Research Project: Development of Enhanced Tools and Management Strategies to Support Sustainable Agricultural Systems and Water Quality

Location: Grassland Soil and Water Research Laboratory

2025 Annual Report

Objectives
Objective 1: In support of LTAR and CEAP, develop management strategies to maintain agronomic resilience through climate extremes that support natural resource conservation and agroecosystem sustainability in the Texas Gulf Coast region. Sub-objective 1.A: Identify, develop, and evaluate strategies to improve precision agronomic management of croplands to incorporate precision conservation that will optimize agronomic, environmental and economic outcomes. Sub-objective 1.B: Evaluate the role of landscape scale spatiotemporal soil, water and genetic variability within representative plant species to determine production potential through extreme climatic events in the Texas Gulf Coast region. Sub-objective 1.C: Catalog, archive and synthesize observational research data across the LTAR network to facilitate collaborate research using advanced database and visualization technologies. Objective 2. Enhance process-based model algorithms and structure using modern programming paradigms and new research findings from LTAR and CEAP. Sub-objective 2.A: Improve the SWAT+ model using streamlined code, data structures and upgraded algorithms to better address U.S. and global environmental challenges. Sub-objective 2.B: Improve ALMANAC predictive capacity using enhanced phenology algorithms and updated plant parameters derived from LTAR-Phenocam observational data on fraction leaf cover. Objective 3: Develop tools using enhanced models and other new technologies to support improved agroecosystems management and policy formulation from the field to national scales. Subobjective 3.A: Develop a trans-scale unified national modeling framework to support CEAP and LTAR. Subobjective 3.B: Develop decision support tools to address unmet agricultural and environmental problems by synthesizing observational data and model predictions using emerging technologies (ML, AI, Drones).

Approach
The overall goal of this research is to improve agricultural and environmental sustainability by providing producers and policymakers with scientifically credible information to make good decisions. There is a strong need for improved data driven decision support tools which can predict the effects of both human activity and climate variability on agricultural production systems and the environment. These tools are needed and requested by producers, conservation/watershed planners, USDA leadership, State Agencies, Federal Agencies (NRCS, FPAC, EPA, NOAA, USGS), Non-Governmental Organizations (The Nature Conservancy, Environmental Defense Fund, Field to Market), and local stakeholder groups. The Grassland, Soil and Water Research Laboratory (GSWRL) is well positioned to address this need using complementary programs in field/ monitoring and hydrologic/water quality modeling. There are three interlinked principal components of this project: 1) collection and synthesis of field data to aid in the evaluation of environmental and agro-economic impacts to support the development of more sustainable production strategies; 2) enhancement and testing of the Soil and Water Assessment Tool (SWAT) and Agricultural Land Management Alternative with Numerical Assessment Criteria (ALMANAC) model algorithms that represent field-, farm-, and watershed-scale processes using up-to-date scientific knowledge from Conservation Effects Assessment Program (CEAP), Long-Term Agro-ecosystem Research (LTAR), and other applied research programs; and 3) development of decision support tools using emerging technologies for conservation management, planning, and policy development at local, regional, and national scales. Model enhancement is a foundational component of this project, models developed at GSWRL are critical components of local/regional, USDA, and legislative decision-making. These models are being used to assess USDA conservation policy in the second generation of the Office of Management and Budget (OMB) and congressionally mandated CEAP program. These models are widely used in Europe, Asia, Africa, and South America, and their enhancement has significant global impact.

Progress Report
Sub-objective 1.A: Adaptive management of precision conservation work is progressing on schedule. Cotton is being grown as the 3th year of the rotation, after which analysis can be completed on agronomic, economic and water quality outcomes to determine the role this practice plays in furthering sustainable agronomic systems to foster productive and economically viable farms. Sub-objective 1.B: The scientist responsible for this objective retired prior to FY25. The position has not been refilled at this time. Sub-objective 2.A: Additional enhancements have been integrated into the Soil and Water Assessment Tool (SWAT+) model. The groundwater and salt components have been significantly enhanced and applied to our National Agroecosystems Model (NAM). Progress on improved soil carbon routines continues, and these are currently undergoing testing. Phosphorus dynamics have been incorporated into the groundwater component and are being tested in the Choptank, Le Sueur, and Big Sunflower watersheds in collaboration with three other ARS locations. SWAT+ source code is now managed within the Enterprise GitHub framework as an open-source project to promote collaboration and accelerate improvements. Sub-objective 2.B: The scientist responsible for this objective retired prior to FY25. The position has not been refilled at this time. Sub-objective 3.A: National Agroecosystems Model (NAM) is available for collaborative projects and is currently being used by other federal partners, non-governmental ogranizations, university researchers and private sector partners. These include: Natural Resources Conservation Service, U.S. Geological Survey (USGS), Environmental Protection Agency, The Nature Conservancy, Environmental Defense Fund, Field to Market, and University partners at Colorado State, Kansas, Iowa State, Michigan State, University of Michigan, Ohio State, University of Wisconsin–Madison. NAM calibrations are being updated with new Artificial Intelligence (AI) based instream load estimates based on USGS water quality monitoring data. NAM evapotranspiration predictions are being evaluated using OpenET data at both the field-scale and HUC8-scale across the 17 western states. Daily weather inputs are being evaluated against gridMET data across the contiguous U.S., with a focus on reference evapotranspiration. Sub-objective 3.B: A draft decision support tool has been developed in cooperation with The Nature Conservancy to identify management strategies that improve wildlife resources in the Upper Mississippi River Basin. This tool utilizes NAM models to generate recommendations and simulate management scenarios. A field-level version of the Agricultural Conservation Reduction Estimator (ARCE) was developed and deployed to the web during FY25. The servers were temporarily taken offline pending the hiring of a replacement web developer.

