Scholarly Works, Virginia Agricultural Experiment Station

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https://hdl.handle.net/10919/24309

VAES faculty are located at 11 Agricultural Research and Extension Centers in Virginia and three colleges at Virginia Tech (CALS, CNRE, and VMRCVM).

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Integrated crop-livestock farming systems influence the incidence of Salmonella, Listeria monocytogenes, Shiga toxin-producing Escherichia coli, and indicator bacteria on fresh produce
Goodwyn, Brian; Millner, Patricia; Punchihewage-Don, Anuradha Jeewantha; Schwarz, Melinda; Bowers, John; Haymaker, Joseph; Hashem, Fawzy; Kim, Chyer; Biswas, Debabrata; Parveen, Salina (American Society for Microbiology, 2025-10)
Concerns remain about foodborne pathogen contamination risks to integrated crop-livestock farm (ICLF) fresh produce due to farm animal proximity to crop fields and use of biological soil amendments of animal origin (BSAAO). This study aimed to evaluate the extent of microbial contamination in Maryland's Eastern Shore ICLFs and compare results with those from corresponding samples from crop-only farms (COFs), farmers' markets, and supermarkets. Altogether, 1,782 soil, animal reservoir, water, and produce samples from ICLFs, COFs, farmers' markets, and supermarkets were analyzed following standard methods. Total aerobic bacterial counts and generic Escherichia coli were enumerated using petrifilms, whereas Salmonella, Listeria monocytogenes (L. monocytogenes), and Shiga toxin-producing E. coli (STEC) and virulence factors (VFs) were detected by culturing and PCR confirmation. ICLF soil health parameters were evaluated pre- and post-BSAAO incorporation. Animal pen samples and BSAAOs, which generally improved ICLF soil health parameters, harbored more pathogens and generic E. coli. Samples of ICLF produce (0.39%, 1.95%, and 13.62%) and soils (2.04%, 2.72%, and 20.86%) had higher Salmonella, L. monocytogenes, and STEC/VF-gene prevalence than COF produce (0.00%, 0.00%, and 5.33%) and soils (1.33%, 0.00%, and 20.00%), respectively. Pathogen contamination was relatively low in retail environments where Salmonella and L. monocytogenes were each isolated from one farmers' market produce, whereas STEC/VF-genes were found in one supermarket produce. Overall, the most frequent Salmonella serovars were Bareilly and Newport, whereas the highest detected STEC serovars and VF-genes were O103 and stx2. Produce contamination in ICLFs and farmers' markets was greater than that of traditional counterparts, indicating the importance of continued research/education regarding produce safety for producers and consumers.IMPORTANCEShifts in consumer demand have led to notable increases in integrated crop-livestock farms (ICLFs), which are major contributors to popular farmers' markets. However, production practices in these settings, like manure-based soil amendments and the lack of food safety regulation enforcement, have raised concerns regarding potential foodborne illness cases and outbreaks. This study provides valuable information on the prevalence and comparison of three major foodborne bacterial pathogens and indicator microorganisms in ICLF, crop-only farm (COF), farmers' market, and supermarket environments. Salmonella, Listeria monocytogenes, and Shiga toxin-producing E. coli were detected more frequently in ICLF and farmers' market samples compared with COF and supermarket samples, respectively. However, at least one pathogen was detected in each setting. Findings from this study highlight that, regardless of studied settings, the contamination risks associated with fresh produce production on the Maryland Eastern Shore will contribute to developing food safety standards in various produce production environments.
