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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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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Exploring Micro-Environmental Conditions of Urban Agrivoltaics: Advancing Sustainable Green Spaces and Agriculture in Temperate Cities

Agrivoltaics, the integrated land use combining renewable energy production and agriculture, can potentially address key challenges faced by urban communities, including limited access to vacant land, fresh produce, and electricity. However, urban agrivoltaics has rarely been considered a viable solution, primarily due to a lack of experimental systems demonstrating the functionality of urban food production or green spaces alongside solar energy production. To evaluate the potential of solar photovoltaics to help mitigate heat stress on urban agriculture and green spaces in a temperate climate, we examined the microclimatic changes introduced by an urban agrivoltaics system (UrAV) when combined with vegetable crops and turfgrass, respectively. Accordingly, we installed an experimental setup instrumented with environmental sensors to compare full-sun conditions with those of an agrivoltaics system designed for urban environments. We found minimal differences for both land cover types between the control and agrivoltaics sites in air and soil temperature. However, during periods of intensified summer heat, temperatures beneath the panels cooled due to reductions in wind speed and relative humidity, which impeded the movement of hot, moist air and reduced reference evapotranspiration. Further, soil moisture in UrAV was highly spatially heterogeneous, influenced by the layout of the photovoltaic panels and their support structure, which redistributed rainfall and controlled where solar radiation could penetrate and drive evapotranspiration. Overall, our results suggest that PV-induced environmental changes in temperate climates are compatible with cultivating turfgrass or appropriate crops. These findings can help planners and designers integrate agrivoltaics into community gardens, farms, and green spaces in temperate cities.

14 SOLAR ENERGY↗
DOE OSTI2021

Science Breakthroughs 2030. Final report

Agriculture is a fundamental societal activity, characterized by many different landscapes, crops, markets, and participants. Food, agricultural, and biofuels products are central to the daily life of all citizens, though most do not recognize the fragility of the environment that brings forth this abundance. As is noted in the 2012 report from the President's Council of Advisors on Science and Technology, Agricultural Preparedness and the United States Agricultural Research Enterprise (PCAST, 2012) the food and agricultural system faces constant challenges in: Managing new pests, pathogens, and invasive plants. Increasing the efficiency of water use. Growing food in a changing climate. Reducing the environmental footprint of agriculture. Managing the production of bioenergy. Producing safe and nutritious food. Assisting with global food security and maintaining abundant yields. Science Breakthroughs 2030 was organized to identify the most compelling research directions in food and agriculture, in particular those empowered by the application of insights and tools from disciplines of science and engineering not typically associated with food and agricultural research. A committee appointed by the Chairman of the National Research Council explored ideas for research directions with input from the scientific community, with the objective of producing a report describing ambitious and achievable scientific pathways to address major problems and create new opportunities in food and agriculture. Following numerous meetings, a jamboree, and town hall, the appointed committee prepared a report that has subsequently become a reference for federal agencies supporting research in the food and agricultural space. It highlights five key areas for research investment with broad application across food and agriculture: integrated systems research; sensor development; data mining and information sciences, genomics; and the microbiome.

09 BIOMASS FUELS↗
DOE OSTI2025

Stepping into the Midwest Bioeconomy: Stakeholder Engagement and Geospatial Tools to Assist in Perennial Bioenergy Crop Decision Making and Entrepreneurship

