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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Comparing the Recovery of Arbuscular and Ectomycorrhizal Stands from Long-Term Nitrogen Fertilization at the Fernow Experimental Forest, WV (2021 and 2022)

This data was generated to answer the research question: After the end of a 30-year nitrogen (N) fertilization experiment in an Eastern temperate forest, to what extent do plant microbial interactions and carbon cycling in arbuscular mycorrhizal (AM) and ectomycorrhizal (ECM) dominated stands follow different recovery trajectories?The soil and the roots used in this dataset were sampled from two watersheds (N-fertilized watershed 3 and Reference watershed 7) with 6 AM and 6 ECM dominated stands each, over the course of three months (June, July, August) in 2021 and 2022. The "All_Roots.csv" data includes fine root biomass (in g) scaled to sampled soil (m2) (Scaled_Rt_Bm_gm2), and AM and ECM root colonization (%) in two soil fractions: the organic horizon (O) and the mineral horizon (B). The "All_Enzymes.csv" data includes extracellular soil enzyme activity (N-acetyl-glucosaminidase (NAG), acid phosphatase (AP), β-glucosidase (BG), phenol oxidase and peroxidase), and the ratios of BG to AP (BG:AP) and BG to NAG (BG:NAG) in three soil fractions: the organic horizon (O), the bulk soil (B), and the rhizosphere (R).The "All_Nmin" data includes inorganic N (NO3- and NH4+) pools (unit: μg of N per g of dry soil) measured pre- (I_Nitrate_ugNgsoil and I_Ammonia_ugNgsoil) and post-incubation (F_Nitrate_ugNgsoil and F_Ammonia_ugNgsoil), as well as calculated nitrification (Nitrification_day) and nitrogen mineralization (Mineralization_day) rates per day, in three soil fractions: the organic horizon (O), the bulk soil (B), and the rhizosphere (R).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Nitrogen fertilization effects on aged Miscanthus × giganteus stands: Exploring biomass yield, yield components, and biomass prediction using in–season morphological traits

For sustainable biomass production of Miscanthus × giganteus (hereafter miscanthus), understanding the impact of stand age and nitrogen (N) fertilization on biomass yield is crucial. This study investigated the effects of varying N fertilization rates (0, 56, 112, and 168 kg N ha –1 ) on yield components (tiller height, density, and weight) and their correlations with end-of-season biomass yield in miscanthus. We also explored end-of-season biomass yield prediction using in-season traits (canopy height, leaf area index, and leaf chlorophyll content [LCC]). The study was conducted at two sites in Illinois: a previously unfertilized 10-year-old miscanthus research stand at Urbana and a 16-year-old commercial stand at Pesotum with a history of annual 56N application. Results from 2018 to 2021 in Urbana and 2020 to 2021 in Pesotum showed increased biomass yields with N fertilization, varying by rate, year, and location. Biomass yield in Pesotum peaked at 56N, while in Urbana, it increased significantly at 112 kg N ha –1 . Biomass yield was strongly correlated with tiller height and weight measured at Urbana across N rates. Morphological traits measured every 2–3 weeks during the 2020 and 2021 growing seasons showed that canopy height was the strongest single predictor of miscanthus biomass yield, followed by LCC. Mid-August to September measurements of these traits were the best predictors of biomass yield. Multiple regressions involving the canopy height and LCC further improved yield predictions. We conclude that while N enhances biomass yields of aging miscanthus, the optimum rate depends on the site, environmental conditions, and management history.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Nitrogen Fertilizer, Arbuscular Mycorrhizal Fungi, and Soil Nematodes Affect Lignin Quality and Quantity in Switchgrass (Panicum virgatum L.)