Accomplishments
1. Enhancing phosphorus indices with new mechanistic insights to improve on-farm decision making. The Phosphorus Index is used across the United States and in countries across the world to help farmers balance crop production and water quality outcomes. However, current Phosphorus Indices often struggle to predict dissolved phosphorus losses that represent the most immediate concern to water quality. This work by an international team of scientists, including USDA scientists from Temple, Texas, and Fort Collins, Colorado, rethinks the foundational model for predicting dissolved phosphorus losses. By integrating new mechanistic insight into the Phosphorus Index, farmers and decision-makers gain more accurate, regionally tailored risk assessments that empower them to prioritize high-impact interventions and design more effective, site-specific nutrient management strategies to ensure this valuable resource remains on the agricultural fields for its intended purpose of producing a crop.

2. Unmanned Arial Vehicle (UAV) and Artificial Intelligence (AI)-driven precision agriculture innovations to boost profitability and reduce waste for U.S. farmers. Advanced precision agriculture research led by ARS scientists in Temple, Texas, is driving substantial innovations that enhance profitability and resource use for U.S. agricultural producers. By integrating cutting-edge sensor technologies and machine learning/AI with field-based research, the team is refining techniques for in-field detection of biophysical and biochemical crop characteristics. This work not only supports more efficient management strategies but also has the potential to deliver large-scale fiscal savings. This work includes the development of a UAV-based methodology for accurate crop stand counts even under weed pressure, as highlighted in a recent publication titled ‘An Innovative UAV-Based Approach for Estimating Crop Stand Counts Amidst Weed Infestation.’ These efforts provide agricultural stakeholders with actionable, data- driven insights that optimize operations and drive long-term economic and environmental benefits.

3. Soil and Water Assessment Tool (SWAT+) enhancements to address modern agricultural challenges. USDA’s widely used SWAT+ is a hydrologic and water quality model utilized in over 25 countries and the subject of more than 6,000 peer-reviewed publications, has recently undergone significant enhancements to better address complex current and emerging agricultural challenges. These updates completed by ARS researchers at Temple, Texas, include the integration of a spatially detailed groundwater model for improved resource assessment and irrigation simulation, a more realistic depiction of nitrate and phosphate transport in aquifers, and new modules for rice paddy management. Further enhancements include improvements to simulate water rights, wastewater reuse, irrigation transfer, soil carbon and nutrient cycles, instream sediment transport, and improved crop yield predictive capacity. These achievements contribute to a more robust and versatile decision support tool enabling stakeholders and policy makers to evaluate land management practices and optimize land and water resource use for enhanced agricultural productivity/profitability and reduced environmental impacts.

4. National Agroecosystems Model: a framework for shared, scalable modeling to strengthen agriculture and water resilience. The National Agroecosystems Model (NAM) is a robust, field-scale modeling platform and decision-support tool developed by ARS researchers at Temple Texas. This tool simulates the impacts of agricultural activities on water resources, soil health, and crop production across the entire United States. Built on USDA’s Soil and Water Assessment Tool (SWAT+) engine, NAM includes over 4 million individual cultivated fields, 2 million streams and rivers, and all lakes and reservoirs nationwide. It incorporates observed weather variability, including droughts and floods, to evaluate how land management practices perform under diverse environmental conditions. NAM plays a central role in USDA initiatives such as the Conservation Effects Assessment Project (CEAP) for Cropland and Wildlife components, and serves as a shared platform for collaboration among federal agencies (NRCS, USGS, EPA), non- governmental organizations (The Nature Conservancy, Environmental Defense Fund, Field to Market), and university partners including Colorado State University, Kansas State University, Iowa State University, Michigan State University, University of Idaho, Ohio State University, and the University of Wisconsin–Madison. This collaborative approach reduces duplication of modeling efforts, lowers development costs for individual projects, and enhances national capacity to evaluate water management, conservation outcomes, and agricultural program impacts.

