Location: Grassland Soil and Water Research Laboratory
2024 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: Progress has continued for precision agriculture/precision conservation. Adoption of precision conservation through the reduction or elimination of inputs to unproductive portions of fields along with water quality and economic analysis continues. Thus far, crop year 2024 has been very different than the drought of the last couple of years, providing excellent variability in crop production parameters. Precision phosphorus(P) fertility applications have been demonstrated to result in crop response to fertilizer rate, form and placement. An unmanned ground vehicle was used to apply fertilizer in FY24, severe wind and hail has damaged the crop and yield is expected to be greatly diminished.
Sub-objective 1.B: Work continued to develop plant parameters for several crops and grasses. These parameters are targeted for inclusion in ALMANAC (Agricultural Land Management Alternative with Numerical Assessment Criteria) and SWAT (Soil and Water Assessment Tool) models. The scientist responsible for this Sub-objective retired during FY24. The position has not been refilled at this time.
Sub-objective 2.A: Significant enhancements have been integrated into the SWAT+ model. A completely revised instream sediment and nutrient transport model has been included and is currently under testing. A spatially detailed groundwater model within the SWAT+ framework was recently included and has been applied to the conterminous United States in an uncalibrated condition. Refinements to SWAT+ soil phosphorus transformations associated with the Legacy P project have been included. The models soil carbon routines have also been enhanced in collaboration with university and non-governmental organization partners.
Sub-objective 2.B: Subobjective 3.A: NAM (National Agroecosystems Model) is being utilized by several collaborators and has resulted in several joint publications. An enhanced calibration to instream data has been developed which utilizes a dynamically dimensioned search algorithm in a multiprocessor environment. Sediment and nutrient loads at the local process domain have been developed for 2,121 watersheds comprising the conterminous United States.
Subobjective 3.B: A draft field level version of the Agricultural Conservation Reduction Estimator (ARCE) has been developed. This decision support tool links fields derived from USDA’s Crop Sequence Boundaries to existing ACRE databases to provide edge of field sediment and nutrient reductions associated with altered conservation or management practices. Progress towards aerial imagery/lidar collection has been successful. In addition to the aerial data collection, in-situ data has also been collected for the plots that will establish a foundation for the machine learning/artificial intelligence methodologies to be explored soon.
Accomplishments
Review Publications
Menefee, D.S., Lee, T.O., Flynn, K.C., Chen, J., Abraha, M., Baker, J.M., Suyker, A. 2023. Machine learning algorithms improve MODIS GPP estimates in United States croplands. Frontiers in Remote Sensing. 4. Article 1240895. https://doi.org/10.3389/frsen.2023.1240895.
Kiniry, J.R., Williams, A.S., Jacot, J., Avila, A. 2024. Eastern gamagrass model simulation parameters for diverse ecotypes: Leaf area index, light extinction coefficient, and radiation use efficiency. Agronomy. 14(3). Article 441. https://doi.org/10.3390/agronomy14030441.
Kiniry, J.R., Williams, A.S., Jacot, J., Kim, S., Schantz, M.C. 2024. Eastern gamagrass responds inconsistently to nitrogen application in long-established stands and within diverse ecotypes. Agronomy. 14. Article 907. https://doi.org/10.3390/agronomy14050907.
Baath, G.S., Sarkar, S., Sapkota, B., Flynn, K.C., Northup, B.K., Gowda, P.H. 2023. Forage yield and nutritive value of summer legumes as affected by row spacing and harvest timing. Farming System. https://doi.org/10.1016/j.farsys.2023.100069.
Kiniry, J.R., Williams, A.S., Jacot, J., Mcbryde, G., Shadow, A., Brakie, M., Burson, B., Jessup, R., Cordsiemon, R., Kim, S., Avila, A., Elias, S. 2023. Diverse eastern gamagrass ecotypes: General characteristics, ploidy levels, and biogeography. Crop Science. https://doi.org/10.1002/csc2.21103.
Kiniry, J.R., Fernandes, J., Aziz, F., Jacot, J., Williams, A.S., Meki, M., Osorio, J., Baez-Gonzalez, A., Johnson, M. 2023. Tropical tree simulation with a process-based, daily timestep simulation model (ALMANAC): Description of model adaptation and examples with coffee and cocoa simulations. Agronomy. https://doi.org/10.3390/agronomy13020580.