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ARS Home » Plains Area » El Reno, Oklahoma » Grazinglands Research Laboratory » Agroclimate and Natural Resources Research » Research » Research Project #432323

Research Project: Towards Resilient Agricultural Systems to Enhance Water Availability, Quality, and Other Ecosystem Services under Changing Climate and Land Use

Location: Agroclimate and Natural Resources Research

2021 Annual Report


Objectives
Objective 1: Quantify states, fluxes, and cycling of water, carbon, and hydrologic constituents within the soil-plant-hydrologic-atmospheric systems of selected landscapes, watersheds, and agricultural systems of the Southern Great Plains. Objective 2: Develop tools and techniques for the selection, placement, and evaluation of conservation and agricultural practices to improve watershed integrity and ecosystems services. Objective 3: As part of the LTAR network, and in concert with similar long-term, land-based research infrastructure in the region, use the Little Washita River/Fort Cobb Reservoir Experimental Watersheds LTAR site to improve the observational capabilities and data accessibility of the LTAR network and support research to sustain or enhance agricultural production and environmental quality in agroecosystems characteristic of the Southern Plains region. Research and data collection are planned and implemented based on the LTAR site application and in accordance with the responsibilities outlined in the LTAR Shared Research Strategy, a living document that serves as a roadmap for LTAR implementation. Participation in the LTAR network includes research and data management in support of the ARS GRACEnet and/or Livestock GRACEnet projects.


Approach
The project builds upon the prior 5-year project and is structured around three inter-related research objectives that: 1) develop, maintain, and expand long-term observational research infrastructure and databases to elucidate water-related agroecosystem processes for agricultural systems, 2) conducts studies that help understand processes and improve algorithms of commonly used hydrologic and water quality models, and 3) develops tools and techniques for the selection, placement, and evaluation of conservation and agricultural practices to improve watershed integrity and ecosystems services. Our long-term objective is to elucidate key hydrologic and agroecosystem processes and to bridge the gap between farm management goals and landscape or watershed goals that are shared across farms and communities, using long-term research sites and research watersheds as the primary outdoor laboratories to address these issues of global relevance. Research approaches include field studies, remote sensing analyses, mathematical and statistical assessment of climate, farm to watershed scale process modeling, and development of integrative optimization tools. This research will assist farmers, land owners, governmental action agencies, and residents to contribute to more resilient mixed land-use watersheds, in part by providing tools that help them evaluate and optimize multiple management objectives for mixed-enterprise agricultural systems.