Plant lignin content and composition, which limit cell wall digestibility and efficiency of cellulose conversion to bioethanol, can be influenced by belowground biotic and abiotic factors. Switchgrass (Panicum virgatum L.) is a leading lignocellulosic biofuel crop and forms strong belowground associations with arbuscular mycorrhizal fungi (AMF), is susceptible to belowground plant-parasitic nematodes (PPN), and when grown in monoculture generally requires nitrogen (N) fertilization. The main objectives of the study were to investigate the effects of N fertilizer and belowground organisms on lignin content and composition of switchgrass. Here, leaf, stem, and root tissues were evaluated separately to test whether these factors had varying belowground (local) or aboveground (systemic) effects on plants. These factors were manipulated in a field study in 2017 using biocide applications to reduce soil fungi and nematodes. Combined biocide application reduced p-hydroxyphenyl (H) unit abundance in the leaves by 14% and increased the syringyl:guaiacyl (S:G) ratio in stems by 2%. Application of fungicide alone increased stem syringyl (S) unit by 12.4% as compared with control plots, and 11.1% as compared with nematicide plots. Overall, fertilizer increased total stem lignin by 3%, stem S unit by 6.7%, and stem S:G ratio by 10%, whereas it reduced the amount of H-unit in the roots by 11%. While the effects of N fertilizer were more pronounced in this study, changes to soil organisms had similar magnitudes of effect for some measures of lignin, indicating that these belowground interactions may be important for growers to consider in the future.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Identifying the Best High‐Biomass Sorghum Hybrids Based on Biomass Yield Potential and Feedstock Quality Affected by Nitrogen Fertility Management Under Various Environments

The growing interest in high-biomass sorghum ( Sorghum bicolor L. Moench), hereafter referred to as sorghum, as a bioenergy feedstock in the United States requires an understanding of geographical adaptation to identify the most suitable hybrids for the Midwest. In this study, 13 sorghum hybrids (H1–H13) were evaluated for biomass yield potential in central and southern IL over two growing seasons (2022 and 2023). In addition to biomass yield, the effects of nitrogen (N) fertilization on yield, nutrient removal (N, P, and K), and feedstock composition (cellulose, hemicellulose, lignin, and soluble fractions) were determined to identify the best-performing sorghum hybrid across environmental gradients. The experimental design was a split-plot arrangement within a randomized complete block design with four replications at each of two locations: N rates (0 and 112 kg-N ha −1 ) as a whole plot factor and 13 sorghum hybrids as a subplot factor. As a result, complex genotypes (13 hybrids) by environment (2 sites and 2 years) and management (2 N rates) interactions were observed in biomass yield. The best hybrids at both sites were H1 (ATx2932/F10702_PSL) and H13 (TX08001), which were very photoperiod sensitive (PS). These hybrids produced superior biomass yield, and they also exhibited less nutrient removal and high energy-rich feedstock compositions (cellulose, hemicellulose, and lignin). Biomass yield potential was associated with morphological and phenological traits according to environmental conditions. Low-yielding hybrids were short-stature (H5 and H6) with pollinators (F10801_PSL-3dw and F10805_PSL-3dw) that are recessive at the Dw3 locus. Moderate PS hybrids (H7, H8, H11, and H12) that produced grain panicles at harvest showed high biomass yield plasticity and excessive nutrient removal as they accumulated high K concentrations in biomass tissues and high N and P in grain panicles.

09 BIOMASS FUELS↗
DOE OSTI2025

Data for Identifying the best high-biomass sorghum hybrids based on biomass yield potential and feedstock quality affected by nitrogen fertility management under various environments

Data were collected from agronomy fields in Urbana and Ewing, IL, during the 2022 and 2023 growing seasons. The dataset includes dry biomass yield, nitrogen, phosphorus, and potassium concentrations and removals, and chemical composition elements (cellulose, hemicellulose, lignin, and soluble fractions) for 13 high-biomass sorghum hybrids. data_sharing.xlsx contains 20 columns and 104 rows. Below is the explanation of all variables in the file: Year: 2022; 2023 Location: Urbana, IL; Ewing, IL N rate (kg-N/ha): 0; 112 Hybrid #: H1-H13 Pedigree: Pedigree for 13 hybrids Dry biomass yield (Mg/ha): Aboveground dry biomass yield N (g/kg): Nitrogen concentration in plant tissue P (g/kg): Phosphorus concentration in plant tissue K (g/kg): Potassium concentration in plant tissue N (kg/ha): Nitrogen removal by aboveground biomass P (kg/ha): Phosphorus removal by aboveground biomass K (kg/ha): Potassium removal by aboveground biomass Cellulose (g/kg): Cellulose concentration in plant tissue Hemicellulose (g/kg): Hemicellulose concentration in plant tissue Lignin (g/kg): Lignin concentration in plant tissue Soluble (g/kg): Soluble concentration in plant tissue Cellulose (Mg/ha): Cellulose content in aboveground biomass Hemicellulose (Mg/ha): Hemicellulose content in aboveground biomass Lignin (Mg/ha): Lignin content in aboveground biomass Soluble (Mg/ha): Soluble content in aboveground biomass

environmental adaptability↗
DOE OSTI2022

Data for Responsiveness of Miscanthus and Switchgrass Yields to Stand Age and Nitrogen Fertilization: A Meta-regression Analysis

