Destination Areas (DAs)

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Destination Areas provide faculty and students with new tools to identify and solve complex, 21st-century problems in which Virginia Tech already has significant strengths and can take a global leadership role. The initiative represents the next step in the evolution of the land-grant university to meet economic and societal needs of the world.

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AgroSeek: a system for computational analysis of environmental metagenomic data and associated metadata
Liang, Xiao; Akers, Kyle; Keenum, Ishi M.; Wind, Lauren L.; Gupta, Suraj; Chen, Chaoqi; Aldaihani, Reem; Pruden, Amy; Zhang, Liqing; Knowlton, Katharine F.; Xia, Kang; Heath, Lenwood S. (2021-03-10)
Background Metagenomics is gaining attention as a powerful tool for identifying how agricultural management practices influence human and animal health, especially in terms of potential to contribute to the spread of antibiotic resistance. However, the ability to compare the distribution and prevalence of antibiotic resistance genes (ARGs) across multiple studies and environments is currently impossible without a complete re-analysis of published datasets. This challenge must be addressed for metagenomics to realize its potential for helping guide effective policy and practice measures relevant to agricultural ecosystems, for example, identifying critical control points for mitigating the spread of antibiotic resistance. Results Here we introduce AgroSeek, a centralized web-based system that provides computational tools for analysis and comparison of metagenomic data sets tailored specifically to researchers and other users in the agricultural sector interested in tracking and mitigating the spread of ARGs. AgroSeek draws from rich, user-provided metagenomic data and metadata to facilitate analysis, comparison, and prediction in a user-friendly fashion. Further, AgroSeek draws from publicly-contributed data sets to provide a point of comparison and context for data analysis. To incorporate metadata into our analysis and comparison procedures, we provide flexible metadata templates, including user-customized metadata attributes to facilitate data sharing, while maintaining the metadata in a comparable fashion for the broader user community and to support large-scale comparative and predictive analysis. Conclusion AgroSeek provides an easy-to-use tool for environmental metagenomic analysis and comparison, based on both gene annotations and associated metadata, with this initial demonstration focusing on control of antibiotic resistance in agricultural ecosystems. Agroseek creates a space for metagenomic data sharing and collaboration to assist policy makers, stakeholders, and the public in decision-making. AgroSeek is publicly-available at https://agroseek.cs.vt.edu/ .
Assessing Strontium and Vulnerability to Strontium in Private Drinking Water Systems in Virginia
Scott, Veronica; Juran, Luke; Ling, Erin; Benham, Brian L.; Spiller, Asa (MDPI, 2020-04-08)
A total of 1.7 million Virginians rely on private drinking water (PDW) systems and 1.3 million of those people do not know their water quality. Because most Virginians who use PDW do not know the quality of that water and since strontium poses a public health risk, this study investigates sources of strontium in PDW in Virginia and identifies the areas and populations most vulnerable. Physical factors such as rock type, rock age, and fertilizer use have been linked to elevated strontium concentrations in drinking water. Social factors such as poverty, poor diet, and adolescence also increase social vulnerability to health impacts of strontium. Using water quality data from the Virginia Household Water Quality Program (VAHWQP) and statistical and spatial analyses, physical vulnerability was found to be highest in the Ridge and Valley province of Virginia where agricultural land use and geologic formations with high strontium concentrations (e.g., limestone, dolomite, sandstone, shale) are the dominant aquifer rocks. In terms of social vulnerability, households with high levels of strontium are more likely than the average VAHWQP participant to live in a food desert. This study provides information to help 1.7 million residents of Virginia, as well as populations in neighboring states, understand their risk of exposure to strontium in PDW.
Assessing the Effects of Climate Change on Water Quantity and Quality in an Urban Watershed Using a Calibrated Stormwater Model
Alamdari, Nasrin; Sample, David J.; Steinberg, Peter; Ross, Andrew C.; Easton, Zachary M. (MDPI, 2017-06-27)
Assessing climate change (CC) impacts on urban watersheds is difficult due to differences in model spatial and temporal scales, making prediction of hydrologic restoration a challenge. A methodology was developed using an autocalibration tool to calibrate a previously developed Storm Water Management Model (SWMM) of Difficult Run in Fairfax, Virginia. Calibration was assisted by use of multi-objective optimization. Results showed a good agreement between simulated and observed data. Simulations of CC for the 2041–2068 period were developed using dynamically downscaled North American Regional CC Assessment Program models. Washoff loads were used to simulate water quality, and a method was developed to estimate treatment performed in stormwater control measures (SCMs) to assess water quality impacts from CC. CC simulations indicated that annual runoff volume would increase by 6.5%, while total suspended solids, total nitrogen, and total phosphorus would increase by 7.6%, 7.1%, and 8.1%, respectively. The simulations also indicated that within season variability would increase by a larger percentage. Treatment practices (e.g., bioswale) that were intended to mitigate the negative effects of urban development will need to deal with additional runoff volumes and nutrient loads from CC to achieve the required water quality goals.
