Objective 1: As part of the LTAR network, and in concert with similar long-term, land-based research infrastructure in the Texas Gulf Region, use the Texas Gulf 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 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. Sub-objective 1A: Evaluate differences in the environmental and agro-economic impacts of conventional and aspirational cropping systems. Sub-objective 1B: Quantify landscape and climatic factors regulating C, N and P loss to surface waters at the field, stream, and river basin scales. Sub-objective 1C: Create “business as usual” and “aspirational” production and ecosystem service system scenarios as outlined by the LTAR common experiment. Assess the sustainability of both systems and develop new strategies to enable greater sustainability. Objective 2: Use new findings from CEAP and other applied research to enhance and validate model algorithms for watershed processes to meet emerging stakeholder needs. Sub-objective 2A: Develop and incorporate SWAT model enhancements to improve logistics, streamline application, foster collaborative development, and meet emerging national and international modeling needs. Sub-objective 2B: Improve Agricultural Land Management Alternatives with Numerical Assessment Criteria (ALMANAC) simulation of bioenergy, rangeland, pastureland, and wetland plants by using field data to develop new phenology algorithms and associated plant parameters. Objective 3: Utilize enhanced models to develop decision support tools for conservation management, planning, and policy at local and national scales to mprove water resources. Sub-objective 3A: Enhance model-based decision support tools to support field and small watershed management decision making to improve ecosystem services. Sub-objective 3B: Enhance and streamline large-scale resource models and decision support tools to support CEAP requirements and other national and international stakeholder needs.
Cropped agricultural fields at the Riesel Watersheds will be monitored for agroenviornmental response to climatic drivers and operations will be recorded to assess the economics between aspirational and business-as-usual treatments. Smaller scale mechanistic studies will be used to evaluate the potential influence of climatic drivers and changing nutrient transport in stream and river networks. Land-use and hydrologic pathway will also be used to evaluate coupled C/N/P biogeochemistry in soils and runoff. The Soil and Water Assessment Tool (SWAT) model will be updated to improve the process-based modeling capabilities for gully erosion, flood plain interactions, riparian wetlands and grazing management. The Agricultural Land Management Alternative with Numerical Assessment Criteria (ALMANAC) model will be updated to better represent wetlands, pasturelands and biofuels cropping systems. The modeling capabilities of the GSWRL will be utilized to develop frameworks and databases that will then be used in a national conservation effects assessment project (CEAP). The GSWRL CEAP tools will be the basis of accounting for the environmental benefits of conservation practice implementation by other USDA programs and other state and federal agencies.
Objective 1. The Long-Term Agroecosystem Research (LTAR) network croplands common experiment in Riesel continued with Austrian winter pea as cover crop on the aspirational treatment and corn grown as the cash crop for both aspirational and business as usual treatments. Partners at Texas A&M University continued monitoring eddy covariance and phenocams. Water quality samples from watersheds associated with LTAR were collected and processed from at least a dozen runoff events during a very wet 2019. Preliminary information on plant, soil, and water response to aspirational crop production was collected and analyzed. Soil samples have been collected monthly from the aspirational and business-as-usual cultivated fields as part of the LTAR network and analyzed for treatment effects on plant uptake, soil nutrient availability, runoff quality, and soil microbial communities using phospholipid fatty acid (PFLA) techniques. PFLA analyses of microbial communities showed significant differences among treatments and were related to the level of soil organic matter. In situ chambers to measure soil greenhouse gas emissions have not yet been installed, but three flux towers are currently collecting data on carbon dioxide emissions cropping systems. A preliminary assessment of the carbon (C), nitrogen (N) and phosphorus (P) budgets were made for the conventional and aspirational cropping systems. This assessment identified gaps in information regarding the deposition and loss of N to the atmosphere and N and P losses through leaching. Data collection will be expanded to correct and collect information on these parameters. Stakeholder interest about the role of precipitation chemistry on runoff water quality persists. Subobjective 2A. Development of Soil and Water Assessment Tool plus (SWAT+) continued for national environmental and conservation assessments. The pesticide routines were enhanced to track pesticide fate and transport through the entire watershed including channels, reservoirs, and aquifers. A new salt module was developed in collaboration with scientists at Colorado State University. The salt routines simulate the transport of 8 salt ions including sulfate, calcium, magnesium, sodium, potassium, chloride, carbonate, and bicarbonate. A geomorphic groundwater flow model was developed that simulates flow by stream order, allowing smaller streams