Rapid and Nondestructive Determination of Oil Content and Distribution of Potato Chips Using Hyperspectral Imaging and Chemometrics
Sun, Yue; Nayani, Nikhita Sai; Xu, Yixiang; Xu, Zhanfeng; Yang, Jun; Feng, Yiming (American Chemical Society, 2024-06-03)
Conventional techniques used to measure oil content in the food are laborious, rely on chemical agents, and have a negative environmental impact. In this study, near-infrared hyperspectral imaging was used as a rapid and nondestructive tool to determine the oil content and its distribution in commercial flat-cooked and batch-cooked potato chips. By evaluating various algorithmic models, such as partial least-squares regression (PLSR), ridge regression, random forest, gradient boosting, and support vector regression, in combination with preprocessing methods like multiplicative scattering correction, standard normal variable (SNV) transform, Savitzky-Golay filtering, normalization, and baseline correction, the most effective preprocessing method and model combination was determined to be SNV-PLSR. Moreover, by employing the optimized PLSR model, a highly accurate oil content prediction model was developed, achieving a coefficient of determination (R2) of 0.95. To identify the wavelengths that contributed most significantly to the model's predictive power, variable importance in projection (VIP) analysis was utilized. A dimensionally reduced PLSR model using only 68 selected wavelengths was developed based on the VIP analysis. This simplified model maintained similar performance to that of the full-spectrum model while using a smaller data set. The model was also used to apply the hyperspectral images of potato chips at the pixel level to visualize the oil distribution in potato chips with the intent to provide a real-time approach to quality control for the potato chip industry.
A Case Study of Irrigation Pondwater and Soilless Substrate Quality across Nine Large Nurseries in Eastern Virginia
Criscione, Kristopher S. (2025-11-11)
Nursery producers depend on clean irrigation and efficient soilless substrates to successfully produce salable plants. Little information is available regarding the quality of irrigation and pine bark used in large eastern Virginia nurseries, despite the impact eastern Virginia nurseries have on Virginia horticulture sales. The objective of this case study was to investigate whether large nurseries located in eastern Virginia maintained irrigation and substrate quality within recommended standards. Irrigation pondwater (n = 8) samples were collected and tested for chemical properties, including pH, electric conductivity, alkalinity, total suspended solids, hardness, calcium, iron, and sodium. Additionally, pine bark-based substrates (n = 9) were collected and tested for substrate physical properties, including water storage, air-filled porosity, total porosity, and bulk density. The results of this case study showed that nursery irrigation pondwater had lower alkalinity values than recommended standards, while pine bark-based substrates used by large nurseries slightly exceeded air-filled porosity recommendations. However, all other parameters measured were generally within acceptable recommended standards.
Managing the reduction of soil phosphorus can prolong global reserves of fertilizer phosphorus and improve water quality
McDowell, R. W.; Simpson, Z. P.; Doscher, C.; Steinfurth, K.; Mott, Joshua; Margenot, A. J.; Appelhans, S. C.; Elledge, A. E.; Thornton, C. M.; Moore, P. A.; Blackwell, M. S. A.; Cade-Menun, B. J.; Ros, M. B. H.; Pavinato, P. S.; Zavattaro, L.; Soltangheisi, A.; Zhang, T. Q.; Haygarth, P. M.; Burkitt, L.; Fenton, O. (Elsevier, 2025-09)
Excess phosphorus in agricultural soils threatens freshwater quality and long-term fertilizer security. Globally, 27% of soils exceed crop phosphorus needs (plant-available soil test phosphorus as Olsen phosphorus), contributing to runoff that degrades water quality for 3 billion people. Reducing surplus phosphorus through fertilizer cessation (“drawdown”) is low cost, but rates remain poorly understood. We analyzed ∼12,700 observations from 225 trials in 21 countries to model the time for Olsen phosphorus to reach optimal agronomic thresholds across major crops and improved grassland. Drawdown rates ranged from 9 (Oceania and Asia) to 14 (Europe) years. Our model suggests that global drawdown could save ∼190,430 kt of fertilizer, 10 times the annual global use. These findings highlight opportunities to maintain yields, improve water quality, and deliver economic benefits, supporting better-informed agricultural practice and environmental polices worldwide.
