VTechWorks
VTechWorks provides global access to Virginia Tech scholarship, including journal articles, books, theses, dissertations, conference papers, slide presentations, technical reports, working papers, administrative documents, videos, images, and more by faculty, students, and staff. Faculty can deposit items to VTechWorks from Elements, including journal articles covered by the University open access policy. Email vtechworks@vt.edu for help.
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Virginia Tech's open access policy enables researchers to deposit the accepted version of scholarly articles with no embargo.
Theses and Dissertations
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More than 50 freely available and openly licensed textbooks are among our most downloaded items.
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Recent Submissions
Comparison of standard pruning with cordon pruning on blackberry yield and quality
Hernandez, Enrique (Virginia Tech, 2026-09-09)
Blackberries (Rubus spp.) are an economically important specialty fruit crop cultivated in temperate and subtropical regions worldwide. Advances in cultivar development, trellis systems, and postharvest handling have contributed to rising global production and growing market demand. Among cultural management practices, pruning and trellising play critical roles in shaping canopy architecture and, in turn, influencing fruit quality and yield potential in commercial blackberry production systems.
This study evaluated the effects of two pruning strategies, standard pruning, and cordon pruning, on fruit yield and fruit quality in floricane-bearing 'Ouachita' blackberry plants trained on a modified V-trellis system. A randomized complete block design with four replications was implemented over two growing seasons (2023–2024). Mixed model analyses were conducted for six response variables, U.S. No.1 (Grade 1) berries, U.S. No.1 (Grade 2) berries, white drupelet disorder, non-marketable fruit, °Brix, and harvest time, to assess the effects of pruning system (cordon vs. standard), year, and their interaction.
Pruning system significantly affected Grade 2 berry yield (F = 8.3, p = 0.01), with cordon pruning producing more Grade 2 berries (LSM = 1,247.0 g) than standard pruning (LSM = 964.6 g), a difference of 282.4 g (SE = 97.9). A significant year × pruning interaction was also detected for Grade 2 berry yield (p = 0.01), indicating that the magnitude of this effect varied between growing seasons. Grade 1 berry yield had no statistical differences between treatments (p = 0.2). White drupelet disorder and non-marketable fruit showed non-significant trends associated with pruning system (p = 0.07 and p = 0.5, respectively), with cordon pruning numerically associated with fewer non-marketable fruit. °Brix and harvest time, evaluated only in the second growing season, were not significantly affected by pruning system (p = 0.2 and p = 0.5, respectively).
Overall, the results revealed no significant difference in performance between the cordon and standard pruning treatments. Environmental variability between growing seasons, particularly rainfall during harvest, appears to have influenced treatment effects across several response variables, contributing to periods of overripe fruit when rain prevented timely harvest. Cane dieback was also observed during the study and may have further confounded yield outcomes for both pruning systems. Additionally, fruit production slowed earlier in the season under standard pruning compared to cordon pruning, suggesting a difference in cropping duration between the two training methods. These findings offer useful insight into the tradeoffs growers may face when selecting a pruning and canopy management strategy for blackberry production in the Mid-Atlantic region.
Reframing "The Veteran Problem": How a WWII Era Theory Can Improve Veteran Policy in the Twenty-First Century
McDonald, Todd A. (Veterans Studies Association, 2026-03)
I use framing theory and Willard Waller’s 1944 book, The Veteran Comes Back, to structure an analysis of the Virginia Military Survivors and Dependents Education Program (VMSDEP) and make policy recommendations to improve it. VMSDEP is a state program that waives mandatory tuition and fees at public colleges and universities for the spouse or child of a deceased military service member or of a disabled veteran. In 2024, the program’s benefits were briefly restricted in response to its rapidly rising cost but quickly restored as public outcry ensued. I examine VMSDEP legislation, reports, and development by innovating Waller’s description of “the veteran problem” to highlight a persistent moral tension within veteran and military-affiliated policy debates in the WWII era and today. I describe one moral concern and set of policy priorities as the “veteran friendly” frame, a phrase stated in Virginia Code and state agency documents to emphasize Virginia’s commitment to the veteran and military-affiliated community. Another set of moral concerns and policy priorities are characterized as the “administrative neutrality” frame, referring to certain state agency’s commitment to remain a neutral, non-partisan party in policy debates. I explain how these frames form a moral conflict and lack a clear vision for VMSDEP’s future. Using these insights and the latest data on the program, I reveal how the General Assembly should understand and resolve these challenges, ensuring the program remains effective and sustainable for future generations.
A Multi-Source Artificial Intelligence Framework for Non-Invasive Peanut Maturity Mapping Using Aerial Spectral Imagery and Weather Data
Emmanuel Sahayaraj, Sathish Raymond (Virginia Tech, 2026-09-16)
Virginia-type peanut (Arachis hypogaea L.) maturity assessment is a critical challenge in precision agriculture because peanut pods develop belowground and mature asynchronously within the same plant and field. Conventional methods such as pod blasting, hull scrape evaluation, and maturity profile board classification remain important for harvest decisions, but they are destructive, labor intensive, spatially limited, and dependent on expert judgment. This dissertation developed a complete research chain for Virginia-type peanut maturity assessment, beginning with field sampling and ground reference data collection, progressing through the evaluation of field and surrounding agroclimatic factors, advancing into remote sensing and machine learning based maturity prediction, and culminating in the implementation of trained models as a deployable decision support tool for precision harvest planning.
