Search NASA⌕ Search

SEARCH · Search NASA

Results for “resilient crops”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 127 records · Page 7

Data for The Stem Cell-Type Transcriptome of Bioenergy Sorghum Reveals the Spatial Regulation of Secondary Cell Wall Networks

Bioenergy sorghum is a low-input, drought-resilient, deep-rooting annual crop that has high biomass yield potential enabling the sustainable production of biofuels, biopower, and bioproducts. Bioenergy sorghum’s 4-5 m stems account for ~80% of the harvested biomass. Stems accumulate high levels of sucrose that could be used to synthesize bioethanol and useful biopolymers if information about stem cell-type gene expression and regulation was available to enable engineering. To obtain this information, Laser Capture Microdissection (LCM) was used to isolate and collect transcriptome profiles from five major cell types that are present in stems of the sweet sorghum Wray. Transcriptome analysis identified genes with cell-type specific and cell-preferred expression patterns that reflect the distinct metabolic, transport, and regulatory functions of each cell type. Analysis of cell-type specific gene regulatory networks (GRNs) revealed that unique TF families contribute to distinct regulatory landscapes, where regulation is organized through various modes and identifiable network motifs. Cell-specific transcriptome data was combined with a stem developmental transcriptome dataset to identify the GRN that differentially activates the secondary cell wall (SCW) formation in stem xylem sclerenchyma and epidermal cells. The cell-type transcriptomic dataset provides a valuable source of information about the function of sorghum stem cell types and GRNs that will enable the engineering of bioenergy sorghum stems.

Software↗

The final piece of the Triangle of U: Evolution of the tetraploid Brassica carinata genome

Abstract Ethiopian mustard (Brassica carinata) is an ancient crop with remarkable stress resilience and a desirable seed fatty acid profile for biofuel uses. Brassica carinata is one of six Brassica species that share three major genomes from three diploid species (AA, BB, and CC) that spontaneously hybridized in a pairwise manner to form three allotetraploid species (AABB, AACC, and BBCC). Of the genomes of these species, that of B. carinata is the least understood. Here, we report a chromosome scale 1.31-Gbp genome assembly with 156.9-fold sequencing coverage for B. carinata, completing the reference genomes comprising the classic Triangle of U, a classical theory of the evolutionary relationships among these six species. Our assembly provides insights into the hybridization event that led to the current B. carinata genome and the genomic features that gave rise to the superior agronomic traits of B. carinata. Notably, we identified an expansion of transcription factor networks and agronomically important gene families. Completion of the Triangle of U comparative genomics platform has allowed us to examine the dynamics of polyploid evolution and the role of subgenome dominance in the domestication and continuing agronomic improvement of B. carinata and other Brassica species.

Biochemistry & Molecular Biology↗

Cell‐type‐specific transcriptomics uncovers spatial regulatory networks in bioenergy sorghum stems

SUMMARY Bioenergy sorghum is a low‐input, drought‐resilient, deep‐rooting annual crop that has high biomass yield potential enabling the sustainable production of biofuels, biopower, and bioproducts. Bioenergy sorghum's 4–5 m stems account for ~80% of the harvested biomass. Stems accumulate high levels of sucrose that could be used to synthesize bioethanol and useful biopolymers if information about cell‐type gene expression and regulation in stems was available to enable engineering. To obtain this information, laser capture microdissection was used to isolate and collect transcriptome profiles from five major cell types that are present in stems of the sweet sorghum Wray. Transcriptome analysis identified genes with cell‐type‐specific and cell‐preferred expression patterns that reflect the distinct metabolic, transport, and regulatory functions of each cell type. Analysis of cell‐type‐specific gene regulatory networks (GRNs) revealed that unique transcription factor families contribute to distinct regulatory landscapes, where regulation is organized through various modes and identifiable network motifs. Cell‐specific transcriptome data was combined with known secondary cell wall (SCW) networks to identify the GRNs that differentially activate SCW formation in vascular sclerenchyma and epidermal cells. The spatial transcriptomic dataset provides a valuable source of information about the function of different sorghum cell types and GRNs that will enable the engineering of bioenergy sorghum stems, and an interactive web application developed during this project will allow easy access and exploration of the data ( https://mc‐lab.shinyapps.io/lcm‐dataset/ ).

