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NASA NTRSDate not supplied

Enhancing Air Quality Applications in the Hindu Kush-Himalayan Region Using Satellite, Model, and Machine Learning Techniques

Air pollution in the Hindu Kush Himalayan (HKH) region of South Asia is a severe issue, as increases in emissions over the past two decades have degraded air quality (AQ) across the region, which poses major threats to human health, the ecosystem, climate, and agriculture. A diversity of anthropogenic and natural emission sources including transportation, power plants, industries, open biomass burning of crop residue, forest fires, cooking and heating fires, and dust storms contribute to unhealthy AQ and transboundary pollution issues in the region. Further complicating matters is the importance of meteorology and terrain on AQ, especially in the Kathmandu Valley where extreme haze episodes frequently develop from the atmospherically stable weather conditions during the winter monsoon. This study uses state-of-the-art satellite observations and modeling capabilities in conjunction with machine learning techniques to develop a comprehensive toolkit for enhancing AQ monitoring and forecasting in HKH. The toolkit incorporates new generation satellite observations from the TROPOspheric Monitoring Instrument (TROPOMI), Geostationary Environment Monitoring Spectrometer (GEMS), and Advanced Meteorological Imager (AMI), which provide unprecedented resolution on aerosols and trace gases, including nitrogen dioxide, formaldehyde, sulfur dioxide, carbon monoxide, and ozone, and aerosol optical depth. Value-added products, such as level 4 PM2.5 products, are developed from the suite of satellite observations to further improve AQ monitoring capabilities in the region. The satellite products are also used to assimilate a high-resolution chemical transport model tailored for the HKH region, which is providing daily, 54-hour AQ forecasts with horizontal grid spacings of 12- and 4-km. This presentation will provide an overview of the suite of satellite- and model-based products in the AQ toolkit and application and performance of the toolkit for AQ monitoring and forecasting in HKH.

Forecasting↗
NASA NTRS2009

The Crop Evaluation Research for Environmental Strategies (CERES) Remote Sensing 2008 Project Activities

In recent years, the use of Plant Incorporated Protectant (PIP) corn by American producers has been increasing dramatically. PIP corn contains genetically inserted traits that produce toxins in the plant that provide narrowly targeted protection against specific insect pests. The plant producing t oxms can offer significant reductions in the application of broad -spectrum pesticides that have ecological and human health consequences. PIP corn as a percentage of total corn acreage planted in the US is expected to continue to increase as these protective traits are "stacked" with other desirable traits by seed companies, and producers are seeing considerable increases in corn yield as a result. The introduction of corn as a bio-fuel source for ethanol has increased production by over 6 million hectares in 2007. The United States Environmental Protection Agency (USEPA), which is responsible for the registration of PIP crops under the Federal Insecticide, Fungicide and Rodenticide Act, views the use of PIP corn as positive. Broad spectrum pesticide use has declined since the PIP traits have been introduced. As the agricultural landscape sees a higher percentage of corn acres using the PIP technology, the risk of the targeted insect pest populations developing resistance to the toxins, thereby rendering the in will increase as well. This result would negate the effectiveness of the PIP corn traits and could reduce production of a US field corn crop valued at $33 billion dollars in 2006 and place US food and now energy security at risk. Concerns over insect pest resistance development to PIP traits have led the USEPA to team with NASA and the Institute for Technology Development (ITD) to develop geo-spatial technologies designed to proactively monitor the corn production landscape for insect pest infestation and possible resistance development. USEPA resistance management simulation models are combined with NASA remote sensi ng products to monitor the corn landscape for resistance development. The two agencies have entered into an agreement which could potentially lead to the development of next generation NASA sensors that will more specifically address the requirements of the USEPA's resistance development strategy and offer opportunities to study the ever changing ecosystem complexities. The USEPA/NASA/ITD team has developed a broad research project entitled CERES (Crop Evaluation Research for Environmental Strategies). CERES is a research effort leading to decision support system tools that are designed to integrate multi-resolution NASA remote sensing data products and USEPA geo -spatial models to monitor the potential for insect pest resistance development from the regional to the landscape and then to the field level.

