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 Agroclimate and Hydraulics Research Unit: El Reno, OK
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ARS Home Plains Area El Reno, Oklahoma Oklahoma and Central Plains Agricultural Research Center Agroclimate and Hydraulics Research Unit Research





- Fortuna, Ann Marie
- 2023
- Publications
- Water Availability and Watershed Management (211)
- Soil and Air (212)
- Sustainable Agricultural Systems Research (216)

Research Project: **Location:
                            Agroclimate and Hydraulics Research Unit** Project Number: 3070-13000-015-000-D
                        
                        Project Type: In-House Appropriated Start Date: Jun 16, 2023
                        
                        End Date: Jun 15, 2028 **Objective:**
                        
                        Objective 1: Evaluate and compare performance of alternative and business as usual agricultural production systems with respect to water availability, selected environmental indicators, and productivity.
Sub-objective 1.A: In accordance with the LTAR Common Experiment G x E x M framework, identify, quantify, and elucidate differences between BAU and ASP systems in terms of concentrations of N and P in surface water runoff, water use efficiencies (WUE), and pre-planting soil water content.
Sub-objective 1.B: For the BAU and ASP systems, investigate impact of agricultural system (M and G) and climate (E), on soil microbial community activity and structure and subsequent changes in fractions of soil C and N, soil water content, and quality of surface water runoff.


Objective 2: Develop hydrologic modeling tools to improve evaluation of the effects of land management, conservation practices, and climate variability on water and soil resources for agricultural practices in the Southern Plains.
Sub-objective 2.A: Develop process-based distributed hydrologic and transport models to evaluate changes in soil and water quality under different management practices.
Sub-objective 2.B: Evaluate and compare performance of RUSLE2 and WEPP models to predict soil loss under different land management systems.
Sub-objective 2.C: Quantify the impacts of static vs dynamic land use on hydrologic model simulation performance.
Sub-objective 2.D: Develop, incorporate, and evaluate new irrigation algorithm in SWAT to improve water budget predictions for increasing accuracy of water quantity and quality simulations.

Objective 3: Develop, implement, or evaluate artificial intelligence, remote sensing, and spatial analysis tools for quantifying water, soil, and plant variables.
Sub-objective 3.A: Develop a predictive and eXplainable artificial intelligence (XAI) framework to quantify climate-related risks for water resources and crop production and to assess adaptation pathways in the Southern Great Plains.
Sub-objective 3.B: Use field-based radiometry to develop an in-the-field technique that would facilitate application in field research, increase the timeliness of results, reduce laboratory chemical wastes, reduce costs, and increase sample analysis throughput of soil and plant samples.
Sub-objective 3.C: Use a GIS-based Revised Universal Soil Loss Equation (RUSLE) linked to a sedimentation module to predict (estimate) reservoir sedimentation. **Approach:**
                        
                        This research is guided primarily by two USDA national research initiatives, the Conservation Effects and Assessment Program (CEAP) and the Long-Term Agroecosystem Research (LTAR) network and builds upon the prior 5-year project. The project is structured around three interrelated research objectives that: 1) improve the understanding of the impact of agricultural production systems on water availability, selected environmental indicators, and productivity, 2) develop hydrologic modeling tools to improve evaluation of the effects of land management, conservation practices, and climate variability on water and soil resources, and 3) develop artificial intelligence, remote sensing, and spatial analysis tools for quantifying water, soil, and plant variables. Objective 1 primarily deals with LTAR research goals. Objectives 2 and 3, though both tool-based, differ in their research thrust. Objective 2 is driven by the CEAP program goals and objectives, and addresses hydrologic model improvement, assessment, and environmental applications.  Objective 3 is not limited to hydrologic models but seeks to develop, apply, or assess remote sensing, geospatial (e.g., Geographical Information Systems), machine learning, and data driven methods to address agricultural and natural resources problems. Our long-term goal is to elucidate system-wide performance indicators of the impacts of land management and climate variability on water, soil, productivity, and other ecosystem services at farm, watershed, and regional scales using long-term field, farm, and watershed research sites as the primary outdoor laboratories. Research approaches include field studies, remote sensing analyses, mathematical and statistical assessment of climate, and plot-to-watershed scale modeling. This research will assist agricultural producers, landowners, and governmental action agencies to contribute towards adopting more resilient and sustainable agricultural production systems by providing knowledge and tools that help them evaluate and optimize multiple management objectives for mixed-enterprise agricultural systems.


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