The compiled datasets include plot level observations of energy crops (miscanthus and switchgrass) from recent experimental field trials in the US including dry biomass yield, location, state, region, harvest year, growing season degree days (GDD), winter season heating degree days (HDD), growing season cumulative precipitation, annual nitrogen application rate, age of the pant when harvested, National Commodity Crop Productivity Index (NCCPI) values, and cultivar type (switchgrass) from various published and unpublished sources. The stata codes include estimation procedures for four different specifications, i.e., Model A includes deterministic effect without interaction terms; Model B includes deterministic effect with interaction terms (N2, age2, N × age, GDD2, precip2, N × NCCPI); Model C includes deterministic effect with interaction terms, study, and location random effect; Model D includes deterministic effect with interaction terms, harvest year augmented study, and location random effect.

Age↗
DOE OSTI2021

Legacy Effects of Intercropping and Nitrogen Fertilization on Soil N Cycling, Nitrous Oxide Emissions, and the Soil Microbial Community in Tropical Maize Production

Maize-forage grasses intercropping systems have been increasingly adopted by farmers because of their capacity to recycle nutrients, provide mulch, and add C to soil. However, grasses have been shown to increase nitrous oxide (N 2 O) emissions. Some tropical grasses cause biological nitrification inhibition (BNI) which could mitigate N 2 O emissions in the maize cycle but the reactions of the N cycle and the microbial changes that explain the N 2 O emissions are little known in such intercropping systems. With this in mind, we explored intercropping of forage grasses ( Brachiaria brizantha and Brachiaria humidicola ) with distinct BNI and yield potential to increase N cycling in no-till maize production systems compared to monocrop with two N rates (0 and 150 kg ha −1 ) applied during the maize season. These grasses did not strongly compete with maize during the period of maize cycle and did not have a negative effect on grain yield. We observed a legacy of these grasses on N mineralization and nitrification through the soil microbiome during maize growth. We observed that B. humidicola , genotype with higher BNI potential, increased net N mineralization by 0.4 mg N kg −1 day −1 and potential nitrification rates by 1.86 mg NO 3 -N kg −1 day −1 , while B. brizantha increased the soil moisture, fungi diversity, mycorrhizal fungi, and bacterial nitrifiers, and reduced saprotrophs prior to maize growth. Their legacy on soil moisture and cumulative organic inputs (i.e., grass biomass) was strongly associated with enhanced mineralization and nitrification rates at early maize season. These effects contributed to increase cumulative N 2 O emission by 12.8 and 4.8 mg N 2 O-N m −2 for maize growing after B. brizantha and B. humidicola , respectively, regardless of the N fertilization rate. Thus, the nitrification inhibition potential of tropical grasses can be outweighed by their impacts on soil moisture, N recycling, and the soil microbiome that together dictate soil N 2 O fluxes.

Canisares, Lucas P.↗
DOE OSTI2021

Unbalancing Symbiotic Nitrogen Fixation: Can We Make Effectiveness More Effective?

