College of Agriculture & Natural Resources
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The collections in this community comprise faculty research works, as well as graduate theses and dissertations.
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Item MODELING GROUNDWATER FLUCTUATIONS IN THE COASTAL PLAIN OF MARYLAND: AN ANN POWERED STRATEGY(2024) Steeple, Jennifer Lynne; Negahban-Azar, Masoud; Shirmohammadi, Adel; Environmental Science and Technology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Groundwater management in the face of climate change presents a critical challenge with far-reaching implications for water resource sustainability. This study evaluates the effectiveness of Artificial Neural Networks (ANNs) as predictive tools for estimating current groundwater levels and forecasting future groundwater levels in the Aquia aquifer in the Coastal Plain ofMaryland. The groundwater levels of the Aquia aquifer have declined under the pressures of land use change, increases in agricultural irrigation, and population growth. We tested, trained, and employed eight county-level artificial neural network (ANNs) models to predict and project Aquia aquifer groundwater levels for the near (2030-2050) and far (2050-2100) future under two socio-economic pathways (SSP245 and SSP585). The models exhibited significant predictive performance during testing (R²= 0.82-0.99). Minimum temperature and population were the most influential variables across all county-based models. When used to forecast groundwater level under two climate scenarios, the models predicted declining groundwater levels over time in Calvert, Caroline, Queen Anne’s, and Kent counties, aligning with regional trends in the Aquia aquifer. Conversely, Anne Arundel, Charles, St. Mary’s, and Talbot counties exhibited projected increases in groundwater levels, likely influenced by correlations with the variable irrigated farm acreage, underscoring the importance of considering nonlinear relationships and interactions among variables in groundwater modeling. The study highlights the ability of ANNs to accurately predict county-scale groundwater levels, even with limited data, indicating their potential utility for informing decision-making processes regarding water resource management and climate change adaptation strategies. This study also assessed the usability of multiple methods to fill in the missing data and concluded that using the repeated groundwater level data still resulted in powerful ANN models capable of both predicting and forecasting ground water levels in the Coastal Plain of Maryland.Item WATER QUALITY IN MANAGEMENT INTENSIVE GRAZING AND CONFINED FEEDING DAIRY FARM WATERSHEDS(2005-07-12) Gilker, Rachel Esther; Weil, Ray R.; Plant Science and Landscape Architecture (PSLA); Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Dairy farm size has increased in the United States, while the profit margin has decreased. An alternative to confined feeding dairy farming is management intensive grazing (MIG), a grass-based system relying on rotational grazing for most of the herd's dietary requirements. Previous research has measured high levels of nitrate leaching under MIG, citing the liquid nature and high nitrogen (N) content of urine. However, this research included heavy N fertilizer applications or was conducted on monolith lysimeters with artificial leaching processes and did not accurately represent mid-Atlantic MIG dairy farms. Phosphorus (P) losses have typically been attributed to runoff and erosion but are now being ascribed to leaching as well. To measure the magnitude of N and P losses to groundwater, we sampled shallow groundwater and pore water on one confined feeding and two MIG-based Maryland dairy farms between 2001 and 2004. Transects of nested piezometers and ceramic-tipped suction lysimeters were installed in two watersheds on each farm. Two streams running through two of the grazed watersheds were also sampled to measure the effects of grazing on surface water. For three years, groundwater and surface water samples were collected biweekly and pore water was collected when conditions made it possible. Samples were analyzed for inorganic N and dissolved reactive P and were digested for determination of dissolved organic N and P, pools previously not considered major sources of nutrient loss. Seasonal mean nitrate concentrations under the grazed watersheds remained below the EPA maximum contaminant load of 10 mg L-1 with only two exceptions on the grazed watersheds. Mean nitrate concentrations in the four grazed watersheds ranged from 3 to 7.44 mg L-1. Nitrogen losses were closely correlated to farm N surpluses. Groundwater P concentrations exceeded the EPA surface water critical levels in all six watersheds. Geologic factors, rather than dairy farm management, played a large role in P losses. In all watersheds, substantial pools of dissolved organic N and P were measured in groundwater. Low nitrate losses under MIG as well as the environmental advantages inherent in a grass-based system make grazing a viable Best Management Practice.