Towards an End-to-End Digital Framework for Precision Crop Disease Diagnosis and Management Based on Emerging Sensing and Computing Technologies: State over Past Decade and Prospects
Nkwocha, Chijioke Leonard; Chandel, Abhilash Kumar (MDPI, 2025-10-16)
Early detection and diagnosis of plant diseases is critical for ensuring global food security and sustainable agricultural practices. This review comprehensively examines latest advancements in crop disease risk prediction, onset detection through imaging techniques, machine learning (ML), deep learning (DL), and edge computing technologies. Traditional disease detection methods, which rely on visual inspections, are time-consuming, and often inaccurate. While chemical analyses are accurate, they can be time consuming and leave less flexibility to promptly implement remedial actions. In contrast, modern techniques such as hyperspectral and multispectral imaging, thermal imaging, and fluorescence imaging, among others can provide non-invasive and highly accurate solutions for identifying plant diseases at early stages. The integration of ML and DL models, including convolutional neural networks (CNNs) and transfer learning, has significantly improved disease classification and severity assessment. Furthermore, edge computing and the Internet of Things (IoT) facilitate real-time disease monitoring by processing and communicating data directly in/from the field, reducing latency and reliance on in-house as well as centralized cloud computing. Despite these advancements, challenges remain in terms of multimodal dataset standardization, integration of individual technologies of sensing, data processing, communication, and decision-making to provide a complete end-to-end solution for practical implementations. In addition, robustness of such technologies in varying field conditions, and affordability has also not been reviewed. To this end, this review paper focuses on broad areas of sensing, computing, and communication systems to outline the transformative potential of end-to-end solutions for effective implementations towards crop disease management in modern agricultural systems. Foundation of this review also highlights critical potential for integrating AI-driven disease detection and predictive models capable of analyzing multimodal data of environmental factors such as temperature and humidity, as well as visible-range and thermal imagery information for early disease diagnosis and timely management. Future research should focus on developing autonomous end-to-end disease monitoring systems that incorporate these technologies, fostering comprehensive precision agriculture and sustainable crop production.
Hydraulic constraints to stomatal conductance in flooded trees
Brennan, Marisa J.; Criscione, Kristopher S.; Olichney, Jacob A.; Ding, Junyan; Fang, Yilin; McDowell, Nate; Wolfe, Brett T. (Springer, 2025-09-10)
Stomatal closure is a pervasive response among trees exposed to flooded soil. We tested whether this response is caused by reduced hydraulic conductance in the soil-to-leaf hydraulic continuum (ktotal), and particularly by reduced root hydraulic conductance (kroot), which has been widely hypothesized. We tracked stomatal conductance at the leaf level (gs) and canopy scale (Gs) along with physiological conditions in two temperate tree species, Magnolia grandiflora and Quercus virginiana, that were subjected to flood and control conditions in a greenhouse experiment. Flooding reduced gs, Gs, kroot and ktotal. Path analysis showed strong support for direct effects of ktotal on gs and for flood duration on ktotal, but not kroot on ktotal. A process-based model that accounted for the ktotal reduction predicted the timeseries of Gs in flood and control treatment trees reasonably well (predicted versus observed Gs R2 = 0.80 and 0.51 for M. grandiflora and Q. virginiana, respectively). However, accounting only for kroot reduction in flooded trees was insufficient for predicting observed Gs reduction. Together, these results suggest that hydraulic constraints were not limited to roots and highlight the need to account for flooding effects on ktotal when projecting forest ecosystem function using process-based models.
The Effects of Endophyte-Infected Tall Fescue on the Production and Reproductive Performance Parameters of Beef Cattle and Calves
Taylor, Amber A.; Fike, John H.; Mercadante, Vitor R. G.; Pent, Gabriel J. (MDPI, 2025-07-15)
Records from 2012 to 2019 for two herds were analyzed to determine how tall fescue (Schedonorus arundinaceus (Schreb.) Dumont) endophyte (Epichloë coenophialum) status affected the productivity of spring-calving cows and calves. Pastures either contained tall fescue with wildtype endophyte (high levels of ergot alkaloids) or novel- or endophyte-free tall fescue (largely ergot alkaloid free). The experimental design was a randomized complete block with year as the replication unit. Forage samples from the farm with toxic endophyte-infected tall fescue contained 1136 ± 413 ppb total ergot alkaloids, while forage from the non-toxic pastures on the second farm contained 118 ± 83 ppb total ergot alkaloids. Artificial insemination pregnancy rates and calving rates were greater (p < 0.05) for cows that grazed non-toxic tall fescue (51.2 ± 2.8% and 93.5 ± 1.4%, respectively) than for cows on toxic endophyte-infected tall fescue (43.3 ± 2.8% and 88.8 ± 1.4%, respectively). Birth weights and weaning weights were greater (p < 0.05) for calves from the non-toxic tall fescue system (37 ± 1 kg and 278 ± 8 kg, respectively) than for calves from the toxic endophyte-infected tall fescue system (33 ± 1 kg and 254 ± 8 kg, respectively). Raising cattle on tall fescue without the toxic endophyte improved cow and calf productivity.
