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At least 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Deeply Rooted: Evaluating Plant Rooting Depth as a Means for Enhanced Soil Carbon Sequestration (Full Technical Report)

Soils store three times as much carbon (C) as the atmosphere, but are not at capacity, and enhanced soil C storage is considered an essential strategy to mitigate rising atmospheric CO 2 levels. Agricultural soils have experienced substantial C loss in the past century due to poor agricultural practices and erosion. A shift towards deep-rooting crops and low-impact soil management could potentially increase long-term sequestration of C fixed by plants and stored in their root tissues, particularly for crops that have naturally deep root systems (>1 meter). A substantial amount of the CO 2 taken up by plants is allocated to their root systems, and because C deposited in deep soil layers has a longer residence time (up to millennia, in contrast to C deposited in topsoils), C increases at depth may have better long-term C sequestration potential than topsoils. However, the accrual, turnover, and stabilization of C in subsoils is a critical knowledge gap. We investigated a deeply rooted plant, switchgrass (Panicum virgatum), as a means of increasing carbon stocks in marginal and agricultural soils. We hypothesized that deep (>30 cm) SOC stocks would be greater under bioenergy crops relative to stocks under shallow-rooted conventional crop cover. To test this hypothesis, we compared soil depth profiles beneath deeply rooted switchgrass (cultivated for 4-30 years) and paired shallowrooted annual controls. We studied 12 field sites, 3 that were collected in 2018 before the start of the project as part a Department of Energy (DOE) Sustainable Biofuels study, and 9 that we collected in 2019 on a national field sampling campaign across the eastern US. In our publication from the 2018 study, which was written in collaboration with the LLNL Soil Microbiome Scientific Focus Area (SFA), we found that C stocks increased under switchgrass, but that the increases were dependent on soil texture. In the 2019 study, we found that carbon accrual tended to occur most consistently in low C soil in the southern US, which could indicate that perennial grasses may be a viable strategy to increase SOC in marginals soils in this region. We have published two studies from the 2019 sampling campaign thus far, we measured microbial growth parameters that will aid in modeling subsoil carbon cycling, and we found that switchgrass appears to move water upward in the soil profile and could promote drought tolerance, which a phenomenon commonly performed by trees known as ‘hydraulic redistribution;’ our study is the first to show this can occur in deep-rooted grasses. Finally, we published a modeling paper in collaboration with the LLNL Soil Microbiome SFA, where we found that poorly crystalline minerals are abundant and strongly correlated with organic C in geographically limited zones with enhanced weathering rates. Our results will inform technological development in the agricultural carbon sequestration sector as well as future negative emissions policies.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2025

From quantitative trait loci towards mechanisms: Linkage Integration Hypothesis Testing (LIgHT) sheds light on the mechanisms of genetically modulated stress tolerance

The goal of this work is to assess the mechanistic bases of natural genetic variations in plant responses of photosynthesis to stress. To achieve this goal, we devised the Linkage Integration Hypothesis Testing (LIgHT) approach, comparing chromosomal locations of quantitative trait loci (QTLs) for multiple phenotypes to distinguish between hypothetical mechanisms. As a use case, we explored genetic variations in photosynthesis-related processes under chilling stress in recombinant inbred lines of cowpea ( Vigna unguiculata L. Walp.). We focused on photosynthesis-related parameters measurable in high throughput and indicative of proposed chilling responses, including the states of PSI and PSII, photoprotective non-photochemical quenching, PSII photodamage, and nyctinastic leaf movements (NLMs). The patterns of QTL linkages indicated that chilling stress tolerance is genetically controlled by avoiding PSII photodamage rather than PSI damage or NLMs. This model was validated in a separate experiment measuring the rates of PSII photodamage and repair. Additional linkages suggest that chilling-induced damage to PSII is controlled by the thylakoid proton motive force and redox state of PSII. This regulation appears to be modulated by thylakoid fatty acid composition, previously associated with the same genetic loci and now supported by broader mechanistic evidence. We propose that the LIgHT approach can be broadly applied to test mechanisms underlying genetic variations.

