Location: Grassland Soil and Water Research Laboratory
2022 Annual Report
Objectives
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.
Approach
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.
Progress Report
This is the final report for this project which terminated in January 2022. See the report for the replacement project, 3098-13610-009-000D, “Development of Enhanced Tools and Management Strategies to Support Sustainable Agricultural Systems and Water Quality” for additional information.
Sub-objective 1A: The Long-Term Agroecosystem Research (LTAR) network croplands common experiment in Riesel continued for the duration of the project with corn grown as the cash crop for the business-as-usual treatments with Austrian winter pea as cover crop added for aspirational treatments. Partners at Texas A&M University continue to monitor eddy covariance and phenocams. 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. Precision agriculture technologies have been adopted at the Riesel Watersheds and Temple farms, and treatments that push the envelope of precision agriculture toward precision conservation were assessed. Collaborative efforts with Texas A&M AgriLife in both Temple and Stephenville, Texas, were undertaken to collect data from small plots incorporating dairy manure, biochar, and synthetic fertilizers for bermudagrass and maize. Data collected by unmanned aerial vehicle (UAV)-based hyperspectral remote sensors have been organized and will continue to be collected over the next few years to establish a long-term dataset over the plots. Additionally, publications will follow year two or three of data collection.
Sub-objective 1B: At the field scale, an assessment of the Carbon (C), Nitrogen (N), and Phosphorus (P) budgets were made for the conventional and aspirational cropping systems under LTAR. This assessment identified gaps in information regarding the deposition and loss of N to the atmosphere and N and P losses through leaching. At the river basin scale, a conceptual model of C/N/P stoichiometric ratios was developed and tested using data from the River Thames (United Kingdom) which included nutrient and chlorophyll-a concentrations. The conceptual model was able to predict when P deficiencies limited algal productivity. This conceptual framework is being tested using other datasets to evaluate the influence of land use and hydrologic flow path on C/N/P stoichiometry.
Sub-objective 1C: The LTAR cropland common experiment continues at Riesel. Data continue to be collected to assess the common experiment treatments based on productivity, economics, and environmental responses linked to sustainability including greenhouse gas emissions and microbial community. No-tillage and banding fertilizer treatments were found to produce less nitrous oxide (N2O) emissions than conventional systems. Nitrous oxide emissions were greater for surface applied fertilizer than banded and were higher following rain events. Microbial communities were significantly affected by the level of disturbance (e.g. native, conventional till, no-till), soil organic carbon, and soil chemical properties and correlated well with the Soil Health Index. Treatment analyses will continue in successive years under the new plan. The radio spectrum for the automation of mobile/near-real-time data collection to support LTAR/AgCROS initiatives is being evaluated.
Sub-objective 2A: The Soil and Water Assessment Tool (SWAT) hydrologic and water quality model was recoded and refined to produce more accurate and scientifically-sound conservation and environmental assessments. The model was completely recoded into a modular, object-oriented structure, thus improving maintenance and development efficiencies. The newly developed flexible routing structure enables the simulation of additional landscape processes including overbank flood flows, flood plain wetlands, and numerous riparian processes. In addition, the improved input and output file structures allow hundreds of thousands of fields to be modeled efficiently in a single simulation. Model enhancements during the life of the project include the addition of 1) spatially distributed groundwater flow model, 2) comprehensive salt fate and transport model, 3) comprehensive carbon fate and transport model, 4) water allocation model to simulate the transfer of water to competing municipal, industrial, and agricultural water demands, and 5) simulation of emerging management technologies including two-stage ditches, saturated buffers, and drainage water management. The improved SWAT model is the modeling engine for the National Agroecosystem Model that is being used in Cropland Conservation Effects Assessment Project (CEAP) and LTAR assessments, providing a science-based framework for national conservation policy development.