Assessment and validation of total water storage in the Chesapeake Bay watershed using GRACE
Sridhar, Venkataramana; Ali, Syed Azhar; Lakshmi, Venkataraman (Elsevier, 2019-05-22)
The Chesapeake Bay is the largest estuary in the United States, and its catchment has heterogeneous hydrological and geomorphologic characteristics. It includes seven major river basins: James, Patuxent, Potomac, Rappahannock, Susquehanna, Western Shore, Eastern Shore, and York. Remote sensing data, along with in-situ observations of streamflow and simulated water budget components, can provide significant understanding of variability in water resources availability in this diverse watershed. In this study, we quantify the terrestrial water storage using both remote sensing and in-situ data and hydrologic model outputs in the Chesapeake Bay watershed. Total water storage change (TWSC) was calculated based on the combination of three methods to identify the best approach in estimating TWSC. These methods evaluated different sources of data, including Parameter elevation Regression on Independent Slopes Model (PRISM) precipitation, MODIS ET, U.S. Geological Survey observed streamflow, and the Variable Infiltration Capacity (VIC) model. Estimated TWSC were in close agreement with GRACE-derived TWSC when we employed VIC-simulated streamflow after calibration with observed streamflow. However, the use of VIC-simulated ET or MODIS-derived ET yielded similar results for TWSC. Assessment of TWSC during extreme events (drought) during the summer months revealed that predicting ET is critical for TWSC in June–August and that VIC-simulated TWSC could be a reliable proxy for GRACE data to assess the water availability in the watershed.
Assessment of rice yield gap under a changing climate in India
Debnath, Subhankar; Mishra, Ashok; Mailapalli, D. R.; Raghuwanshi, N. S.; Sridhar, Venkataramana (2021-06)
Climate change evokes future food security concerns and needs for sustainable intensification of agriculture. The explicit knowledge about crop yield gap at country level may help in identifying management strategies for sustainable agricultural production to meet future food demand. In this study, we assessed the rice yield gap under projected climate change scenario in India at 0.25 degrees x 0.25 degrees spatial resolution by using the Decision Support System for Agrotechnology Transfer (DSSAT) model. The simulated spatial yield results show that mean actual yield under rainfed conditions (Y-a) will reduce from 2.13 t/ha in historical period 1981-2005 to 1.67 t/ha during the 2030s (2016-2040) and 2040s (2026-2050), respectively, under the RCP 8.5 scenario. On the other hand, mean rainfed yield gap shows no change (approximate to 1.49 t/ha) in the future. Temporal analysis of yield indicates that Y-a is expected to decrease in the considerably large portion of the study area (30-60%) under expected future climate conditions. As a result, yield gap is expected to either stagnate or increase in 50.6 and 48.7% of the study area during the two future periods, respectively. The research outcome indicates the need for identifying plausible best management strategies to reduce the yield gap under expected future climate conditions for sustainable rice production in India.
Climate Change Adaptation for Agriculture. Mitigating Short- and Long-Term Impacts of Climate on Crop Production
Easton, Zachary M.; Faulkner, Joshua W. (Virginia Cooperative Extension, 2014-09-24)
Climate change and climate variability pose a great risk to agricultural production and farm livelihoods, and producers will need to adapt to a changing climate that is expected to be significantly more variable in order to meet these challenges. Agricultural producers have a long record of successful adaptation to a host of internal and external pressures and have made remarkable strides in the face of these pressures.
