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Category: Information Systems
West Africa is particularly vulnerable to impacts of land use changes due to prevalent deforestation, urbanization, and agricultural expansion. Remote sensing observations provide decades of time series information regarding environmental and land surface conditions. Many studies have used these datasets to conduct ecological and climatic trend analysis within the West African region, exploring change patterns of classification types or singular variables, but there remains a significant gap in comprehending causal linkages among different variables and underlying structures of the relationships. This study aims to understand the relationships among remotely sensed vegetation indices and environmental indicators with patterns of landcover and demographic change in Ghana using exploratory factor analysis (EFA). EFA will enable the evaluation of the influence of environmental and biophysical variables on patterns of demographic and land cover changes in the region. This analysis will allow for the interpretation of multivariate correlations of each individual factor (i.e. precipitation) with spatial patterns of trends (e.g. change in forest cover) and will simultaneously help interpret the variations in the strengths of interdependence of these factors across Ghana.
Julie Peeling
Advisor: Dr. Jasmeet Judge
Live on Zoom: Thursday, November 19 (3-4 p.m.)
Category: Biological Engineering, Protein Therapeutics
Although misfolded α-synuclein (α-syn) conformers play a central role in Parkinson’s Disease (PD) pathogenesis and progression, clearance of misfolded α-syn in the midbrain remains an unmet clinical need. Brain delivery of wild-type (WT) neurosin was found to degrade WT α-syn and alleviate PD-like symptoms in mouse models of multiple system atrophy (MSA) and Lewy Body Dementia (LBD). While promising, recombinant WT neurosin degrades WT α-syn oligomers slowly and shows little to no catalytic activity against common point mutants of α-syn (A53T, A30P). Moreover, α-syn is neither the preferred nor the only in vivo substrate of neurosin. Rather, WT neurosin processes dozens of physiological substrates, including protease-activated receptors (PAR) and myelin basic protein, proteins whose overactivation are linked to neuroinflammation, neuronal injury and glutamate neurotoxicity. While these obstacles currently limit the potential of neurosin as a therapeutic, an engineered neurosin variant with high catalytic activity and α-syn selectivity may offer unique therapeutic opportunities for PD and other synucleopathies. We have at our disposal a unique engineering platform that selects for both catalytic activity and selectivity, two key aspects in evolving proteases. A targeted proteolytic degradation strategy may lead to higher efficacy compared to monoclonal antibodies because of enzymatic turnovers rather than stoichiometric binding and a distinct mechanism that could induce a neuroprotective environment to help fight PD.
Spencer Serrano
Advisor: Dr. Carl Denard (UF Department of Chemical Engineering)
Live on Zoom: Wednesday, November 18 (3-4 p.m.)
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Category: Land and Water
Surface energy balance models are developed in contrasting geographical and agrometeorological conditions, and their suitability for a particular climatic setting needs to be assessed before considering the use of these models in decision making. In this study, four commonly used remote sensing models viz. METRIC [Mapping Evapotranspiration at High Resolution with Internalized Calibration], SEBAL [Surface Energy Balance Algorithm for Land], S-SEBI [Simplified Surface Energy Balance Index], and SEBS [Surface Energy Balance System] were used to quantify and map the spatio-temporal variation of crop evapotranspiration (ETc) in semi-arid to arid region of Big Horn Basin, Wyoming (Landsat Path/Row: 37/29). Model retrievals from 19 cloud-free Landsat 7 and 8 images were validated with Bowen Ratio Energy Balance System (BREBS) flux stationed on a center pivot irrigated field during 2017 (Sugarbeet), 2018 (Dry bean), and 2019 (Barley) growing season. METRIC proved to be superior of all the models on estimating crop ETc owing to its lower and consistent RMSE values (0.07 – 0.09 mm hr-1). A mid-season scatterplot and relative difference map between the models considered showed METRIC and SSEBI model close on their estimates of instantaneous ETc with RMSE of 0.07 mm hr-1 and relative difference of 0.01 mm hr-1. METRIC model was further used to map and quantify monthly, and seasonal ETc where METRIC underestimated growing season ETc in the range between 3.2 to 6 %. Likewise, Seasonal ETc by land-use type showed significant variation over the study area with crop ETc was 52% higher than natural vegetation ETc.