09 BIOMASS FUELS↗

Quantitative phenotyping of crop roots with spectral electrical impedance tomography: a rhizotron study with optimized measurement design

Background: Root systems are key contributors to plant health, resilience, and, ultimately, yield of agricultural crops. To optimize plant performance, phenotyping trials are conducted to breed plants with diverse root traits. However, traditional analysis methods are often labour-intensive and invasive to the root system, therefore limiting high-throughput phenotyping. Spectral electrical impedance tomography (sEIT) could help as a non-invasive and cost-efficient alternative to optical root analysis, potentially providing 2D or 3D spatio-temporal information on root development and activity. Although impedance measurements have been shown to be sensitive to root biomass, nutrient status, and diurnal activity, only few attempts have been made to employ tomographic algorithms to recover spatially resolved information on root systems. In this study, we aim to establish relationships between tomographic electrical polarization signatures and root traits of different fine root systems (maize, pinto bean, black bean, and soy bean) under hydroponic conditions. Results: Our results show that, with the use of an optimized data acquisition scheme, sEIT is capable of providing spatially resolved information on root biomass and root surface area for all investigated root systems. We found strong correlations between the total polarization strength and the root biomass (R 2 = 0.82) and root surface area (R 2 = 0.8). Our findings suggest that the captured polarization signature is dominated by cell-scale polarization processes. Additionally, we demonstrate that the resolution characteristics of the measurement scheme can have a significant impact on the tomographic reconstruction of root traits. Conclusion: Our findings showcase that sEIT is a promising tool for the tomographic reconstruction of root traits in high-throughput root phenotyping trials and should be evaluated as a substitute for traditional, often time-consuming, root characterization methods.

59 BASIC BIOLOGICAL SCIENCES↗

Future Farming: National Predictive Modeling Tool Initiative (NPMTI) [Slides]

The National Predictive Modeling Tool Initiative (NPMTI) is a multi-year program to develop research-based “tools” to forecast incidences of diseases and mycotoxins affecting U.S. crops. The overall goals of NPMTI are to: (1) ensure crop sustainability and crop quality; (2) provide climate change resilience; (3) improve soil health; (4) monitor pathogens and microbial diversity in the environment, but not limited to crop residues, soils, and air; (5) improve disease management thereby reducing yield losses; (6) increase precision of in-season disease management tactics; and (7) increase precision of pesticide use.

59 BASIC BIOLOGICAL SCIENCES↗

Invertebrate and Plant Community Diversity of an Illinois Corn–Soybean Field with Integrated Shrub Willow Bioenergy Buffers

Perennial bioenergy crop production within intensively managed agricultural landscapes has the potential to improve the sustainability, resiliency, and diversity of these landscapes. Perennial crops are ideal because of their high production potential on marginal lands relative to grain crops (e.g., corn and soybean) and their ability to provide additional ecosystem service benefits. When agricultural landscapes are designed to target specific services, determining the non-targeted services of perennial bioenergy crops can further promote their adoption. This 3-year study addresses this proposition by evaluating the canopy invertebrates and understory plant (non-target crop) communities using bee bowls and point measurement of ground coverage, respectively, within a grain field integrated with shrub willow buffer systems designed for nutrient loss reduction. Greater plant diversity and richness were observed under willow than under grain, resembling that of the surrounding riparian community with more perennial, native species. However, the same relationship did not hold true for invertebrates, with seasonality having a significant influence resulting in similar communities observed in willow and grain plots. The presence of unique plant and invertebrate species in both willow and grain crops as well as foraging pollinators on both crop and non-target crop species highlights the importance of habitat heterogeneity for supporting biodiversity and the potential benefits of buffer bioenergy landscape designs.

09 BIOMASS FUELS↗

Plant science decadal vision 2020–2030: Reimagining the potential of plants for a healthy and sustainable future