This project, “Ecosystem Services and Farm Entrepreneurship Technical Assistance,” was a three-year project originally planned for FY22–FY24. Due to a late start and a few extensions, it is being completed in early FY25. This project explored opportunities to support the deployment of a bioeconomy with a circular, more sustainable supply chain. Using a tool developed by Argonne to identify agricultural areas suitable for use in the bioeconomy, we sought to create opportunities in the bioeconomy as biomass producers, bioenergy users, and environmental entrepreneurs. We proposed to focus at the beginning on enhancing the tool’s capabilities, while engaging with key stakeholders to improve and expand the tool’s functionality for all potential stakeholders in the bioeconomy. We believe that expanding our tools and technologies, coupled with conversations in agricultural spaces, will be needed as we continue to explore how best to offer farmers whole-of supply-chain opportunities to participate in the bioeconomy. Through this project we have continued to gain a better understanding of the ways in which farmers, landowners, bioenergy users, and environmental entrepreneurs may approach the bioeconomy. In addition, as we improve our analytic toolkit, we can continue to refine our communication and the ways in which we can valuate the bioeconomy. Refining these tools allows us to dive deeper into conversations around plausible policies and drivers for future bioeconomy investment and engagement by stakeholders. By working with farmers and agricultural landowners to enable a sustainable bioeconomy business model, enhance their energy options, and recover resources from their waste streams, this project directly responds to the Bioenergy Technology Office’s (BETO) priorities of building a resilient energy economy. It addresses BETO’s focus on fostering the development and adoption of energy technologies that enable the conversion of waste to energy, efficient land use, and robust job creation. By establishing a technical assistance program that develops capabilities and practices in agricultural areas to implement a bioeconomy future, this program will develop an important linkage between technology being developed at U.S. Department of Energy national laboratories and the agricultural communities of the Midwest. This project focuses on farmers with lower productivity farmland. Because less productive lands create a more difficult revenue stream for conventional crops, these farmers may therefore be more open to alternative agricultural land management regimes. Consequently, the technical assistance program and the methodologies for targeting perennial bioenergy crop application on marginal land provide a distinct opportunity to engage with and invest in the bioeconomy in these economically stressed areas. Stakeholders in this project include farmers and landowners, local conservation organizations (NRCS, SWCS, etc.), universities, non-profit environmental and agricultural entities, farm consultants, environmental regulators, and industry, including the industries working on conversion technologies, anaerobic digestion, pyrolysis, and biochar generation, and the companies interested in trading or purchasing/supporting the valuation of ecosystem services (ES).

09 BIOMASS FUELS↗
DOE OSTI2020

Empirical Assessment of the Appliance-Level Load Shape and Demand Response Potential in India

Over the next 15 years, electricity demand from the key residential and commercial appliances is projected to be nearly 300 GW or ~65% of India’s total peak demand. The objective of this study is to characterize appliance level demand and temporal variation, and identify the overall DR potential in India. We use Bangalore Electricity Supply Company territory (peak load of 3,505 MW in 2016) as a case study, using actual one-minute resolution load data for 2,979 distribution feeders and a detailed load survey. Our results show that agricultural pumping and space cooling (residential, commercial, and industrial) are the main contributors to the peak demand – with shares of 23-27% and 14-23%, respectively. Both sectors have about 1,000 MW of DR potential – agricultural pumps offering load shifting service while space cooling offering shimmy service that is capable of dynamically adjusting to react to short-run ramps and grid disturbances. Residential electric water heaters contribute nearly 18% of the winter morning peak demand and can also offer about 500 MW in shimmy service. Overall, we find that shifting and shimmy services offer 1,199 MW and 1,511 MW total DR potential, respectively.

demand response↗
DOE OSTI2020

Diazotrophs Show Signs of Restoration in Amazon Rain Forest Soils with Ecosystem Rehabilitation

Biological nitrogen fixation can be an important source of nitrogen in tropical forests that serve as a major CO 2 sink. Extensive deforestation of the Amazon is known to influence microbial communities and the biogeochemical cycles they mediate. However, it is unknown how diazotrophs (nitrogen-fixing microorganisms) respond to deforestation and subsequent ecosystem conversion to agriculture, as well as whether they can recover in secondary forests that are established after agriculture is abandoned. To address these knowledge gaps, here we combined a spatially explicit sampling approach with high-throughput sequencing of nifH genes. The main objectives were to assess the functional distance decay relationship of the diazotrophic bacterial community in a tropical forest ecosystem and to quantify the roles of various factors that drive the observed changes in the diazotrophic community structure. We observed an increase in local diazotrophic diversity (α-diversity) with a decrease in community turnover (β-diversity), associated with a shift in diazotrophic community structure as a result of the forest-to-pasture conversion. Both diazotrophic community turnover and structure showed signs of recovery in secondary forests. Changes in the diazotrophic community were primarily driven by the change in land use rather than differences in geochemical characteristics or geographic distances. The diazotroph communities in secondary forests resembled those in primary forests, suggesting that at least partial recovery of diazotrophs is possible following agricultural abandonment. IMPORTANCE The Amazon region is a major tropical forest region that is being deforested at an alarming rate to create space for cattle ranching and agriculture. Diazotrophs (nitrogen-fixing microorganisms) play an important role in supplying soil N for plant growth in tropical forests. It is unknown how diazotrophs respond to deforestation and whether they can recover in secondary forests that establish after agriculture is abandoned. Using high-throughput sequencing of nifH genes, we characterized the response of diazotrophs’ β-diversity and identified major drivers of changes in diazotrophs from forest-to-pasture and pasture-to-secondary-forest conversions. Studying the impact of land use change on diazotrophs is important for a better understanding of the impact of deforestation on tropical forest ecosystem functioning, and our results on the potential recovery of diazotrophs in secondary forests imply the possible restoration of ecosystem functions in secondary forests.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Adapting Agrivoltaics for Solar Mini-Grids in Haiti