Bibek Acharya
Advisor: Dr. Vivek Sharma
Live on Zoom: Thursday, November 19 (3-4 p.m.)
Category: Land and Water, Irrigation
Increasing demand on Florida’s water systems is predicted to exceed the available supply in vulnerable regions by the year 2035. Much of the residential water supply in Florida is used to irrigate landscapes and turfgrass. Previous studies have shown that soil compaction and lack of soil organic matter in new lots can pose issues for the establishment of turfgrass, and that incorporation of a soil amendment can improve landscape outcomes. This study aims to evaluate the rate that amendments should be applied to a new lot, the characteristics that define successful soil amendments, and potential reduction in irrigation rates from current recommended rates. A 120-plot study was designed in which five organic soil amendments were incorporated into the top six inches of sandy soil at rates of 0 (control), 1, 2, and 4 yd3/1000 ft2. St. Augustine sod was established in Fall 2019, after which plots received uniform irrigation. Beginning in June 2020 , 0%, 25%, 50%, and 75% of the IFAS recommended irrigation rates for St. Augustine grass in Central Florida were initiated. Turfgrass quality was assessed on an approximately weekly basis, and soil samples were collected from plots at the beginning of the growing season. Soil samples were analyzed for soil bulk density, volumetric water content, soil organic matter, and soil texture. Preliminary results show improvements in turf quality in amended plots compared to the unamended control, and that improvements were consistent across amendment types. Results also suggest that acceptable turf quality can be achieved at an amendment rate of 2 yd3/1000 ft2 and at the 25% irrigation rate, which would reduce material costs for developers and conserve significant quantities of water.
Ronald Fox
Advisor: Dr. Eban Bean
Live on Zoom: Thursday, November 19 (3-4 p.m.)
Category: Machine Learning
Sensor data has a profound impact across the agricultural supply chain, improving decision capabilities, but the data analysis is a major challenge for using biosensor to measure small molecules in complex solution, since the biosensor cannot directly reflect the sample solution’s physical states. Here, the biosensor was used to monitor the water quality, and different statistical machine learning(SML) methods were used to analyze the data. The data had 8 concentrations and each concentration was measured by 15 different biosensors. We transferred the concentration to different types of responses in SML model and evaluated the performance of different methods for three 3 scenarios: new concentration, new biosensor, and new concentration & new sensor. We found the best data analysis method and established the most accuracy model for different scenarios. The prediction accuracy was highly increased by using our data analysis method and based on our R code, we would develop a mobile phone-based demonstration focused on predicting the concentration of bacteria in the sample solution, which is important information to decide whether we can recycle utilization of water resource.
Hanyu Qian
Advisor: Dr. Nikolay Bliznyuk and Dr. Eric McLamore
Live on Zoom: Wednesday, November 18 (3-4 p.m.)
Category: Land and Water, Biological Engineering
To improve the nitrogen removal capability of constructed wetlands, the biochar, produced from bamboo, activated with HCl and coated with Fe (FeCl3·6H2O), and then was added as a substrate into the systems. Three horizontal subsurface flow constructed wetlands (HSCWs) was established to treat the tailwater from the wastewater treatment plant: C-HSCW(quartz sand+soil), B-HSCW(quartz sand+soil+unmodified biochar), and FeB-HSCW(quartz sand+soil+Fe-modified biochar). Under different combinations of hydraulic retention time and nitrogen loading, the FeB-HSCW revealed extremely effective nitrogen removal. The highest removal efficiencies of NO3−-N (95.30%), Total N (86.68%), NH4+-N (86.33%), NO2−- N (79.35%) were obtained in FeB-HSCW with the hydraulic retention time of 96 h. and low influent nitrogen loading. Nitrogen mass balance analysis showed that microbial processes played the most important role of nitrogen removal in HSCWs and the Fe-modified biochar significantly enhanced the microbial nitrogen removal. The contributions of microorganisms, substrate storage and plant uptake on the total amount of nitrogen removal in the FeB-HSCW was 92.69%, 2.97% and 4.34%, respectively. Moreover, FeB significantly increased the abundances of genes involved in nitrogen removal. Thus, Fe-modified biochar provides a feasible and effective amendment for constructed wetlands to improve the nitrogen removal, particularly nitrate-N, for low C/N wastewaters by enhancing the microbial nitrogen removal capacity.
