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

Location: Agroclimate and Hydraulics Research Unit

2023 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: Data collection from our long-term watershed sites, several eddy covariance flux sites, a network of soil moisture sensors deployed around a Cosmic-ray Soil Moisture Observing System (COSMOS), and four other COSMOS sites is ongoing. One COSMOS site was damaged and is currently offline awaiting repair and reinstallation. A network of soil moisture sensors was installed on the Watershed Runoff and Erosion (WRE) watersheds to support experiments from point to watershed scales and to improve satellite soil moisture products. Through the Oklahoma State University (OSU) cooperative agreement, ARS scientists in El Reno, Oklahoma, hired a postdoc to work on the development and incorporation of a new irrigation method into the Soil and Water Assessment Tool (SWAT) model that contributes to the new research project. Research collaboration with an ARS Scientist in Kimberly, Idaho, University of Idaho to test current and improved irrigation routines in SWAT continues. A doctoral student at the University of Idaho has completed building a basic SWAT model project and is in the process of parameterizing and calibrating the model. As part of the OSU agreement, completed our analysis of weather station aridity effects across the Oklahoma Mesonet, especially in western Oklahoma where most irrigated agricultural lands are located. ARS scientists in El Reno, Oklahoma, also synthesized the results of the analysis of long-term trends (1951-2021) in the nine climate divisions of Oklahoma. An Oak Ridge Institute for Science and Education (ORISE) Fellow was hired to work on a project to advance agricultural drought early warning capabilities. Objective 2. Digital elevation models for 12 watersheds were developed from 2-m horizontal spatial resolution. USDA-Natural Resources Conservation Service (NRCS) Light Detection and Ranging (LiDAR) soil data from the USDA-NRCS Web Soil Survey were used to develop a set of geomorphic, pedologic, and topographic variables to assess and improve the utility of a Geographic Information System (GIS)-based Revised Universal Soil Loss Equation (RUSLE) model for estimating reservoir sedimentation. All data have been analyzed, and a manuscript will be submitted for review before the end of FY 2023. Land cover products developed by the Multi-Resolution Land Characteristics Consortium were used to develop nine land use maps for the Fort Cobb Reservoir Experimental Watershed (FCREW). These will be used in the new research project to determine the impact of dynamic land use on model performance and outputs. Soil moisture and National Aeronautics and Space Administration (NASA) crop data are being collected for use in the new research project to develop a predictive artificial intelligence tool to quantify climate-related risks for water resources and crop production in the FCREW. Objective 3: ARS scientists in El Reno, Oklahoma, will proceed with repairs and renovation to the 10-paddock GREEN Farm. In collaboration with the ARS Livestock, Forage, and Pasture Management Research Unit in El Reno, Oklahoma, and the University of Oklahoma, eddy covariance systems were installed in 2018 on eight of the ten fields to measure fluxes of carbon dioxide (CO2), methane (CH4), and evapotranspiration (ET). Other measurements at these sites include biomass, leaf area index, chlorophyll concentration, and hyperspectral canopy reflectance. Data from our Long-Term Agricultural Research (LTAR) weather station continues to be measured and uploaded to the Ag Data Commons.

Accomplishments
1. Water quality in the Fort Cobb Reservoir Experimental Watershed in Oklahoma improved to meet Environmental Protection Agency (EPA) standards. ARS scientists at El Reno, Oklahoma, quantified water quality improvements in the Fort Cobb Reservoir Experimental Watershed (FCREW) in southwestern Oklahoma from historic data. Insufficient scientific information and data gaps in soil and watershed health exist. FCREW was identified by the Oklahoma Conservation Commission and Oklahoma Water Resources Board in 2003 as an impaired watershed that could be improved by farmer adoption of conservation practices (e.g., conversion to grassland and/or no-till). Chosen as a project site in the USDA, Natural Resource Conservation Service, Effects Assessment Project (CEAP), ARS researchers at El Reno, Oklahoma studied the effects of land use on soil health (SH) and water quality (WQ) across the FCREW using historic data. Adoption and continued application of conservation practices and conversion to grassland between 2003 and November 2006 improved water quality (WQ). Specifically, Environmental Protection Agency (EPA) standards for total dissolved solids, sediment, and nitrates in water were met. Identified soil and water indicators from this study could be used to develop a sampling protocol to monitor WQ within the FCREW. Methodology protocols followed in this study could be adopted by federal and state agencies to determine whether watersheds could be removed from the EPA 303 impaired water lists or could be used to develop new or improve Farm Bill incentive programs for farmers and producers adopting specific conservation practices.