MultispeQ↗
DOE OSTI2022

Blue Rock CropSpanPV - Low Cost Racking for Agricultural Solar Photovoltaics

In this work, a total of four solar panel layouts, applied to Agrivoltaics, are investigated. This study presents two hypotheses: 1. Prefabrication - Lower total cost can be achieved by constructing a pre-assembled, pre-wired solar array in a factory, with automation, then rapidly deploying it in the field. 2. Tension Structures - Using tension structures in a way to suspend an overhead solar array also reduces costs when compared to conventional support methods. Designs are presented for frames to hold the panels. Configurations are advanced to connect the frames into arrays. Supports and foundations to hold the arrays in the desired layout are calculated, selected and presented. Cost models are prepared for the four layouts, which include definition of the business enterprise, the manufacturing operation, and the required facility for production at scale. Cost models include, detail material and task/labor take-offs for both manufacturing and installation. Comparisons with conventional system adapted to Agrivoltaics reveal that the two hypotheses do not hold true. A prefabricated array requires additional material that is not needed in the base system and the cost of this additional material is not outweighed by the cost savings of reduced field installation. The cost of wire rope for a tension structure does result in an economy of material, however, the cost of end connections and tensioning devices adds significantly to the cost of the overall tension structure. Foundation loads with tension structures, especially the large wind uplift seen with a fixed tilt solar array, impose significant constraints and high costs. The following conclusions are recommended for further consideration: 1. Best Case Agrivoltaic Configuration - The best value/lowest cost configuration for agrivoltaics is the installation of solar panels mounted on a single axis tracker in either a 2-in-portrait or 2-in landscape arrangement. This is useful for grazing lands and for staple crops which require high light levels. These applications represent the vast majority of the potential agrivoltaic market. Important elements in this configuration are the specific geometric layout coordinated with the various field operations and a control system linked between the farm equipment and single axis tracker. The control system is to monitor location and orientation, then actively tilt the solar panels or brake the field equipment to provide clearance and avoid collisions. 2. Long Span Applications - The use of tension structures for an overhead fixed tilt array is beneficial where long spans are a necessity, especially where the base support in the project location has consolidated rock near the surface or some other solid structure. One application is spanning wide irrigation canals. Another may be as an installation at the top deck of parking structures. 3. Direct Ground Mount Configuration – Having skids at the base of a pre-fabricated array that extend when the array is deployed, could be a successful design. This could be a useful strategy for dense, flat, ground-mount configurations like that employed by a couple of successful commercial operations (5G Maverick and Erthos). Arrays would be orientated north and south, deployed very low on prepared grade. Arrays would be aligned closely next to each other and anchored with small ground screws or simply with ballast. This same product arrangement could be very useful for rapid deployment and set-up of solar arrays for temporary deployments, disaster response and the military operations

14 SOLAR ENERGY↗
DOE OSTI2024

Genomic prediction of regional-scale performance in switchgrass ( Panicum virgatum ) by accounting for genotype-by-environment variation and yield surrogate traits

Switchgrass is a potential crop for bioenergy or carbon capture schemes, but further yield improvements through selective breeding are needed to encourage commercialization. To identify promising switchgrass germplasm for future breeding efforts, we conducted multisite and multitrait genomic prediction with a diversity panel of 630 genotypes from 4 switchgrass subpopulations (Gulf, Midwest, Coastal, and Texas), which were measured for spaced plant biomass yield across 10 sites. Our study focused on the use of genomic prediction to share information among traits and environments. Specifically, we evaluated the predictive ability of cross-validation (CV) schemes using only genetic data and the training set (cross-validation 1: CV1), a subset of the sites (cross-validation 2: CV2), and/or with 2 yield surrogates (flowering time and fall plant height). We found that genotype-by-environment interactions were largely due to the north–south distribution of sites. The genetic correlations between the yield surrogates and the biomass yield were generally positive (mean height r = 0.85; mean flowering time r = 0.45) and did not vary due to subpopulation or growing region (North, Middle, or South). Genomic prediction models had CV predictive abilities of –0.02 for individuals using only genetic data (CV1), but 0.55, 0.69, 0.76, 0.81, and 0.84 for individuals with biomass performance data from 1, 2, 3, 4, and 5 sites included in the training data (CV2), respectively. To simulate a resource-limited breeding program, we determined the predictive ability of models provided with the following: 1 site observation of flowering time (0.39); 1 site observation of flowering time and fall height (0.51); 1 site observation of fall height (0.52); 1 site observation of biomass (0.55); and 5 site observations of biomass yield (0.84). The ability to share information at a regional scale is very encouraging, but further research is required to accurately translate spaced plant biomass to commercial-scale sward biomass performance.