Yicheng Yang
Advisor: Dr. Bin Gao
Live on Zoom: Wednesday, November 18 (3-4 p.m.)
UNDERGRADUATE STUDENTS FIRST-YEAR GRADUATE STUDENTS GRADUATE STUDENTS BACK TO TOP
Category: Water Resource Monitoring
Water resource monitoring solutions available today are often cost-prohibitive to water resource professionals. These solutions usually have proprietary components, preventing their users from modifying/customizing as required. They also lack the ability to record spatial variation in water quality metrics. GatorByte’s primary objective is to provide the water managers a low-cost, real-time, high-frequency, and mobile datalogging platform that can be easily customized as needed. The major components of the platform are- a water-tight buoy (datalogger) that collects the metrics, a cloud-based server (datahub), and a web-based dashboard to visualize the past and real-time data using charts and maps. The buoy has the following basic water-quality sensors- pH, temperature, Dissolved Oxygen, and electroconductivity. The datalogger also collects spatial data using an onboard GPS, which will allow users to getting better insights into the variation of water-quality metrics over both space and time. The buoy has a form factor of a 32 oz. bottle (10 in. x 3 in. Ø), that will allow it to float down shallow streams and stormwater drains with ease. The datalogger is built using inexpensive and easily available components and sensors, 3D-printed housing, and inhouse-designed circuit board to enhance the availability, reproducibility, customizability, and maintenance of the system.
Piyush Agade
Advisor: Dr. Eban Bean
Live on Zoom: Thursday, November 19 (3-4 p.m.)
Category: Greenhouses Environmental Control
The most common systems to cool greenhouses in arid and semi-arid regions are fan-and-pad evaporative cooling systems (FAPECS) due to their simplicity and high efficiency in low humidity conditions. Still, this efficiency drops when operating in humid regions and can produce relative humidity (Rh) levels above optimal for plant production. Thus, methods to improve the efficiency of FAPECS, such as using liquid desiccant systems (LDS), are desired. Here, we evaluated a calcium chloride liquid desiccant (CaCl2-H2O) potential to control Rh in a FAPECS under two concentrations and temperatures. These results were compared to a lithium chloride liquid desiccant (LiCl-H2O) effectiveness. Then, the use of CaCl2-H2O under a range of temperature and concentration combinations was studied to compare the FAPECS efficiencies. To regenerate the desiccant and recover water, a single slope solar still was used. The still was modeled using computational fluid dynamic (CFD) software (SolidWorks®) and evaluated to determine the effects of hydrophilic and hydrophobic coatings on the droplet area and the solar still productivity. The results suggested that LiCl-H2O outperformed CaCl2-H2O under both temperatures and concentrations in lowering Rh. However, the benefits of using CaCl2-H2O outweighed the issues associated with using LiCl-H2O. Although there was increased heat removal with the FAPECS coupled with LDS (CaCl2-H2O), the sensible cooling gain was limited, suggesting limited applications. Additionally, applying a hydrophilic coating on the still's cover resulted in a 23 % increase in productivity than uncoated glass. The CFD model simulation and experimental results were in good agreement (R2 = 0.99).
Waleed Bin Masoud
Advisor: Dr. Melanie Correll
Live on Zoom: Thursday, November 19 (3-4 p.m.)
Category: Modeling
Agent Based Models (ABM) are stochastic models that are often used to simulate complex coupled human and natural systems. As ABMs complexity increase (more agents, rules and stochastic interactions), they behave more like black boxes. Analysis of ABMs require of an ensemble of diverse techniques to extract information of the inside processes and their final outputs. Global Sensitivity Analysis (GSA) is a High-Dimensional Variance Decomposition technique able to estimate the contribution of each model input (and associated processes) onto the predicted outcomes, helping to manage output and input uncertainties. Currently, ABM literature only focuses on output uncertainty due to input uncertainty when applying GSA. As stochastic models, ABM also exhibit the intrinsic stochastic uncertainty due to its random processes. This work presents a methodological ABM GSA analysis framework to account for both input and stochastic uncertainty and discusses the importance of including both component for exploration and management of complex systems.
Alvaro Carmona-Cabrero
Advisor: Dr. Rafael Munoz-Carpena
Live on Zoom: Wednesday, November 18 (3-4 p.m.)