Progress Report
Objective 1: With respect to Subobjective 1A, ARS researchers at El Reno, Oklahoma, continue to collect soil moisture data from the installed in-situ soil moisture network around a COsmic-ray Soil Moisture Observing System (COSMOS) site for purposes of evaluating one-time vs. multi-temporal calibration. In addition, scientists continue to perform quality assurance/quality control on the collected data. These soil moisture measurement sites have been upgraded to include telecommunications systems for automated data download and archive. Sub-objective 1B: With three years of irrigation flux and efficiency data at several fields across the Fort Cobb Reservoir Experimental Watershed (FCREW), a final report was prepared and submitted by the Oklahoma State University collaborators. In addition, the findings on irrigation management and efficiencies from this cooperative project have been disseminated to stakeholders through field days, local meetings, and online videos. With respect to Sub-objective 1C no progress was made due to issues related to the new irrigation algorithm implemented in the Soil and Water Assessment Tool (SWAT) model. With respect to Sub-objective 1D, the Revised Universal Soil Loss Equation (RUSLE) in a geograhic information system (GIS) context model is being used in the Little Washita River Experimental Watershed (LWREW) to determine potential soil erosion as a function of land use, determine most vulnerable areas, and correlate potential soil erosion to measured reservoir sedimentation. Although there were some delays due to COVID-19, scientists have completed RUSLE scenarios (for 12 reservoirs averaging about 8 years per reservoir). In addition, in the LWREW, a study focused on concentrations of arsenic and chromium heavy metals in reservoir sediments and their potential ecological risks to organisms living in reservoirs within cropland, forest, and grassland areas has been completed. The collaborators from Florida A&M University completed analyses on sediment core and water samples with respect to nutrients, specifically phosphorus. The collaborators wrote and submitted the final report. Objective 2: As reported previously, due to retirement of key researchers and shifting roles of the research staff, the study in Research Goal 2B.1 has been modified in regards to research site and source of Light Detection and Ranging (LiDAR) data. This work has been further delayed because of travel restrictions, social distancing, and other factors due to the COVID-19 pandemic. ARS researchers at El Reno, Oklahoma, have entered into collaborative arrangements with Natural Resources Conservation Service (NRCS) and our sister ARS laboratory in Stillwater, Oklahoma, to accomplish two broad objectives. The emphasis of the first objective is to evaluate LiDAR data more generally for assessment of selected flood plain characteristics such as upstream and downstream assessment of stream sinuosity, degradation, and land use change and impact on selected conservation structures. The second objective is to conduct preliminary study to qualitatively assess channel stream banks, begin work linking processed LiDAR data to ARS's Bank Stability and Toe Erosion Model (BSTEM), and to determine vulnerable areas that require conservation practices. A study site in central Oklahoma was identified for Unmanned Aircraft Systems LiDAR data collection and preliminary data have been collected and evaluated. With respect to Sub-objective 2D, we plan to use the SWAT- Landuse Update Tool, the user-friendly graphical software for updating landuse in SWAT recently developed by ARS scientists along with collaborators, in the next research project to determine the impact of dynamic landuse on model performance and outputs. Also, ARS scientists worked with collaborators in University of Texas at El Paso, Texas to use the new Management Optimization Tool for Soil and Water Assessment Tool (SWAT-MOT) to determine optimal spatial placement of soybeans, winter wheat, grain sorghum, upland cotton, and peanuts cropping systems under no-till in the FCREW. Objective 3: A number of studies have been conducted describing the seasonal and annual fluxes of evapotranspiration (ET) and carbon dioxide (CO2) from the business-as-usual (BAU, winter wheat) and aspirational (ASP, canola) under till and minimum till conditions. Due to 2019 storm damage to infrastructure on the primary Cropland Common Experiment (i.e., the GREEN Farm), the physical experimental layout was redesigned to reduce the land area involved and to expedite data collection and analysis for comparison of "business-as-usual" to an "aspirational" system. COVID-19 induced maximized telework has delayed repair on the GREEN Farm, and it is now anticipated to be at full experimental capacity by late fall of 2021. Additional measurement equipment has been purchased for installation on our secondary Common Cropland Experimental site (the Water and Erosion Experimental (WRE) watersheds) to allow us to measure components of the water balance. Soil and plant biomass measurements have been completed for fall-winter and spring-summer 2020. Collected samples have been weighed and prepared for storage for future analysis. A new long-term experiment designed to address the effects of Environment x Rotation x Rhizobiome x Innoculants x Cultivar on Climatic Adaptation (ERRICCA) was initiated in the fall of 2020. ERRICCA is comprised of 144 plots within a 270 X 160 ft (~82 m X 49 m) area. Several combinations of treatments have been implemented to plots in a winter wheat (Triticum aestivium L.), cotton (Gossypium hirsutum L.) rotation, an unfertilized control (no NPK fertilizer), bioinoculant no NPK fertilizer, bioinoculant applied to the wheat entry point only, bioinoculant applied to the cotton entry point only, and no bioinoculant (N fertilizer only). Soil data loggers were installed in the spring of 2021 to monitor soil temperature and moisture. Also, plant and soil samples are and will continue to be taken at critical growth stages (spring green up, stem elongation, boot, flowering, and final yield) to measure N uptake, forage and fiber quality, and yield (biomass and grain, seed-cotton and lint). Soil samples are and will continue to be collected to 15 cm depth to determine nutrient status and microbial populations before planting each crop and at critical growth stages of plants. Research that tested the use of less expensive hyperspectral radiometers to predict the effects of land use and conservation practices on biologically active and resistant stocks of soil organic carbon was completed and published. Overall, the findings compared well to the baseline results, indicating that the less expensive sensors produced at least satisfactory results while the research grade field radiometer achieved good performance for all soil constituents.


Accomplishments
1. Soil health as a tool for land managers. Globally, retention and distribution of nutrients vary with landscape, soil type, and anthropogenic management. Currently, a gap exists in inventorying the impact of land use and management on soil resources. Therefore, reducing the number of samples required to monitor soil carbon, nitrogen, and mineral constituents (indicators of soil health) using proximal sensing techniques such as hyperspectral radiometry can limit the cost and personnel required to monitor natural resources. In furtherance of this objective, we conducted experiments on three watersheds to address the effects of spatial variability, land management, and conservation practices on indicators of soil health. Collected measurements were used to monitor soil constituents as well as parameterize and validate hydrologic models used to predict the effects of alternative land management on soil health and water quality. Our research confirms that hyperspectral diffuse reflectance data (400–1000 nm) collected with relatively inexpensive radiometers can be used to develop good prediction equations relative to results acquired using the full spectrum (350–2500 nm). This approach facilitates application of in-situ field research, increases the timeliness of results, and reduces laboratory chemical waste/cost. The ability to predict soil constituents with fewer direct laboratory measures coupled with the added benefit of lightweight, small inexpensive sensors in the 350–1000 nm range will enable researchers to monitor larger areas for shifts in soil health with land use and management. These approaches are currently being incorporated into a Long-Term Agroecosystem Research network project.