Review Publications
Tuladhar, A., Bailey, R.T., Abbas, S.A., Shanmugam, M.S., Arnold, J.G., White, M.J. 2025. Quantifying the impact of climate change and land use change on surface-subsurface nutrient dynamics in a Chesapeake Bay watershed system. Journal of Environmental Management. https://doi.org/10.1016/j.jenvman.2025.125101.
Abbas, S.A., Bailey, R.T., White, J.T., Arnold, J.G., White, M.J. 2024. Quantifying the role of calibration strategies on surface-subsurface hydrologic model performance. Hydrological Processes. https://doi.org/10.1002/hyp.15298.
H K, C., Flynn, K.C., Baath, G., Gowda, P.H., Northup, B.K., Ashworth, A.J. 2025. Monitoring legume nutrition with machine learning: The impact of splits in training and testing data. Applied Soft Computing. https://doi.org/10.1016/j.asoc.2025.113186.
Figueiredo Moura Da, E.H., Da Silva, M., Kothari, K., Pattey, E., Battisti, R., Boote, K.J., Archontoulis, S.V., Cuadra, S.V., Faye, B., Grant, B., Hoogenboom, G., Jing, Q., Marin, F.R., Nendel, C., Qian, B., Smith, W., Srivastava, A., Thorp, K.R., Vieira, N.A., Salmeron, M. 2025. Inter-comparison of soybean models for the simulation of evapotranspiration in a humid continental climate. Agricultural and Forest Meteorology. 365. Article 110463. https://doi.org/10.1016/j.agrformet.2025.110463.
Abbas, S.A., Bailey, R.T., Arnold, J.G., White, M.J., Mirchi, A. 2025. Modeling agro-hydrological surface-subsurface processes in a semi-arid, intensively irrigated river basin. Journal of Hydrology: Regional Studies. 57. Article 102188. https://doi.org/10.1016/j.ejrh.2025.102188.
Abbas, S.A., Bailey, R.T., White, J.T., Arnold, J.G., White, M.J. 2025. Estimation of groundwater storage loss using surface–subsurface hydrologic modeling in an irrigated agricultural region. Scientific Reports. https://doi.org/10.1038/s41598-025-92987-6.
Nao-Yarasca, E., Osorio Leyton, J., White, M.J., Gao, J., Arnold, J.G. 2025. Assessing the use of alternative soil data in hydrological and water quality modeling with SWAT+: SSURGO and POLARIS at subbasin and field scales. Water Research. https://doi.org/10.3390/w17050670.
Bailey, R.T., Abbas, S.A., Arnold, J.G., White, M.J. 2025. Assessing selenium fate and transport in a semi-arid river basin with and without human influence. Water Research. https://doi.org/10.1016/j.watres.2025.123335.
Almahawis, M.K., Bailey, R.T., Abbas, S.A., Arnold, J.G., White, M.J. 2024. Investigating the impact of irrigation practices on hydrologic fluxes in a highly managed river basin. Agricultural Water Management. 301. https://doi.org/10.1016/j.agwat.2024.108954.
Abbas, S.A., Bailey, R.T., Almahawis, M.K., White, J.T., Arnold, J.G., White, M.J. 2024. Calibration guide for watershed modeling with distributed groundwater modeling: Application for the SWAT+ model. Hydrological Sciences Journal. https://doi.org/10.1080/02626667.2024.2393414.
Baath, G.S., Bawa, A., Sapkota, B., Flynn, K.C., Sakar, S., Smith, D.R. 2025. An innovative UAV-based approach for estimating crop stand counts amidst weed infestation. Smart Agricultural Technology. https://doi.org/10.1016/j.atech.2025.101030.
Flynn, K.C., Smith, D.R., Lee, T.O., Martinez, D.L., Ma, S.R., Zhou, Y. 2024. Evaluating maize (Zea mays L.) management practices implementing sensitivity analysis of vegetation indices. Soil and Tillage Research. 244. Article 106266. https://doi.org/10.1016/j.still.2024.106266.
Flynn, K.C., Witt, T.W., Baath, G., H.K., C., Smith, D.R., Gowda, P.H., Ashworth, A.J. 2024. Hyperspectral reflectance and machine learning for multi-site monitoring of cotton growth. Smart Agricultural Technology. 9. Article 100536. https://doi.org/10.1016/j.atech.2024.100536.