Sub-objective 2B: We developed a new method of using results from the Phenocam network to update and improve the phenology parameters related to how the leaf area cover for a crop or grass develops over the growing season. These improved parameters were demonstrated with the Agricultural Land Management Alternative with Numerical Assessment Criteria (ALMANAC) to improve model simulations for rangeland plants, annual crops, and pasture grasses that were included in the Phenocam network. Also, results on phenology of a large number of eastern gamagrass ecotypes were improved by field measurements in a common garden in Temple. These included the development of leaf area cover over the growing season and the radiation use efficiency of several ecotypes. Other ALMANAC parameter refinements during this project included dry bean in tropical environments, grain sorghum in more tropical environments, common vegetables in the Texas Winter Garden Region, canola in saline sites near the Rio Grande, wetland plants in saline sites near the Rio Grande, and wetland plants in diverse wetland sites in the United States.
Sub-objective 3A: During this project, significant advancements have been made in the development of decision support tools. The Agricultural Conservation Reduction Estimator (ACRE) was developed and deployed. ACRE is a web-based conservation planning tool that allows a user to select specific field location, soils, and conditions and then run conservation scenarios. ACRE predicts reductions in runoff, sediment, and nutrient losses resulting from conservation. It is available on the web at: https://acre.brc.tamus.edu/ and is detailed in a publication.
Sub-objective 3B: The development of the National Agroecosystem Model (NAM) progressed throughout this project with a focus on meeting the demands of the Cropland Conservation Effects Assessment Project (CEAP) and LTAR. Version 1.0 was finalized and used in the Agricultural Operations Planning Tool and for the CEAP Wildlife effort in the upper Mississippi River Basin. The framework was developed with 2,109 individual regional SWAT+ models. Detailed information for 4.5 million cultivated agricultural fields is included and represented as a separate simulation unit in NAM. Model revision for Version 2.0 continues under the new project plan with enhanced irrigation, tile, conservation practice, soil test P, instream parameters, and groundwater data. We have and are continuing to collect remote sensing data as well as the collection of in-situ measures in conjunction with objective 1. We have also initiated the incorporation of remote sensing vegetation indices in NAM version 2.0, leading to novel/experimental applications of large-scale remote sensing in CEAP modeling efforts.
Accomplishments
1. Enhanced SWAT+ model. River basin models are needed to determine the impact of climate and land use changes on regional and national water supplies, water quality, and crop production. Science-based solutions are needed by USDA in conservation planning, United States Environmental Protection Agency (USEPA) in environmental planning, and LTAR to assess inspirational scenarios. To meet these important national needs, development continued on the SWAT+ (Soil and Water Assessment Tool) model. A comprehensive watershed scale carbon fate and transport model has been coded and validated in the SWAT model. The carbon code is currently being translated into object-oriented code for SWAT+ inclusion. Work also continued on developing a comprehensive fate and transport model for salt in SWAT+. A new groundwater model was developed and incorporated into SWAT+. During the last year, the model was parameterized and applied in the Mississippi delta and the Chesapeake Bay basin. A new water allocation submodel was developed and implemented in SWAT+. The model simulates water transfer and allocation from multiple sources (rivers, reservoirs, and aquifers) to competing water demand objects (municipal/industrial and irrigation demand from agricultural fields). The water allocation submodel is a critical addition to the model since most rivers in the United States have competing demands for water. The impact of this research is a validated, science-based tool that provides the framework for assessing and defining regional and national water, environmental, and conservation policy.
Review Publications
Rathjens, H., Kiesel, J., Miguez, M.B., Winchell, M., Arnold, J.G., Sur, R. 2022. Simulation of pesticide and metabolite concentrations using SWAT+ landscape routing and conditional management applications. Water. 14. Article 1332. https://doi.org/10.3390/w14091332.
Arnold, J.G., White, M.J., Allen, P.M., Gassman, P.W., Bieger, K. 2021. Conceptual framework of connectivity for a national agroecosystem model based on transport processes and management practices. Journal of the American Water Resources Association. 57(1):154-169. https://doi.org/10.1111/1752-1688.12890.