Abstract Plants, and the biological systems around them, are key to the future health of the planet and its inhabitants. The Plant Science Decadal Vision 2020–2030 frames our ability to perform vital and far‐reaching research in plant systems sciences, essential to how we value participants and apply emerging technologies. We outline a comprehensive vision for addressing some of our most pressing global problems through discovery, practical applications, and education. The Decadal Vision was developed by the participants at the Plant Summit 2019, a community event organized by the Plant Science Research Network. The Decadal Vision describes a holistic vision for the next decade of plant science that blends recommendations for research, people, and technology. Going beyond discoveries and applications, we, the plant science community, must implement bold, innovative changes to research cultures and training paradigms in this era of automation, virtualization, and the looming shadow of climate change. Our vision and hopes for the next decade are encapsulated in the phrase reimagining the potential of plants for a healthy and sustainable future. The Decadal Vision recognizes the vital intersection of human and scientific elements and demands an integrated implementation of strategies for research (Goals 1–4), people (Goals 5 and 6), and technology (Goals 7 and 8). This report is intended to help inspire and guide the research community, scientific societies, federal funding agencies, private philanthropies, corporations, educators, entrepreneurs, and early career researchers over the next 10 years. The research encompass experimental and computational approaches to understanding and predicting ecosystem behavior; novel production systems for food, feed, and fiber with greater crop diversity, efficiency, productivity, and resilience that improve ecosystem health; approaches to realize the potential for advances in nutrition, discovery and engineering of plant‐based medicines, and "green infrastructure." Launching the Transparent Plant will use experimental and computational approaches to break down the phytobiome into a "parts store" that supports tinkering and supports query, prediction, and rapid‐response problem solving. Equity, diversity, and inclusion are indispensable cornerstones of realizing our vision. We make recommendations around funding and systems that support customized professional development. Plant systems are frequently taken for granted therefore we make recommendations to improve plant awareness and community science programs to increase understanding of scientific research. We prioritize emerging technologies, focusing on non‐invasive imaging, sensors, and plug‐and‐play portable lab technologies, coupled with enabling computational advances. Plant systems science will benefit from data management and future advances in automation, machine learning, natural language processing, and artificial intelligence‐assisted data integration, pattern identification, and decision making. Implementation of this vision will transform plant systems science and ripple outwards through society and across the globe. Beyond deepening our biological understanding, we envision entirely new applications. We further anticipate a wave of diversification of plant systems practitioners while stimulating community engagement, underpinning increasing entrepreneurship. This surge of engagement and knowledge will help satisfy and stoke people's natural curiosity about the future, and their desire to prepare for it, as they seek fuller information about food, health, climate and ecological systems.

59 BASIC BIOLOGICAL SCIENCES↗

Modelling Bambara Groundnut Yield in Southern Africa: Towards a Climate-Resilient Future

Current agriculture depends on a few major species grown as monocultures that are supported by global research underpinning current productivity. However, many hundreds of alternative crops have the potential to meet real world challenges by sustaining humanity, diversifying agricultural systems for food and nutritional security, and especially responding to climate change through their resilience to certain climate conditions. Bambara groundnut (Vigna subterranea (L.) Verdc.), an underutilised African legume, is an exemplar crop for climate resilience. Predicted yield performances of Bambara groundnut by AquaCrop (a crop−water productivity model) were evaluated for baseline (1980−2009) and mid-century climates (2040−2069) under 20 downscaled Global Climate Models (CMIP5-RCP8.5), as well as for climate sensitivities (AgMIPC3MP) across 3 locations in Southern Africa (Botswana, South Africa, Namibia). Different land - races of Bambara groundnut originating from various semi-arid African locations showed diverse yield performances with diverse sensitivities to climate. S19 originating from hot-dry conditions in Namibia has greater future yield potential compared to the Swaziland landrace Uniswa Red-UN across study sites. South Africa has the lowest yield under the current climate, indicating positive future yield trends. Namibia reported the highest baseline yield at optimum current temperatures, indicating less yield potential in future climates. Bambara groundnut shows positive yield potential at temperatures of up to ~31degC, with further warming pushing yields down. Thus, many regions in Southern Africa can utilize Bambara groundnut successfully in the coming decades. This modelling exercise supports decisions on genotypic suitability for present and future climates at specific locations.

farm crops↗

Linking Plant and Microbial Traits to Soil Carbon for Reliable and Resilient Bioenergy Systems

