Location: Agroclimate and Hydraulics Research Unit
2025 Annual Report
Objectives
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.
Progress Report
Objective 1: ARS scientists at El Reno, Oklahoma, collected soil moisture data and water, energy, and carbon flux data using soil sensors and eddy covariance systems on the Grazinglands Research agroEcosystems and the ENvironment (GREEN) Farm. Continuously monitored field soil water content on a 5-minute scale on the Water Resources and Erosion watersheds (WRE) and measured plant biomass (or yield) from business-as-usual (BAU) and aspirational (ASP) agricultural systems. We continued to collect and process precipitation, soil water content, and microbial community samples, soil carbon (C) and nitrogen (N) fractions and available water holding capacity (AWC) in BAU and ASP agricultural systems on the WRE. Measured soil water content and currently and previously collected data were used to model surface runoff in BAU and ASP using Agricultural Policy/Environmental eXtender (APEX) model. Data is included in a cross site Long-Term Agroecosystem Research (LTAR) network project to assess effects of conservation practices on natural resources. Objective 2: ARS scientists at El Reno, Oklahoma, calibrated and compared Water Erosion Prediction Project (WEPP) model and Revised Universal Soil Loss Equation (RUSLE v. 2) using measured sediment discharge at the watershed outlet as well as the spatially distributed soil erosion data within the watershed derived using an isotopic tracer of element cesium-137. Soil physical and chemical properties, time series of soil moisture data, event surface runoff, sediment discharge at the watershed outlet, historical cropping and tillage management records, and daily precipitation data including rainfall breakpoint data at the Water Resource and Erosion (WRE) watersheds at El Reno, Oklahoma, were compiled. The cesium-137 inventories at 10-m grids for No. 6 of the WRE watersheds were collected. All those datasets were used to build four input files (slope, soil, climate, and crop management) for the WEPP model. The model was calibrated with measured annual runoff volumes and subsequently with measured annual soil losses from the WRE watersheds. The calibrated model was then used to simulate soil erosion rates along a representative hillslope of WRE watershed No. 6, and the simulated spatial variation of soil erosion rates was compared to those estimated with the cesium-137 method to evaluate the model’s skill in simulating erosion distribution in space. Similarly, RUSLE v.2 will be evaluated with the same dataset and the results will be prepared with those of the WEPP model. Working with an ARS scientist at Ames, Iowa, research continued setting up and parameterizing selected hydrological models. The models of MIKE Systeme Hydrologique Europeen (MIKE-SHE), WEPP, Sediment Transport Model (STM), and ECO-lab were calibrated and evaluated. In collaboration with Oklahoma State University in Stillwater, Oklahoma, research continued building static and dynamic Soil and Water Assessment Tool (SWAT) and incorporating irrigation components. Sub-objective 3A.2: Research continues in developing a skillful seasonal forecast tool for use in a wheat grazing decision support tool. Outside collaborators at the University of Texas, at Arlington, Texas, developed seasonal forecast program and delivered the program in June 2023. Work continued in tuning and optimizing this program for study location (El Reno, Oklahoma) until October 2024. At this point, outside collaborators came to the El Reno, Oklahoma, location to help improve the forecast system, when it was discovered that the program given to the lead ARS scientist was an outdated program and thus was not usable. The correct program was then provided to the lead ARS scientist and tuning for the study location took place again. It was then determined that tuning and optimizing could not improve this system and two different directions were determined: 1) utilize Climate Prediction Center forecasts along with fitted precipitation distributions to forecast precipitation amounts for the coming months and 2) develop new k-nearest neighbors method that utilizes all available potential matches to find the best fits rather than a subset of the nearest neighbors as done previously. Work is ongoing in the development of these two new directions. Sub-objectives 3B, C: ARS scientists at El Reno, Oklahoma, developed calibration relationships for use in monitoring carbon seasonally, annually and in perpetuity. The choice of pretreatment or lack of pretreatment and type of sensor was determined by the level of accuracy required of the soil carbon measurement. Specifically, baseline carbon measurements received the most labor-intensive treatment and were scanned with the more expensive accurate sensor, after which, changes in soil organic carbon were monitored with less accurate, but robust portable radiometers. Periodic monitoring using the cheaper, but less accurate sensor was sufficient to estimate carbon stocks, unless an extreme management practice was applied to the field site such as tilling a previously never tilled southern tall grass prairie. Spectral libraries can then be used to standardize and interpret variations among different radiometers and environmental conditions. Future research will apply this approach across multiple sites within the United States representing variable soil, agronomic land use, and climate. In addition, the evaluation on the impacts of change in land cover and sediment delivery ratio on reservoir sedimentation was completed and published. The work was presented as one of the accomplishments in the Fiscal Year 2024 annual report.
Accomplishments
1. Alternative cool and warm season forage mixes foster crop yield stability in the Southern Plains region. Integrated crop-livestock systems are the predominant agricultural production systems in the Southern Plains. The 5.4 million hectares of integrated crop-livestock systems in the region lack alternative management practices to mitigate a forage gap, so crop productivity under variable weather patterns can be maintained. ARS researchers at El Reno, Oklahoma, through their Long-Term Agroecosystem Research site, verified that alternative cool and warm season forage mixes fostered yield stability while increasing ground cover. As a result, potential reductions in sediment and nutrient loading to surface waters and enhancement to soil health and water holding capacity are possible. Adoption of these alternative forage mixes by farmers across the Southern Plains will fill a forage gap and foster crop yield stability in the Southern Plains region.
