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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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64 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Increasing thermostability of the key photorespiratory enzyme glycerate 3‐kinase by structure‐based recombination

As global temperatures rise, improving crop yields will require enhancing the thermotolerance of crops. One approach for improving thermotolerance is using bioengineering to increase the thermostability of enzymes catalysing essential biological processes. Photorespiration is an essential recycling process in plants that is integral to photosynthesis and crop growth. The enzymes of photorespiration are targets for enhancing plant thermotolerance as this pathway limits carbon fixation at elevated temperatures. We explored the effects of temperature on the activity of the photorespiratory enzyme glycerate kinase (GLYK) from various organisms and the homologue from the thermophilic alga Cyanidioschyzon merolae was more thermotolerant than those from mesophilic plants, including Arabidopsis thaliana. To understand enzyme features underlying the thermotolerance of C. merolae GLYK (CmGLYK), we performed molecular dynamics simulations using AlphaFold-predicted structures, which revealed greater movement of loop regions of mesophilic plant GLYKs at higher temperatures compared to CmGLYK. Based on these simulations, hybrid proteins were produced and analysed. These hybrid enzymes contained loop regions from CmGLYK replacing the most mobile corresponding loops of AtGLYK. Two of these hybrid enzymes had enhanced thermostability, with melting temperatures increased by 6 °C. One hybrid with three grafted loops maintained higher activity at elevated temperatures. Whilst this hybrid enzyme exhibited enhanced thermostability and a similar Km for ATP compared to AtGLYK, its Km for glycerate increased threefold. This study demonstrates that molecular dynamics simulation-guided structure-based recombination offers a promising strategy for enhancing the thermostability of other plant enzymes with possible application to increasing the thermotolerance of plants under warming climates.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Feature selection and causal analysis for microbiome studies in the presence of confounding using standardization

Abstract Background Microbiome studies have uncovered associations between microbes and human, animal, and plant health outcomes. This has led to an interest in developing microbial interventions for treatment of disease and optimization of crop yields which requires identification of microbiome features that impact the outcome in the population of interest. That task is challenging because of the high dimensionality of microbiome data and the confounding that results from the complex and dynamic interactions among host, environment, and microbiome. In the presence of such confounding, variable selection and estimation procedures may have unsatisfactory performance in identifying microbial features with an effect on the outcome. Results In this manuscript, we aim to estimate population-level effects of individual microbiome features while controlling for confounding by a categorical variable. Due to the high dimensionality and confounding-induced correlation between features, we propose feature screening, selection, and estimation conditional on each stratum of the confounder followed by a standardization approach to estimation of population-level effects of individual features. Comprehensive simulation studies demonstrate the advantages of our approach in recovering relevant features. Utilizing a potential-outcomes framework, we outline assumptions required to ascribe causal, rather than associational, interpretations to the identified microbiome effects. We conducted an agricultural study of the rhizosphere microbiome of sorghum in which nitrogen fertilizer application is a confounding variable. In this study, the proposed approach identified microbial taxa that are consistent with biological understanding of potential plant-microbe interactions. Conclusions Standardization enables more accurate identification of individual microbiome features with an effect on the outcome of interest compared to other variable selection and estimation procedures when there is confounding by a categorical variable.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Object-oriented analysis as a foundation for building climate storylines of compounding short-term drought and crop heat stress

Introduction: Crops are vulnerable to precipitation and heat extremes during late spring through summer. Methods: We analyzed for a north-central U.S. region short-term drought and agricultural heat stress during April-May-June-July. We used the 4-km Parameter Elevation Regression on Independent Slopes Model (PRISM) for observations, aggregated to a 25-km grid, and two 25-km Regional Climate Model version 4 (RegCM4) simulns used either GFDL- or MPI-GCM boundary conditions. We chose 1981-2000 as our contemporary time period, and 2041- 2060 as our scenario time period, which used the Representative Concentration Pathway 8.5 emissions scenario. We used object-oriented analysis to identify events of interest in observations and simulations by identifying objects in a space-time domain that meet specified criteria, such as exceeding a heat-stress temperature threshold. The event diagnosis allowed analysis of compound events, occurring when temperature and drought objects overlap. Results: Identified objects yielded events that can undermine agricultural productivity and which are thus relevant to decision makers, making them building blocks for possible climate storylines. The observations and simulations showed similar spatial distributions of event frequencies across the analysis region. However, the simulations attained this distribution by having fewer events that tend to cover larger areas compared to observed events, suggesting that the effective resolution of the simulations was coarser than their 25-km grids. Short-term drought frequency increased and heat-stress frequency decreased in transitioning to the scenario climate. When compounding occurred heat-stress events generally preceded the short-term drought events. The overlapping, compound events tended to be more extreme compared to non-overlapping events of either type. Discussion: The information yielded projected changes in these agriculturally motivated events. One prominent conditional behavior emerging from the work was that a heat-stress event should be a warning to watch for potential drought, as both could compound each other to more intense levels.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Corn stover harvest reduces soil CO 2 fluxes but increases overall C losses