09 BIOMASS FUELS↗
DOE OSTI2025

Impacts of year-to-year weather variability and inter-panel spacing on agrivoltaic crop yields in Massachusetts

Deployment of utility-scale solar power plants could lead to agricultural land-use changes. Agrivoltaics, a dual land use combining solar and agriculture on the same land, can provide multiple environmental benefits, including improving soil quality and water use efficiency. The body of agrivoltaic field data is still growing, and crop responses to different solar configurations under different local climates are highly varied. We investigate the impact of adding spacing between adjacent solar panels in a fixed-tilt system to improve light diffusion to crops. For four crops (broccoli, peppers, kale, Swiss chard) grown across 3 years in an agrivoltaic system in Massachusetts, we found that only kale had a linearly increasing trend as the inter-panel spacing increased from 0.6 m to 1.5 m (2 ft to 5 ft). However, there were significant year-to-year differences in the yield of agrivoltaic versus control fields. Agrivoltaic and full sun fields produced equivalent yields in a hot, dry year, whereas the full-sun control beds produced more salable yield for all four crops in a warm, wet year. This demonstrates variability of agricultural outcomes and the need for more multi-year studies to ensure agrivoltaic impacts are not under- or overestimated.

14 SOLAR ENERGY↗
DOE OSTI2022

Biomass yield improvement in switchgrass through genomic prediction of flowering time

The seasonal timing of the transition from vegetative to reproductive growth has a major impact on biomass accumulation in switchgrass. Late-flowering switchgrass cultivars produce greater biomass, a critical trait for sustainable bioenergy production. Genomic prediction (GP) may allow rapid selection of late-flowering individuals with reduced time and expense for field evaluations. To evaluate GP, two flowering time traits (heading date and anthesis date) were collected on 1532 genotypes from four breeding populations: Midwest, Gulf, Atlantic, and Hybrid. These were sequenced using genotype-by-sequencing (530,792 single-nucleotide polymorphisms). Predictive ability of single-trait and multi-trait models were evaluated by cross-validation, by prediction of a progeny trial (n = 122), and through prediction of yield performance in a parallel experiment (n = 52). Predictive ability was not improved by sharing information among breeding groups. Overall, multi-trait models provided an advantage during cross-validation, but a smaller advantage during progeny prediction. Within populations, GP resulted in lower per-cycle progress than previously reported field evaluations (3.1 vs. 5.0 day –1 cycle –1 ). However, GP cycles are potentially much faster than field evaluations. When directly predicting biomass yield, the Hybrid training population had a predictive ability of 0.54–0.63. This reinforces the strong linkage between biomass yields in swards and flowering time. Furthermore, these results highlight the value of GP for rapid yield improvement in switchgrass, particularly in a breeding program designed to share information between biomass yield trials and low-cost flowering time evaluations.