With less than 2% of the rural population with access to electricity and almost half the population facing acute hunger, Haiti faces interconnected challenges of energy poverty and food insecurity. One solution to help address energy poverty in Haiti has been the development of distributed solar, particularly solar mini-grids. However, often the land best suited for deploying solar generation is also best suited for agriculture by smallholder farmers, thereby creating a potentially complicated tension between energy access and food security. To address this tension, solar developers, agricultural specialists, and researchers are jointly examining a novel solution called "agrivoltaics." Agrivoltaics is a shared land-use solution that is rapidly expanding in established solar markets like the United States, Europe, and Asia that pairs solar with agriculture, producing electricity and providing space for crops and animal grazing under and between panels. As part of the Energy Access Partnership for Haiti with the U.S. Agency for International Development (USAID), the National Renewable Energy Laboratory (NREL) performed an initial feasibility analysis and stakeholder engagement project to evaluate the potential for agrivoltaics in mini-grid contexts in Haiti. The analysis considered typical 100-kW and larger 1-MW mini-grids in towns across Haiti and developed two example agrivoltaic archetypes based on key local inputs, including solar irradiance, production data from the agricultural census, market prices, stakeholder interviews, and existing agrivoltaic research. See NREL/TP-7A40-89399 for the Haitian Creole translation of this document.

14 SOLAR ENERGY↗
DOE OSTI2022

CubeSat constellations provide enhanced crop phenology and digital agricultural insights using daily leaf area index retrievals

Satellite remote sensing has great potential to deliver on the promise of a data-driven agricultural revolution, with emerging space-based platforms providing spatiotemporal insights into precision-level attributes such as crop water use, vegetation health and condition and crop response to management practices. Using a harmonized collection of high-resolution Planet CubeSat, Sentinel-2, Landsat-8 and additional coarser resolution imagery from MODIS and VIIRS, we exploit a multi-satellite data fusion and machine learning approach to deliver a radiometrically calibrated and gap-filled time-series of daily leaf area index (LAI) at an unprecedented spatial resolution of 3 m. The insights available from such high-resolution CubeSat-based LAI data are demonstrated through tracking the growth cycle of a maize crop and identifying observable within-field spatial and temporal variations across key phenological stages. Daily LAI retrievals peaked at the tasseling stage, demonstrating their value for fertilizer and irrigation scheduling. An evaluation of satellite-based retrievals against field-measured LAI data collected from both rain-fed and irrigated fields shows high correlation and captures the spatiotemporal development of intra- and inter-field variations. Novel agricultural insights related to individual vegetative and reproductive growth stages were obtained, showcasing the capacity for new high-resolution CubeSat platforms to deliver actionable intelligence for precision agricultural and related applications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Chemical identity of carbon substrates drives differences in denitrification and N 2 O reduction within agricultural soils

Rates of nitrous oxide (N 2 O) production from agricultural soils are highly variable across space and time. Improving predictions of N 2 O emissions will require improving our understanding of the drivers of denitrification and the sources of variability in the rates of N 2 O production between soils and over time. While the amount of available carbon (C) is a known control on denitrification and N 2 O reduction, relatively little attention has been paid to the effect of the chemical identity of C substrates on rates of denitrification and N 2 O reduction. We investigated the effects of twelve different C-substrate additions on the production and reduction of N 2 O in five soils taken from two distinct agricultural locations in Michigan under multiple land uses. We provided additions of glucose, cellulose, N-acetyl-glucosamine, chitin, amino acids, protein, vanillyl alcohol, lignin, citrate, succinate, methanol, and water in laboratory denitrification potential assays to determine the effects of denitrifier C preference on denitrification rates. We found that amino acids, protein, and organic acids stimulated the greatest rates of denitrification potential across all land uses. Similarly, we found these same substrates caused the most N 2 O reduction, resulting in the lowest net concentrations of N 2 O. Soils from agricultural rotations without cover crops had overall lower rates of denitrifier activity, leading to less net N 2 O production compared to soils from other land uses. In general, C-utilization patterns were similar among all soils, and C-substrate identity had a much stronger effect than land use. Here, we demonstrate that the chemical identity of available C gives rise to wide variability in rates of denitrification and N 2 O reduction.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Spaced out: An economic framework to explore the impacts of PV panel spacing on large-scale farming in Colorado