Category: Land and Water, Information Systems, Modeling
Progress in hydrological science and water resource management is largely limited by the rate at which water quality data can be collected and transformed into actionable information. Water quality data is needed to understand the overall health of water bodies and the underlying physical, chemical, biological, and ecological processes so that resource managers can make well-informed decisions. In recent studies, spectroscopy-based sensing technologies have been used to generate high-frequency data for multiple water quality parameters. While several devices designed to perform in situ spectroscopy are currently available off-the-shelf, their broad use is limited by cost (in the range of $20,000) and lack of control over how the system operates and manages data. Recent technological advancements have enabled investigators to custom build spectroscopy-based in situ water quality data collection systems. A low-cost (~$2,500) benchtop prototype was developed which can detect multiple water quality parameters using a miniature spectrophotometer and light source housed in a 3D-printed enclosure and controlled using a microcomputer. In the future, this system will be adapted to perform high-frequency collection of water quality data, in situ, and used to investigate the effects of data collection frequency on hydrological model performance in urban settings and to analyze nutrient dynamics within urban stormwater infrastructure. Beyond the goals of this study, the same data collection framework can be used in future applications that require highly controlled in situ water quality data collection; such as adaptive control of stormwater systems, watershed fingerprinting, and improving pollutant detection through advanced statistical techniques.
Joe Barrett Carter
Advisor: Dr. Eban Bean and Dr. Aditya Singh
Live on Zoom: Wednesday, November 18 (3-4 p.m.)
Category: Climatology, Modeling
Precipitation is a crucial meteorological parameter in agriculture, hydrology, and weather derivatives. Accurate estimates of precipitation and understanding its spatial and temporal characteristics are of profound importance. This paper aims to address the lack of precipitation detection issue of remote-sensing precipitation products. We first found a single threshold on the Global Precipitation Measurement (GPM) precipitation estimate cannot well define the precipitation event. Hence, we adopted an array of machine learning algorithms to identify precipitation events over Florida. Our best-resulting model had remarkable improvement from the GPM estimates. The improvement was mainly achieved by correcting spatial errors, incorporating diurnal temperature amplitude information, and calibrating gauge observations. To further understand the merging process, we explored the spatial and temporal characteristics of precipitation by designing investigative models. These models were designed to track the optimal information flow from the GPM estimates, temporal and spatial information, and temperature information to the observed precipitation events. We found the most informative GPM estimates for identifying precipitation events was always the one or two nearby GPM pixels, and their position had a clear trend across seasons. Moreover, we found that the diurnal temperature amplitude was related to precipitation events, especially in the cold season.
Yi Han
Advisor: Dr. Nikolay Bliznyuk
Live on Zoom: Wednesday, November 18 (3-4 p.m.)
Category: Modeling
Social and hydrological dynamics are coupled, nonlinear, and complex. To clarify and enhance our understanding of such dynamics, we developed a stylized model that combines hydrological and social dynamics of a generic coupled human-water system. In this model, neither too much (flood) nor too little water (drought) is desirable, and the population self-organizes to respond to relative benefits they derive from the water system and outside opportunities. Despite its simplicity, the model yields seven different regimes, governed by hydrological and socioeconomic factors. As external drivers change, the conditions giving rise to these regimes shift, and with them, social consequences such as migration patterns. Clear understanding of such regime boundaries (thresholds) derived from this simple model contributes to insights on how one might cope with a complex socio-hydrological system under change.
Mehran Homayounfar
Advisor: Dr. Rachata Muneepeerakul
Live on Zoom: Thursday, November 19 (3-4 p.m.)
Category: Agriculture Production, Modeling
"Since its introduction to Florida in the 1800s, the Brazilian peppertree (Schinus terebinthifolia) has proven to be one of the most aggressive invasive species in the state. In addition to displacing native plant species and reducing biodiversity, it is a host to citrus pests, including the root weevil. Over the decades, Schinus has been found to be resilient to costly chemical and mechanical control measures, prompting research into biological control (biocontrol) agents. Recently, laboratory experiments have shown that thrips selectively feed on Schinus shoot meristems; however, their overall effectiveness as a biocontrol agent in the wild remains unknown.
