Location: Hydrology and Remote Sensing Laboratory
Project Number: 8042-66000-002-000-D
Project Type: In-House Appropriated
Start Date: Jun 1, 2021
End Date: May 31, 2026
Objective 1: Quantify agricultural and environmental processes in the Lower Chesapeake Bay (LCB) along with other LTAR and USDA network locations to facilitate the development and assessment of agricultural management and conservation practices leading to the sustainable intensification of agricultural production. Subobjective 1.1: Maintain existing and establish new long-term data streams for the LCB-LTAR watershed site to assess agroecosystem status and trends and for use in modeling efforts. Subobjective 1.2: Quantify the spatial and temporal variability and assess atmospheric ammonia fate on the Delmarva Peninsula. Subobjective 1.3: Use LCB-LTAR data streams collected to assess pollutant fate as a function of spatial differences in land use and temporal changes. Subobjective 1.4: Characterize groundwater lag time for agricultural watersheds across climatic regions and different drainage conditions (e.g., well drained, karst hydrology, ditch drained, and tile drained). Objective 2: Advance, develop, and validate remote sensing methods to assess crop condition and conservation practices. Subobjective 2.1: Develop and validate remote sensing methods for assessing winter cover crop operations. Subobjective 2.2: Improve remote sensing methods for assessing summer crop conditions. Subobjective 2.3: Develop remote sensing methods to assess crop residue cover and soil tillage intensity at field to watershed scales. Subobjective 2.4: Develop new methods to assess crop growth and N status using remote sensing for precision agriculture. Objective 3: Quantify the environmental factors regulating interconnected atmosphere, soil, and water processes within agricultural landscapes to identify the potential risks associated with pollutants, assess conservation and management practices, and develop remediation strategies. Subobjective 3.1: Develop enhanced measurement and modeling techniques for accurately quantifying the emission and atmospheric transport of agrochemicals that are required to design and evaluate both management and remediation strategies. Subobjective 3.2: Evaluate the use of compost and grass buffers to remediate pollutants in soils. Subobjective 3.3: Evaluating conservation practice performance in agricultural landscapes. Subobjective 3.4: Improve representation of wetland location and biogeochemistry within process-based models to support the assessment of wetland functions within the LCB-LTAR region.
Increase in agricultural production while maintaining natural resources and environmental quality requires a deeper understanding of natural processes in agricultural systems, new and better measurement techniques, robust decision support tools, and improved management practices. To address these needs, this project by focuses on improving techniques to assess agricultural practices, developing novel in-situ and remote sensing methods for measuring natural and agricultural processes, and both creating and maintaining long-term datasets through the Long-Term Agroecosystem Research (LTAR) and other USDA networks. Specifically, this project will continue the current data collection for the LTAR network as the Lower Chesapeake Bay (LCB) watershed site while creating new data streams focused on nutrient loading in Chesapeake Bay waterways for research efforts and to meet network goals (Objective 1). It will also develop and ascertain the utility of remote sensing to monitor crop conditions and tillage practices, assess the impacts of cover crops, and measure pesticide volatilization (Objective 2 and 3). The project will also explore new insights into optimizing agricultural management practices at landscape and regional scales which will improve rural prosperity (Objective 3). The results will lead to improved techniques for measuring ground water lag time within watersheds for modeling efforts and a deeper understanding the fate of agricultural and agroecosystem emissions, including ammonia, methane, agrochemicals, and particulate matter. The new measurement and modeling techniques, along with the other products of this research will benefit diverse customers including agricultural producers, policymakers, and non-governmental organizations.