CONTEXT Agrivoltaic systems co-locate solar technologies with agricultural operations on an integrated plot of land and potentially provide benefits to both energy and agricultural systems. To date, large-scale (>5-MW) agrivoltaic projects in the United States have been limited to grazing and ecovoltaic applications, raising questions about the impact and scalability of agrivoltaic crop systems. Many agrivoltaic designs raise the height of the solar panels to accommodate agricultural practices while keeping energy density high. However, raising the panels results in increased photovoltaic (PV) development costs, which often are higher than the economic returns of crop production underneath the panels. This leads to unfavorable project economics and the need for other agrivoltaic solutions than raising panels. OBJECTIVE To explore other solutions, we perform an initial feasibility analysis for an agrivoltaic solution that can integrate with large-scale farming practices by increasing the row spacing in between panels. Increased PV row spacing is a low-cost approach for scaling agrivoltaics to accommodate crop production and this spacing can be tailored to required crop equipment for different regions. Increasing row spacing will reduce the power density (PV installed per acre), but in areas that are not land limited, these agrivoltaic designs could be economically feasible. Our analysis establishes a framework for a feasibility analysis for where and with what crops spaced out panel agrivoltaic solutions might be economical. METHODS Using a case study for large-scale agriculture crops in Colorado, we establish a framework for wide-row agrivoltaic economic feasibility analysis. We utilized the System Advisor Model to calculate technoeconomic metrics to compare different row spacing solutions and capture tradeoffs of these system designs. RESULTS AND CONCLUSIONS We find that, in some circumstances, wider row agrivoltaic solutions that allow for continued mechanized crop production can provide economic benefits over a traditional utility-scale PV system. For most crops examined in this analysis, roughly $\$$200/acre in agricultural profit justified spacing out the panels to at least 31.7 ft. to accommodate agrivoltaic configurations versus PV only configurations. Additionally, opportunities for increased agricultural revenue with agrivoltaic systems allow PV project economics to tolerate a larger range of CAPEX variability while remaining economically viable relative to the PV only configurations. SIGNIFICANCE This framework can be adapted for a wide variety of crops and regions and allows for examination of economically favorable sites for future agrivoltaic systems that utilize different configuration and expand opportunities for agrivoltaics.

14 SOLAR ENERGY↗
DOE OSTI2026

Design and Integration of a Compact Mobile Outdoor Soil Sampling Agricultural Robot

The lack of feedback on soil management for crop systems necessitates the collection of soil samples to derive insights into prevailing soil conditions. Achieving an accurate model of these plantations requires a substantial number of soil samples, which is a labor-intensive process that can suffer from inconsistencies in both the methodology of collection and the locations from which samples are gathered. In addition, the close spacing of crop rows constrains the maneuverability of mobile agricultural equipment during sample collection. This article proposes the use of agricultural robotics to facilitate the acquisition of composite soil samples with minimal human intervention, allowing for precise data collection for a Populus plant system. More specifically, this article presents a case study on the design and construction of a compact agricultural robot for soil sample collection. The performance of the robot, including its soil collection efficacy, maneuverability, and positional accuracy, is evaluated.

42 ENGINEERING↗
DOE OSTI2024

Network science can improve the sustainable development of solar energy

Abstract The recent emergence of agrivoltaic and ecovoltaic approaches to ground-mounted photovoltaic (PV) energy provides a much-needed alternative to the environmentally disruptive practices employed in utility-scale solar development. Research on such land-sharing approaches has grown rapidly, with an emphasis on characterizing how PV arrays impact ecosystem processes and agricultural productivity. Although these studies have done well to quantify a variety of dual-use solar practices by employing site-specific sampling designs, this approach has limited our ability to synthesize results across sites, regions, and globally. We call for a network science approach for improved cross-site synthesis of dual-use solar research. We contend that a common approach for data collection and synthesis will facilitate a more rigorous investigation of the agricultural and ecological impacts of PV development across space and over time. The products of this scientifically informed approach can be directly applied to improve sustainable land management.