Bioenergy systems in the United States offer a dual opportunity to supply renewable feedstocks while enhancing ecosystem services such as hydrologic regulation, erosion control, and soil carbon (C) storage. National assessments highlight the potential to grow perennial energy crops to improve soil function and ecosystem resilience. Realizing this potential requires understanding the ecological mechanisms that govern how C is added, transformed, and stabilized in soils. Plant traits determine the quantity, depth, and chemistry of organic inputs, while microbial processes—including carbon use efficiency, necromass formation, and trophic interactions—mediate their transformation and partitioning among soil carbon pools. These biological pathways are shaped by soil physical and chemical properties, including aggregation, texture, and mineralogy, and by environmental drivers such as temperature, moisture, and disturbance, leading to context-dependent outcomes across landscapes. Management practices that diversify feedstocks, minimize disturbance, and maintain soil cover can promote both biomass production and C retention, while microbial amendments and rhizosphere engineering offer emerging, but often context-dependent, tools to optimize plant–microbe interactions. Trade-offs between biomass yield and soil carbon storage may arise when systems favor rapid aboveground productivity at the expense of belowground inputs and microbial processing, underscoring the importance of trait combinations that support both functions. Advances in monitoring, reporting, and verification—spanning precision agriculture, remote sensing, and biosensing—are improving predictive capacity through microbial-explicit process models and model–experiment (ModEx) frameworks. By connecting soil, plant, and microbial processes with advances in modeling and biosensing, this review outlines research priorities focused on trait-based parameterization and ModEx integration. These priorities will support the design of bioenergy systems that are both reliable and resilient, enhancing renewable energy production and ecosystem sustainability.

bioenergy systems↗

Space Crop Considerations for Human Exploration

NASA has been actively working to both determine how many crops will be needed for early exploration missions as well as updating the “Crop Readiness Level” (CRL) for a library of crops that can be selected for supporting a long-term mission. The Crop Readiness Level (CRL) is modelled after NASA’s Technology Readiness Level (TRL) approach for developing and advancing new technologies for space, first suggested by Barry Finger and published by Wheeler and Strayer [2]. The CRL model has nine levels from “crop identification” to “consumed in space.” The number and variety of crops needed is impacted by both primary factors (nutrition, menu fatigue, behavioral health system resiliency) as well as secondary factors such as ECLSS considerations, crop robustness, and hardware considerations.

Gioia D Massa↗

VEG-05 Tomato Crop Testing on the International Space Station

Production of fresh, nutritious, and tasty produce for astronauts during spaceflight may provide health-promoting, bioavailable nutrients, enhance the dietary experience, and reduce menu fatigue as we move into longer-duration missions. Growing and caring for plants may also reduce the psychological stresses associated with spaceflight and enhance connection to Earth. A diversity of crops will be required to provide nutrition, variety, and resiliency, however requirements for consistent growth under spaceflight environmental conditions remain unclear. VEG-05 is part of a series of experiments with pick-and-eat salad crops to better define best practices for crop production in space. VEG-05 and predecessor experiments VEG-04A and VEG-04B, grew salad crops in the Veggie vegetable production facilities on the International Space Station using different lighting treatments. In VEG-05 we grew ‘Red Robin’ dwarf cherry tomatoes under two different red: blue lighting spectra. Light can impact the growth habit, yield, nutritional composition, microbial levels, and even flavor attributes within crops, and our goal was to assess these characteristics in ‘Red Robin’ tomatoes during VEG-05. Considerable pre-flight verification testing was performed prior to launch in Nov. 2022. Prior to the flight and ground experiments, lighting in both Veggie units on ISS was measured and lights were standardized between flight and ground hardware. VEG-05 flight operations ran between December 2022 and March 2023, with a ground control initially 48 hours delayed. Due to environmental challenges of very low humidity during the first week of the experiment, seed germination was low for both flight and ground plants. The flight experiment ultimately had 4 - 5 plants per treatment out of the planned 6 plants, but the initial ground control had only three plants in one treatment and none in the other, so this ground control was restarted at the beginning of Feb. 2023 and ran through May, with successful growth of all 12 plants. Both flight and ground control ran 100 days, with harvests of fruit at day 83, day 90, and day 100. Flight plants had uneven growth, and following the early drying events, excess water was frequently observed, which led to a variety of plant stress responses including uneven plant growth, excess adventitious root formation, flower and fruit abortion, and visible microbial growth. In total, from the five surviving red-rich lighted plants, only 5 ripe fruit were produced, and from the four surviving blue-rich lighted plants, 10 fruit were produced with only 6 of these ripe by day 100. Because of the small fruit number and the unsatisfactory growth, crew members were not allowed to consume the tomatoes, and all fruit, as well as large branches with leaves, samples of the adventitious roots, two plant rooting pillows from each treatment, microbial sampling swabs, and some water samples were returned for analysis. Because of the small sample sizes and factors affecting growth on the ISS, objectives of assessing light quality effect (red: blue light treatments) will not be achieved. Revised objectives of this study include to compare stressed flight plants with normal ground plants to determine the impact of plant overwatering stress in space on food safety and the plant microbial community, to determine nutrient content changes in fruit and leaves from stressed plants, and to evaluate stress metabolism changes in returned tissue by transcriptomic analysis. Postflight analysis is underway with the following analyses being conducted: A. culturable microbiology and food safety as well as molecular microbial community analysis of 1. ripe fruit, 2. leaves, stems, and adventitious roots, 3. pillow components (roots, wicks, and substrates), 4. swabs, and 5. water samples from root mats before and after growth. B. transcriptomics of leaf tissue and adventitious roots, and C. elemental analysis of leaf tissue. If sufficient tissue remains elemental analyses will also be conducted on fruit. While not generating the desired information on spaceflight growth responses of healthy crops, our team is hopeful that these analyses will shed light on tomato responses to stress in this environment as plant overwatering stress is a mission-relevant condition that could occur in future space crop growth systems. This research was co-funded by the Human Research Program and Space Biology (MTL#1075) in the ILSRA 2015 NRA call.

