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This dataset contains leaf photosynthetic and biochemical traits, plant biomass, and yield in five C3 crops (chickpea, rice, snap bean, soybean, wheat) and four C4 crops (sorghum, maize, Miscanthus × giganteus, switchgrass) grown under ambient and elevated O3 concentration ([O3]) in the field at free-air O3 concentration enrichment (O3-FACE) facilities over the past 20 years.
G-quadruplexes are nucleic acid secondary structures formed by a stack of square planar G-quartets. G-quadruplexes were implicated in many biological functions including telomere maintenance, replication, transcription, and translation, in many species including humans and plants. For wheat, however, though it is one of the world’s most important staple food, no G-quadruplex studies have been reported to date. Here, we computationally identify putative G4 structures (G4s) in wheat genome for the first time and compare its distribution across the genome against five other genomes (human, maize, Arabidopsis, rice, and sorghum). We identified close to 1 million G4 motifs with a density of 76 G4s/Mb across the whole genome and 93 G4s/Mb over genic regions. Remarkably, G4s were enriched around three regions, two located on the antisense and one on the sense strand at the following positions: 1) the transcription start site (TSS) (antisense), 2) the first coding domain sequence (CDS) (antisense), and 3) the start codon (sense). Functional enrichment analysis revealed that the gene models containing G4 motifs within these peaks were associated with specific gene ontology (GO) terms, such as developmental process, localization, and cellular component organization or biogenesis. We investigated genes encoding MADS-box transcription factors and showed examples of G4 motifs within critical regulatory regions in the VRN-1 genes in wheat. Furthermore, comparison with other plants showed that monocots share a similar distribution of G4s, but Arabidopsis shows a unique G4 distribution. Our study shows for the first time the prevalence and possible functional roles of G4s in wheat.
This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-Bi2 Bouldin Island corn. This is the FLUXNET version of the carbon flux data for the site US-Bi2 Bouldin Island corn produced by applying the standard ONEFlux (1F) software. Site Description - Corn field on Bouldin Island in the Sacramento San Joaquin Delta. This site has deep peat that was drained for farming. Over the past 100 years there has been excessive subsidence of the peat in the Delta region. The corn is usually harvested for silage cattle feed. Corn was planted in the field 2017-2023. Sorghum was planted in the field 2024.
This is the AmeriFlux version of the carbon flux data for the site US-IAC Iowa State University SE tower. Site Description - This is a 4 ha (200 m x 200 m) sorghum-corn-soybean rotation established at the Sustainable Advanced Bioeconomy Research (SABR) farm at Iowa State University. The site has a long history of conventional row-cropping, predominantly corn-soy rotations. In the immediately preceding growing season, soybeans were grown at the SABR farm.
This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-IAC Iowa State University SE tower. This is the FLUXNET version of the carbon flux data for the site US-IAC Iowa State University SE tower produced by applying the standard ONEFlux (1F) software. Site Description - This is a 4 ha (200 m x 200 m) sorghum-corn-soybean rotation established at the Sustainable Advanced Bioeconomy Research (SABR) farm at Iowa State University. The site has a long history of conventional row-cropping, predominantly corn-soy rotations. In the immediately preceding growing season, soybeans were grown at the SABR farm.