Casas, Joseph C.↗
DOE OSTI2022

Land use-land cover gradient demonstrates the importance of perennial grasslands with intact soils for building soil carbon in the fertile Mollisols of the North Central US

The impact of land use change and agricultural management on the cycling of soil organic carbon (SOC) is not well understood, limiting our ability to manage for, and accurately model, soil carbon changes at both local and regional scales. To address this issue, here we combined long-term soil incubations with acid-hydrolysis and dry combustion to parse total SOC (C t ) into three operationally defined SOC pools (active, slow, and recalcitrant) from 9 long-term sites with varying land uses on current and former tallgrass prairie soil. Land uses represented a gradient of soil disturbance histories including remnant prairie, restored prairie, grazed pasture, annual crop rotations, and continuous maize. Dry combustion was used to estimate total carbon (C t , physical), while acid hydrolysis of both the active (C a ) and slow (C s ) pools was used to estimate a recalcitrant carbon pool (C r , chemical). Non-linear modeling of CO 2 efflux data from the long-term incubations was then used to estimate C a , and the decomposition rates of both C a and C s (k a and k r , biological). The size of the slow pools C s was then defined mathematically as C t- (C a + C r ). Remnant prairie had the highest C t , while cool-season pasture and a 35-y-old restored prairie had higher C t than the other agricultural systems. All agricultural systems, including pasture, had the highest fraction of C t as C r (~50%), whose mean residence time (MRT) in these soils is ≥500 years (Paul et al., 2001a) demonstrating that this fraction persists, while the more labile fractions were lost over the course of a few months (C a ) to a few decades (C s ) as a result of tillage-intensive agriculture. The two- to four-decade MRT time of C s indicated a pool likely to be more responsive to the 20 to 40 years of land-use practices used at some of the sites. The C s pool was largest in the remnant- and 35-y-old prairies indicating significant C accrual and stabilization compared to the agricultural ecosystems. Interestingly, the remnant prairie maintained the highest C a pool as well, demonstrating the strong connection between the quantity of fresh C inputs and the potential for long-term C stabilization and accrual. The accumulation of C in active (≈labile) pools as a first step toward long-term stabilization highlights the tenuous nature of early carbon gains, which can be quickly lost in response to climate change or poor management.

54 ENVIRONMENTAL SCIENCES↗
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 OSTI2025

JGI Plant Transformation Workshop, May 20-21, 2025

Domestic biomass crops such as sorghum, switchgrass, Miscanthus, and poplar can provide United States industries with renewable feedstocks while also supporting low-input farming systems and strengthening supply chains for biofuels, biochemicals and biomaterials. The U.S. leads globally in biomass crop genomics, yet progress in engineering traits is constrained by slow, genotype-dependent transformation methods and lengthy Design-Build-Test-Learn (DBTL) cycles. At a May 2025 workshop, a panel of experts recommended establishing a DOE Plant Transformation Capability (PTC) to overcome these barriers. The PTC would unite two missions: advancing research to achieve genotype-independent, automated methods, and delivering scalable transformation services through a user-facility model. With expected gains of 10–100x in efficiency, including transformation and cost reduction, the PTC would accelerate the path from discovery to engineered plants, expand community access and training, and support downstream applications and workflows including field trials and regulatory navigation. By enabling rapid and predictable crop engineering, the PTC would strengthen U.S. supply chains, enhance industrial competitiveness, and ensure that DOE’s genomic investments deliver national impact.

09 BIOMASS FUELS↗
NASA NTRS2018

Intercomparison of MODIS AQUA and VIIRS I-Band Fires and Emissions in an Agricultural Landscape - Implications for Air Pollution Research