The dissertation was organized around six connected objectives. First, current traditional, sensing based, and artificial intelligence driven approaches for peanut maturity assessment were reviewed to identify major limitations and future needs. Second, field maturity dynamics were quantified across cultivars, locations, years, growth regulator treatments, research trials, and commercial grower fields. Third, growing degree day relationships with peanut maturity were evaluated under humid subtropical environments to understand the influence of accumulated agroclimatic conditions. Fourth, aerial multispectral imagery, vegetation indices, and accumulated growing degree days were integrated into a single-view machine learning framework to establish a baseline for non-destructive maturity prediction. Fifth, a multi-view stacked ensemble learning framework was developed to preserve and combine spectral, vegetation, and weather information for improved cultivar specific prediction and spatial mapping. Sixth, the resulting top models were implemented in a web based expert decision support system to generate peanut maturity index maps, days to maturity estimates, harvest readiness summaries, and automated reports.
Field studies were conducted across Virginia and North Carolina during the 2022 to 2024 growing seasons using breeder seed trials, peanut variety quality evaluation (PVQE) trials, and commercial grower fields. Virginia-type cultivars including Bailey-II, Emery, NC-20, Sullivan, and Walton were evaluated through systematic pod sampling, pod blasting, mesocarp color classification, and peanut maturity index quantification. Maturity progression was strongly influenced by days after planting, cultivar, location, and environmental factors. Optimal maturity generally occurred between 140 and 150 days after planting for Bailey-II, Emery, NC-20, and Sullivan, while Walton showed later and more variable maturity. Growing degree day (GDD) analyses showed moderate to strong relationships with maturity, and the inclusion of additional weather factors such as soil temperature, relative humidity, rainfall, and photosynthetically active radiation provided broader agroclimatic context for interpreting maturity progression under humid subtropical conditions.
Modeling efforts translated field and agroclimatic understanding into predictive maturity estimation. Single-view machine learning framework demonstrated that combined spectral reflectance, vegetation indices, and accumulated growing degree days could estimate peanut maturity, although performance varied by cultivar and data split. The multi-view stacked ensemble learning framework improved this approach by modeling each information domain separately and then combining their predictions through meta learning. This enabled more interpretable and spatially explicit prediction of peanut maturity index and days to maturity. Finally, these models were operationalized in the Peanut Readiness Evaluation Platform, a web based R Shiny expert decision support system that supports data ingestion, preprocessing, vegetation masking, feature extraction, model inference, interactive spatial visualization, and report generation. Overall, this dissertation advances peanut maturity assessment from manual field sampling alone to an integrated, data driven, and deployable decision support framework for precision harvest management in Virginia-type peanut production systems.
Bacteria, We Can See Your Halo: Chitinases in the Amphibian Skin Microbiome
Snead, Mychala Antonia (Virginia Tech, 2026-09-17)
Microbial communities can play an important role in protecting hosts against pathogens, but the mechanisms underlying microbiome-mediated host defense are still not fully understood. Amphibian skin microbiomes are an important system for studying these interactions because some bacteria found on amphibian skin can inhibit the growth of the fungal pathogen Batrachochytrium dendrobatidis (Bd), which has contributed to global amphibian declines. My dissertation research focused on expanding our understanding of the mechanisms involved in microbiome-mediated host defense, with a primary focus on the role of bacterial chitinase genes within the amphibian skin microbiome. My dissertation builds on previous work conducted in the Belden lab using a collection of 666 bacterial isolates cultured from skin swabs of four amphibian species in Virginia. Prior work demonstrated that dominant members of the amphibian skin microbiome were more likely to inhibit Bd growth and identified several candidate genetic pathways potentially associated with antifungal activity, including chitinase genes. These findings provided the foundation for investigating the functional importance of bacterial chitinases in amphibian host defense. In chapter two, I investigated the prevalence, diversity, and functional activity of chitinase genes in amphibian skin bacterial isolates. I established an in vitro plate assay to test for chitin degradation in 27 isolates with annotated genomes and combined these functional assays with genomic analyses to characterize chitinase diversity. I also used PCR to screen 124 additional amphibian skin isolates for the presence of chiA genes to better understand how widespread these genes are within the amphibian skin microbiome. Although multiple bacterial genera contained predicted catalytic chitinase domains, only Janthinobacterium and Streptomyces isolates demonstrated observable chitin hydrolysis. In addition, neither Bd inhibition ability nor functional chitin degradation predicted the presence of chitinase genes, suggesting that genomic predictions alone may not accurately reflect antifungal activity. One important finding from this chapter was that Streptomyces isolates contained the greatest diversity and abundance of chitinase genes. In chapter three, I expanded this work through a comparative genomics study of amphibian skin Streptomyces isolates collected from Panama. Since Streptomyces are medically important bacteria and major producers of natural products, this project also had broader applications beyond amphibian systems. Across 11 unique isolates, only three demonstrated functional chitin hydrolysis, and no relationship was found between catalytic domain copy number and chitin degradation. Comparative genomic analyses also identified 17 biosynthetic gene clusters with the potential to produce siderophores, antibiotics, and antifungal compounds. In my final chapter, I broadened my research focus beyond amphibian systems to investigate relationships between mucosal microbiomes and disease status in shelter-housed kittens with upper respiratory infections. Using bacterial amplicon sequencing, I characterized ocular and throat microbiomes in kittens of varying health statuses including healthy, sick,. Healthy kittens had similar bacterial richness and dominant genera across ocular and throat samples, while samples from sick kittens were dominated by members of the families Mycoplasmataceae. Overall, my dissertation integrates microbiology, comparative genomics, and ecology to better understand the host microbiome and its potential roles in disease dynamics. This work highlights the challenge of linking microbial genomic traits with functional activity and demonstrates that predicted antimicrobial genes do not necessarily correspond with observed antifungal function.