Exploring the potential of advanced distillable solvents as efficient biomass pretreatment agents is critical for biorefineries, enhancing fermentable sugar yields while enabling solvent recovery and recycling without suffering significant losses. Here, we employ distillable amine-based solvents for pretreating a wide range of lignocellulosic feedstocks, aiming to facilitate the industrial release of fermentable sugars from diverse feedstocks through enzymatic hydrolysis. Twenty-two diverse feedstocks, sourced from different geographical regions and representing various biomass categories, were surveyed for chemical (mainly carbohydrates and lignin) and lignin (S, G, and H units) profiles. Several solvents, including ethanolamine, ethanolammonium acetate, butylamine, butylammonium acetate, and triethylamine, were tested for the pretreatment of eight selected biomasses. Among these solvents, butylamine emerged as the most effective due to its favorable sugar release, excellent solvent removal rate, and low boiling point, facilitating solvent recovery and recycling. Extending butylamine pretreatment to all 22 feedstocks demonstrated desirable sugar yields and highly efficient solvent removal in the majority of the biomass sources tested. Agricultural residues and their mixtures showed particularly favorable sugar release. Despite minimal changes in cellulose crystallinity, XRD characterization of sorghum, poplar, and pine before and after butylamine pretreatment showed a decrease in intensity and a slight shift of certain peaks, indicating alterations in cellulose structure. Fourier-transform infrared spectroscopy and thermogravimetric analysis analyses suggested disruption of biomass linkages in hemicellulose and lignin, enhancing enzymatic digestibility. Scale-up experiments of the mixed agricultural feedstocks in a 1 L Parr reactor achieved over 90% glucose liberation and more than 99% butylamine removal, highlighting the scalability of the method. The resulting hydrolysates supported the growth of diverse bacterial and fungal strains, indicating downstream compatibility with commercial fermentation processes. This study presents butylamine as an effective, recoverable pretreatment solvent for a wide range of lignocellulosic feedstocks, offering a promising solution to key biorefinery challenges. The demonstrated scalability and compatibility with various biomass types and blends underscore its potential for industrial application, advancing sustainable biofuel and biochemical production.
A transparent and easy-to-use tool for feedstock-specific, farm-level CI calculation of feedstocks is helpful. With the ARPA-E support, we have developed a tool–the Feedstock Carbon Intensity Calculator (FD-CIC). The first version of FD-CIC was released with the GREET® model in 2020 (Wang et al., 2020) so that corn feedstock producers could use this publicly available tool (https://greet.es.anl.gov/tool_fd_cic) to quantify corn grain CIs with farm-level input data and management practices. In the 2021 version, we expand the tool’s capabilities by including additional feedstocks such as soybeans, sorghum, and rice. Similar to corn, it calculates the farm-level CI for these feedstocks by allowing user-defined farm-level farming inputs and incorporating the GHG emission intensities of these inputs from GREET (in particular, GREET1, the fuel cycle model of GREET).
The United States (U.S.) Department of Energy (DOE) developed the Billion-Ton Vision to enable production of one-billion tons of sustainable, reliable biomass for the bioenergy industry by 2030 (Perlack et al., 2005). The Sun Grant Regional Feedstock Partnership (RFP) was organized to fill information gaps and validate biomass yield assumptions related to the Billion-Ton Study (Owens, 2018; Owens, Karlen, and Lacey, 2016). Along with the more than 130 scientific publications generated from these studies, yield and sustainability data from the RFP field trials not only validated the Billion-Ton estimates, but were critical in developing both the U.S. Billion-Ton Update report in 2011 and the 2016 Billion-Ton Report (DOE, 2011; 2016). The intention of this biomass quality assessment report is to build on these initial successes from the RFP field trials by focusing on variability in biomass quality data necessary to evaluate conversion performance. This report contains a summary of chemical quality results from samples collected as part of the RFP field trials. This report focuses on assessment of the impact of experimental agronomic designs on biomass properties followed by analyses of the impact of environmental and production variables on biomass properties. Datasets include species and other genetic variables, fertilizer treatments, harvest information, and yield, as well as other publicly available data such as precipitation, temperature, soil properties, and drought. The key outcomes from this chemical quality focused assessment have included: • Complete evaluation of the impacts of agronomic designs, genetics, and environmental conditions on chemical properties for Miscanthus, switchgrass, sorghum, energycane, mixed perennial grasses, and shrub willow short-rotation feedstocks • Over 30 peer review publications and technical reports focused on variability in quality data • Development of spatial and temporal environmental quality prediction maps for Miscanthus and switchgrass feedstocks allowing for comprehensive evaluation of variability in feedstock chemical quality across U.S. regions and over multiple harvest years