Gao, J., White, M.J., Bieger, K., Arnold, J.G. 2021. Design and development of a python-based interface for processing massive data with the LOAD ESTimator (LOADEST). Environmental Modelling & Software. 135. Article 104897. https://doi.org/10.1016/j.envsoft.2020.104897.
Gassman, P.W., Jeong, J., Boulange, J., Narasimhan, B., Kato, T., Somura, H., Watanabe, H., Eguchi, S., Cui, Y., Sakaguchi, A., Tu, L., Jiang, R., Kim, M., Arnold, J.G., Ouyang, W. 2022. Simulation of rice paddy systems in SWAT: A review of previous applications and proposed SWAT+ rice paddy module. International Agricultural Engineering Journal. 15(1):1-24. https://doi.org/10.25165/j.ijabe.20221501.7147.
Wagner, P.D., Bieger, K., Arnold, J.G., Fohrer, N. 2022. Representation of hydrological processes in a rural lowland catchment in Northern Germany using SWAT and SWAT+. Hydrological Processes. 36(5). Article 14589. https://doi.org/10.1002/hyp.14589.
Menefee, D.S., Rajan, N., Shafian, S., Cui, S. 2022. Modeling carbon uptake of dryland maize using high resolution satellite imagery. Frontiers in Remote Sensing. 3. Article 810030. https://doi.org/10.3389/frsen.2022.810030.
Liu, W., Dong, J., Du, G., Zhang, G., Hao, Z., You, N., Zhao, G., Flynn, K.C., Yang, T., Zhou, Y. 2022. Biophysical effects of paddy rice expansion on land surface temperature in Northeastern Asia. Agricultural and Forest Meteorology. 315. Article 108820. https://doi.org/10.1016/j.agrformet.2022.108820.
Wells, J., Crow, S., Khanal, S., Turn, S., Hashimoto, A., Kiniry, J.R., Meki, N. 2021. Anaerobic digestion and hot water pretreatment of tropically grown C4 energy grasses: Mass, carbon, and energy conversions from field biomass to fuels. Agronomy. 11. Article 838. https://doi.org/10.3390/agronomy11050838.
Kiniry, J.R., Arthur, C.E., Banick, K.M., Fritschi, F.B., Wu, Y., Hawkes, C.V. 2020. The effects of plant-soil feedback on switchgrass productivity depend on microbial origin. Agronomy. 10. Article 1860. https://doi.org/10.3390/agronomy10121860.
Escobar-Silva, E., Bourscheidt, V., Daughtry, C.S., Kiniry, J.R., Backes, A. 2022. A grass growth model adapted to urban areas: a case study with bahiagrass (Paspalum notatum flugee) in San Carlos, Brazil. Environmental Modelling & Software. 73:127583. https://doi.org/10.1016/j.ufug.2022.127583.
Zhou, Y., Flynn, K.C., Gowda, P.H., Wagle, P., Ma, S., Kakani, V.G., Steiner, J.L. 2021. The potential of active and passive remote sensing to detect frequent harvesting of alfalfa. International Journal of Applied Earth Observation and Geoinformation. 104. Article 102539. https://doi.org/10.1016/j.jag.2021.102539.
van Tol, J., Bieger, K., Arnold, J.G. 2021. A hydropedological approach to simulate streamflow and soil water contents with SWAT+. Hydrological Processes. 35(6). Article e14242. https://doi.org/10.1002/hyp.14242.
Xu, Y., Elbakidze, L., Yen, H., Arnold, J.G., Gassman, P.W., Hubbart, J., Strager, M.P. 2021. Integrated assessment of nitrogen runoff to the Gulf of Mexico. Resource and Energy Economics. 67. Article 101279. https://doi.org/10.1016/j.reseneeco.2021.101279.