Gioia D. Massa↗

Imitating the “breeder's eye”: Predicting grain yield from measurements of non‐yield traits

Abstract Plant breeding relies on information gathered from field trials to select promising new crop varieties for release to farmers and to develop genomic prediction models that can enhance the efficiency of genetic improvement in future breeding cycles. However, generating the genetic marker data required to apply genomic prediction at the early stages of a breeding program remains costly for many public‐sector breeding programs as well as for many plant breeders operating in developing countries. As the pace of climate change intensifies, the time lag of developing and deploying new crop varieties requires plant breeders to make selection decisions without knowing the future environments those crop varieties will encounter in farmers’ fields. Therefore, both lower cost and higher accuracy methods for prediction of crop performance are essential for creating and maintaining resilient agricultural systems in the latter half of the 21 st century. To address this challenge, we conducted linked yield trials of 752 public maize ( Zea mays ) genotypes in two distinct environments. We developed and trained a phenotypic prediction model to predict yield from manually scored plant traits. The phenotypic prediction approach we employed outperformed genomic prediction in predicting yields in a second environment, with 8.7%–63% higher R 2 and 4%–13% less root mean square error than the genomic prediction. The phenotypic prediction has the potential to be applied to a wider range of breeding programs, including those that lack the resources to genotype large populations, such as programs in the developing world, breeding programs for specialty crops, and public sector programs.

60 APPLIED LIFE SCIENCES↗

Reducing stomatal density by expression of a synthetic epidermal patterning factor increases leaf intrinsic water use efficiency and reduces plant water use in a C4 crop

Abstract Enhancing crop water use efficiency (WUE) is a key target trait for climatic resilience and expanding cultivation on marginal lands. Engineering lower stomatal density to reduce stomatal conductance (gs) has improved WUE in multiple C3 crop species. However, reducing gs in C3 species often reduces photosynthetic carbon gain. A different response is expected in C4 plants because they possess specialized anatomy and biochemistry which concentrates CO2 at the site of fixation. This modifies the relationship of photosynthesis (AN) with intracellular CO2 concentration (ci), such that photosynthesis is CO2 saturated and reductions in gs are unlikely to limit AN. To test this hypothesis, genetic strategies were investigated to reduce stomatal density in the C4 crop sorghum. Constitutive expression of a synthetic epidermal patterning factor (EPF) transgenic allele in sorghum led to reduced stomatal densities, reduced gs, reduced plant water use, and avoidance of stress during a period of water deprivation. In addition, moderate reduction in stomatal density did not increase stomatal limitation to AN. However, these positive outcomes were associated with negative pleiotropic effects on reproductive development and photosynthetic capacity. Avoiding pleiotropy by targeting expression of the transgene to specific tissues could provide a pathway to improved agronomic outcomes.