Coenzyme Engineering of a Hyperthermophilic 6-Phosphogluconate Dehydrogenase from NADP(+) to NAD(+) with Its Application to Biobatteries
Chen, Hui; Zhu, Zhiguang; Huang, Rui; Zhang, Y. H. Percival (Nature Publishing Group, 2016-11-02)
Engineering the coenzyme specificity of redox enzymes plays an important role in metabolic engineering, synthetic biology, and biocatalysis, but it has rarely been applied to bioelectrochemistry. Here we develop a rational design strategy to change the coenzyme specificity of 6-phosphogluconate dehydrogenase (6PGDH) from a hyperthermophilic bacterium Thermotoga maritima from its natural coenzyme NADP(+) to NAD(+). Through amino acid-sequence alignment of NADP(+)-and NAD(+)-preferred 6PGDH enzymes and computer-aided substrate-coenzyme docking, the key amino acid residues responsible for binding the phosphate group of NADP(+) were identified. Four mutants were obtained via site-directed mutagenesis. The best mutant N32E/R33I/T34I exhibited a x 6.4 x 10(4)-fold reversal of the coenzyme selectivity from NADP(+) to NAD(+). The maximum power density and current density of the biobattery catalyzed by the mutant were 0.135 mW cm(-2) and 0.255 mA cm(-2), similar to 25% higher than those obtained from the wide-type 6PGDH-based biobattery at the room temperature. By using this 6PGDH mutant, the optimal temperature of running the biobattery was as high as 65 degrees C, leading to a high power density of 1.75 mW cm(-2). This study demonstrates coenzyme engineering of a hyperthermophilic 6PGDH and its application to high-temperature biobatteries.
Combined statistical and spatially distributed hydrological model for evaluating future drought indices in Virginia
Kang, Hyunwoo; Sridhar, Venkataramana (Elsevier, 2017-06-06)
Study region: Virginia, United States. Study focus: Climate change is expected to impact the intensity and severity of droughts; therefore, it is necessary to simulate future drought conditions using temperature and precipitation projections and hydrological models to derive reliable hydrological variables and drought indices. The objective of this study was to evaluate climate change influences on future drought potential and water resources in five major river basins in Virginia. In this study, the Soil and Water Assessment Tool (SWAT) and Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models were used to compute a Standardized Soil Moisture Index (SSI), a Multivariate Standardized Drought Index (MSDI), and a Modified Palmer Drought Severity Index (MPDSI) for both historic and future periods. The drought conditions were evaluated, and their occurrences were determined at river basin scales. New hydrological insights for the region: The results of the ensemble mean of SSI indicated that there was an overall increase in agricultural drought occurrences projected in the New (> 1.3 times) and Rappahannock (> 1.13 times) river basins due to increases in evapotranspiration and surface and groundwater flow. However, MSDI and MPDSI exhibited a decrease in projected future drought, despite increases in precipitation, which suggests that it is essential to use hybridmodeling approaches and to interpret application-specific drought indices that consider both precipitation and temperature changes.
Communicating Climate Change to Agricultural Audiences
Easton, Zachary M.; Faulkner, Joshua W. (Virginia Cooperative Extension, 2016-11-15)
Discusses climate change and challenges related to climate change, in relation to agriculture.
Comparison of short-term streamflow forecasting using stochastic time series, neural networks, process-based, and Bayesian models
Wagena, Moges B.; Goering, Dustin; Collick, Amy S.; Bock, Emily; Fuka, Daniel R.; Buda, Anthony R.; Easton, Zachary M. (2020-04)
Streamflow forecasts are essential for water resources management. Although there are many methods for forecasting streamflow, real-time forecasts remain challenging. This study evaluates streamflow forecasts using a process-based model (Soil and Water Assessment Tool-Variable Source Area model-SWAT-VSA), a stochastic model (Artificial Neural Network -ANN), an Auto-Regressive Moving-Average (ARMA) model, and a Bayesian ensemble model that utilizes the SWAT-VSA, ANN, and ARMA results. Streamflow is forecast from 1 to 8 d, forced with Quantitative Precipitation Forecasts from the US National Weather Service. Of the individual models, SWAT-VSA and the ANN provide better predictions of total streamflow (NSE 0.60-0.70) and peak flow, but underpredicted low flows. During the forecast period the ANN had the highest predictive power (NSE 0.44-0.64), however all three models underpredicted peak flow. The Bayesian ensemble forecast streamflow with the most skill for all forecast lead times (NSE 0.49-0.67) and provided a quantification of prediction uncertainty.