The dynamic spatiotemporal distribution of light and micro climatic variables (wind speed, atmospheric CO 2 concentration, humidity, pressure, and air temperature) within the plant canopy as determined by the canopy architecture play a critical role in land-biosphere-atmosphere (LBA) photosynthetic carbon uptake, transpiratory water loss and energy partitioning. Vegetation models that include effects of sun and shade flecks, leaf temperature and energy balance partitioning between sensible and latent heat fluxes, and reduced turbulent mixing in dense vegetation canopies can accurately predict canopy performance, particularly its shaded leaves, which can constitute 80% of leaf area and contribute up to 50% of carbon update and 65% of water loss. However, current vegetation models with their bulk characterization of shaded leaves poorly represent the complex three dimensional (3D) canopy architecture are insufficient to capture the spatiotemporal dynamics of shaded leaves. Here, we propose to employ an x-ray computed tomography (XCT) imaging technique in combination with an automated, high throughput, image processing pipeline (3DArch, to be developed) to obtain an explicit 3D triangulated surface mesh of a plant canopy (Fig 1). 3DArch would provide a much needed technological jump in 3D LBA modeling where computer generated artificial 3D models suffer from lack of high resolution detailed canopy architecture parameters. The 3D canopy surface mesh output from 3DArch is coupled with an explicit 3D forward ray tracing model (fastTracer) to obtain a high resolution representation of the dynamic spatiotemporal light environment within the canopy over the course of a day (Fig 1). The results from the light attenuation model are then coupled with a process based multi-scale bio-physical canopy and leaf model of photosynthesis (PlantGro) to get fluxes of carbon, water and energy at the leaf and canopy scale, in addition to estimating whole plant carbon uptake, and water loss. Our proposed model framework would be the first of its kind to provide a breakthrough capability in the field of process-based plant modeling that bridges the key technological gap in shaded leaf modeling through explicit representation of 3D canopy architecture. Computer generated artificial 3D model canopy simulations on grasses show that the canopy light distribution does not follow an exponential behavior as assumed in most 1D vegetation models, but rather exhibits a heterogeneous behavior, with a small but significant portion of interior canopy leaves experiencing peak light levels due to sun flecks while the rest of the canopy receives substantially lower light levels. These results indicate a bias in traditional models that overestimates shaded leaf contribution towards whole canopy photosynthesis by up to 15% and underestimate transpiration by up to 12% with an associated increase in sensible heat fluxes since photosynthesis, transpiration and energy partitioning are tightly coupled processes. However, these test results need to be validated using the 3D architecture obtained from a real plant imaged in 3D using 3DArch, rather than an artificial model plant. Our proposed model framework would allow us to identify inefficiencies in canopy light distribution and prescribe optimal canopy architectures (leaf optical properties, leaf area, leaf angles, row spacing, planting density, etc.) to traditional and genetic engineering based crop breeders, for maximizing target functions such as light use efficiency, water use efficiency, drought tolerance, crop yield, crop resilience etc., with important applications in biofuel crops such as sorghum, and switchgrass. Our model can also be applied for designing optimal canopy architecture that increase surface albedo as a mitigation strategy for global warming and projections of crop yield for future food and water security under climate change.
This final technical report details the accomplishment and findings of a project that developed technology related to imaging metabolic processes in plants. The report gives technical details of the design, construction and characterization of a Raman microscope system capable of identifying chemical species using the wavelength shifts of scattered excitation light. Some of the unique aspects of the design include: use of a Bessel beam excitation source, which allow long excitation lines that are resistant to scattering in complex media such as plants; excitation at 785 nm to avoid fluorescence emission that can occur in photosynthetic organisms; and a flat optical design that makes construction and alignment significantly simpler. This project also used stable isotope labeling to look at photorespiratory flux in the leaves of three different types of plants that have different photosynthesis pathways. Gas chromatography–mass spectrometry was used to look at which metabolites and amino acids had altered oxygen, carbon and hydrogen uptake under various conditions of environmental oxygen. This generated a rich data set that is being further analyzed to understand metabolic pathways in plants. This project also used the natural fluorescence in sorghum and rice to conduct an extensive experiment across different developmental stages. Spectral differences were identified at most of the developmental stages and these can be attributed to differential pigment and pigment-protein complexes present in different relative abundances as the plant develops.