Quantifying emissions from crop residue burning is crucial as it is a significant source of air pollution. In this study, we first compared the fire products from two different sensors, the Visible Infrared Imaging Radiometer Suite (VIIRS) 375 m active fire product (VNP14IMG) and Moderate Resolution Imaging Spectroradiometer (MODIS) 1 km fire product (MCD14ML) in an agricultural landscape, Punjab, India. We then performed an intercomparison of three different approaches for estimating total particulate matter (TPM) emissions which includes the fire radiative power (FRP) based approach using VIIRS and MODIS data, the Global Fire Emissions Database (GFED) burnt area emissions and a bottom-up emissions approach involving agricultural census data. Results revealed that VIIRS detected fires were higher by a factor of 4.8 compared to MODIS Aqua and Terra sensors. Further, VIIRS detected fires were higher by a factor of 6.5 than Aqua. The mean monthly MODIS Aqua FRP was found to be higher than the VIIRS FRP; however, the sum of FRP from VIIRS was higher than MODIS data due to the large number of fires detected by the VIIRS. Besides, the VIIRS sum of FRP was 2.5 times more than the MODIS sum of FRP. MODIS and VIIRS monthly FRP data were found to be strongly correlated (r2 = 0.98). The bottom-up approach suggested TPM emissions in the range of 88.19-91.19 Gg compared to 42.0-61.71 Gg, 42.59-58.75 Gg and 93.98-111.72 Gg using the GFED, MODIS FRP, and VIIRS FRP based approaches, respectively. Of the different approaches, VIIRS FRP TPM emissions were highest. Since VIIRS data are only available since 2012 compared to MODIS Aqua data which have been available since May 2002, a prediction model combining MODIS and VIIRS FRP was derived to obtain potential TPM emissions from 2003-2016. The results suggested a range of 2.56-63.66 (Gg) TPM emissions per month, with the highest crop residue emissions during November of each year. Our results on TPM emissions for seasonality matched the ground-based data from the literature. As a mitigation option, stringent policy measures are recommended to curtail agricultural residue burning in the study area.

Global Fire Emissions Database (GFED)↗
NASA NTRS1972

Corn blight review: Sampling model and ground data measurements program

The sampling plan involved the selection of the study area, determination of the flightline and segment sample design within the study area, and determination of a field sample design. Initial interview survey data consisting of crop species acreage and land use were collected. On all corn fields, additional information such as seed type, row direction, population, planting date, ect. were also collected. From this information, sample corn fields were selected to be observed through the growing season on a biweekly basis by county extension personnel.

Allen, R. D.↗
DOE OSTI2021

The Implications of Global Change for the Co-Evolution of Argentina's Integrated Energy-Water-Land Systems

This study seeks to understand how Argentina’s energy, water, and land (EWL) systems will co-evolve under a representative array of human and earth system influences, including socioeconomic change, climate change, and climate policy. To capture Argentina’s sub-national EWL dynamics in the context of global change, we couple the Global Change Analysis Model with a suite of consistent, gridded sectoral downscaling models to explore multiple stakeholder-engaged scenarios. Across scenarios, Argentina has the economic opportunity to use its vast land resources to satisfy growing domestic and international demand for crops, such as oil (e.g., soy) and biomass. The human (rather than earth) system produces the most dominant changes in mid-century EWL resource use. A Reference scenario characterized by modest socioeconomic growth projects a 40% increase in Argentina’s agricultural production by 2050 (relative to 2020) by using 50,000 km 2 of additional cropland and 40% more water. A Climate Policy scenario designed to achieve net-zero carbon emissions globally shortly after mid-century projects that Argentina could use 100,000 km 2 of additional land (and 65% more water) to grow biomass and other crops. The burden of navigating these national opportunities and challenges could fall disproportionately on a subset of Argentina’s river basins. The Colorado and Negro basins could experience moderate-to-severe water scarcity as they simultaneously navigate substantial irrigated crop demand growth and climate-induced declines in natural water availability. Argentina serves as a generalizable testbed to demonstrate that multi-scale EWL planning challenges can be identified and managed more effectively via integrated analysis of coupled human-earth systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS2015

Is There Ecological Information in Optical Polarization Data?