2. Impact of tillage practices on the ability of the Agricultural Policy/Environmental eXtender (APEX) model to predict ET and daily biomass. Farmers rely on recommendations on best production systems based on model outputs. Tillage practices impact agricultural productivity, soil erosion, and evapotranspiration (ET), which is a major component of the water cycle. Therefore, it is important to ensure that APEX and other hydrologic models are properly parameterized for different tillage systems to ensure accurate model outputs are used to provide agricultural production recommendations. ARS scientists in El Reno, Oklahoma, determined model parameters that affect ET and biomass prediction in the APEX model for winter wheat fields managed under conservation and conventional tillage systems, and evaluated the ability of APEX to predict ET and daily biomass for these tillage systems in central Oklahoma. Tillage system had an impact on the number and type of APEX parameters sensitive for prediction of ET. Therefore, it is recommended to perform a sensitivity analysis for study areas with different tillage management practices to determine appropriate parameters for inclusion in the calibration process in order to ensure accurate model outputs. Overall, APEX predicted ET and biomass relatively well for both tillage systems, with average predicted ET and daily biomass values within 3% and 25% of the measured ET and daily biomass, respectively. The results of this study contribute to the USDA Agricultural Research Service Long-Term Agroecosystem Research network that seeks to evaluate current agricultural production systems, develop sustainable one, and provide recommendations to farmers and other stakeholders.

3. Water fluxes of irrigated fields in the Fort Cobb Reservoir Experimental watershed quantified. Irrigation withdrawal for crop production is the biggest use of water resources in the southern Great Plains. Accurate measurements or estimation of different water fluxes in irrigated agriculture is the first step towards developing and implementing scheduling approaches to improve water use efficiency in irrigated agriculture and reduce water losses. ARS scientists in El Reno, Oklahoma, and Oklahoma State University collaborators conducted a 3-year study at the Fort Cobb Reservoir Experimental watershed to quantify irrigation fluxes and to compare the actual fluxes with those calculated assuming well-watered (no stress) conditions. Crop evapotranspiration (ET), runoff (RO), and deep percolation (DP) were estimated using the root zone Soil Water Balance and the HYDRUS models. Measured applied irrigation data revealed that nearly all studied fields were under-irrigated, with an average amount that was only 30% of stress-free well-watered crop conditions. Overall, the soil water content model outputs computed based on the actual irrigation fluxes were comparable to in-situ measurements from sensors installed at four depths at each study site. However, model results indicated that ET, RO, and DP fluxes computed using actual irrigation fluxes were 82%, 50%, and 33%, respectively, of those computed assuming well-watered conditions commonly used in hydrologic modeling studies. Since farmers and water resources managers depend on model outputs to make water management decisions, the results highlight the importance of using actual irrigation in order to estimate water fluxes accurately. These results contribute to the Conservation Effects Assessment Project and the Long-term Agroecosystems Research network studies.