09 BIOMASS FUELS↗
DOE OSTI2026

Climate-Driven Divergence in Biophysical and Economic Impacts of Agrivoltaics

Increasing global demands for food and energy necessitate innovative land-use solutions. Agrivoltaics, colocating solar photovoltaics with agriculture, shows promise, but its widespread adoption faces complex biophysical and economic trade-offs in a changing climate. Here, we develop an integrated biophysical-economic modeling framework to quantify how agrivoltaics affect biophysical and economic impacts across the Midwestern United States under both current and project climate conditions. We find strong regional divergences driven by climate gradients. In the humid eastern Midwest, solar panel shading limits photosynthesis, leading to reduced yields (maize -24%; soybean -16%) and lower farmers' profitability (maize -16%; soybean -2%) compared to conventional agriculture. Conversely, in the semiarid western region, shading alleviates heat and water stress, moderating yield reductions for maize (-12%) and even boosting soybean yields (+6%), resulting in improved economic returns (-6% for maize; +9% for soybean), for a scenario with 33% photovoltaic ground coverage ratio. Although agrivoltaics generate substantial electrical energy across all regions, high upfront installation costs challenge solar developers compared to standalone solar photovoltaics. However, our analysis identifies “win-win” opportunities where soybean-based agrivoltaics in the semiarid region produce economic benefits for both farmers and solar developers, highlighting the necessity for region-specific designs tailored to local climate conditions. Critically, future climate projections indicate eastward expansion of semiarid conditions, broadening areas where agrivoltaics can mitigate crop yield penalties (even boosting yield) and improve overall profitability, especially under high-emission scenarios. The results provide a mechanistic and economically integrated understanding essential for developing evidence-based and region-specific strategies to scale agrivoltaics in a changing climate.

14 SOLAR ENERGY↗
DOE OSTI2020

The Utility of Satellites and Autonomous Remote Sensing Platforms for Monitoring Offshore Aquaculture Farms: A Case Study for Canopy Forming Kelps

The emerging sector of offshore kelp aquaculture represents an opportunity to produce biofuel feedstock to help meet growing energy demand. Giant kelp represents an attractive aquaculture crop due to its rapid growth and production, however precision farming over large scales is required to make this crop economically viable. These demands necessitate high frequency monitoring to ensure outplant success, maximum production, and optimum quality of harvested biomass, while the long distance from shore and large necessary scales of production makes in person monitoring impractical. Remote sensing offers a practical monitoring solution and nascent imaging technologies could be leveraged to provide daily products of the kelp canopy and subsurface structures over unprecedented spatial scales. Here, we evaluate the efficacy of remote sensing from satellites and aerial and underwater autonomous vehicles as potential monitoring platforms for offshore kelp aquaculture farms. Decadal-scale analyses of the Southern California Bight showed that high offshore summertime cloud cover restricts the ability of satellite sensors to provide high frequency direct monitoring of these farms. By contrast, daily monitoring of offshore farms using sensors mounted to aerial and underwater drones seems promising. Small Unoccupied Aircraft Systems (sUAS) carrying lightweight optical sensors can provide estimates of canopy area, density, and tissue nitrogen content on the time and space scales necessary for observing changes in this highly dynamic species. Underwater color imagery can be rapidly classified using deep learning models to identify kelp outplants on a longline farm and high acoustic returns of kelp pneumatocysts from side scan sonar imagery signal an ability to monitor the subsurface development of kelp fronds. Current sensing technologies can be used to develop additional machine learning and spectral algorithms to monitor outplant health and canopy macromolecular content, however future developments in vehicle and infrastructure technologies are necessary to reduce costs and transcend operational limitations for continuous deployment in an offshore setting.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Data for A Hybrid Biophysical-Machine Learning Framework for Diurnal Surface Energy Flux Estimation Using Proximal Sensing