One of the most critical uses of carbon fuels is in generating nitrogen fertilizer. Atmospheric dinitrogen is too stable to be used directly by plants, so plants need other chemical forms of nitrogen. Nitrogen fertilizer production uses ~4% of the world’s natural gas. Making N fertilizers leverages methane’s energy content by enabling additional energy to be captured into food and fiber through increased photosynthesis. Manufacturing fertilizer is essential—without it there would only be enough combined nitrogen to feed ~3 billion of the world’s ~7 billion people. How to alter this addiction is not obvious, but the best chance to secure substantial and sustainable amounts of future N might be through symbiotic nitrogen fixation (SNF). SNF describes mutualistic interactions where nitrogen-fixing bacteria provide fixed nitrogen to their plant hosts, freeing them from the need for nitrogen fertilizers. SNF already occurs on a large scale; "biological nitrogen” contributes about 5 Tg N/yr to American agriculture, a quarter of the nitrogen needed. Improving SNF and replacing inorganic fertilizers are both important goals in establishing sustainable and more energy-efficient farming. SNF has been used in agriculture for millennia, largely by using legumes like beans or alfalfa in mixed cropping systems. Legume SNF occurs in root nodules, organs that develop after a growing root is infected by bacteria called rhizobia. In the plant cells of a legume nodule, specialized forms of rhizobia fix nitrogen and can produce enough ammonia to supply the growing plant. The exchange of photosynthetically derived plant carbon compounds for nitrogen reduced by the bacteria is the engine that drives the symbiosis. Important details about the organization of development and metabolism in SNF remain to be determined, such as how plant and bacterial metabolism are coordinated as nitrogen is being fixed and what governs the overall level of nitrogen fixation. Most bacterial mutations that affect SNF completely block fixation (e.g. are Fix – ), providing a limited window into the feedback interactions that must be part of the process. We have investigated an unusual mutant of Sinorhizobium meliloti, a symbiotic partner of alfalfa, that fixes dinitrogen at a normal rate (i.e., it is Fix + ) but is not effective in supporting plant growth (i.e., it is ineffective or Eff – ). We have shown that the mutant has a specific mutation in the glnD gene, which codes for a protein that regulates the bacterial response to nitrogen stress, among other key pathways. A Fix + Eff – phenotype contains an unavoidable puzzle—how is it possible for the bacteria to fix nitrogen at a normal rate and without benefiting the nitrogen starved plant host? After eliminating other possibilities, we proposed that the bacteria synthesize a nitrogen-containing compound that the plant can’t use so that acquiring this compound does not relieve plant nitrogen stress. Extracts of nodules made by glnD mutant strains have high concentrations of pyruvate canaline oxime (PCO), a derivative of the plant defensive compound canaline, a toxic analog of ornithine, an amino acid. Many legumes produce canaline and the related arginine mimic canavanine. For reasons we do not yet understand, the mutation in glnD appears to trigger significant overproduction of canaline in a defense response that leads to substantial synthesis of PCO. We expected the bacteria to be able to use PCO, but we have not been able to grow S. meliloti on PCO under several conditions. PCO thus appears to be a metabolic dead end for both the plant and bacteria. We also examined bacterial catabolic pathways that degrade arginine and canavanine and showed that mutation of these did not alter the symbiosis in an obvious way. In related experiments to examine the metabolic development of root nodules, we carried out an ambitious experiment to simultaneously measure metabolites and protein changes during nodule maturation. For these experiments we used a related symbiotic interaction between S. medicae and Medicago truncatula. M. truncatula has a simpler genome than alfalfa so we could identify plant proteins based on predictions from the DNA sequence. Nodules formed on Medicago grow linearly, with the least mature tissues at the tip. Because development of nitrogen fixation is accompanied by the production of the pigmented plant oxygen carrier protein leghemoglobin, we were able to locate and manually separate the tip, transitional, and mature nitrogen-fixing regions from each other. Advances in proteomic and metabolomic techniques allowed us to analyze these tissues from a pool of as few as 10 nodules. We observed correlated transitions of various enzymes in the plant and bacterial proteomes from those characteristic of free-living metabolism to the microaerobic metabolism of the nitrogen-fixing tissues. The metabolome could not be separated into plant and bacterial domains but was consistent with this overall transition. A subsequent paper examined proteolysis in nodule bacteria. Protein turnover in mature regions of the nodule is significant. We were able to show complexes between various proteins and important proteases, using precipitation capture techniques to isolate the proteases and sensitive proteomic analysis to reveal the associated proteins.