Bacon, Taylor (ORCID:0009000518578569)↗
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

Finding Promising Candidates for Wet Growing Conditions: The Effect of Two Row Spacings on Biomass Production of Four Bioenergy Prairie Cordgrass Populations in a Wet Marginal Land

Demand for energy and the environmental consequences associated with fossil fuel emissions has created a need for alternative energy sources. To avoid conflict over the conversion of existing agricultural land from food production to fuel production, bioenergy crops could instead be cultivated on marginal land. Here, this study compared the performances, biomass yield, tiller density, and lignocellulosic bioenergy feedstock composition of four prairie cordgrass populations with two-row spacings cultivated on a waterlog-prone marginal land, as well as three other bioenergy candidate crops used as controls: big bluestem, M. × giganteus , and switchgrass. Across all populations and spacings, annual biomass yield of prairie cordgrass was 13 Mg ha −1 . Row spacing had significant impacts on prairie cordgrass productivity, with higher biomass yields observed in the 45 cm spacing than in the 90 cm spacing. Feedstock composition (cellulose, hemicellulose, ADL, and ash concentration) was not influenced by row spacing and did not deviate from expected values for growth on agricultural lands. However, biomass yields of the control species M . × giganteus and switchgrass planted in 45 cm spacing were higher than the prairie cordgrass. Our results provide evidence that prairie cordgrass could be a good energy crop with comparable biomass yield production to the energy crops M . × giganteus and switchgrass on waterlogged marginal land.

Bioenergy crops↗
DOE OSTI2025

Combining geometric-optical and spectral invariants theories for modeling canopy fluorescence anisotropy

The spectral invariants theory ( p -theory) has received much attention in the field of quantitative remote sensing over the past few decades and has been adopted for modeling of canopy solar-induced chlorophyll fluorescence (SIF). However, the spectral invariant properties (SIP) in simple analytical formulae have not been applied for modeling canopy fluorescence anisotropy primarily because they are parameterized in terms of leaf total scattering, which precludes the differentiation between forward and backward leaf SIF emissions. In this study, we have developed the canopy-SIP SIF model by combining geometric-optical (GO) theory to account for asymmetric leaf SIF forward and backward emissions at the first-order scattering and by modeling multiple scattering based on the p-theory, thus avoiding the dependence on radiative transfer models. The applicability of the model simulations especially over 3D heterogeneous canopies was improved by incorporating canopy structure through multi-angular clumping index, and by modeling single scattering from the four components of the scene in view according to the GO approach. The results show good consistency with both the state-of-the-art SIF models and multi-angular field SIF observations over grass and chickpea canopies. Further, the coefficient of determination (R²) between the simulated SIF and field measurements was 0.75 (red) and 0.74 (far-red) for chickpea, and 0.65 (both red and far-red) for grass. The average relative error was approximately 3% for 1D homogeneous scenes when comparing the canopy-SIP SIF model simulations to the SCOPE model simulations, and around 4% for the 3D heterogeneous scene when comparing to the LESS model simulations. The results indicate that the proposed approach for separating asymmetric leaf SIF emissions is a robust way to keep a balance between satisfactory simulation accuracy and efficiency. Model simulations suggest that neglecting the leaf SIF asymmetry can lead to an underestimation of canopy red SIF by 6.3% to 42.6% for various leaf biochemical and canopy structural parameters. This study presents a simple but efficient analytical approach for canopy fluorescence modeling, with potential for large-scale canopy fluorescence simulations.