59 BASIC BIOLOGICAL SCIENCES↗

A temporal analysis and response to nitrate availability of 3D root system architecture in diverse pennycress (Thlaspi arvense L.) accessions

Introduction Roots have a central role in plant resource capture and are the interface between the plant and the soil that affect multiple ecosystem processes. Field pennycress ( Thlaspi arvense L.) is a diploid annual cover crop species that has potential utility for reducing soil erosion and nutrient losses; and has rich seeds (30-35% oil) amenable to biofuel production and as a protein animal feed. The objective of this research was to (1) precisely characterize root system architecture and development, (2) understand plastic responses of pennycress roots to nitrate nutrition, (3) and determine genotypic variance available in root development and nitrate plasticity. Methods Using a root imaging and analysis pipeline, the 4D architecture of the pennycress root system was characterized under four nitrate regimes, ranging from zero to high nitrate concentrations. These measurements were taken at four time points (days 5, 9, 13, and 17 after sowing). Results Significant nitrate condition response and genotype interactions were identified for many root traits, with the greatest impact observed on lateral root traits. In trace nitrate conditions, a greater lateral root count, length, density, and a steeper lateral root angle was observed compared to high nitrate conditions. Additionally, genotype-by-nitrate condition interaction was observed for root width, width:depth ratio, mean lateral root length, and lateral root density. Discussion These findings illustrate root trait variance among pennycress accessions. These traits could serve as targets for breeding programs aimed at developing improved cover crops that are responsive to nitrate, leading to enhanced productivity, resilience, and ecosystem service.

59 BASIC BIOLOGICAL SCIENCES↗

Near-Term Effects of Perennial Grasses on Soil Carbon and Nitrogen in Eastern Nebraska

Incorporating native perennial grasses adjacent to annual row crop systems managed on marginal lands can increase system resiliency by diversifying food and energy production. This study evaluated (1) soil organic C (SOC) and total N stocks (TN) under warm-season grass (WSG) monocultures and a low diversity mixture compared to an adjacent no-till continuous-corn system, and (2) WSG total above-ground biomass (AGB) in response to two levels of N fertilization from 2012 to 2017 in eastern Nebraska, USA. The WSG treatments consisted of (1) switchgrass (SWG), (2) big bluestem (BGB), and (3) low-diversity grass mixture (LDM; big bluestem, Indiangrass, and sideoat grama). Soils were sampled at fixed depth increments (0–120 cm) in the WSG plots and in the adjacent corn experiment in 2012 and 2017. Soil stocks (Mg ha−1) of SOC and TN were calculated on an equivalent soil mass (ESM) basis and compared within the three WSG treatments as well as between experiments (corn compared to the mean of all WSGs). Soil organic C and TN stocks within soil layers and cumulative stocks responded to the main effect of WSG (PWSG < 0.05) but were no different when comparing the WSGs to corn (Pexpt = NS). Both SOC/TN stocks and cumulative stocks were generally greater in the LDM compared to the BGB. Neither SOC nor TN changed over time under either the WSGs or corn. Warm-season grass AGB responded to a three-way interaction of year, N rate, and WSG (p = 0.0007). Decreases in AGB over time were significant across WSGs and N levels except for SWG at 56 kg N ha−1 and LDM at 112 kg N ha−1. Above-ground biomass was generally greater in the LDM after the first harvest year (2013). Results suggest that incorporating WSGs into marginal cropland can maintain SOC and TN stocks while providing a significant source of biomass to be used in energy production or in integrated livestock systems.

Ramirez, II, Salvador (ORCID:000000030152153X)↗

NASA POWER: Providing Analysis-Ready, Cloud-Optimized Data for AI /ML Training and Applications in Earth Science