Review Publications
Steiner, J.L., Fortuna, A. 2020. Climate change, greenhouse gas emissions, and carbon sequestration: Challenges and solutions for natural resources conservation through time. In: Delgado, J., Gantzer, C., Sasssenrath, G., editors. Soil and Water Conservation: A Celebration of 75 Years. Journal of Soil and Water Conservation Society. p. 229-240.
Masasi, B., Taghvaeian, S., Gowda, P.H., Moriasi, D.N., Starks, P.J. 2020. Assessment of heat unit availability and potential lint yield of cotton in Oklahoma. Applied Engineering in Agriculture. 36(6):943-954. https://doi.org/10.13031/aea.14006.
Ngatia, L.W., Oliveira, L.M., Betiku, O.O., Fu, R., Moriasi, D.N., Steiner, J.L., Verser, J.A., Taylor, R.W. 2020. Relationship of arsenic and chromium availability with carbon functional groups, aluminum and iron in Little Washita River experimental watershed reservoirs, Oklahoma, USA. Ecotoxicology and Environmental Safety. 207. https://doi.org/10.1016/j.ecoenv.2020.111468.
Ngatia, L.W., Moriasi, D.N., Grace III, J.M., Fu, R., Gardner, C., Taylor, R.W. 2021. Land use change affects soil organic carbon: An indicator of soil health. In: Otsuki, T., editor. Environmental Health. Intech. p. 1-15. http://dx.doi.org/10.5772/intechopen.95764.
Masasi, B., Taghvaeian, S., Boman, R., Moriasi, D.N., Starks, P.J. 2020. Impacts of variable irrigation regimes on cotton yield and fiber quality. Agricultural & Environmental Letters. 5(1):e20031. https://doi.org/10.1002/ael2.20031.
Tsegaye, T.D., Moriasi, D.N., Bryant, R.B., Bosch, D.D., Locke, M.A., Heilman, P., Goodrich, D.C., King, K.W., Pierson Jr, F.B., Buda, A.R., Kleinman, P.J. 2020. Water availability for agriculture in the United States. In: Delgado, J., Gantzer, C., Sasssenrath, G., editors. Soil and Water Conservation: A Celebration of 75 Years. Journal of Soil and Water Conservation Society. p. 95-114.
Samimi, M., Mirchi, A., Moriasi, D.N., Ahn, S., Alian, S., Taghvaeian, S., Sheng, Z. 2020. Modeling arid/semi-arid irrigated agricultural watersheds with SWAT: Applications, challenges, and solution strategies. Journal of Hydrology. 590:125418. https://doi.org/10.1016/j.jhydrol.2020.125418.
Tadesse, H.K., Moriasi, D.N., Gowda, P.H., Wagle, P., Starks, P.J., Steiner, J.L., Talebizadeh, M., Neel, J.P., Nelson, A.M. 2020. Comparison of evapotranspiration and biomass simulation in winter wheat under conventional and conservation tillage systems using APEX model. Ecohydrology & Hydrobiology. https://doi.org/10.1016/j.ecohyd.2020.08.003.
Yildirim, T., Zhou, Y., Flynn, K.C., Gowda, P.H., Ma, S., Moriasi, D.N. 2021. Evaluating the sensitivity of vegetation and water indices to monitor drought for three Mediterranean crops. Agronomy Journal. 113:123-134. https://doi.org/10.1002/agj2.20475.
Dong, J., Crow, W.T., Tobin, K., Cosh, M.H., Bosch, D.D., Starks, P.J., Seyfried, M.S., Holifield Collins, C.D. 2020. Comparison of microwave remote sensing and land surface modeling for surface soil moisture climatology estimation. Remote Sensing of Environment. 242:111756 . https://doi.org/10.1016/j.rse.2020.111756.
Kim, H., Wigneron, J., Kumar, S., Dong, J., Wagner, W., Cosh, M.H., Bosch, D.D., Holifield Collins, C.D., Starks, P.J., Seyfried, M.S., Lakshmi, V. 2020. Global scale error assessments of soil moisture estimates from microwave-based active and passive satellites and land surface models over forest and mixed irrigated/dryland agriculture regions. Remote Sensing of Environment. 251:112052. https://doi.org/10.1016/j.rse.2020.112052.
Shelito, P., Kumar, S., Santanello, J., Lawston, P., Bolton, J., Cosh, M.H., Bosch, D.D., Holifield Collins, C.D., Livingston, S.J., Prueger, J.H., Seyfried, M.S., Starks, P.J. 2020. Assessing the impact of soil layer specification on the observability of modeled soil moisture and brightness temperature. Journal of Hydrometeorology. 21(9):2041-2060. https://doi.org/10.1175/JHM-D-19-0280.1.
Fang, L., Zhan, X., Yin, J., Schull, M., Walker, J., Wen, J., Cosh, M.H., Lakankar, T., Holifield Collins, C.D., Bosch, D.D., Starks, P.J., Caldwell, T. 2020. An intercomparison study of algorithms for downscaling SMAP radiometer soil moisture retrievals. Journal of Hydrometeorology. 21(8):1761-1775. https://doi.org/10.1175/JHM-D-19-0034.1.
Kang, C.S., Zhao, T., Shi, J.C., Cosh, M.H., Chen, Y., Starks, P.J., Holifield Collins, C.D., Wu, S., Sun, R., Zheng, J. 2020. Global soil moisture retrievals from the Chinese FY-3D microwave radiation imager. IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/TGRS.2020.3019408.
Coopersmith, E., Cosh, M.H., Starks, P.J., Bosch, D.D., Holifield Collins, C.D., Seyfried, M.S., Livingston, S.J., Prueger, J.H. 2021. Understanding temporal stability: A long-term analysis of USDA ARS watersheds. International Journal of Digital Earth. https://doi.org/10.1080/17538947.2021.1943550.
Bajgain, R., Xiangming, X., Wagle, P., Kimball, J., Brust, C., Basara, J., Gowda, P.H., Starks, P.J., Neel, J.P. 2020. Comparing evapotranspiration products of different temporal and spatial scales in native and managed prairie pastures. Remote Sensing. 82(13). https://doi.org/10.3390/rs13010082.