In the present study, a method of remotely determining if Brazilian peppertrees are thrip-damaged is developed using hyperspectral imaging data. Three groups (thrip-inoculated, mechanically damaged, and control) of mature plants were imaged using the UF ABE SPOT Facility’s hyperspectral sensor every three days for a month. Following radiometric calibration, quadratic discriminant analysis and support vector machines were used to classify plant pixels as belonging to thrip-damaged, mechanically damaged, or healthy plants. Well-trained models may be used in conjunction with hyperspectral data collected from drones to characterize thrip damage in the wild."
Stephen Lantin
Advisor: Dr. Aditya Singh and Dr. Melanie Correll
Live on Zoom: Thursday, November 19 (3-4 p.m.)
Category: Modeling
Seasonal streamflow forecasts help make efficient environmental and economic decisions, such as water supply management and planning for flood/drought hazards, by providing a narrower range of possible outcomes that are likely to occur. Probabilistic forecasts, rather than point predictions commonly provided by machine learning, are preferable because they enable decision-makers to take risks and benefits into account and provides the information needed for exceedance probability analysis, which is important in water management. However, the forecasting can be challenging due to the complex relationship between large-scale climate periodic phenomenon and regional streamflow. Aiming to provide the methodological framework of using machine learning for probabilistic forecasting in hydro-meteorological applications, this work developed probabilistic streamflow forecasts using machine learning that captures the relationships between streamflow and Niño indices which reflects the impact of the El Niño-Southern Oscillation (ENSO). The results show that the probabilistic forecasts provided by Quantile Regression Forest and quantile regression with Boosting Trees are generally skillful in the January-March season in West-Central Florida. Extreme observations are better caught by multiple linear regression with Elastic Net selection. The importance of predictors provided by random forest shows that for longer lags there is more importance in Niño indices than that in historical streamflow, reflecting the lagged influence of ENSO. Machine learning was found to provide skillful probabilistic forecasting for seasonal streamflow and showed improved forecasting over climatology for extreme observations, indicating its strengths in hydro-meteorological applications.
Jia-Yi Ling
Advisor: Dr. Christopher Martinez and Dr. Nikolay Bliznyuk
Live on Zoom: Wednesday, November 18 (3-4 p.m.)
Category: Modeling
Cassava is the second largest supplier of starch in the world after maize and is used by food, paper, textile, and pharmaceutical industries. The quality of cassava is defined by its starch content of fresh roots. A high starch content equates to optimal quality, increased extraction efficiency, and premium payments to farmers. Starch content and dry matter cassava root measurements were assembled from 49 published experiments. The data showed a linear relationship between starch content and the percentage of dry matter in fresh cassava roots. Based on the literature review, up to 60% of the observed variation of dry matter content in fresh cassava roots is linked to the variation among cultivars. In addition, a decline in the water supply during the month prior to harvest tends to increase dry matter content in fresh cassava roots, but this pattern varies with cultivars and other growing conditions. The relationship between starch and dry matter content in fresh cassava roots that was developed based on the literature survey together with dry matter content as a function of genetic variation and water supply before harvesting, can be added as a new module to existing cassava models to simulate cassava quality.
Patricia Moreno Cadena
Advisor: Dr. Gerrit Hoogenboom and Dr. Senthold Asseng
Live on Zoom: Wednesday, November 18 (3-4 p.m.)
Category: Land and Water
The natural environment is characterized by variability and change over a wide range of space and time scales. Superimposed on these are population growth-related changes due to the demands on natural resources, which outpace global population growth. The hydrologic cycle is an integrator of the effects of all these changes. In heavily impacted river basins, water-related management is responsive, aimed at minimizing impacts on the water resources. However, less impacted river basins present an opportunity for proactive management, where potential impacts of drivers of change can be used to inform pro-active management strategies. The transboundary Cubango-Okavango River Basin in southern Africa is one of the few large, near-pristine river basins globally. However, it is faced with the emerging problems of increasing rates of change due to diverse anthropogenic activities. The riparian states of the basin have different, non-compatible aspirations of the water resources of the basin. Changes in the upper reaches of the basin will be propagated downstream, where they will add on to the existing challenges within each country’s portion of the basin. Variations and reduction of hydrologic flows, which will induce changes in the abundance and distribution of flora in the downstream Okavango Delta, have been identified as some of the key water-related issues in the basin. The proposed study will assess the potential, combined effects of climate and land-use changes, including dam construction, on the socio-ecological-hydrological system of the Cubango-Okavango River Basin. Attention will be focused on the importance of each factor in the change-impact matrix.
