (R² = 0.86), while a multivariate combination of the GNDVI and normalized difference red-edge index (NDRE) enhanced prediction accuracy (R² = 0.88). Cellulose was best predicted using the NDRE (R² = 0.53), whereas hemicellulose prediction was most effective with a multivariate model combining the GNDVI, NDRE, NDVI, and green ratio vegetation index (GRVI) (R² = 0.44). These findings demonstrate the potential of UAV-based VIs for the in-season estimation of biomass yield and cellulose concentration.

Bacterial spot disease of cucurbits, caused by Xanthomonas cucurbitae , is a major problem in cucurbit‐growing areas worldwide. In the Midwestern region of the United States, pumpkin and squash fields can have greater than 90% infected fruits, leading to high yield losses. While reference genomes are available for this bacterial species, the genetic diversity between different strains and populations is unknown. After performing restriction‐site associated DNA sequencing (RAD‐seq) analysis of X . cucurbitae isolates collected from the Midwestern region, we selected five representative isolates for further characterization, which included whole‐genome sequencing and in vitro enzyme and in planta virulence assays. Our results suggest that minimal genetic diversity exists between these isolates, and that the isolates have differential virulence on different cucurbit host plants. This study contributes to our understanding of X . cucurbitae population dynamics in the Midwestern region and may assist with developing additional management strategies for controlling bacterial spot disease of cucurbits.