Thermal infrared-based remote sensing of surface energy fluxes has traditionally relied on high spatial resolution satellite data with revisit frequencies on the order of weeks. In this study, we evaluate a biophysics-based analytical surface energy balance model for predicting latent energy (LE) and sensible heat (H) fluxes using proximal sensing observations. The Surface Temperature Initiated Closure (STIC1.2) model has been extensively validated across a wide range of spatial and temporal scales using various satellite-derived thermal infrared data sets. Here we extend this validation by applying STIC at sub-hourly temporal resolution over multiple growing seasons for four distinct agricultural systems. We further develop and evaluate novel STIC variants that incorporate machine learning (ML) techniques to eliminate the need for surface energy balance observations, specifically net radiation and soil heat flux, thereby enhancing model applicability in data-sparse settings. The integration of a ML component to estimate surface available energy is shown to have strong predictive performance for both LE (R2 = 0.81–0.94) and H (R2 = 0.46–0.72) across all agricultural systems examined here, demonstrating the potential of hybrid biophysical-machine learning approaches for surface energy balance modeling with minimal data requirements. This study concludes with a novel application of explainable machine learning (exML) to diagnose sources of model error. This exML framework attributes residual prediction errors to both model input variables and environmental drivers not explicitly included in the simulation experiments. This approach provides a new pathway for improving model design and integrating previously overlooked yet influential variables into future model iterations.

AI/ML↗
DOE OSTI2021

Phenotyping stomatal closure by thermal imaging for GWAS and TWAS of water use efficiency-related genes

Abstract Stomata allow CO2 uptake by leaves for photosynthetic assimilation at the cost of water vapor loss to the atmosphere. The opening and closing of stomata in response to fluctuations in light intensity regulate CO2 and water fluxes and are essential for maintaining water-use efficiency (WUE). However, a little is known about the genetic basis for natural variation in stomatal movement, especially in C4 crops. This is partly because the stomatal response to a change in light intensity is difficult to measure at the scale required for association studies. Here, we used high-throughput thermal imaging to bypass the phenotyping bottleneck and assess 10 traits describing stomatal conductance (gs) before, during and after a stepwise decrease in light intensity for a diversity panel of 659 sorghum (Sorghum bicolor) accessions. Results from thermal imaging significantly correlated with photosynthetic gas exchange measurements. gs traits varied substantially across the population and were moderately heritable (h2 up to 0.72). An integrated genome-wide and transcriptome-wide association study identified candidate genes putatively driving variation in stomatal conductance traits. Of the 239 unique candidate genes identified with the greatest confidence, 77 were putative orthologs of Arabidopsis (Arabidopsis thaliana) genes related to functions implicated in WUE, including stomatal opening/closing (24 genes), stomatal/epidermal cell development (35 genes), leaf/vasculature development (12 genes), or chlorophyll metabolism/photosynthesis (8 genes). These findings demonstrate an approach to finding genotype-to-phenotype relationships for a challenging trait as well as candidate genes for further investigation of the genetic basis of WUE in a model C4 grass for bioenergy, food, and forage production.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A plant host, Nicotiana benthamiana, enables the production and study of fungal lignin-degrading enzymes

Abstract Lignin has significant potential as an abundant and renewable source for commodity chemicals yet remains vastly underutilized. Efforts towards engineering a biochemical route to the valorization of lignin are currently limited by the lack of a suitable heterologous host for the production of lignin-degrading enzymes. Here, we show that expression of fungal genes in Nicotiana benthamiana enables production of members from seven major classes of enzymes associated with lignin degradation (23 of 35 tested) in soluble form for direct use in lignin activity assays. We combinatorially characterized a subset of these enzymes in the context of model lignin dimer oxidation, revealing that fine-tuned coupling of peroxide-generators to peroxidases results in more extensive C-C bond cleavage compared to direct addition of peroxide. Comparison of peroxidase isoform activity revealed that the extent of C-C bond cleavage depends on peroxidase identity, suggesting that peroxidases are individually specialized in the context of lignin oxidation. We anticipate the use of N. benthamiana as a platform to rapidly produce a diverse array of fungal lignin-degrading enzymes will facilitate a better understanding of their concerted role in nature and unlock their potential for lignin valorization, including within the plant host itself.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Switchgrass cropping systems affect soil carbon and nitrogen and microbial diversity and activity on marginal lands