Edwin Ntong Mosimanyana
Advisor: Dr. Rafael Muñoz-Carpena
Live on Zoom: Wednesday, November 18 (3-4 p.m.)
Category: Land and Water, Modeling
In diverse environmental challenges, we observe interdependencies between humans and nature. Existing studies have more focused on what kind of social and natural factors should be included in their models. However, this viewpoint is insufficient for capturing emergent behaviors as complex human-nature interactions themselves may be critical. Therefore, we center on “how” social and natural factors should be incorporated (factor configuration) into the models of coupled natural-human systems (CNHSs). Our research questions are 1) do different factor configurations produce unique transient patterns?; and 2) If shocked, do CNHSs behave differently according to factor configurations? In this research, we developed a proof-of-concept agent-based model (ABM) of environmentally induced migration to solve the research questions. We compared two emergent patterns of migration, the spatial distribution of populations and the mixing of cultural groups. We interpreted the underlying mechanism of transient migration behaviors based on how each factor configuration is theoretically defined. Then, we tested what-if scenarios to explore how a shock changes migration patterns in each factor configuration. We found that setting different factor configurations is critical to migration patterns and produces varying post-shock responses. In a broader view, these findings provide a reasonable basis for considering how social and natural factors are incorporated in the designs of other coupled system models.
Woi Sok Oh
Advisor: Dr. Rachata Muneepeerakul
Live on Zoom: Thursday, November 19 (3-4 p.m.)
Category: Land and Water
Watershed management can have a significant influence on water quantity and quality, and habitat restoration efforts can have significant effects on groundwater resources. Tree- and stand-level transpiration was estimated in two stands: a mature sand pine (Pinus clausa) plantation and a restored scrub-oak stand. Results showed some significant differences in species unit transpiration and drought tolerance. Under optimum conditions, estimated average sap flux density was similar among 3 oak species, within and among the two sites (167 g H2O cm-2 sapwood day-1 for myrtle oak (Quercus myrtifolia), sand live oak (Quercus geminata), chapman oak (Quercus chapmanii)) while sand pine, was significantly lower (78 g H2O cm-2 sapwood day-1). Stem diameter at breast height (DBH) averaged 17.4 cm for sand pines, while the chapman, sand live, and myrtle oaks were 3.9, 6.8, and 8.6 cm in average DBH, respectively. Average tree-level water use under optimal conditions at the sand pine site was 4.71 kg day-1, 1.38 kg day-1 and 6.30 kg day-1 for sand pine, chapman oak and myrtle oak respectively, while whole-tree water use at the oak-scrub site was 6.66 kg day-1, 3.86 kg day-1 and 1.22 kg day-1 for myrtle oak, sand live oak, and chapman oak, respectively. Under limiting soil moisture conditions, sand pine, sand live oak, and myrtle oak showed approximately 24%, 29%, and 43% reduction in transpiration, respectively, while chapman oak showed no change with water stress. Although average tree size was higher for the pine site than the oak-scrub site, stem density was much lower. High stem density in the oak-scrub site resulted in significantly higher canopy interception of precipitation than in the sand pine stand. Compared to the pine plantation, the oak-scrub site had higher transpiration (301 mm versus 214 mm) and higher interception (455 versus 147 mm) over the 10 month study period.
Israel Ojo
Advisor: Dr. Sanjay Shukla and Dr. Yiannis Ampatzidis
Live on Zoom: Thursday, November 19 (3-4 p.m.)
Category: Biological Engineering
Oil spills pose a large threat to marine ecosystems. The current cleanup methods include chemical dispersion agents, in-situ burning, and collection of oil from the water surface through physical processes. These current methods are harmful to the environment and can be extremely costly and time consuming. Therefore, an ecofriendly, highly efficient, and excellent absorbent is needed. Here, a potential oil absorbant was successfully prepared by a mild method of modified Alkali Lignin (EAL), trimethylolpropane tris(3-mercaptopropionate) (TMMP) in dimethyl sulfoxide (DMSO) catalyzed by sodium hydroxide (NaOH). The formation of oil-absorbent properties were confirmed by Fourier Transform Infrared (FTIR) Spectroscopy, Thermal Gravimetric Analysis (TGA), Scanning Electron Microscopy (SEM) and Brunauer–Emmett–Teller (BET). The oil removal capacity was performed at different contact times in an oil as well as oil/water mixture, and the sponge showed hydrophobicity as well as a high adsorption capacity up to 4.35g oil/g absorbent for Paraffin oil. The absorbant was made using different percent ratios of lignin to DMSO to create different pore sizes and evaluate the impact on oil absorption. It was found that the 3% ratio of lignin to DMSO yielded the highest absorption capacity. The absorbant can also be washed with an organic solvent and reused for additional oil absorption.