First Report of Two-Spot Cotton Leafhopper (Amrasca biguttula Ishida) (Hemiptera: Cicadellidae) on Commercial Cotton in the Southeastern United States
Esquivel, Isaac L.; Bryant, Tim; Malone, Sean; Jacobson, Alana L.; Graham, Scott H.; Gimenez-Cremonez, Paulo S.; Roberts, Phillip; Paula-Moreas, Silvana; Reisig, Dominic; Huseth, Anders; Greene, Jeremy; Reay-Jones, Francis P. F.; Taylor, Sally (MDPI, 2025-09-15)
The two-spot cotton leafhopper, Amrasca biguttula (Ishida, 1913) (Hemiptera: Cicadellidae), is a polyphagous pest native to the Indian subcontinent, where it is a significant pest of cotton (Gossypium spp.), okra (Abelmoschus esculentus, Moench), and other crops. At adult and immature stages, they feed on leaf cell contents, causing characteristic “hopperburn” symptoms and yield losses of up to 40% in cotton in its native range. In July 2025, A. biguttula was detected for the first time during the cotton growing season in commercial and experimental fields across multiple counties in Alabama, Florida, Georgia, and South Carolina. Identification was confirmed through morphological examination of diagnostic external features and male genitalia. Within four weeks, the pest was found in 101 counties, with injury symptoms ranging from mild chlorosis to severe necrosis and defoliation. Given the southeastern United States’ average of 979,339 hectares of cotton valued at USD 1.7–USD 2.8 billion annually, the potential for economic impact is considerable. Rapid symptom development, broad host range, and multiple generations per year underscore the need for coordinated monitoring and research to determine preliminary control measures and to identify potential alternative hosts. This report documents the first confirmed occurrence of A. biguttula in U.S. cotton and highlights its potential as an established recurring pest threat in the region.
Evaluation of Peanut Physiological Responses to Heat and Drought Stress Across Growth Chamber and Field Environments
Vennam, Ranadheer Reddy; Beard, Keely M.; Haak, David C.; Balota, Maria (MDPI, 2025-08-28)
Heat-exacerbated drought stress is becoming increasingly common in crop production systems, including peanuts, yet limited information exists on how peanut cultivars respond to this combined stress. While controlled environments allow for the isolation of these stress effects, their relevance to field conditions remains unclear. In this study, five Virginia-type peanut cultivars were evaluated under four treatments in a growth chamber environment, i.e., control, heat, drought, and combined heat and drought stress; and under two treatments in the field environment, i.e., rainfed control, and combined heat and drought stress using rainout shelters. The physiological traits assessed included stomatal conductance and transpiration rate, as well as leaf temperature difference. In both environments, combined heat and drought resulted in a significant decline in physiological performance compared to control conditions. On average, stomatal conductance decreased by 65% in the growth chamber and 21% in the field under combined heat and drought stress, while transpiration was reduced by 49% and 24%, respectively. In the growth chamber, leaf temperature difference increased by 40% under combined stress, whereas it was not statistically different under field conditions. Correlations of the physiological responses between growth chamber and field were stronger under combined stress conditions than under control conditions. Principal component analysis revealed clear genotypic separation based on gas exchange and thermal traits, with NC 20 and Sullivan consistently associated with higher stomatal conductance and transpiration under stress across environments, indicating greater physiological resilience, while Emery clustered with traits linked to stress susceptibility. These findings underscore the significant impacts of combined stress in peanut production and highlight the importance of evaluating cultivar responses under both controlled and field environments to guide crop improvement strategies.