09 BIOMASS FUELS↗
DOE OSTI2025

Mapping Nitrogen Pathways in Organic Manure through GCAM

As the world has become more and more globalized, a need for a comprehensive and extensive modelling system that maps out the vast majority of processes and items around the world and can display how interdependent they are. Furthermore, such a model should be able to predict future processes and values such that it can guide scientific thinking and policy. This is where the importance of GCAM becomes so apparent. GCAM, the Global Change Analysis Model, that incorporates many different fields and can predict such a wide variety of processes. As the model expands and encompasses more fields and goes into greater detail, there is a need for previously incorporated processes to be further fleshed out. My project is dedicated to creating the organic nitrogen fertilizer pathway, such that there are two nitrogen fertilizer pathways, synthetic and organic (manure). Previously, only synthetic fertilizer was included, with all organic fertilizer (manure) being omitted from the model. Developing the synthetic path is relatively easy, as the process for creating synthetic fertilizer is an industrial process that has been perfected over decades, and thus has a very precise input/output coefficient of nitrogen, but determining that same coefficient for organic manure is much harder, because, well, digestion is not an industrial process, especially considering the many different types of animals have different processes, and a good amount of the manure they produce won’t be applied to the soil. I obtained data from the UN’s FAO on manure nitrogen applied to soil, and filled in the gaps and created many different visualizations to help comprehend the data. These findings will foster a greater understanding of nitrogen flows, which is crucial to fields like industrial agriculture, polluting runoff, and nitrifying bacteria and archaea in the soil.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Airborne hyperspectral imaging of nitrogen deficiency on crop traits and yield of maize by machine learning and radiative transfer modeling

Nitrogen is an essential nutrient that directly affects plant photosynthesis, crop yield, and biomass production for bioenergy crops, but excessive application of nitrogen fertilizers can cause environmental degradation. To achieve sustainable nitrogen fertilizer management for precision agriculture, there is an urgent need for nondestructive and high spatial resolution monitoring of crop nitrogen and its allocation to photosynthetic proteins as that changes over time. Here, we used visible to shortwave infrared (400–2400 nm) airborne hyperspectral imaging with high spatial (0.5 m) and spectral (3–5 nm) resolutions to accurately estimate critical crop traits, i.e., nitrogen, chlorophyll, and photosynthetic capacity (CO 2 -saturated photosynthesis rate, V max,27 ), at leaf and canopy scales, and to assess nitrogen deficiency on crop yield. We conducted three airborne campaigns over a maize (Zea mays L.) field during the growing season of 2019. Physically based soil-canopy Radiative Transfer Modeling (RTM) and data-driven approaches i.e. Partial-Least Squares Regression (PLSR) were used to retrieve crop traits from hyperspectral reflectance, with ground truth of leaf nitrogen, chlorophyll, V max,27 , Leaf Area Index (LAI), and harvested grain yield. To improve computational efficiency of RTMs, Random Forest (RF) was used to mimic RTM simulations to generate machine learning surrogate models RTM-RF. The results show that prior knowledge of soil background and leaf angle distribution can significantly reduce the ill-posed RTM retrieval. RTM-RF achieved a high accuracy to predict leaf chlorophyll content (R 2 = 0.73) and LAI (R 2 = 0.75). Meanwhile, PLSR exhibited better accuracy to predict leaf chlorophyll content (R 2 = 0.79), nitrogen concentration (R 2 = 0.83), nitrogen content (R 2 = 0.77), and V max,27 (R 2 = 0.69) but required measured traits for model training. We also found that canopy structure signals can enhance the use of spectral data to predict nitrogen related photosynthetic traits, as combining RTM-RF LAI and PLSR leaf traits well predicted canopy-level traits (leaf traits × LAI) including canopy chlorophyll (R 2 = 0.80), nitrogen (R 2 = 0.85) and V max,27 (R 2 = 0.82). Compared to leaf traits, we further found that canopy-level photosynthetic traits, particularly canopy V max,27 , have higher correlation with maize grain yield. This study highlights the potential for synergistic use of process-based and data-driven approaches of hyperspectral imaging to quantify crop traits that facilitate precision agricultural management to secure food and bioenergy production.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Nitrogen-Fixing Gamma Proteobacteria Azotobacter vinelandii—A Blueprint for Nitrogen-Fixing Plants?