2. Multiscale soil moisture prediction artificial intelligence tool developed to create efficiency in assessing climate change impact. Reliable and trustworthy prediction of rainfed crop yields under future climate is useful to develop short- or long-term irrigation and agriculture management plans that accommodate climate adaptation and mitigation strategies to ensure regional or national food security. Soil moisture is a critical variable affecting crop yields, but local soil moisture data are often sparse due to the lack of automated soil monitoring. Limited soil moisture data is the major drawback for Artificial Intelligence (AI)-based crop yield predictions. To address this challenge, ARS researchers at El Reno, Oklahoma, developed a new AI-based data generator known as Multiscale Extrapolative Learning Algorithm (MELA) capable of extrapolating limited local hydroclimatic measurements using information from data available for longer periods at larger spatial resolutions from external sources such as remote sensing and existing climate gauges. Multiscale soil moisture prediction artificial intelligence tool successfully and efficiently assesses soil moisture 72% of the time. These long-term monthly soil moisture data along with climatic data were used to predict annual winter wheat yield with 81% prediction accuracy. The AI-based analyses in conjunction with future climate projections for the study area suggest potential reductions in rain-fed crop yields between 2050 and 2100 in the absence of climate-resilient mitigation and adaptation plans. Rapidly assessing the impact of climate change on soil moisture and crop yields through this decision support tool could advise policy makers, lending institutions on what agricultural production systems and land management practices provide the best protection for their investment.