Vessel Behavior, Navigational Constraint Exposure, and Cargo Economics in the Port of Virginia
Rabena, Frank Michael (Virginia Tech, 2026-09-17)
This dissertation examines how vessel movement behavior changed in the Port of Virginia approach area between 2019 and 2024, how selected navigational conditions were associated with distinct behavioral signatures, and how those changes translate into scenario-based operational and economic exposure. Organized as a three-manuscript study, the dissertation combines approximately 9.69 million cleaned Automatic Identification System observations with Virginia Pilot Association movement and draft records, publicly charted bathymetry, and transparent maritime risk, fuel, and cargo-capacity assumptions.
Chapter 2 establishes the behavioral foundation. Relative to the 2019 baseline, mean vessel speed declined from 8.34 knots to 5.74 knots in 2024, mean course-over-ground-to-heading divergence increased from 19.94 to 37.47 degrees, mean course-change rate increased from 2.21 to 3.20 degrees per minute, and turning-event frequency rose from 13.68 percent to 20.11 percent. The annual pattern was uneven, with the largest simultaneous year-to-year changes occurring between 2021 and 2022. Condition-based comparisons further showed that Whale-season, CVOW, and Depth indicators were not behaviorally interchangeable: the CVOW condition developed the strongest later-year corridor-level burden signature, while the localized Depth core displayed a profile consistent with possible anticipatory route selection or avoidance.
Chapter 3 translates the AIS-observed changes into two operational consequence pathways. A relative CVOW concentration index increased by 47.6 percent between 2019 and 2024. Under the chapter's central illustrative calibration, this change corresponds to approximately $0.24 million in incremental annual collision-risk exposure. Under an illustrative adverse fuel-rate scenario, the observed 31 percent speed reduction, combined with an assumed 20-nautical-mile approach segment and 2,000 annual transits, produces a conditional annual fuel-cost penalty of $1.32 million, approximately 9,320 additional metric tons of carbon dioxide, and an associated carbon-cost equivalent of $0.47 million. The direction of the actual fleet-level fuel effect cannot be determined without vessel-specific fuel curves.
Chapter 4 evaluates the cargo-capacity pathway using class-specific vessel movement and draft records. The 2024 modeled exposure population includes 40 container movements and 335 outbound coal movements, for a total of 375 movements exceeding the modeled 45-foot threshold and falling within the applicable class-specific upper bounds. Under the central assumption that 50 percent of potentially exposed movements are assigned to cargo reduction, annual Pathway 3 exposure is $136.45 million, with low and high routing-share scenarios of $68.23 million and $204.68 million. The dissertation treats these values as economic exposure rather than as observed realized loss, and treats the six-foot effective-depth-loss and 20 percent under-keel-clearance inputs as explicit scenario assumptions rather than as measured site conditions or universal operating requirements.
Taken together, the dissertation contributes an integrated empirical and scenario-based framework that moves from observed vessel behavior to condition-specific interpretation and then to safety, efficiency, emissions, and cargo-capacity consequences. The results show that navigational constraints can remain physically passable while materially changing the economic quality of access. Three results were not anticipated in the original framework design: the largest simultaneous year-to-year behavioral inflection occurred in 2022 rather than at the 2020 pilot-project completion; the localized Depth indicator produced a counterintuitive lower-burden signature rather than the higher within-area burden originally expected; and cargo-capacity exposure accounted for approximately 98.5 percent of modeled incremental economic exposure under the central scenario, a dominance that the framework structure did not predetermine. The framework is intended to support marine spatial planning, port operations, and prospective evaluation of offshore infrastructure while making the distinction between observed evidence and modeled assumptions explicit.