Exploring Substrata Flexibility and Peat Reduction with Wood Fiber in Stratified Substrates
Fields, Jeb S.; Criscione, Kristopher S. (American Society for Horticultural Science, 2025-08-29)
Considerable research has investigated solutions for alternative substrates in reducing horticulture peat applications. Among many options, soilless substrate stratification has been shown to reduce peat inputs by upwards of 50%, and coconut coir and wood fiber are two popular alternatives to peat in many soilless substrates. Most stratified studies have used a pine bark–based substrata; however, scant research has explored substrata variations to promote more flexibility in stratified substrate management decisions. Therefore, the objective of our study was to explore different variations of the top-strata and substrata materials to identify the potential of reduced-peat and no-peat production of greenhouse-grown petunias. A commercial peatlite or coirlite (7:3 blend of peat/coir:perlite by volume) was layered over pine bark or wood fiber (HydraFiber) at a 50/50-by-volume ratio, as well as an unstratified peatlite or coirlite control. Results show that a petunia plant can be produced successfully with equal quality growth using 50% less peat-based media when pine bark or wood fiber is layered below. Moreover, greenhouse petunias can still be grown to salable and marketable quality (with slightly less shoot, root, and flower development) using systems with 100% peat elimination in coir-based unstratified and stratified (coirlite layered over pine bark or wood fiber) profiles. This work provides more options for growers seeking flexible solutions.
The Economic Contributions of the Virginia Seafood Industry and the Effects of Virginia Seafood Products in Retail Stores and Restaurants in 2023
Gonçalves, Fernando H.; van Senten, Jonathan; Schwarz, Michael H. (MDPI, 2025-08-02)
Virginia’s coastal location and abundant marine resources make its seafood industry a vital contributor to the state’s economy, supporting both local communities and tourism. This study applied input–output models and updates the economic contributions of the Virginia seafood industry using 2023 data, building on models developed for 2019 that capture both direct effects and broader economic ripple effects. In 2023, the industry generated USD 1.27 billion in total economic output and supported over 6500 jobs—including watermen, aquaculture farmers, processors, and distributors—resulting in USD 238.3 million in labor income. Contributions to state GDP totaled USD 976.7 million, and tax revenues exceeded USD 390.4 million. The study also evaluates the economic role of Virginia seafood products sold in retail stores and restaurants, based on secondary data sources. In 2023, these sectors generated USD 458 million in economic output, supported more than 3600 jobs, produced USD 136.7 million in labor income, and USD 280.8 million in value-added. Combined tax contributions surpassed USD 74 million. Importantly, the analysis results for the Virginia seafood products from retail and restaurant should not be summed to the seafood industry totals to avoid double-counting, as seafood products move as output from one sector as an input to another. These results provide evidence-based insights to guide decision-making, inform stakeholders, and support continued investment in Virginia’s seafood supply chain and related economic activities.
Lime Calibration for Soilless Media- A Tool for Greenhouse and Nursery Producers
Criscione, Kristopher S. (2025)
Rootzone pH is important to manage to produce marketable nursery and greenhouse stock. This is primarily because mineral nutrients are more available under certain conditions (within specific pH levels). Soilless substrates are essentially inert and contain little nutritional value. Thus, the producer is responsible for supplementing nearly all applied mineral nutrients and water for proper plant development. This goes beyond simple fertilization, where maintaining optimal rootzone pH levels is critical to ensure that applied nutrients are available to the plant. If not, plants can exhibit symptoms of nutrient deficiency or toxicity, resulting in delayed or decreased yield or reduced quality. The first step in ensuring that rootzone conditions are healthy for the plant is gauging the current pH status of the rootzone and correcting it through proper lime adjustments. This extension article highlights the importance of rootzone pH in soilless substrates and explains how to adjust rootzone conditions prior to production by performing lime calibrations.