Arianna Partow
Advisor: Dr. Zhaohui Tong
Live on Zoom: Wednesday, November 18 (3-4 p.m.)
Category: Information Systems, Modeling
Climate variability and change plays an important role in the production risks that farmers face nowadays. Adverse climate conditions can set the stage for crop failures associated with either a lack or an excess of rainfall as well as the incidence of pests and diseases. The soybean crop is a major commodity in the US, being a source of oil, animal feeding, an isolated protein, and fresh food used for cooking. Soybean grain demand is increasing constantly with global population growth, thus it is necessary to improve production. However, variations in weather conditions may result in yield losses by way of different mechanisms. Water deficit causes a reduction in crop growth with resulting implications on yield. Selected growth stages such as flowering and grain-filling are particularly sensitive to the extremes in temperature and/or precipitation. In this project, we develop a weather index-based insurance to help farmers and risk managers to mitigate yield losses in soybean production using mathematical models to quantify the soybean losses during crop growth due to weather events such as drought.
Daniel Perondi
Advisor: Dr. Clyde Fraisse
Live on Zoom: Wednesday, November 18 (3-4 p.m.)
Category: Land and Water, Climatology, Modeling
Low floodplain wetlands such as the western Everglades in South Florida are vulnerable to extreme weather events, and their water quality and ecosystem functions largely vary depending on changes in water levels and discharges. The future climate is projected to result in increased frequency and magnitude of extreme events, which can negatively affect the hydroecological function of the wetlands. Wetland management practices have been commonly implemented to protect wetlands and the functions, but it is not clear if the current management practices still can be effective under projected climate change scenarios. This study evaluated climate change impacts on the runoff and total phosphorus (TP) of natural (L28 Gap) and managed (L28) wetland watersheds in the Western Everglades. For the assessment, we employed future climate projections made using 29 General Circulation Models (GCMs), and a watershed loading model, Watershed Assessment Model (WAM). Modeling results showed that the natural wetland watershed would be more vulnerable to projected climate change than the managed wetland basin. The impacts of projected climate changes on daily runoff and TP loads were modulated by water control facilities and practices in the managed watershed, highlighting the significance of watershed management practice for improved water quality under projected climate change. This study demonstrated how the local natural and managed wetland watersheds distinctly respond to the global-scale changes and emphasized the role of the water management practices in wetland basins, which is expected to help develop effective climate change adaptation plans to improve the sustainability of the Greater Everglades System.
Satbyeol "Joy" Shin
Advisor: Dr. Young Gu Her
Live on Zoom: Wednesday, November 18 (3-4 p.m.)
Category: Land and Water, Agriculture Production, Climatology, Irrigation, Modeling
Impacts of climate change have been posing a big challenge to food security. Several climate change mitigation measures have been tested. This study investigated the potentials of irrigation as a climate change adaptation measure for sorghum production. Two irrigation levels (deficit and full irrigation) were tested in two major sorghum production areas in Ethiopia. The Decision Support System for Agrotechnology Transfer (DSSAT v4.7.6) was used to simulate the impacts of climate change on sorghum yield using twenty Global Simulation Models (GCMs) under two Representative Concentration Pathways (RCPs; RCP 4.5 and RCP 8.5) and three future periods: 20225s (2010-2039), 2055s (2040-2069), and 2085s (2070-2099. The CERES-sorghum model of DSSAT was calibrated using eight years of sorghum experimental data. Results showed that the average temperature is likely to increase by up to 5.6 oC. Meanwhile, mixed results were observed for annual rainfall with reductions of up to 12% and increases of up to 194% compared to the baseline period. Sorghum yield under rainfed production is projected to decrease by up to 50%. However, up to 60% yield increases are projected under full irrigation production systems. This study highlights the potentials of irrigation as a climate change adaptation practice for sorghum production and results from this study would help to inform policy and management decisions about possible climate change adaptation.