3D heterogeneous structure↗
DOE OSTI2022

Resolve the Clear-Sky Continuous Diurnal Cycle of High-Resolution ECOSTRESS Evapotranspiration and Land Surface Temperature

Although numerous evapotranspiration (ET) estimates have been developed from remote sensing measurements, a diurnally resolved product with high spatial resolution (<100 m) is still lacking, which is critically needed for agricultural and ecological monitoring. The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) provides, for the first time, ET at 70 m resolution with diurnal samplings, enabling a step toward filling this data gap. This study developed the first framework to resolve the full diurnal cycle of ET and Land Surface Temperature (LST) from sporadic ECOSTRESS measurements. We first constructed 70 m diurnal LST utilizing a diurnal temperature cycle (DTC) model that fused ECOSTRESS and Geostationary Operational Environmental Satellite (GOES) LST. Next, we derived 70 m diurnal ET from the diurnally resolved LST, along with ancillary meteorological and reflectance data sets, using the Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) algorithm. This framework was tested in two regions: A relatively homogeneous semiarid grassland landscape (centered at the US-Seg Ameriflux site) and a heterogeneous humid cropland landscape (centered at US-ARM). Our diurnally resolved LST and ET successfully reproduced the spatial patterns in the native ECOSTRESS measurements during overpasses for both landscapes (LST: r > 0.97, RMSE ~2°C; ET: r > 0.99, and RMSE = 1–7 W m -2 ). The constructed time series also well captured the temporal variability of the in situ measurements (especially under clear-sky conditions) at both US-Seg and US-ARM, albeit performance varying with availability and quality of ECOSTRESS samples. This study sets the stage for testing and applying our framework to broader climates, biomes, and landscapes toward eventually generating diurnally resolved 70 m global operational LST/ET products to enhance ecological/agricultural applications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A General Spatiotemporal Imputation Framework for Missing Sensor Data

Many applications from precision agriculture, environmental monitoring and transportation networks rely on data collected across space and time over a large geographic area. Missing data poses a significant challenge for any data-driven inference and control tasks. Data imputation or the estimation of missing data can help fill these gaps by utilizing inherent spatial relationships and temporal patterns. A variety of spatiotemporal imputation models have been developed to address missing data in spatiotemporal datasets. However, these classical methods rely on the assumption that the underlying data follows a smooth trend and fail to provide accurate estimates when there is a large number of missing points in the data. Even though there are machine learning driven tensor completion approaches such as convolutional neural network based tensor completion (CoSTCo) that capture the non-linear relationships in the dataset, the transductive nature makes the algorithm less scalable. Thus, existing approaches for estimating the missing information do not effectively capture all dimensions of the spatiotemporal data structure, resulting in erroneous predictions and poor performance. The main contributions of this paper are: (1) We propose a novel inductive framework (G-LSTM) for missing data imputation that integrates a graph neural network with LSTMs to effectively capture both spatial and temporal dependencies. (2) Experimental results on a traffic dataset demonstrate that the proposed GNN integrated with an LSTM framework achieves improved imputation and maintains steady performance even when there are extreme missing conditions in comparison with the state-of-the-art imputation framework (i.e, CoSTCo). (3) The simulation results on a traffic network show up to 69% reduction in mean absolute error and 61% reduction in root mean square error when compared to CoSTCo.

Tharzeen, Aabila↗
DOE OSTI2023

Home range and resource selection of Virginia opossums in the rural southeastern United States

The Virginia opossum (Didelphis virginiana) has a rapidly expanding distribution in North America, but many aspects of its ecology remain relatively understudied, particularly in rural areas of its core range. We collected GPS telemetry data from 93 opossums in a rural, non-agricultural landscape in South Carolina, USA (2018–2019) to examine factors influencing space use and resource selection. Estimated male home ranges (99% utilization distributions) were on average 50% larger than those of females (mean home range 115.9 ± 103.7 ha vs 76.7 ± 75.0 ha). The home range size decreased on average by 20% with each 20% increase in deciduous land cover but was not affected by season or other landscape factors. Core area sizes (65% utilization distributions) were not influenced by sex (mean core area size 29.1 ± 23.7 ha and 22.4 ha ± 13.8 for males and females, respectively) or season, but the core area size decreased by 14% with each 400 m increase in distance from a permanent water source. Resource selection by opossums primarily occurred at the landscape level. Both males and females generally selected for wetlands while avoiding pine forests and developed/open areas, likely the result of differences in resource availability and predation risk between habitats. Opossums also tended to select for linear features such as unpaved roads and edge habitat, which may facilitate movement across the landscape. Finally, the home ranges we documented are among the largest recorded for opossums in the USA, likely the result of the relatively low resource abundance throughout our study area due to comparatively minimal anthropogenic influence.

59 BASIC BIOLOGICAL SCIENCES↗