Moderate Increases in Substrate Packing Density Can Improve Petunia Root Development
Fields, Jeb S.; Criscione, Kristopher S. (American Society for Horticultural Science, 2025-10)
Greenhouse horticulture relies on manual labor for plug transplanting, which is subject to variability in substrate packing density. Little research exists on the effect variable substrate packing density has root morphological development. Petunia hybrid ‘Supertunia Honey’ plugs were grown in peat-based substrates packed at four densities (0.08, 0.10, 0.12, and 0.14 g·cm⁻³). The results indicated that root development was improved with moderately increased substrate density.
Infected Grapevines Are Poor Hosts But Can Serve as Source of Pathogen Transmission for SLF
Islam, Md Tariqul; Kudla-Williams, Crosley; Harner, Andrew D.; Centinari, Michela; Rosa, Cristina (2025)
The potential of the invasive spotted lanternfly (Lycorma delicatula White; SLF) to serve as vector of plant pathogens is especially a concern for grapevine growers, as SLF are known to invade and can heavily infest vineyards, where the insects may encounter grapevines with multiple diseases. In this study, we have found that, when given the choice, SLF preferentially fed on healthy vines and that, when forced, feeding on Pierce’s disease (PD)-infected vines had negative effects on nymph development. Upon transmission trials, most of the recipient vines showed scorching symptoms typical of PD and one of the recipient vines resulted positive by qPCR. Our study suggests that SLF could be a vector of PD, however further experiments are needed to determine if transmission would occur under field conditions.
Feeding by Adult Spotted Lanternfly Impacts Carbon Allocation Post Infestation in Young Grapevines
Harner, Andrew D.; Rowles, Taran K.; Kar, Suraj; Briggs, Lauren; Centinari, Michela (American Society for Enology and Viticulture, 2025-06-01)
Background and goals: The spotted lanternfly (SLF), Lycorma delicatula (White), is an invasive sap-feeding planthopper that can negatively affect grapevine carbon assimilation and allocation, but it is unclear if impacts persist post infestation. The goals of this study were to test if adult SLF feeding impacts carbon allocation after SLF removal and confirm the impacts of prolonged adult SLF feeding on starch storage in young vines. Methods and key findings: 13C pulse-labelling was used to measure 13C content of vegetative tissues in young, container-grown Cabernet franc grapevines. We measured total nonstructural carbohydrates in stems and roots. Feeding by SLF impacted carbon allocation: SLF-infested vines had about two times greater 13C content in stems and over four times less 13C in roots than control vines, 12 days after SLF removal. We confirmed that SLF feeding can inhibit carbon allocation to roots, as demonstrated via reductions in root 13C. Conclusions and significance: This study demonstrates that the impacts of adult SLF feeding on carbon allocation may persist following SLF removal, suggesting that carbon reserve refilling may be limited following substantial late season feeding. These results highlight the importance of controlling the exposure time of vines to high populations of adult SLF to avoid impacts on carbon allocation and storage.
What you eat is what we need: using ants to detect spotted lanternfly (Lycorma delicatula) DNA
Lin, Wei-Jiun; Liu, Fang-Ling Chloe; Huang, Xun-Yi; Del Pozo-Valdivia, Alejandro I.; Leskey, Tracy C.; Yang, Chin-Cheng Scotty (Wiley, 2025)
BACKGROUND: Early detection of invasive species such as the spotted lanternfly (SLF, Lycorma delicatula) is critical for effective management including eradication efforts and limiting further spread. SLF excretes honeydew containing detectable DNA, providing a unique opportunity to leverage environmental DNA (eDNA) for its detection. This study introduces the ant-derived DNA (antDNA) approach, utilizing ants as ‘honeydew samplers’ to detect SLF DNA. We validated the effectiveness of this method through three experiments. RESULTS: Using SLF-specific polymerase chain reaction (PCR), we consistently detected SLF DNA in ants foraging or nesting near SLF infestations. We then showed that after a single honeydew meal, SLF DNA persisted in ants for at least 5 days, even when, subsequently, ants were fed plain honey solution. Lastly, ants collected from honey-baited lure stations along transects radiating from SLF infestations yielded positive detections up to 100 mfrom the core infestations, demonstrating the method's extensive detection range. These findings confirm that ants, through their active foraging and feeding on environmental honeydew and ability to retain the ingested material, are highly reliable SLF DNA samplers. CONCLUSION: Combined with ants' ecological dominance and the ease and low cost of ant collection, the antDNA method offers a sensitive, efficient and practical alternative to traditional, labor-intensive approaches for detecting SLF and potentially other honeydew-producing insects.