Fikadu Getachew Welidehnna
Advisor: Dr. Haimanote Bayabil
Live on Zoom: Wednesday, November 18 (3-4 p.m.)
Category: Land and Water, Modeling
As world population increases, agricultural production must increase to meet demand, particularly in food insecure regions. In contrast to other methods of increasing food production, sustainable agricultural intensification (SAI) aims to produce more food per unit of land while preserving important ecosystem services and providing resilience to system shocks and stresses. Although these practices are implemented in North America, most studies have focused on commercial farms and field-scale impacts while using models that may not be sufficiently parsimonious for assessing specific SAI scenarios. Few studies quantify teleconnections between agricultural areas and remote ecosystems, specifically savannas. Therefore, this research seeks to answer how SAI practices, and specifically conservation tillage and increased water use efficiency, impact ecosystems at the watershed scale. Laikipia county, in the savanna landscape of central Kenya, is a representative example of the global need to increase food production while preserving natural resources. A link and node hydrological model will be developed and applied to the region along its steep rainfall gradient to quantify impacts of SAI adoption depending on the level of adoption, landscape position, and regional rainfall. Primary drivers of hydrology will be assessed via global sensitivity and uncertainty analysis. Key savanna species will be linked to ecosystem function through water availability under SAI scenarios. Expected results include that SAI practices consequentially improve streamflow and quality adjacent to SAI practices, but these improvements do not translate to savanna ecosystem improvements. These results can be used by government agencies to improve context specific extension practices and management policies.
Lory Willard
Advisor: Dr. Rafael Munoz-Carpena and Dr. Cheryl Palm
Live on Zoom: Thursday, November 19 (3-4 p.m.)
Category: Land and Water
Phosphorus (P), a vital growth-limiting nutrient, is often lost in wastewater discharge, which may not only deteriorate water quality but also accelerate P depletion. Engineered biochars (EBCs) loaded with metal oxides/hydroxides have been used as sorbents to remove and recycle phosphate (P) from wastewater. In this study, engineered biochar produced through pyrolysis from two feedstocks with different loadings of magnesium (Mg), aluminum (Al), and iron (Fe) metal contents. Comparative investigation indicates the Al-EBCs showed the highest aqueous stability with little metal dissolution, while Mg-EBC had the largest P adsorption capacity (119.6 mg P/g), mainly through the combination of surface precipitation and electrostatic interaction mechanisms. Furthermore, a facile ball-milling method was developed as an alternative method to synthesize MgO/biochar nanocomposites as EBCs. The composites achieved nano-scaled morphologies and porous structure with MgO nanoparticles 20 nm dispersed uniformly on the surface of the biochar. Ball milling, as a facile and promising method, lends the advantage of operational flexibility and chemical adjustability for targeted remediation of diverse environmental pollutants.
Patrick Zheng
Advisor: Dr. Bin Gao
Live on Zoom: Wednesday, November 18 (3-4 p.m.)
Category: Precision Agriculture
Strawberry is one of the high-value crops in the United States. However, strawberry production is severely affected by the two-spotted spider mite (TSSM). TSSM usually can be found on the underside of the strawberry leaf and they can make a negative effect on leaf photosynthesis. Growers usually use a magnifier to count the number of TSSM on the leaf, and they need to apply pesticide or release predatory mite before the pest density reaches the economic threshold. Therefore, intensive sampling of TSSM in the field is necessary for integrated pest management. However, manually counting is time-consuming and biased by the observer. Therefore, this study developed an artificial intelligence-based method to automatically count the TSSM and mite egg on the leaf. A smartphone and macro lens were used to collect hundreds of TSSM images for the model training. The mean average precision of the model was 0.6. The detection of TSSM and mite egg can be further improved when more images are collected to train the model. This study can guide growers to apply the right amount of pesticide or release the right number of predatory mites at the right location, which not only reduces the costs but also helps to protect the environment.
UNDERGRADUATE STUDENTS FIRST-YEAR GRADUATE STUDENTS GRADUATE STUDENTS BACK TO TOP
Congliang Zhou
Advisor: Dr. Won Suk Lee
Live on Zoom: Thursday, November 19 (3-4 p.m.)