Optical linear polarization? In remote sensing it's due to specular reflection. The first surface that incident light encounters - a smooth water surface or the waxy first surface of a leaf's cuticle, if it's even somewhat smooth (i.e. shiny) - will specularly reflect and linearly polarize the incident light. We provide three examples of the types of ecological information contained in remotely sensed optical linear polarization measurements. Remove the surface reflection to better see the interior. The linearly polarized light reflected by leaf surfaces contains no information about cellular pigments, metabolites, or water contained in the leaf interiors of a plant canopy, because it never enters the leaf interior to interact with them. Thus, for purposes of remotely sensing the leaf interiors of a plant canopy, the linearly polarized light should be subtracted from the total reflected light, because including it would add noise to the measurement. In particular 'minus specular' vegetation indices should allow improved monitoring of a plant canopy's physiological processes. Estimate plant development stage and yield. Wheat and sorghum grain heads, following emergence, rapidly extend upward and very quickly tower over nearby leaves, partially blocking our view of the sunlight reflected by those leaf surfaces. The resulting decrease in the amount of surface reflected and polarized sunlight, if monitored over time, potentially allows per-field estimates of the dates of the heading and flowering development stages to be interleaved with weather data in models, which is key to better estimating per-field grain yield. Similar polarization changes may occur in other grasses, such as oats, barley, corn and rice, each a crop so widely grown that it potentially affects climate at the regional scale. Wetlands Mapping. The sunlight specularly reflected by surface waters is blindingly bright, spectrally flat and polarized - all of which telegraphs that the ground area is inundated. Inundated soils exchange methane with the atmosphere; non-inundated soils, carbon dioxide. Aquatic plants growing through the water surface pipe the soil-produced methane via the stomata to the atmosphere, enhancing exchanges rates by factors of 10-20 compared to ebullition (bubbling) or diffusion through the water column to the atmosphere. Thus, mapping wetland areas into three community types - inundated areas with emergent vegetation, open water and uplands - provides potentially key information to water, carbon and energy budgets at landscape to global scales.

Water↗
DOE OSTI2023

The role of social support on midwestern farmers’ willingness to grow perennial bioenergy crops

The lack of farmers' willingness to grow perennial bioenergy crops (PBCs) presents a critical barrier to the emergence of cellulosic biofuel production. The willingness relies on a complex network of economic, environmental, and social drivers, among which the influence of social factors (e.g., the influence of neighborhood, community, and communication) is less understood. This study addresses this knowledge gap via a survey analysis of midwestern farmers. The survey data are analyzed through ordinary least square regression and structural equation model, which together investigate the individual and interactive impacts of multiple factors on farmers' decisions to adopt PBCs. Based on a farm-scale analysis, six statistically significant predictors of farmer willingness to grow PBCs are identified: perception of PBCs' environment benefits, education level, willingness to take risks, familiarity with PBCs, portion of peers already growing PBCs, and support of biorefineries locating in the local community. Among these, the latter three predictors are social support variables. It is found that familiarity with the crops is the most significant predictor of willingness; familiarity is also an important intermediate variable that mediates the influence of many other predictors. In addition, peer adoption can both directly and indirectly affect willingness via its influence on familiarity. Furthermore, these findings suggest that it is a pressing need to improve farmers’ knowledge of PBCs to promote the adoption of such crops.

09 BIOMASS FUELS↗
NASA NTRS1981

Predicting the timing and potential of the spring emergence of overwintered populations of Heliothis spp

The current state of knowledge dealing with the prediction of the overwintering population and spring emergence of Heliothis spp., a serious pest of numerous crops is surveyed. Current literature is reviewed in detail. Temperature and day length are the primary factors which program H. spp. larva for possible diapause. Although studies on the interaction of temperature and day length are reported, the complete diapause induction process is not identified sufficiently to allow accurate prediction of diapause timing. Mortality during diapause is reported as highly variable. The factors causing mortality are identified, but only a few are quantified. The spring emergence of overwintering H. spp. adults and mathematical models which predict the timing of emergence are reviewed. Timing predictions compare favorably to observed field data; however, prediction of actual numbers of emerging moths is not possible. The potential for use of spring emergence predictions in pest management applications, as an early warning of potential crop damage, are excellent. Research requirements to develop such an early warning system are discussed.