Cyberbiosecurity: A New Perspective on Protecting US Food and Agricultural System
Duncan, Susan E.; Reinhard, Robert; Williams, Robert C.; Ramsey, A. Ford; Thomason, Wade E.; Lee, Kiho; Dudek, Nancy; Mostaghimi, Saied; Colbert, Edward; Murch, Randall Steven (Frontiers, 2019-03-29)
Our national data and infrastructure security issues affecting the "bioeconomy" are evolving rapidly. Simultaneously, the conversation about cyber security of the U.S. food and agricultural system (cyber biosecurity) is incomplete and disjointed. The food and agricultural production sectors influence over 20% of the nation's economy ($ 6.7T) and 15% of U.S. employment (43.3M jobs). The food and agricultural sectors are immensely diverse and they require advanced technologies and efficiencies that rely on computer technologies, big data, cloud-based data storage, and internet accessibility. There is a critical need to safeguard the cyber biosecurity of our bio economy, but currently protections are minimal and do not broadly exist across the food and agricultural system. Using the food safetymanagement Hazard Analysis Critical Control Point systemconcept as an introductory point of reference, we identify important features in broad food and agricultural production and food systems: dairy, food animals, row crops, fruits and vegetables, and environmental resources (water). This analysis explores the relevant concepts of cyber biosecurity from food production to the end product user (such as the consumer) and considers the integration of diverse transportation, supplier, and retailer networks. We describe common challenges and unique barriers across these systems and recommend solutions to advance the role of cyber biosecurity in the food and agricultural sectors.
Data on floating treatment wetland aided nutrient removal from agricultural runoff using two wetland species
Spangler, Jonathan T.; Sample, David J.; Fox, Laurie J.; Owen, James S. Jr.; White, Sarah A. (Elsevier, 2018-12-15)
The data presented in this article are related to the research article entitled “Floating treatment wetland aided nutrient removal from agricultural runoff using two wetland species” (Spangler et al., 2018). This Data in Brief article provides data on concentrations of common ions, macro- and micro-nutrients and metals every other week during a floating treatment wetland (FTW) mesocosm experiment, and macro- and micro-nutrient contents in cumulative plant tissues, data on continuously monitored water temperature, and nitrogen and phosphorus removal curves assessed every other week. The full data set is made available to enable critical or extended analysis of the research.
Denitrification Management
Easton, Zachary M.; Lassiter, Emily (Virginia Cooperative Extension, 2013-03-27)
Provides an explanation of denitrification and how it occurs including descriptions of the nitrogen cycle, environmental impacts of nitrogen levels, denitrification management, limitation, and unknowns.
Denitrifying Bioreactors: An Emerging Best Management Practice to Improve Water Quality
Lassiter, Emily; Easton, Zachary M. (Virginia Cooperative Extension, 2013-04-12)
This fact sheet discusses denitrifying bioreactors, what they are, how they work, applications, current research, expected costs and includes a glossary of terms.
Deriving the Reservoir Conditions for Better Water Resource Management Using Satellite-Based Earth Observations in the Lower Mekong River Basin
Ali, Syed Azhar; Sridhar, Venkataramana (MDPI, 2019-12-03)
The Mekong River basin supported a large population and ecosystem with abundant water and nutrient supply. However, the impoundments in the river can substantially alter the flow downstream and its timing. Using limited observations, this study demonstrated an approach to derive dam characteristics, including storage and flow rate, from remote-sensing-based data. Global Reservoir and Lake Monitor (GRLM), River-Lake Hydrology (RLH), and ICESat-GLAS, which generated altimetry from Jason series and inundation areas from Landsat 8, were used to estimate the reservoir surface area and change in storage over time. The inflow simulated by the variable infiltration capacity (VIC) model from 2008 to 2016 and the reservoir storage change were used in the mass balance equation to calculate outflows for three dams in the basin. Estimated reservoir total storage closely resembled the observed data, with a Nash-Sutcliffe efficiency and coefficient of determination more than 0.90 and 0.95, respectively. An average decrease of 55% in outflows was estimated during the wet season and an increase of up to 94% in the dry season for the Lam Pao. The estimated decrease in outflows during the wet season was 70% and 60% for Sirindhorn and Ubol Ratana, respectively, along with a 36% increase in the dry season for Sirindhorn. Basin-wide demand for evapotranspiration, about 935 mm, implicitly matched with the annual water diversion from 1000 to 2300 million m³. From the storage–discharge rating curves, minimum storage was also evident in the monsoon season (June–July), and it reached the highest in November. This study demonstrated the utility of remote sensing products to assess the impacts of dams on flows in the Mekong River basin.