to recede and stop flowing before the larger rivers. This process is critical in understanding the hydrologic impact on fish and wildlife health and habitat. Proper calibration of a model is critical to developing realistic conservation scenarios and ultimately national conservation policies. Of particular importance is ensuring water, sediment, and nutrient budgets are accurate. To ensure accurate simulating of processes in SWAT+, a simple heuristic procedure was developed and incorporated into SWAT+ for “soft” calibration. Users input average annual budgets and within 20 model runs, SWAT+ will automatically adjust input parameters to match user input budgets. This “soft” calibration procedure will help ensure accurate and meaningful national conservation policy. Subobjective 2B. New plant parameters were developed to allow Agricultural Land Management Alternative with Numerical Assessment Criteria (ALMANAC) to more accurately simulate bermudagrass and bahiagrass. Research has also begun to parameterize various varieties of eastern gamagrass for use in ALMANAC. This work has resulted in an abstract by a summer intern at the upcoming Agronomy Society meetings and at a switchgrass meeting at University of Illinois in July 2019. Subobjective 3A. The ALMANAC model is now downloadable not only for all the U.S. (with soils and weather data) but also for the country of Mexico (https://www.ars.usda.gov/plains-area/temple-tx/grassland-soil-and-water-research-laboratory/docs/almanacmex/). Plans are being made for a researcher to visit from Argentina in September to extend this work even further, into South America. Subobjective 3B. The development of datasets to drive the National Agroecosystem Model (NAM) was continued. NAM uses the SWAT+ model to address the needs of the Conservation Effects Assessment Project (CEAP) and LTAR. A draft national model containing 2,109 individual regional models was developed. Unlike previous models at this scale, detailed information for 4.5 million cultivated agricultural fields are included. Each field is represented as a separate simulation unit in NAM. Data for each field is derived from multiyear crop and landcover data and digital elevation models. High resolution stream data have also been incorporated into NAM, to allow the evaluation of impoundment and riparian conservation practices by the USDA.
1. Hydrology and land-use affects nutrient stoichiometry. Agriculture is often identified as one of the contributors of nitrogen and phosphorus which induce algal blooms in surface waters, such as Lake Erie, Chesapeake Bay or Gulf of Mexico. Aquatic algae generally demand nutrients at stoichiometric ratio close to 106 carbon atoms to 16 nitrogen atoms to 1 phosphorus atom, which is known as the Redfield Ratio. Using a visualization technique previously developed within this project, ARS researchers at Temple, Texas, used years of water quality data to evaluate how hydrology and land-use impacted the stoichiometric ratio of runoff samples. While water collected from streams during drought conditions was depleted in nitrogen relative to carbon and phosphorus, samples collected at extreme high flow exhibited stoichiometric ratios very near the Redfield Ratio, which indicates these high flow samples could readily induce algal proliferation if other factors (i.e., water temperature and light) were also right. In an agricultural setting, field scale surface runoff was generally depleted in nitrogen relative to carbon and phosphorus, yet increasing rates of poultry litter in both cropped fields and pastures shifted the stoichiometric ratios incrementally toward the Redfield Ratio, which could be detrimental to water quality. These two studies provide a first step toward a better understanding of how we may better manage agricultural runoff to disrupt algal proliferation by focusing on the stoichiometric carbon, nitrogen and phosphorus levels in runoff in a mechanistic fashion. This could give conservation planners and watershed managers additional tools to improve the selection of conservation practices in watersheds that are highly susceptible to harmful and nuisance algal blooms.
2. Official release of SWAT+ to the public. The Soil and Water Assessment Tool plus (SWAT+) is a revised modular version of SWAT with improved flexibility in watershed configuration, a new relational input file structure, and additional capabilities in water allocation and reservoir operation. The SWAT+ model was officially released at the 2019 International SWAT Conference in Vienna in July, 2019. The first SWAT+ workshop was conducted to train participants in the use of the QGIS (Geographic Information System) interface and associated input file editor. Model code and example data sets were made available at a website used for archiving and version control. In addition, a user manual is now available at the SWAT website https://swat.tamu.edu/software/plus/. SWAT is one of the most widely used hydrologic and water quality models in the U.S. and internationally. With the development of SWAT+ researchers can more accurately predict the impact of human activities on the environment, especially at the local level. SWAT+ is currently being used as the modeling engine behind the National Agroecosystem Model (NAM), a nationwide model used for research and policy development in the USDA. NAM and SWAT+ are integral components of the Conservation Effects Assessment (CEAP) project and ARS Long Term Agroecosystem Research (LTAR) project, both of which are designed to guide agricultural related policy at the federal level.