Small molecule metabolites drive inter- and intraspecies communication and dependencies in diverse biological systems, yet a large proportion of these important chemical compounds remain uncharacterized in plants and microbes. Approximately 90% of the metabolites in root exudate profiles are unknown compounds, despite the importance of root exudate composition in plant-microbe interactions. We need advanced analytical capabilities that will support rapid discovery and structural elucidation of metabolites from biological samples that may be limited in quantity and high in complexity. To fill this gap, this project aimed to develop an integrated workflow involving metabolite extraction, separation, and crystallization from plant root exudates followed by characterization using nuclear magnetic resonance (NMR) spectroscopy, mass spectrometry, and microcrystal electron diffraction (MicroED). Using crude root exudates from sorghum, this project successfully developed higher throughput exudate fractionation strategies to obtain pure compounds for crystallization and identified crystals in multiple fractions that diffracted. Additional efforts to increase the throughput of high-quality crystal generation for MicroED, such as crystallization screening and crystallization chaperone exploration, will be needed to further advance root exudate metabolite identification. The overall optimized sample preparation process can then be integrated with the existing data collection and data analysis pipelines for MicroED at PNNL to facilitate more rapid natural product discovery.
A collaborative team from Texas A&M AgriLife Research, ABQMR Inc., the Soil Health Institute, the Athinoula A. Martinos Center for Biomedical Imaging, and NIST developed low-field magnetic resonance imaging (LF-MRI) instrumentation capable of imaging intact soil-root systems. The system measured root biomass, architecture, 3D mass distribution, and growth rates, providing a non-destructive means to evaluate ideal plant characteristics based on root metrics. It also successfully generated three-dimensional images of soil water content, a key property influencing root growth and exploration. Operating much like an MRI used in a medical setting, the system functioned in field conditions without damaging plants, overcoming the limitations of traditional methods such as trenching, soil coring, and root excavation. Over the course of the project, the team designed and built three functional prototype systems. These prototypes provided new insights into root–water–soil interactions that drive processes such as nutrient uptake, water use, and carbon management. This information contributed to efforts to optimize plants for carbon sequestration without sacrificing economic yield. The project also supported the identification of desirable traits for energy sorghum, including high root growth rates, more vertical root angles, and enhanced drought resilience under water-limiting conditions.
Domestic biomass crops such as sorghum, switchgrass, Miscanthus, and poplar can provide United States industries with renewable feedstocks while also supporting low-input farming systems and strengthening supply chains for biofuels, biochemicals and biomaterials. The U.S. leads globally in biomass crop genomics, yet progress in engineering traits is constrained by slow, genotype-dependent transformation methods and lengthy Design-Build-Test-Learn (DBTL) cycles. At a May 2025 workshop, a panel of experts recommended establishing a DOE Plant Transformation Capability (PTC) to overcome these barriers. The PTC would unite two missions: advancing research to achieve genotype-independent, automated methods, and delivering scalable transformation services through a user-facility model. With expected gains of 10–100x in efficiency, including transformation and cost reduction, the PTC would accelerate the path from discovery to engineered plants, expand community access and training, and support downstream applications and workflows including field trials and regulatory navigation. By enabling rapid and predictable crop engineering, the PTC would strengthen U.S. supply chains, enhance industrial competitiveness, and ensure that DOE’s genomic investments deliver national impact.
This dataset contains data on agricultural residue production by county from 2022 to 2041. The agricultural residue includes barley straw, corn stover, oats straw, sorghum stubble, and wheat straw. The dataset was obtained from the database of the BT23 (Davis et al., 2024) for the mature-market medium scenario with biomass market prices from $50 to $130 per dry ton. For access to this dataset, please use the contact form and indicate the dataset by name.
The combustion of petroleum-based fuels contributes to increases in atmospheric CO2, contributing to climate change. As other sectors electrify, current battery technology makes this impractical for the aviation industry. The fastest pathway to reducing the carbon contributions of aviation is to create low or no net carbon-emitting drop-in petroleum alternatives. This research explores 3 potential sorghum derived jet fuel molecules from 4 different production routes, hydro-processed esters and fatty acids (HEFA), and Jet A, to identify how blends including these fuels could offer improved performance (MJ/kg, MJ/L) and improved emissions (gCO2/MJ) while minimizing costs. Different applications will value each target metric differently. This research utilizes the Jet Fuel Blend Optimizer (JudO) to create a 4-dimensional Pareto front across potential solutions. The molecules considered will not offer reduced cost compared to conventional jet fuel (Jet A), but they can be utilized to create blends with higher specific energy, greater energy density, and reduced greenhouse gas emissions. Considerations including carbon credits, the sale of byproducts, and the valuation of improved performance will make the proposed molecules more commercially viable. At the lowest region of GHG solutions, coupled with equivalent LCFS approximations, JudO determined blends that could achieve as high as 69% overall carbon reductions at a premium of $0.34/L.