Description of future drought indices in Virginia
Kang, Hyunwoo; Sridhar, Venkataramana (Elsevier, 2017-07-20)
This article presents projected future drought occurrences in five river basins in Virginia. The Soil and Water Assessment Tool (SWAT) and the Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models were used to derive input variables of multiple drought indices, such as the Standardized Soil Moisture index (SSI), the Multivariate Standardized Drought Index (MSDI), and the Modified Palmer Drought Severity Index (MPDSI) for both historic and future periods. The results of SSI indicate that there was an overall increase in agricultural drought occurrences and that these were caused by increases in evapotranspiration and runoff. However, the results of the MSDI and MPDSI projected a decrease in drought occurrences in future periods due to a greater increase in precipitation in the future. Furthermore, GCM-downscaled products (precipitation and temperature) were verified using comparisons with historic observations, and the results of uncertainty analyses suggest that the lower and upper bounds of future drought projections agree with historic conditions.
Environmental health disparities in the Central Appalachian region of the United States
Krometis, Leigh-Anne H.; Gohlke, Julia M.; Kolivras, Korine N.; Satterwhite, Emily M.; Marmagas, Susan West; Marr, Linsey C. (De Gruyter, 2017-09-26)
Health disparities that cannot be fully explained by socio-behavioral factors persist in the Central Appalachian region of the United States. A review of available studies of environmental impacts on Appalachian health and analysis of recent public data indicates that while disparities exist, most studies of local environmental quality focus on the preservation of nonhuman biodiversity rather than on effects on human health. The limited public health studies available focus primarily on the impacts of coal mining and do not measure personal exposure, constraining the ability to identify causal relationships between environmental conditions and public health. Future efforts must engage community members in examining all potential sources of environmental health disparities to identify effective potential interventions.
Evaluating the bio-hydrological impact of a cloud forest in Central America using a semi-distributed water balance model
Caballero, Luis A.; Easton, Zachary M.; Richards, Brian K.; Steenhuis, Tammo S. (De Gruyter, 2013-03-01)
Water scarcity poses a major threat to food security and human health in Central America and is increasingly recognized as a pressing regional issues caused primarily by deforestation and population pressure. Tools that can reliably simulate the major components of the water balance with the limited data available and needed to drive management decision and protect water supplies in this region. Four adjacent forested headwater catchments in La Tigra National Park, Honduras, ranging in size from 70 to 635 ha were instrumented and discharge measured over a one year period. A semi-distributed water balance model was developed to characterize the bio-hydrology of the four catchments, one of which is primarily cloud forest cover. The water balance model simulated daily stream discharges well, with Nash Sutcliffe model efficiency (E) values ranging from 0.67 to 0.90. Analysis of calibrated model parameters showed that despite all watersheds having similar geologic substrata, the bio-hydrological response the cloud forest indicated less plant available water in the root zone and greater groundwater recharge than the non cloud forest cover catchments. This resulted in watershed discharge on a per area basis four times greater from the cloud forest than the other watersheds despite only relatively minor differences in annual rainfall. These results highlight the importance of biological factors (cloud forests in this case) for sustained provision of clean, potable water, and the need to protect the cloud forest areas from destruction, particularly in the populated areas of Central America.
Factors Affecting Phosphorous in Groundwater in an Alluvial Valley Aquifer: Implications for Best Management Practices
Flores-López, Francisco; Easton, Zachary M.; Geohring, Larry D.; Vermeulen, Peter J.; Haden, Van R.; Steenhuis, Tammo S. (MDPI, 2013-05-02)
Many streams in the US are impaired because of high Soluble Reactive Phosphorous (SRP) contributions from agriculture. However, the drivers of ecological processes that lead to SRP loss in baseflow from groundwater are not sufficiently understood to design effective Best Management Practices (BMPs). In this paper, we examine how soil temperature and water table depth influence the SRP concentrations in groundwater for a dairy farm in a valley bottom in the Catskills (NY, USA). Measured SRP concentrations in groundwater and baseflow were greater during the fall, when soil temperatures are warmer, than during winter and spring. The observed concentrations were within the bounds predicted by groundwater temperatures using the Arrhenius equation, except during fall, when concentrations rose above these predictions. These elevated concentrations were likely caused by mineralization and consequent accumulation of phosphorous (P) in summer. In addition, SRP concentrations were greater in near-stream areas, where water tables where higher. In short, SRP concentrations are dependent on temperature, demonstrating the importance of understanding the underlying mechanism of ecological processes. In addition, results suggest BMPs that apply manure on land having a deep groundwater, instead of on land with a shallow water table will lower overall SRP contributions.
Factors When Considering an Agricultural Drainage System
Easton, Zachary M.; Bock, Emily; Collick, Amy S. (Virginia Cooperative Extension, 2017-02-23)
A well designed drainage system can improve crop yield, and lower the variation in crop yield by removing excess water in the soil.

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