Automated Calibration of SWMM for Improved Stormwater Model Development and Application
Ahmadi, Hossein; Scott, Durelle T.; Sample, David J.; Shahed Behrouz, Mina (MDPI, 2025-05-25)
The fast pace of urban development and increasing intensity of precipitation events have made managing urban stormwater an increasingly difficult challenge. Hydrologic models are commonly used to predict flows and assess the performance of stormwater controls, often based on a hypothetical yet standardized design storm. The Storm Water Management Model (SWMM) is widely used for simulating runoff in urban watersheds. However, calibration of SWMM, as with all hydrologic models, is often plagued with issues such as subjectivity, and an abundance of model parameters, leading to delays and inefficiencies in model development and application. Further development of modeling and simulation tools to aid in design is critical in improving the function of stormwater management systems. To address these issues, we developed an integration of PySWMM (a Python wrapper (tool) for SWMM) and Pymoo (a Python package for multi-objective optimization) to automate the SWMM calibration process. The tool was tested using a case study urban watershed in Fredericksburg, VA. This tool can employ either a single-objective or multi-objective approach to calibrate a SWMM model by minimizing the error between prediction and observed values. This tool uses performance metrics including Nash-Sutcliffe Efficiency (NSE), Percent Bias (PBIAS), and Root Mean Square Error (RMSE) Standardized Ratio (RSR) for both single-event and long-term continuous rainfall-runoff processes. During multi-objective optimization calibration, the model achieved NSE, PBIAS, and RSR values of 0.73, 17.1, and 0.52, respectively; while the validation period recorded values of 0.86, 13.1, and 0.37, respectively. Additionally, in the single-objective optimization test case, the model yielded NSE values of 0.68 and 0.73 for the calibration and validation, respectively. The tool also supports parallelized optimization algorithms and utilizes Application Programming Interfaces (APIs) to dynamically update SWMM model parameters, accelerating both model execution and convergence. The tool successfully calibrated the SWMM model, delivering reliable results with suitable computational performance.
Optimizing Maize Agronomic Performance Through Adaptive Management Systems in the Mid-Atlantic United States
Arinaitwe, Unius; Thomason, Wade; Frame, W. Hunter; Reiter, Mark S.; Langston, David (MDPI, 2025-04-27)
Maize (corn) (Zea mays L.) yield is influenced by complex factors, including abiotic and biotic stress and inconsistent nutrient use efficiency, which challenge optimal yield. Standard management recommendations often fall short, prompting interest in intensive management strategies within an Adaptive Maize Management System (ACMS). To investigate this, we employed an addition/omission technique within a randomized complete block design (RCBD) to compare standard maize management recommendations with an intensive management protocol aimed at identifying yield-limiting factors. Our intensive management approach combined early-season biostimulant applications with mid-season supplementation of phosphorus (P), potassium (K), and nitrogen (N) at the V7 stage, followed by foliar fungicides and additional foliar N at the R1 stage. Field trials spanned five Virginia locations over 2022 and 2023 under both irrigated and non-irrigated conditions, yielding ten site-years of data. Analysis via ANOVA in JMP^® Version 18 with Dunnett’s test revealed that the intensive management approach significantly increased grain yield in 3 of 10 experiments. Under non-irrigated conditions, the intensive management practices averaged 5.9% higher yield than the standard management check. We observed a higher response to irrigation in standard management check (34%) than in intensive management check (8.9%). Site-specific irrigation impacts ranged from 14% to 61%. Results emphasize site-specific input recommendations for yield enhancement.