Abstract Switchgrass ( Panicum virgatum L.), as a dedicated bioenergy crop, can provide cellulosic feedstock for biofuel production while improving or maintaining soil quality. However, comprehensive evaluations of how switchgrass cultivation and nitrogen (N) management impact soil and plant parameters remain incomplete. We conducted field trials in three years (2016–2018) at six locations in the North Central Great Lakes Region to evaluate the effects of cropping systems (switchgrass, restored prairie, undisturbed control) and N rates (0, 56 kg N ha −1 year −1 ) on biomass yield and soil physicochemical, microbial, and enzymatic parameters. Switchgrass cropping system yielded an aboveground biomass 2.9–3.3 times higher than the other two systems (Jayawardena et al., unpublished data) but our study found that this biomass accumulation did not reduce soil dissolved organic C, total dissolved N (TDN), or bacterial diversity. The annual aboveground biomass removal for bioenergy feedstock, however, reduced soil microbial biomass C (MBC) and microbial biomass N (MBN) and bacterial richness in the second and third years; despite this, continuous monocropping of switchgrass improved soil TDN, inorganic N, bacterial diversity, and shoot biomass in the second and/or third years compared with the first year. N fertilization increased aboveground biomass yield by 1.2 times and significantly increased soil TDN, MBN, and the shoot biomass of switchgrass compared with the unfertilized control. Locations with higher C and N contents and lower C:N ratio had higher aboveground biomass, MBC, MBN, and the activity of BG, CBH, and UREA enzymes; by contrast, locations with higher pH had higher soil TDN and activity of NAG and LAP enzymes. Our research demonstrates that switchgrass cultivation could improve or maintain soil N content and N fertilization can increase plant biomass yield. The comprehensive data also can inform future biogeochemical models to successfully implement switchgrass for bioenergy production.

09 BIOMASS FUELS↗
DOE OSTI2020

Policy-Driven Sustainable Saline Drainage Disposal and Forage Production in the Western San Joaquin Valley of California

Environmental policies to address water quality impairments in the San Joaquin River of California have focused on the reduction of salinity and selenium-contaminated subsurface agricultural drainage loads from westside sources. On 31 December 2019, all of the agricultural drainage from a 44,000 ha subarea on the western side of the San Joaquin River basin was curtailed. This policy requires the on-site disposal of all of the agricultural drainage water in perpetuity, except during flooding events, when emergency drainage to the River is sanctioned. The reuse of this saline agricultural drainage water to irrigate forage crops, such as ‘Jose’ tall wheatgrass and alfalfa, in a 2428 ha reuse facility provides an economic return on this pollutant disposal option. Irrigation with brackish water requires careful management to prevent salt accumulation in the crop root zone, which can impact forage yields. The objective of this study was to optimize the sustainability of this reuse facility by maximizing the evaporation potential while achieving cost recovery. This was achieved by assessing the spatial and temporal distribution of the root zone salinity in selected fields of ‘Jose’ tall wheatgrass and alfalfa in the drainage reuse facility, some of which have been irrigated with brackish subsurface drainage water for over fifteen years. Electromagnetic soil surveys using an EM-38 instrument were used to measure the spatial variability of the salinity in the soil profile. The tall wheatgrass fields were irrigated with higher salinity water (1.2–9.3 dS m -1 ) compared to the fields of alfalfa (0.5–6.5 dS m -1 ). Correspondingly, the soil salinity in the tall wheatgrass fields was higher (12.5 dS m -1 –19.3 dS m -1 ) compared to the alfalfa fields (8.97 dS mm -1 –14.4 dS mm -1 ) for the years 2016 and 2017. Better leaching of salts was observed in the fields with a subsurface drainage system installed (13–1 and 13–2). The depth-averaged root zone salinity data sets are being used for the calibration of the transient hydro-salinity computer model CSUID-ID (a one-dimensional version of the Colorado State University Irrigation Drainage Model). This user-friendly decision support tool currently provides a useful framework for the data collection needed to make credible, field-scale salinity budgets. In time, it will provide guidance for appropriate leaching requirements and potential blending decisions for sustainable forage production. This paper shows the tie between environmental drainage policy and the role of local governance in the development of sustainable irrigation practices, and how well-directed collaborative field research can guide future resource management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Spectroradiometric detection of competitor diatoms and the grazer Poteriochromonas in algal cultures