3. Development of field scale national modeling framework. The National Agroecosystems Model (NAM) is a framework of national datasets, processing software, and models (Development of Soil and Water Assessment Tool plus - SWAT+) developed to predict the impact of conservation policy, land use and management change, weather, and other human activities on pollutants which degrade the environment. NAM is a continuous effort, developed to address the evolving needs of Long-Term Agroecosystem Research (LTAR) and Conservation Effects Assessment Project (CEAP) and is improved in temporal and spatial scale as additional data and software become available. The current NAM model operates at the field scale for cultivated areas, containing 4.5 million fields within the contiguous U.S. Advances in remote sensing allow crop rotation and topographic characteristics of each individual field to be included as a separate computational unit in SWAT+. County level NAM predictions combined with short term precipitation forecasts are currently being used in Natural Resource and Conservation Service’s (NRCS’s) Agricultural Operations Planning Tool (AgOPT) https://realtimeceap.brc.tamus.edu/ to aid producers in scheduling the application of agrichemicals.
Baez-Gonzalez, A.D., Torres-Meza, M.D., Royo-Marquez, M.O., Kiniry, J.R. 2018. Climate variability and trends in climate extremes in the priority conservation area El Tokio and adjacent areas in Northeastern Mexico. Weather and Climate Extremes. 22:36-47. https://doi.org/10.1016/j.wace.2018.10.001.
Kiniry, J.R., Kim, S., Meki, M.N., Johnson, M.V. 2018. Forage yield estimation with a process-based simulation model. In: Edvan, R.L., Santos, E.M., editors. Forage Groups. IntechOpen. p. 35-52. https://doi.org/10.5772/intechopen.79987
Schilling, K.E., Gassman, P.W., Arenas-Amado, A., Jones, C.S., Arnold, J.G. 2018. Quantifying the contribution of tile drainage to basin-scale water yield using analytical methods and a numerical model. Water Resources Research. 657:297-309. https://doi.org/10.1016/j.scitotenv.2018.11.340.
Kannan, N., Santhi, C., White, M.J., Mehan, S., Arnold, J.G., Gassman, P.W. 2019. Some challenges in hydrologic model calibration for large-scale studies: A case study of SWAT model application to Mississippi-Atchafalaya River Basin. Hydrology. 6(1):17. https://doi.org/10.3390/hydrology6010017.
Sun, X., Bernard-Jannin, L., Grusson, Y., Sauvage, S., Arnold, J.G., Srinivasan, R., Perez, J.S. 2018. Using SWAT-LUD model to estimate the influence of water exchange and shallow aquifer denitrification on water and nitrate flux. Water. 10(4):528. https://doi.org/10.3390/w10040528.
Qi, J., Zhang, X., McCarty, G.W., Sadeghi, A.M., Cosh, M.H., Zeng, X., Gao, F.N., Daughtry, C.S., Haung, C., Lang, M., Arnold, J.G. 2018. Integration of a physically-based soil moisture module into SWAT to improve hydrology model structure. Environmental Modelling & Software. https://doi.org/10.1016/j.envsoft.2018.08.024.
Baez-Gonzalez, A.D., Kiniry, J.R., Meki, M.N., Williams, J.R., Alvarez-Cilva, M., Ramos-Gonzalez, J.L., Magallanes-Estala, A. 2018. Potential impact of future climate change on sugarcane under dryland conditions in Mexico. Journal of Agronomy and Crop Science. 204:515-528. https://doi.org/10.1111/jac.12278.
Pawlowski, M., Meki, M.N., Kiniry, J.R., Crow, S.E. 2018. Carbon budgets of potential tropical perennial grass cropping scenarios for bioenergy feedstock production. Carbon Balance and Management. 13:17. https://doi.org/10.1186/s13021-018-0102-8.
Reichmann, L.G., Collins, H.P., Jin, V.L., Johnson, M.V., Kiniry, J.R., Mitchell, R., Polley, H.W., Fay, P.A. 2018. Inter-annual precipitation variability decreases switchgrass productivity from arid to mesic environments. BioEnergy Research. 11(3):614-622. https://doi.org/10.1007/s12155-018-9922-3.
Smith, D.R., Jarvie, H.P. 2018. Carbon, nitrogen, and phosphorus stoichiometric response to hydrologic extremes in a tributary to Lake Erie, USA. Agricultural and Environmental Letters. 3:180043. https://doi.org/10.2134/ael2018.08.0043.
Macintosh, K.A., Doody, D.G., Withers, P.J., McDowell, R.W., Smith, D.R., Johnson, L.T., Bruulsema, T.W., O'Flaherty, V., McGrath, J.W. 2019. Transforming soil phosphorus fertility management strategies to support the delivery of multiple ecosystem services from agricultural systems. Science of the Total Environment. 649:90-98. https://doi.org/10.1016/j.scitotenv.2018.08.272.