The availability of fixed nitrogen limits overall agricultural crop production worldwide. The so-called modern “green revolution” catalyzed by the widespread application of nitrogenous fertilizer has propelled global population growth. It has led to imbalances in global biogeochemical nitrogen cycling, resulting in a “nitrogen problem” that is growing at a similar trajectory to the “carbon problem”. As a result of the increasing imbalances in nitrogen cycling and additional environmental problems such as soil acidification, there is renewed and increasing interest in increasing the contributions of biological nitrogen fixation to reduce the inputs of nitrogenous fertilizers in agriculture. Interestingly, biological nitrogen fixation, or life’s ability to convert atmospheric dinitrogen to ammonia, is restricted to microbial life and not associated with any known eukaryotes. It is not clear why plants never evolved the ability to fix nitrogen and rather form associations with nitrogen-fixing microorganisms. Perhaps it is because of the large energy demand of the process, the oxygen sensitivity of the enzymatic apparatus, or simply failure to encounter the appropriate selective pressure. Whatever the reason, it is clear that this ability of crop plants, especially cereals, would transform modern agriculture once again. Successfully engineering plants will require creating an oxygen-free niche that can supply ample energy in a tightly regulated manner to minimize energy waste and ensure the ammonia produced is assimilated. Nitrogen-fixing aerobic bacteria can perhaps provide a blueprint for engineering nitrogen-fixing plants. This short review discusses the key features of robust nitrogen fixation in the model nitrogen-fixing aerobe, gamma proteobacteria Azotobacter vinelandii, in the context of the basic requirements for engineering nitrogen-fixing plants.

Microbiology↗
DOE OSTI2021

Varied farm-level carbon intensities of corn feedstock help reduce corn ethanol greenhouse gas emissions

Abstract A reduction in the overall carbon intensity (CI) of a crop-based biofuel can be achieved by cutting down the CI of the biofuel’s feedstock, which in turn correlates significantly to agricultural management practices. Proposals are being made to incentivize low-carbon biofuel feedstocks under U.S. fuel regulatory programs to promote sustainable farming practices by individual farms. For such an incentive scheme to function properly, robust data collection and verification are needed at the farm level. This study presents our collaboration with U.S. private sector companies to collect and verify the corn production data necessary for feedstock-specific CI calculation at the farm level, through a carefully designed questionnaire, to demonstrate the practicality and feasibility of data collection at scale. We surveyed 71 farms that produced 0.2 million metric tons of corn grain in 2018 in a Midwestern U.S. state to obtain information on key parameters affecting corn ethanol feedstock CI, such as grain yields, fertilizer/chemical application rates, and agronomic practices. Feedstock-specific CI was calculated in the unit of grams (g) CO 2 equivalent (CO 2 e) of greenhouse gases per kilogram (kg) of corn produced. Results showed large CI variations—from 119 to 407 g CO 2 e kg −1 of corn—due to the farm-level inventory, while the production-weighted average CI for all surveyed farms was 210 g CO 2 e kg −1 , comparable to the national average CI of 204 g CO 2 e kg −1 . The nitrogen fertilizer type applied and rate were identified as key factors contributing most to CI variations at the farm level. The estimated N 2 O emissions from fertilizer and biomass nitrogen inputs to soil accounted for 51% of the overall farm-level CI and therefore need to be better monitored at farm level with high resolution. We concluded that this feedstock-specific, farm-level CI evaluation has the potential to be used to incentivize low-carbon feedstock for biofuel production.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Genome-Resolved Metagenomics of Nitrogen Transformations in the Switchgrass Rhizosphere Microbiome on Marginal Lands

Switchgrass (Panicum virgatum L.) remains the preeminent American perennial (C4) bioenergy crop for cellulosic ethanol, that could help displace over a quarter of the US current petroleum consumption. Intriguingly, there is often little response to nitrogen fertilizer once stands are established. The rhizosphere microbiome plays a critical role in nitrogen cycling and overall plant nutrient uptake. We used high-throughput metagenomic sequencing to characterize the switchgrass rhizosphere microbial community before and after a nitrogen fertilization event for established stands on marginal land. We examined community structure and bulk metabolic potential, and resolved 29 individual bacteria genomes via metagenomic de novo assembly. Community structure and diversity were not significantly different before and after fertilization; however, the bulk metabolic potential of carbohydrate-active enzymes was depleted after fertilization. We resolved 29 metagenomic assembled genomes, including some from the ‘most wanted’ soil taxa such as Verrucomicrobia, Candidate phyla UBA10199, Acidobacteria (rare subgroup 23), Dormibacterota, and the very rare Candidatus Eisenbacteria. The Dormibacterota (formally candidate division AD3) we identified have the potential for autotrophic CO utilization, which may impact carbon partitioning and storage. Our study also suggests that the rhizosphere microbiome may be involved in providing associative nitrogen fixation (ANF) via the novel diazotroph Janthinobacterium to switchgrass.

60 APPLIED LIFE SCIENCES↗