To address challenges in early detection of pond pests, we have extended a spectroradiometric monitoring method, initially demonstrated for measurement of pigment optical activity and biomass, to the detection of algal competitors and grazers. The method relies upon measurement and interpretation of pond reflectance spectra spanning from the visible into the near-infrared. Reflectance spectra are acquired every 5 min with a multi-channel, fiber-coupled spectroradiometer, providing monitoring of algal pond conditions with high temporal frequency. The spectra are interpreted via numerical inversion of a reflectance model, in which the above-water reflectance is expressed in terms of the absorption and backscatter coefficients of the cultured species, with additional terms accounting for the pigment fluorescence features and for the water-surface reflection of sunlight and skylight. With this method we demonstrate detection of diatoms and the predator Poteriochromonas in outdoor cultures of Nannochloropsis oceanica and Chlorella vulgaris, respectively. Further, the relative strength of these signatures is compared to microscopy and sequencing analysis. Spectroradiometric detection of diatoms is then further assessed on beaker-contained mixtures of Microchloropsis salina with Phaeodactylum tricornutum, Thalassiosira weissflogii, and Thalassiosira pseudonana, respectively, providing an initial evaluation of the sensitivity and specificity of detecting pond competitors.

09 BIOMASS FUELS↗
DOE OSTI2022

Dissecting the Genetic Architecture of Carbon Partitioning in Sorghum Using Multiscale Phenotypes

Carbon partitioning in plants may be viewed as a dynamic process composed of the many interactions between sources and sinks. The accumulation and distribution of fixed carbon is not dictated simply by the sink strength and number but is dependent upon the source, pathways, and interactions of the system. As such, the study of carbon partitioning through perturbations to the system or through focus on individual traits may fail to produce actionable developments or a comprehensive understanding of the mechanisms underlying this complex process. Using the recently published sorghum carbon-partitioning panel, we collected both macroscale phenotypic characteristics such as plant height, above-ground biomass, and dry weight along with microscale compositional traits to deconvolute the carbon-partitioning pathways in this multipurpose crop. Multivariate analyses of traits resulted in the identification of numerous loci associated with several distinct carbon-partitioning traits, which putatively regulate sugar content, manganese homeostasis, and nitrate transportation. Using a multivariate adaptive shrinkage approach, we identified several loci associated with multiple traits suggesting that pleiotropic and/or interactive effects may positively influence multiple carbon-partitioning traits, or these overlaps may represent molecular switches mediating basal carbon allocating or partitioning networks. Conversely, we also identify a carbon tradeoff where reduced lignin content is associated with increased sugar content. The results presented here support previous studies demonstrating the convoluted nature of carbon partitioning in sorghum and emphasize the importance of taking a holistic approach to the study of carbon partitioning by utilizing multiscale phenotypes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Boosting climate change mitigation potential of perennial lignocellulosic crops grown on marginal lands

Nitrogen fertilizer (N F ) is a major uncertainty surrounding the greenhouse gas (GHG) emissions of lignocellulosic biofuels. NF enhances agronomic yields and soil C inputs via plant litters, but results in soil organic carbon (SOC) decomposition, soil N 2 O fluxes, and a large fossil energy footprint. Thus, whether NF is beneficial or detrimental to the GHG mitigation of biofuels is unknown. Here, we show the potential GHG mitigation of fertilizing switchgrass (Panicum virgatum) at the NF rate that minimizes net GHG emissions across 7.1 million ha of marginal lands in the Midwest US, with long-term production advantages surpassing emitted GHG by 0.66 Mg CO 2 e ha –1 yr –1 on the aggregate. Marginal lands limited by poor N fertility could see a much greater benefit, but not SOC-rich lands, limited by low precipitation, or short growing seasons. The objectives of maximizing yield and minimizing GHG overlap only in a few environments, suggesting that maximum yield will reduce the climate benefit of cellulosic biofuels.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Thioredoxin VdTrx1, an unconventional secreted protein, is a virulence factor in Verticillium dahliae