Canopy Transpiration Mapping in an Apple Orchard Using High-Resolution Airborne Spectral and Thermal Imagery with Weather Data
Chandel, Abhilash K.; Khot, Lav R.; Stöckle, Claudio O.; Kalcsits, Lee; Mantle, Steve; Rathnayake, Anura P.; Peters, Troy R. (MDPI, 2025-05-14)
Precision irrigation requires reliable estimates of crop evapotranspiration (ET) using site-specific crop and weather data inputs. Such estimates are needed at high resolutions which have been minimally explored for heterogeneous crops such as orchards. In addition, weather information for estimating ET is very often selected from sources that do not represent conditions like heterogeneous site-specific conditions. Therefore, a study was conducted to map geospatial ET and transpiration (T) of a high-density modern apple orchard using high-resolution aerial imagery, as well as to quantify the impact of site-specific weather conditions on the estimates. Five campaigns were conducted in the 2020 growing season to acquire small unmanned aerial system (UAS)-based thermal and multispectral imagery data. The imagery and open-field weather data (solar radiation, air temperature, wind speed, relative humidity, and precipitation) inputs were used in a modified energy balance (UASM-1 approach) extracted from the Mapping ET at High Resolution with Internalized Calibration (METRIC) model. Tree trunk water potential measurements were used as reference to evaluate T estimates mapped using the UASM-1 approach. UASM-1-derived T estimates had very strong correlations (Pearson correlation [r]: 0.85) with the ground-reference measurements. Ground reference measurements also had strong agreement with the reference ET calculated using the Penman–Monteith method and in situ weather data (r: 0.89). UASM-1-based ET and T estimates were also similar to conventional Landsat-METRIC (LM) and the standard crop coefficient approaches, respectively, showing correlation in the range of 0.82–0.95 and normalized root mean square differences [RMSD] of 13–16%. UASM-1 was then modified (termed as UASM-2) to ingest a locally calibrated leaf area index function. This modification deviated the components of the energy balance by ~13.5% but not the final T estimates (r: 1, RMSD: 5%). Next, impacts of representative and non-representative weather information were also evaluated on crop water uses estimates. For this, UASM-2 was used to evaluate the effects of weather data inputs acquired from sources near and within the orchard block on T estimates. Minimal variations in T estimates were observed for weather data inputs from open-field stations at 1 and 3 km where correlation coefficients (r) ranged within 0.85–0.97 and RMSD within 3–13% relative to the station at the orchard-center (5 m above ground level). Overall, the results suggest that weather data from within 5 km radius of orchard site, with similar topography and microclimate attributes, when used in conjunction with high-resolution aerial imagery could be useful for reliable apple canopy transpiration estimation for pertinent site-specific irrigation management.
Reproductive Performance and Milk Composition of Sows Fed Diets Supplemented with an Immunomodulator
Estienne, Mark J.; Lee, Jung W.; Niblett, R. Tyler; Humphrey, Brooke D.; Monegue, H. James; Lindemann, Merlin D. (MDPI, 2025-05-15)
A cooperative study involving 189 litters from 114 sows (initial BW of 200.8 ± 37.1 kg) at two experiment stations was conducted to investigate the effects of dietary supplementation with OmniGen-AF (OG) (Phibro Animal Health Co., Teaneck, NJ, USA), a nutritional product formulated to improve immune function of animals, on sow reproductive performance and milk composition. Dietary treatments were (1) corn–soybean meal-based control diets or (2) control diets supplemented with OG at 0.75% (~9 g of OG/100 kg BW/d). Supplementation of diets with OG resulted in lesser (p < 0.05) BW changes of sows during lactation (−12.1 vs. −8.2 kg). Litter sizes for control and OG-fed sows were similar, but sows fed OG-based diets had greater (p < 0.05) litter weight for total born (18.3 vs. 19.3 kg) and weaned (63.2 vs. 67.0 kg) and lactation litter gain (47.8 vs. 50.7 kg). Lactation feed intake for the controls and OG-fed sows (5.32 vs. 5.52 kg/d, respectively) did not differ. Supplementing diets with OG increased lactose content (5.78 vs. 5.84%; p = 0.05) and reduced protein content (4.77 vs. 4.68%; p = 0.04) in sow milk. In conclusion, dietary supplementation with OG at 0.75% reduced weight loss during lactation and improved litter weights with marginal effects on the milk composition of sows.

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