As global demand for sustainable development grows, the integration of Earth Observation (EO) data into decision making frameworks has become a primary objective for the scientific community. The NASA Prediction of Worldwide Energy Resources (POWER) project serves as a bridge between NASA EO data and the specialized needs of the renewable energy, sustainable infrastructure and agroclimatology communities. In this poster presentation we will present an overview of POWER data products and services along with its use in diverse research to decision-making workflows. By providing over 40 years of high-resolution historical, hourly and daily solar and meteorological data, POWER transforms satellite observations and global model reanalysis into actionable, Analysis-Ready Dataset (ARD). Currently, the project delivers over 250 industry-friendly parameters to the users from different NASA datasets like CERES SYN1Deg, MERRA-2, and IMERG alongside downscaled CMIP6 climate model data, fulfilling over 16 million requests from 50,000 unique users monthly. To ensure data quality and traceability, these parameters are rigorously validated against the ground-based observations from the Baseline Surface Radiation Network (BSRN) and the Global Surface Summary of the Day (GSOD) – these results will be discussed in the presentation. A newly introduced web-based PaRameter Uncertainty ViEwer (PRUVE) tool will be presented that provides an online validation platform to the users that benchmarks satellite-based and assimilation data products against these surface measurements. To reduce technical barriers to data adoption, POWER data is accessible through RESTful APIs, ESRI ArcGIS Image Services, a web-based Data Access Viewer tool, allowing users to visualize, validate and apply the dataset. For efficient data delivery POWER data is cloud-optimized into Zarr datastore accessible through NASA managed Amazon S3 ensures high-performance allowing users to integrate EO directly into operational pipelines. These customized services will be presented. Use cases from application will be presented from the energy sector - such as for design of generation systems, performance monitoring of solar power plants, in infrastructure sector- optimizing building energy efficiency and thermal comfort, in agriculture – such as driving crop simulation and yield forecasting models to enable climate resilient farming. Furthermore, the shift toward machine learning (ML) in EO research that has positioned POWER as a key provider for training datasets which will be discussed. Use-cases will be presented to showcase how NASA data is enabling the development of predictive tools for climate variability and resource management. The poster will present POWER’s future plans including technology development to enhance data traceability and reproducibility and improving I/O performance to support the rapid integration of new EO products, ensuring that POWER remains a robust scalable backend for the evolving landscape of AI-driven Earth Science. Additionally, POWER is developing an AI Agent and an MCP-Server to enable industry AI-Agentic workflows.

Neha Khadka↗

Turbo‐charging crop improvement: harnessing multiplex editing for polygenic trait engineering and beyond

Multiplex CRISPR editing has emerged as a transformative platform for plant genome engineering, enabling the simultaneous targeting of multiple genes, regulatory elements, or chromosomal regions. This approach is effective for dissecting gene family functions, addressing genetic redundancy, engineering polygenic traits, and accelerating trait stacking and de novo domestication. Its applications now extend beyond standard gene knockouts to include epigenetic and transcriptional regulation, chromosomal engineering, and transgene‐free editing. These capabilities are advancing crop improvement not only in annual species but also in more complex systems such as polyploids, undomesticated wild relatives, and species with long generation times. At the same time, multiplex editing presents technical challenges, including complex construct design and the need for robust, scalable mutation detection. We discuss current toolkits and recent innovations in vector architecture, such as promoter and scaffold engineering, that streamline workflows and enhance editing efficiency. High‐throughput sequencing technologies, including long‐read platforms, are improving the resolution of complex editing outcomes such as structural rearrangements—often missed by standard genotyping—when targeting repetitive or tandemly spaced loci. To fully realize the potential of multiplex genome engineering, there is growing demand for user‐friendly, synthetic biology‐compatible, and scalable computational workflows for gRNA design, construct assembly, and mutation analysis. Experimentally validated inducible or tissue‐specific promoters are also highly desirable for achieving spatiotemporal control. As these tools continue to evolve, multiplex CRISPR editing is poised to become a foundational technology of next‐generation crop improvement to address challenges in agriculture, sustainability, and climate resilience.

59 BASIC BIOLOGICAL SCIENCES↗

Characterizing proteases in sorghum drought response using activity-based protein profiling (ABPP)

Sorghum (Sorghum bicolor) is a C4 grass species that is a major food and bioenergy crop grown worldwide. Understanding the response of sorghum to climate change is therefore critical to future sustainable food and bioenergy production. Major advancements in genome sequencing have provided a wealth of genomic data for DOE-relevant plant species, but the functions of many of these genes remain unknown. Genotype to phenotype characterization of sorghum and other bioenergy crops is needed to identify key genes responsible for plant resilience to drought and other environmental stressors. Proteases, which modulate protein degradation and cellular signaling processes by catalyzing the hydrolysis of proteins into smaller proteins, peptides, or amino acids, are highly implicated in plant drought stress responses. We developed a method for using activity-based protein profiling (ABPP) of selected protease activities (serine and cysteine proteases) to characterize drought stress responses in sorghum roots and leaves. Analysis of these ABPP data identified several known serine and cysteine proteases as well as uncharacterized sorghum proteins which may have protease activities that can be explored in future validation studies.

09 BIOMASS FUELS↗