Understanding how plant pathogenic fungi adapt to their hosts is of critical importance to securing optimal crop productivity. In response to pathogenic attack, plants produce reactive oxygen species (ROS) as part of a multipronged defense response. Pathogens, in turn, have evolved ROS scavenging mechanisms to undermine host defense. Thioredoxins (Trx) are highly conserved oxidoreductase enzymes with a dithiol-disulfide active site, and function as antioxidants to protect cells against free radicals, such as ROS. However, the roles of thioredoxins in Verticillium dahliae , an important vascular pathogen, are not clear. Through proteomics analyses, we identified a putative thioredoxin (VdTrx1) lacking a signal peptide. VdTrx1 was present in the exoproteome of V. dahliae cultured in the presence of host tissues, a finding that suggested that it plays a role in host-pathogen interactions. We constructed a VdTrx1 deletion mutant Δ VdTrx1 that exhibited significantly higher sensitivity to ROS stress, H 2 O 2 , and tert -butyl hydroperoxide ( t -BOOH). In vivo assays by live-cell imaging and in vitro assays by western blotting revealed that while VdTrx1 lacking the signal peptide can be localized within V. dahliae cells, VdTrx1 can also be secreted unconventionally depending on VdVps36, a member of the ESCRT-II protein complex. The Δ VdTrx1 strain was unable to scavenge host-generated extracellular ROS fully during host invasion. Deletion of VdTrx1 resulted in higher intracellular ROS levels of V. dahliae mycelium, displayed impaired conidial production, and showed significantly reduced virulence on Gossypium hirsutum , and model plants, Arabidopsis thaliana and Nicotiana benthamiana . Thus, we conclude that VdTrx1 acts as a virulence factor in V. dahliae .

Tian, Li↗
DOE OSTI2022

Opportunities for Utilization of Low-Cost Algae Resources: Techno-Economic Analysis Screening for Near-Term Deployment (Parts 1 and 2)

This report presents a comprehensive techno-economic analysis (TEA) for the production, collection, or procurement of several low-cost algae resources that may otherwise be considered "waste" biomass materials today, as well as the utilization of these materials through exemplary conversion processes to produce renewable fuels and chemicals. In contrast to conventional TEA models attributed to large-scale algae "farming" approaches, which may be able to produce substantially more biomass and thus fuels/products at a national scale in the future, this assessment focuses on understanding opportunities and costs for such "waste" algal biomass resources as may be available at considerably lower cost today. Economics for base case assumptions and a range of sensitivity scenarios are presented, employing conversion technologies that are simple and well understood, and thus may be deployed at smaller community scale in the near term, as a means to support and expand a nascent algae industry on the way to employing a larger commercial algae farm approach for commodity-scale production. Specifically, three algal biomass resources are considered in this assessment, as may be sourced from (1) municipal wastewater treatment (WWT) utilizing algae in place of more conventional technologies for nitrogen/phosphorus removal, (2) collection and removal of harmful algal bloom (HAB) biomass as proliferates in certain inland water bodies, and (3) procurement of residual biomass following commercial lipid extraction (EXT) operations performed at smaller scale by industry today focused on higher-value nutraceutical applications. These three resources are evaluated through two conversion pathways: (1) combined algal biomass processing (CAP) through a simple/low-complexity configuration, and (2) anaerobic digestion (AD). The CAP pathway produces liquid fuels and chemical coproducts (polymer for off-site upgrading to bioplastics), whereas the AD pathway produces biogas (specifically renewable natural gas [RNG]) and crop fertilizer coproducts. To streamline the discussion, this report is broken into two sections: Part 1 focuses on WWT-derived biomass, and Part 2 on HAB and EXT biomass.

09 BIOMASS FUELS↗