One billion tons of biomass feedstocks have been identified for the production of renewable biofuels and biochemicals. This is one of the key carbon feedstocks to supply energy to the transportation sector for light duty, heavy duty and aviation fuels. Utilization of lignocellulosic feedstocks supports an improved energy security by reducing demand of petroleum imports, agricultural development, job creation, and reducing greenhouse gas emissions. To date, however, operational challenges have stymied the industrial production of large volumes of lignocellulosic-based fuels and chemicals. As a result, significant research investment has been led by the United States Department of Energy to understand and improve operational reliability at pioneer cellulosic biorefineries. In this perspective article lignocellulosic conversion technologies are described that have been adopted from the starch ethanol process. The developed process culminated in successful demonstration of 1,000-h integrated runs using several feedstocks, including switchgrass, energy sorghum, and two types of corn kernel fiber. This report highlights process development that solved several of the issues that plagued—and continue to plague—many in the cellulosic sugars space such as biomass feeding into equipment, high ash content, diversified co-product value, and others.
Ionic liquids (ILs) are seen as a more sustainable alternative to volatile organic solvents that are accelerating innovations in many industries such as energy storage, separations, and bioprocessing. The ability to effectively deconstruct lignocellulosic biomass is a significant hurdle in the biorefining/bioprocessing industry and presents limitations towards the commercial production of bioproducts (such as biofuels, biomaterials, etc. ). Certain ILs have been shown to promote effective lignin removal, cellulose recovery, and sugar yields from various biomass feedstocks such as corn stover, wheat straw, sugarcane bagasse, sorghum, switchgrass, miscanthus, poplar, pine, eucalyptus, and certain mixtures of municipal solid waste. However, these improvements are often counteracted by the limited biocompatibility of ILs, which results in an IL-induced reduction in enzyme activity and stability—an important downstream step in the conversion of biomass to biofuels/bioproducts. As a result, significant efforts have been made to discover and engineer compatible enzyme-IL systems and to improve our understanding on the effect that these ILs have on these systems. This review seeks to examine the impact of ionic liquids on enzymes involved in lignocellulosic biomass deconstruction, with a specific focus on their relevance in the context of pretreatment. Beyond presenting an overview of the ionic liquid pretreatment landscape, we outline the main factors that influence enzyme activity and stability in the presence of ILs This data is consolidated and analyzed to apply this body of knowledge towards new innovations that could lead to improvements in the processing of biomass to biofuels and bioproducts.
Current methods of root sampling typically only obtain small or incomplete sections of root systems and do not capture their true complexity. To facilitate the visualization and analysis of full-sized plant root systems in 3-dimensions, we developed customized mesocosm growth containers. While highly scalable, the design presented here uses an internal volume of 45 ft 3 (1.27 m 3 ), suitable for large crop and bioenergy grass root systems to grow largely unconstrained. Furthermore, they allow for the excavation and preservation of 3-dimensional root system architecture (RSA), and facilitate the collection of time-resolved subterranean environmental data. Sensor arrays monitoring matric potential, temperature and CO 2 levels are buried in a grid formation at various depths to assess environmental fluxes at regular intervals. Methods of 3D data visualization of fluxes were developed to allow for comparison with root system architectural traits. Following harvest, the recovered root system can be digitally reconstructed in 3D through photogrammetry, which is an inexpensive method requiring only an appropriate studio space and a digital camera. We developed a pipeline to extract features from the 3D point clouds, or from derived skeletons that include point cloud voxel number as a proxy for biomass, total root system length, volume, depth, convex hull volume and solidity as a function of depth. Ground-truthing these features with biomass measurements from manually dissected root systems showed a high correlation. We evaluated switchgrass, maize, and sorghum root systems to highlight the capability for species wide comparisons. We focused on two switchgrass ecotypes, upland (VS16) and lowland (WBC3), in identical environments to demonstrate widely different root system architectures that may be indicative of core differences in their rhizoeconomic foraging strategies. Finally, we imposed a strong physiological water stress and manipulated the growth medium to demonstrate whole root system plasticity in response to environmental stimuli. Hence, these new “3D Root Mesocosms” and accompanying computational analysis provides a new paradigm for study of mature crop systems and the environmental fluxes that shape them.