2. Decision tables for crop selection based on producers’ tolerable soil loss levels. Soil erosion by water and wind is generally low under natural conditions, where human activities are limited. Improper land use and management by farmers, producers, and ranchers can cause severe soil erosion worldwide. Soil erosion from agricultural land causes land degradation and reduces crop productivity. To reduce soil erosion, various farming and conservation practices have been widely adopted. ARS researchers at El Reno, Oklahoma, systematically assessed the effectiveness of seventy-one farming and conservation practices (e.g., planting summer cover crops, contour farming (cross slope tillage), and grass filter strips) in reducing soil erosion and surface water runoff in central Oklahoma using a computer program called Water Erosion Prediction Project (WEPP). The odds of soil loss exceeding the predetermined tolerable soil loss thresholds are tabulated for different climate scenarios. The results show conservation tillage and no-till reduce soil erosion considerably as compared to conventional tillage. In addition, annual crop rotations with alfalfa, contour farming, and grass filter strips are effective in reducing soil erosion and the odds of erosion exceeding tolerable soil loss rates. The data including, but not limited to soil loss, surface runoff, and crop yields were used to create decision support tables, so farmers, ranchers, and producers can easily select what crop to grow and how to grow it in central Oklahoma based on the producers’ tolerable levels of soil loss. The decision support tables will be made available online for wide public dissemination.
3. Timing of daily maximum temperature and precipitation impacts wheat production. Knowledge of water availability is critical to the management and operation of agricultural production within the Southern Great Plains. For example, recent drought events (2022 and 2023 growing seasons) impacted the winter wheat harvest, with yield declines as large as 50% in some states. Given the shifts of annual water resources within the region, fully understanding the nature of these changes is critical to the long-term success of agricultural producers. Previous research has shown that the growing season is impacted in shifts in the timing of annual precipitation and temperature maxima within the region through use of their asynchronous difference index (ADI). ARS researchers at El Reno, Oklahoma, used the ADI to investigate the nature of precipitation and temperature for the two different flavors of ADI, namely positive (precipitation maximum leads temperature) and negative (temperature maximum leads precipitation) ADI. Positive ADI years show a wet spring and a hot and dry summer, the typical climate in the region. Negative ADI years show a drier and warmer spring, leading to a hot and dry summer, with a very wet late summer and early fall period. Winter wheat yields during positive ADI years were higher than the long-term average, while negative ADI years showed below normal winter wheat yields. Overall, these results showed the usefulness of ADI in evaluating the regional climate of water availability during the winter wheat growing season. This research provides agricultural producers with new climate information within the region. Namely, in 1 out of every 3 years (about 40%) that precipitation in the mid to late winter wheat growing season will be reduced and water management will be necessary to account for the reduced availability of soil water.
Review Publications
Talebizadeh, M., Moriasi, D.N., Steiner, J.L., Gowda, P.H., Starks, P.J., Verser, J.A. 2024. Spatio-temporal sensitivity analysis for flow and sediment load modeling using SWAT. Water Resources Management. https://doi.org/10.1007/s11269-024-04066-6.
Fortuna, A., Northup, B., Starks, P., Moriasi, D.N., Steiner, J.L., Pradeep, W., Zhang, X.J., Flanagan, P.X., Busteed, P.R., Teet, S.B., Witt, T.W., Hunt, S., Moffet, C., Cibils, A.F., Gunter, S.A. 2024. The LTAR integrated common experiment at Southern Plains. Journal of Environmental Quality. 53(6):930-938. https://doi.org/10.1002/jeq2.20651.
Lee, S., Vahid, M., Danandeh Mehr, A., Moriasi, D.N., Mirchi, A. 2024. Wavelet-entropy enhanced clustering: a comprehensive analysis of drought patterns in the Southern Plains, USA. Journal of Hydrometeorology. 25(12):1809-1822. https://doi.org/10.1175/JHM-D-24-0041.1.
Xiao, Y., He, X., Wei, H., Zhang, X.J., Li, F., Wang, Y., Zhang, Y. 2025. Assessing and modifying the formula for estimating the conditional transition probabilities and daily precipitation variance under the influence of climate change. Journal of Applied Meteorology and Climatology. 64(6):626-636. https://doi.org/10.1175/JAMC-D-24-0193.1.
Lu, Y., Chen, J., Zhang, X.J., Xiong, L. 2025. A stochastic weather generator-based framework for generating ensemble sub-monthly precipitation for streamflow prediction. Journal of Hydrology. 58. Article 102186. https://doi.org/10.1016/j.ejrh.2025.102186.
Peng, P., Chen, J., Zhang, X.J. 2024. Impacts of different El Niño events in the decaying summer on the oceanic source of summer rainfall for eastern China: A perspective from stable isotope. Journal of Water and Climate Change. 15(7). Article 3158. https://doi.org/10.2166/wcc.2024.062.
Flanagan, P.X. 2024. Precipitation and temperature maxima: A study across the Southern Great Plains winter wheat region. Earth Interactions. 28(1). Article e230019. https://doi.org/10.1175/EI-D-23-0019.1.
Whitesel, D., Mahmood, R., Phillips, C., Roundy, J., Rappin, E., Flanagan, P.X., Santanello, J., Nair, U., Pielke Sr, R. 2024. Assessing the convective environment over irrigated and non-irrigated land use with land-atmosphere coupling metrics: Results from grainex. Journal of Hydrometeorology. 25(7):1061-1080. https://doi.org/10.1175/JHM-D-23-0187.1.