Abstract Harvesting corn ( Zea mays L.) stover for use as bioenergy feedstock may provide short‐term economic benefits and perhaps improve grain yield in continuous corn systems, but excessive stover removal may lead to long‐term depletion of soil C stocks. To better quantify the impacts of stover harvest on the soil C balance, we investigated CO 2 fluxes under three harvest treatments (none, moderate, and high) in three continuous corn systems: (a) no tillage and no biochar applications, (b) chisel plowing with biochar amendments, and (c) chisel plowing without biochar amendments were quantified. We sampled static chambers 14, 13, and 15 times in 2010, 2011, and 2012, respectively, and the measurements were used to calibrate the Agricultural Production Systems sIMulator model. Although CO 2 ‐C emissions did not differ among the three systems, both moderate (approximately 30%) and high (approximately 60%) stover removal rates reduced simulated CO 2 ‐C emissions by nearly 10% and more than 22%, respectively. Despite these reductions in CO 2 flux, the sum of CO 2 ‐C and stover C exceeded CO 2 ‐C losses in those plots without stover removal. This finding suggested soil C depletion was occurring for the soil and crop management practices used, even though depletion was not evident after 3 years. Application of biochar increased soil C levels, suggesting it may be able to offset some C losses. We conclude that for sustainable, highly productive agroecosystems that include stover harvest, all agronomic practices must be optimized to minimize C losses and maintain soil quality.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Influence of Vegetation Canopy Architecture on Dynamics of Spatiotemporal Light Distribution and its Effect on Land-Biosphere-Atmosphere Fluxes of Energy, Carbon, and Water

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.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Dynamic life-cycle carbon analysis for fast pyrolysis biofuel produced from pine residues: implications of carbon temporal effects

Abstract Background Woody biomass has been considered as a promising feedstock for biofuel production via thermochemical conversion technologies such as fast pyrolysis. Extensive Life Cycle Assessment studies have been completed to evaluate the carbon intensity of woody biomass-derived biofuels via fast pyrolysis. However, most studies assumed that woody biomass such as forest residues is a carbon–neutral feedstock like annual crops, despite a distinctive timeframe it takes to grow woody biomass. Besides, few studies have investigated the impacts of forest dynamics and the temporal effects of carbon on the overall carbon intensity of woody-derived biofuels. This study addressed such gaps by developing a life-cycle carbon analysis framework integrating dynamic modeling for forest and biorefinery systems with a time-based discounted Global Warming Potential (GWP) method developed in this work. The framework analyzed dynamic carbon and energy flows of a supply chain for biofuel production from pine residues via fast pyrolysis. Results The mean carbon intensity of biofuel given by Monte Carlo simulation across three pine growth cases ranges from 40.8–41.2 g CO 2 e MJ −1 (static method) to 51.0–65.2 g CO 2 e MJ −1 (using the time-based discounted GWP method) when combusting biochar for energy recovery. If biochar is utilized as soil amendment, the carbon intensity reduces to 19.0–19.7 g CO 2 e MJ −1 (static method) and 29.6–43.4 g CO 2 e MJ −1 in the time-based method. Forest growth and yields (controlled by forest management strategies) show more significant impacts on biofuel carbon intensity when the temporal effect of carbon is taken into consideration. Variation in forest operations and management (e.g., energy consumption of thinning and harvesting), on the other hand, has little impact on the biofuel carbon intensity. Conclusions The carbon temporal effect, particularly the time lag of carbon sequestration during pine growth, has direct impacts on the carbon intensity of biofuels produced from pine residues from a stand-level pine growth and management point of view. The carbon implications are also significantly impacted by the assumptions of biochar end-of-life cases and forest management strategies.