Review Publications
Welikhe, P., Williams, M.R., King, K.W., Bos, J.H., Akland, M., Baffaut, C., Beck, G., Bierer, A.M., Bosch, D.D., Brooks, E., Buda, A.R., Cavigelli, M.A., Faulkner, J., Feyereisen, G.W., Fortuna, A., Gamble, J.D., Hanrahan, B.R., Hussain, M., Kovar, J.L., Lee, B., Leytem, A.B., Liebig, M.A., Line, D., Macrae, M., Moorman, T.B., Moriasi, D.N., Mumbi, R., Nelson, N., Ortega-Pieck, A., Osmond, D., Penn, C.J., Pisani, O., Reba, M.L., Smith, D.R., Unrine, J., Webb, P., White, K.E., Wilson, H., Witthaus, L.M. 2023. Uncertainty in phosphorus fluxes and budgets across the U.S. long-term agroecosystem research network. Journal of Environmental Quality. 52(4):837-885. https://doi.org/10.1002/jeq2.20485.
Lee, S., Chu, M.L., Guzman, J.A., Flanagan, D.C., Moriasi, D.N., Fortuna, A., Starks, P.J. 2022. Integrated modeling for simulating sediment production and transport in agricultural landscapes. Environmental Modelling & Software. 160. Article 105605. https://doi.org/10.1016/j.envsoft.2022.105605.
Elias, E.H., Tsegaye, T.D., Hapeman, C.J., Mankin, K.R., Kleinman, P.J., Cosh, M.H., Peck, D.E., Coffin, A.W., Archer, D.W., Alfieri, J.G., Anderson, M.C., Baffaut, C., Baker, J.M., Bingner, R.L., Bjorneberg, D.L., Bryant, R.B., Gao, F.N., Gao, S., Heilman, P., Knipper, K.R., Kustas, W.P., Leytem, A.B., Locke, M.A., McCarty, G.W., McElrone, A.J., Moglen, G.E., Moriasi, D.N., OShaughnessy, S.A., Reba, M.L., Rice, P.J., Silber-Coats, N., Wang, D., White, M.J., Dombrowski, J.E. 2023. A vision for integrated, collaborative solutions to critical water and food challenges. Journal of Soil and Water Conservation. 78(3):63A-68A. https://doi.org/10.2489/jswc.2023.1220A.
Fortuna, A., Lewandowski, A.M., Osterholz, W.R. 2023. Enhancing the soil health-watershed health nexus: introduction. Journal of Environmental Quality. 52(3):407-411. https://doi.org/10.1002/jeq2.20420.
Fortuna, A., Steiner, J., Moriasi, D.N., Starks, P.J. 2023. Use of archived data to derive soil health and water quality indicators for monitoring shifts in natural resources. Journal of Environmental Quality. 52(30):523-536. https://doi.org/10.1002/jeq2.20476.
Rieke, E.L., Bagnall, D.K., Morgan, C., Greub, K., Bean, G.M., Cappellazzi, S.B., Cope, M., Liptzin, D., Norris, C.E., Tracy, P.W., Ashworth, A.J., Baumhardt, R.L., Dell, C.J., Derner, J.D., Ducey, T.F., Fortuna, A., Kautz, M.A., Kitchen, N.R., Leytem, A.B., Liebig, M.A., Moore Jr., P.A., Osborne, S.L., Owens, P.R., Sainju, U.M., Sherrod, L.A., Watts, D.B., et al. 2022. Evaluation of aggregate stability methods for soil health. Geoderma. 428. Article 116156. https://doi.org/10.1016/j.geoderma.2022.116156.
Gao, Y., Colliander, A., Burgin, M., Walker, J., Dinnat, E., Chae, C., Cosh, M.H., Caldwell, T., Berg, A., Martinez-Fernandez, J., Seyfried, M.S., Starks, P.J., Bosch, D.D., Mcnairn, H., Su, Z., Van Der Velde, R. 2022. Multi-frequency radiometer-based soil moisture retrieval and algorithm parameterization using in situ sites. Remote Sensing of Environment. 279. Article 113113. https://doi.org/10.1016/j.rse.2022.113113.
Liu, P., Bindlish, R., Fang, B., Lakshmi, V., O'Neill, P., Yang, Z., Cosh, M.H., Bongiovqnni, T., Bosch, D.D., Holifield Collins, C.D., Starks, P.J., Prueger, J.H., Seyfried, M.S., Livingston, S.J. 2021. Assessing disaggregated SMAP soil moisture products in the United States. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 14:2577-2592. https://doi.org/10.1109/JSTARS.2021.3056001.
Colliander, A., Kerr, Y., Wigneron, J., Al-Yaari, A., Rodriguez-Fernandez, N., Li, X., Chaubell, J., Richaume, P., Mialon, A., Asanuma, J., Berg, A., Bosch, D.D., Caldwell, T., Cosh, M.H., Holifield Collins, C.D., Martinez-Fernandez, J., Mcnaim, H., Seyfried, M.S., Starks, P.J., Su, Z., Thibeault, M., Walker, J. 2023. Performance of SMOS soil moisture products over core validation sites. IEEE Geoscience and Remote Sensing Magazine. 20:1-5. https://doi.org/10.1109/LGRS.2023.3272878.
Paudel, S., Boughton, E., Gomez-Casanovas, N., Chamberlain, S., Wagle, P., Peterson-Munks, B.L., Bajgain, R., Starks, P.J., Basara, J., Bernacchi, C.J., Delucia, E., Goodman, L., Gowda, P.H., Reuter, R., Sparks, J., Swain, H., Xiangming, X., Steiner, J. 2023. Intensification differentially affects the delivery of multiple ecosystem services in subtropical and temperate grasslands. Agriculture, Ecosystems and Environment. 348. https://doi.org/10.1016/j.agee.2023.108398.