Hartstack, A. W.↗
DOE OSTI2020

Bacterial Community Members Increase Bacillus subtilis Maintenance on the Roots of Arabidopsis thaliana

Plant-growth-promoting bacteria (PGPB) are used to improve plant health and promote crop production. However, because some PGPB (including Bacillus subtilis) do not maintain substantial colonization on plant roots over time, it is unclear how effective PGPB are throughout the plant growing cycle. A better understanding of the dynamics of plant root community assembly is needed to develop and harness the potential of PGPB. Although B. subtilis is often a member of the root microbiome, it does not efficiently monoassociate with plant roots. We hypothesized that B. subtilis may require other primary colonizers to efficiently associate with plant roots. We utilized a previously designed hydroponic system to add bacteria to Arabidopsis thaliana roots and monitor their attachment over time. We inoculated seedlings with B. subtilis and individual bacterial isolates from the native A. thaliana root microbiome either alone or together. We then measured how the coinoculum affected the ability of B. subtilis to colonize and maintain on A. thaliana roots. We screened 96 fully genome-sequenced strains and identified five bacterial strains that were able to significantly improve the maintenance of B. subtilis. Three of these rhizobacteria also increased the maintenance of two strains of B. amyloliquefaciens commonly used in commercially available bioadditives. These results not only illustrate the utility of this model system to address questions about plant–microbe interactions and how other bacteria affect the ability of PGPB to maintain their relationships with plant roots but also may help inform future agricultural interventions to increase crop yields.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2016

Safety and Certification Considerations for Expanding the Use of UAS in Precision Agriculture

The agricultural community is actively engaged in adopting new technologies such as unmanned aircraft systems (UAS) to help assess the condition of crops and develop appropriate treatment plans. In the United States, agricultural use of UAS has largely been limited to small UAS, generally weighing less than 55 lb and operating within the line of sight of a remote pilot. A variety of small UAS are being used to monitor and map crops, while only a few are being used to apply agricultural inputs based on the results of remote sensing. Larger UAS with substantial payload capacity could provide an option for site-specific application of agricultural inputs in a timely fashion, without substantive damage to the crops or soil. A recent study by the National Aeronautics and Space Administration (NASA) investigated certification requirements needed to enable the use of larger UAS to support the precision agriculture industry. This paper provides a brief introduction to aircraft certification relevant to agricultural UAS, an overview of and results from the NASA study, and a discussion of how those results might affect the precision agriculture community. Specific topics of interest include business model considerations for unmanned aerial applicators and a comparison with current means of variable rate application. The intent of the paper is to inform the precision agriculture community of evolving technologies that will enable broader use of unmanned vehicles to reduce costs, reduce environmental impacts, and enhance yield, especially for specialty crops that are grown on small to medium size farms.

Hayhurst, Kelly J.↗
DOE OSTI2021

Supply analysis of preferential market incentive for energy crops

This analysis explores the valuation of feedstock quality attributes of switchgrass and miscanthus – two energy crops poised for future expansion – and compares the relative economic availability of these two crops under two scenarios: (i) uniform price assumptions (i.e., no incentive for quality), and (ii) a scenario of a price premium based on convertibility (i.e., an incentive for quality). Given data on cellulose content, hemicellulose content, and their relative convertibility, miscanthus is expected to be 11% more efficient at conversion to biofuels than switchgrass under the biochemical conversion route. Based on this scenario of improved conversion efficiency and associated profit, we simulate an 11% price premium for miscanthus over other feedstocks in a base-case scenario. By adding this price premium, supplies of miscanthus increase over the base case by about 4 million (44%), 94 million (64%), and 166 million (94%) tons in year 0, 10, and 20 after simulated contracts for production are initiated respectively. These results emphasize that custom simulations are needed to quantify feedstock availability if supplies are intended to reflect grower response to industry demands for feedstock quality specifications. Farmers can grow ‘peas or carrots’, and price signals from biorefineries will influence what energy crops they produce. Recognizing that the energy crop mix is tractable according to quality characteristics is relevant both for near-term and long-term biofuels research and development. Here, we recommend accounting for market preferences for quality attributes when estimating potential future supplies of energy crops.