09 BIOMASS FUELS↗
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 OSTI2022

Supply chain optimization of sustainable aviation fuel from carinata in the Southeastern United States

We report carinata is a purpose-grown oilseed feedstock for renewable fuels, including sustainable aviation fuel (SAF) that can replace conventional aviation fuel (CAF). Given carinata is a new crop in the Southeastern United States, it is crucial to analyze its sustainability from a supply chain perspective. This study developed a mixed-integer linear programming (MILP) model and simulated it for 20 years, starting from a farm (county-level data) and ending at the airport. About 2.06 million ha in Alabama, Florida, and Georgia combined were found suitable for carinata production. Given the three-year rotation period, about 0.69 million hectares can be cultivated annually, approximately 65% of which was in Georgia. About 2.4% of the combined SAF annual demand of four major airports (about 210 thousand t) in the study area is satisfied at that level of carinata cultivation. However, all available SAF was supplied to the Atlanta airport as this decision minimizes the supply chain cost. A total of 1343 storage units, one oil extraction mill, and one biorefinery were needed to meet the overall demand. We found that SW Georgia is the top supplier of carinata seeds. The unit cost of production and carbon intensity were estimated to be $\$$0.89 L -1 (or $\$$26.79 GJ -1 ) and 0.91 kg CO 2 e L -1 (or 27.28 kg CO 2 e GJ -1 ), respectively. This carbon intensity of carinata-based SAF was 67.8% lower than that of CAF. With variations included in SAF demand, yield, and soil carbon sequestration, carbon savings remained between 66.5% and 67.8%. Given the GHG advantage of SAF over CAF, there is justification for subsidies required to make SAF competitive.

10 SYNTHETIC FUELS↗
DOE OSTI2020

Simulated Biomass Sorghum GHG Reduction Potential is Similar to Maize

Policy support for cellulosic biofuels is contingent on their achieving much greater reductions in life-cycle greenhouse gas emissions than corn starch ethanol. Biomass sorghum has been suggested as a genetically and agronomically tractable feedstock species to augment near-term cellulosic feedstock production. This study used DayCent modeling to investigate biomass sorghum production emissions relative to corn with and without stover utilization at 3,265 across the rainfed United States. Sorghum produced greater average feedstock dry matter (15.6±1.4 vs. 14.8±2.2 Mg ha -1 yr -1 ) and slightly lower estimated ethanol energy yields (10.6±1.0 vs. 11.8±2.9 MJ m -2 yr -1 ) as corn grain with 75% stover collection. The high biomass removals in both the sorghum and corn stover scenarios led to soil organic carbon losses on 90% and 100% of sites, respectively. Average feedstock production emissions intensities were similar between sorghum and corn with 75% stover removal (17.6±2.8 vs. 18.8±3.0 g CO 2 e MJ -1 ), but were notably lower under sorghum for sites in the southwestern study region (13.6±3.0 vs. 22.5±3.1 g CO 2 e MJ -1 ). Futhermore, these results suggest that biomass sorghum produces cellulosic feedstock with similar emissions to corn grain and at current yield levels is unlikely to meet the Renewable Fuel Standard emissions reduction threshold for cellulosic biofuels.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A regional nuclear conflict would compromise global food security

A limited nuclear war between India and Pakistan could ignite fires large enough to emit more than 5 Tg of soot into the stratosphere. Climate model simulations have shown severe resulting climate perturbations with declines in global mean temperature by 1.8 °C and precipitation by 8%, for at least 5 y. Here we evaluate impacts for the global food system. Six harmonized state-of-the-art crop models show that global caloric production from maize, wheat, rice, and soybean falls by 13 (±1)%, 11 (±8)%, 3 (±5)%, and 17 (±2)% over 5 y. Total single-year losses of 12 (±4)% quadruple the largest observed historical anomaly and exceed impacts caused by historic droughts and volcanic eruptions. Colder temperatures drive losses more than changes in precipitation and solar radiation, leading to strongest impacts in temperate regions poleward of 30°N, including the United States, Europe, and China for 10 to 15 y. Integrated food trade network analyses show that domestic reserves and global trade can largely buffer the production anomaly in the first year. Persistent multiyear losses, however, would constrain domestic food availability and propagate to the Global South, especially to food-insecure countries. By year 5, maize and wheat availability would decrease by 13% globally and by more than 20% in 71 countries with a cumulative population of 1.3 billion people. In view of increasing instability in South Asia, this study shows that a regional conflict using <1% of the worldwide nuclear arsenal could have adverse consequences for global food security unmatched in modern history.

54 ENVIRONMENTAL SCIENCES↗