Thorp, K.R., DeJonge, K.C., Pokoski, T., Gulati, D., Kukal, M., Farag, F., Hashem, A., Erismann, G., Baumgartner, T., Holzkaemper, A. 2024. Version 1.3.0 - pyfao56: FAO-56 evapotranspiration in Python. SoftwareX. 26. Article 101724. https://doi.org/10.1016/j.softx.2024.101724.
DeJonge, K.C., Thorp, K.R., Brekel, J.J., Pokoski, T.C., Trout, T.J. 2024. Customizing pyfao56 for evapotranspiration estimation and irrigation scheduling at the Limited Irrigation Research Farm (LIRF), Greeley, Colorado. Agricultural Water Management. 299. Article e108891. https://doi.org/10.1016/j.agwat.2024.108891.
Thompson, A.L., Thorp, K.R., Herritt, M.T. 2025. Identifying seed cotton yield and abiotic stress response in cotton (Gossypium hirsutum L.) grown in the Arizona low desert. Crop Science. 65(2). Article e70058. https://doi.org/10.1002/csc2.70058.
Chatterjee, A., Thorp, K.R., O'Brien, P.L., Kovar, J.L., Rogovska, N.P., Malone, R.W. 2025. Long-term DSSAT simulation of nitrogen loss to artificial subsurface drainage flow for a corn-soybean rotation with winter rye in Iowa. Agricultural Water Management. https://doi.org/10.1016/j.agwat.2025.109464.
Christensen, C.G., Gohardoust, M.R., Calleja, S., Thorp, K.R., Tuller, M., Pauli, D. 2024. Monitoring cotton water status with microtensiometers. Irrigation Science. 42:995-1011. https://doi.org/10.1007/s00271-024-00930-w.
Chen, X., Dong, H., Qi, Z., Gui, D., Ma, L., Thorp, K.R., Malone, R.W., Wu, H., Liu, B., Feng, S. 2025. Potential deficit irrigation adaptation strategies under climate change for sustaining cotton production in hyper–arid areas. Agricultural Water Management. 312. Article e109417. https://doi.org/10.1016/j.agwat.2025.109417.
White, J.W., Boote, K.J., Kimball, B.A., Porter, C., Salmeron, M., Shelia, V., Thorp, K.R., Hoogenboom, G. 2025. From field to analysis: Strengthening reproducibility and confirmation in research for sustainable agriculture. Sustainable Agriculture. 3(27). https://doi.org/10.1038/s44264-025-00067-z.
Nand, V., Qi, Z., Ma, L., Helmers, M.J., Madramootoo, C.A., Smith, W.N., Zhang, T.Q., Weber, T.K., Pattey, E., Li, Z., Wang, J., Jin, V.L., Jiang, Q., Tenuta, M., Trout, T.J., Chang, H., Harmel, R.D., Kimball, B.A., Thorp, K.R., Boote, K.J., Stockle, C., Suyker, A.E., Evett, S.R., Brauer, D.K., Coyle, G.G., Copeland, K.S., Marek, G.W., Colaizzi, P.D., Acutis, M., Alimagham, S.M., Archontoulis, S., Babacar, F., Barcza, Z., Basso, B., Bertuzzi, P., Constantin, J., Migliorati, M., Dumont, B., Durand, J., Fodor, N., Gaiser, T., Garofalo, P., Gayler, S., Giglio, L., Grant, R., Guan, K., Hoogenboom, G., Kim, S., Kisekka, I., Lizaso, J., Masia, S., Meng, H., Mereu, V., Mukhtar, A., Perego, A., Peng, B., Priesack, E., Shelia, V., Snyder, R., Soltani, A., Spano, D., Srivastava, A., Thomson, A., Timlin, D.J., Trabucco, A., Webber, H., Willaume, M., Williams, K., Van Der Laan, M., Ventrella, D., Viswanathan, M., Xu, X., Zhou, W. 2025. Evaluation of multimodel averaging approaches for ensembling evapotranspiration and yield simulations from maize models. Journal of Hydrology. 661(Part B). Article e133631. https://doi.org/10.1016/j.jhydrol.2025.133631.
Hague, M., Ansari, A.H., Veith, T.L., White, M.J., Costello, C., Spiegal, S.A., Kleinman, P.J., Arnold, J.G., Cibin, R. 2025. Reducing national water degradation: Development and application of a manureshed-identification framework. Agricultural Systems. 227. Article 104349. https://doi.org/10.1016/j.agsy.2025.104349.
Schulz, E.Y., Morrison, R.R., Bailey, R.T., Arnold, J.G., White, M.J. 2024. River corridor beads are important areas of floodplain-groundwater exchange within the Colorado River headwaters watershed. Hydrological Processes. https://doi.org/10.1002/hyp.15282.