09 BIOMASS FUELS↗
DOE OSTI2022

Doubling protected land area may be inefficient at preserving the extent of undeveloped land and could cause substantial regional shifts in land use

Abstract Projection of land use and land‐cover change is highly uncertain yet drives critical estimates of carbon emissions, climate change, and food and bioenergy production. We use new, spatially explicit land availability data in conjunction with a model sensitivity analysis to estimate the effects of additional land protection on land use and land cover. The land availability data include protected land and agricultural suitability and is incorporated into the Moirai land data system for initializing the Global Change Analysis Model. Overall, decreasing land availability is relatively inefficient at preserving undeveloped land while having considerable regional land‐use impacts. Current amounts of protected area have little effect on land and crop production estimates, but including the spatial distribution of unsuitable (i.e., unavailable) land dramatically shifts bioenergy production from high northern latitudes to the rest of the world, compared with uniform availability. This highlights the importance of spatial heterogeneity in understanding and managing land change. Approximately doubling the current protected area to emulate a 30% protected area target may avoid land conversion by 2050 of less than half the newly protected extent while reducing bioenergy feedstock land by 10.4% and cropland and grazed pasture by over 3%. Regional bioenergy land may be reduced (increased) by up to 46% (36%), cropland reduced by up to 61%, pasture reduced by up to 100%, and harvested forest reduced by up to 35%. Only a few regions show notable gains in some undeveloped land types of up to 36%. Half of the regions can reach the target using only unsuitable land, which would minimize impacts on agriculture but may not meet conservation goals. Rather than focusing on an area target, a more robust approach may be to carefully select newly protected land to meet well‐defined conservation goals while minimizing impacts to agriculture.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

U.S. Agrivoltaics Irradiance Database

This is a foundational data set for research and deployment of agrivoltaics, which is the co-location of agriculture and solar power plants on the same land. This irradiance and shading dataset can be utilized to determine the suitability of agrivoltaics configurations for a given region and crop-type. The data is hourly, 4x4 km resolution across the contiguous United States and Hawaii. It is calculated from the National Solar Radiation Database sites, using the System Advisor Model (SAM) to simulate the shading patterns for 10 common agrivoltaics configurations. Sunlight availability data is reported for 10 locations on the ground between adjacent rows of solar panels, as well as averaged across areas of interest such as the average irradiance in the edge-to-edge open area or across 3-6 planting beds. Other available metrics include the input meteorological data from the NSRDB (e.g. global horizontal irradiance, wind speed, etc.) and estimates for comparing energy and agricultural characteristic across the 10 configurations, including power output per acre or per kW installed capacity and farmable land area per acre.

14 SOLAR ENERGY↗
DOE OSTI2023

Integrated Land Suitability Assessment for Depots Siting in a Sustainable Biomass Supply Chain

A sustainable biomass supply chain would require not only an effective and fluid transportation system with a reduced carbon footprint and costs, but also good soil characteristics ensuring durable biomass feedstock presence. Unlike existing approaches that fail to account for ecological factors, this work integrates ecological as well as economic factors for developing sustainable supply chain development. For feedstock to be sustainably supplied, it necessitates adequate environmental conditions, which need to be captured in supply chain analysis. Using geospatial data and heuristics, we present an integrated framework that models biomass production suitability, capturing the economic aspect via transportation network analysis and the environmental aspect via ecological indicators. Production suitability is estimated using scores, considering both ecological factors and road transportation networks. These factors include land cover/crop rotation, slope, soil properties (productivity, soil texture, and erodibility factor) and water availability. This scoring determines the spatial distribution of depots with priority to fields scoring the highest. Two methods for depot selection are presented using graph theory and a clustering algorithm to benefit from contextualized insights from both and potentially gain a more comprehensive understanding of biomass supply chain designs. Graph theory, via the clustering coefficient, helps determine dense areas in the network and indicate the most appropriate location for a depot. Clustering algorithm, via K-means, helps form clusters and determine the depot location at the center of these clusters. An application of this innovative concept is performed on a case study in the US South Atlantic, in the Piedmont region, determining distance traveled and depot locations, with implications on supply chain design. The findings from this study show that a more decentralized depot-based supply chain design with 3depots, obtained using the graph theory method, can be more economical and environmentally friendly compared to a design obtained from the clustering algorithm method with 2 depots. In the former, the distance from fields to depots totals 801,031,476 miles, while in the latter, it adds up to 1,037,606,072 miles, which represents about 30% more distance covered for feedstock transportation.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