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.
Sub-objective 1A. Initiated collaborative efforts with Texas A&M AgriLife in both Temple and Stephenville. Data collection over small plots incorporating dairy manure, biochar, and synthetic fertilizers for bermudagrass, maize, and sorghum has begun. The study has both a greenhouse and a field effort. In the greenhouse experiment, our portion of the research has been the collection of hyperspectral data. This data will be used in conjunction with statistical/machine learning analytics to determine various soil attributes measured in a laboratory. The field plots are in the beginnings of establishment; thus, our station is waiting on those to be able to collect unmanned aerial vehicle (UAV) -based hyperspectral remote sensing data. Once established, the fields will be subject to similar analytics to that of the greenhouse experiment. We continued to evaluate the effect of tillage on nitrous oxide emissions in a corn/corn/cotton rotation under different tillage systems at sites in Temple, Texas. Corn cropping produced less nitrous oxide emissions than cotton under conventional tillage than either strip-till or no-till. Monthly samples have been analyzed for microbial diversity and activity for crop years 2017-2020 from six field-scale watersheds (4.0-8.4 ha) at the Riesel Watersheds. 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 score. Sub-objective 1C. The fourth year of the Long-Term Agroecosystem Research (LTAR) cropland common experiment was completed at Riesel. Cotton was planted for the first time in more than 30 years at Riesel. Data are being collated to assess the common experiment treatments based on productivity, economics, and environmental response. Development of Soil and Water Assessment Tool plus (SWAT+) continued under Sub-objective 2A to support Conservation Effects Assessment Project (CEAP) national conservation scenario assessments and LTAR aspirational scenario analysis. To develop a pesticide component for the National Agroecosystem Model (NAM), SWAT+ algorithms were developed and enhanced to track pesticide fate through soil, aquifers, rivers, and reservoirs. Code was added to track multiple pesticides moving through the watershed and routines were added to track metabolites. There is considerable interest recently to use flood plain management to mitigate non-point source pollution. To simulate flood plain management, algorithms were incorporated into SWAT+ to move overbank flood flow to the flood plain and into riparian wetlands. The algorithms utilize flow rating curves developed by National Oceanic and Atmospheric Administration (NOAA) for use in flood forecasting. The rating curves are available for over 3 million streams and rivers in the U.S. and provides required input for flood routing equations and developing flood inundation maps. Use of the rating curves has allowed continued development of flood routing equations including the Muskingum equation. SWAT+ code is currently being recoded to allow users to routinely simulate sub-hourly routing and overbank flooding. The sub-hourly flood routing is also critical when modeling channel erosion on small, flashy streams simulated in the NAM. Channel bank erosion and downcutting equations were enhanced in SWAT+ and a national database of important channel parameters were developed including bank full depth, flood plain width and slope, and median particle diameter (D50). An object-oriented framework was developed to simulate salt impact on plant growth and transport through the watershed. Modular code was developed to simulate individual salt ion to efficiently model salt transport and fate. An existing salt transport code is currently being rewritten into the SWAT+ object-oriented code. Flow duration curves are routinely used by engineers and ecologists to estimate stream bank stability and fish and wildlife health and habitat. To meet the need, SWAT+ code was modified to output flow duration curves for all river segments simulating in a watershed and ultimately for all 3 million river segments simulated in the NAM. Heat units, or growing degree days, to maturity is a common input to most plant growth models, used to estimate phenological stage of growth. Heat units to maturity for individual crops at a specific location is difficult and confusing for users to estimate. In SWAT+, code was added to calculate heat units to maturity within the model, using weather data that was already input for each field. Thus, eliminating the need to input the variable and providing a reasonable value for the model. Sub-objective 2B. Agricultural Land Management Alternative with Numerical Assessment Criteria (ALMANAC) plant parameters were further developed and refined to enable simulation modeling in a wider range of assessments for conservation. These refinements 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, and wetland plants in diverse wetland sites in the U.S. In addition, data from the Phenocam network were used to refine leaf area simulations of rangeland plants, annual crops, and pasture grasses. Sub-objective 3A. Work continued to develop the Agricultural Conservation Reduction Estimator (ACRE)-Field, a web-based conservation planning tool which allows a user to select a specific field from a map and run conservation scenarios. Individual fields from the NAM model have been incorporated and initial simulation of runoff and crop yields by field have been performed. Predictions for 4 million individual fields have been developed and stored on SQL Server for linkage to ACRE. Additional simulations with and without selected conservation practices have been performed to estimate effectiveness spatially. Sub-objective 3B. The development of NAM has continued into the refinement and calibration stage. NAM has been successfully calibrated to upland water balance data from the United States Geological Survey (USGS) using a procedure that is the topic of a research article in preparation. Another paper is being drafted to describe the calibration of NAM to county level crop yield estimates provided by the National Agricultural Statistics Service (NASS). This calibration included all major field crops. NAM connectivity files have been further refined to more accurately route flow from individual stream segments. Nutrient and sediment loads for more than 2,000 USGS gage locations have been developed for model calibration and validation. Draft software to calibrate NAM to these data has been developed. The calibration procedure is being tested in the Upper Mississippi River Basin to support a CEAP Wildlife collaboration with the Natural Resource Conservation Service (NRCS).
1. Enhanced plant simulation modeling. Plant simulation modeling in wetlands, tropical environments, and saline conditions was greatly advanced by parametrizing new species and refinement of existing plant parameters. Improved simulations were developed for dry bean in Mexico, sorghum in Argentina, vegetables in the Winter Garden region of Texas, and wetland plants in diverse environments across the U.S. This research also provided an improved understanding of canola and switchgrass production in saline conditions near the Rio Grande and carbon dynamics in Hawaii. In addition, we developed techniques using the remotely sensed Phenocam data to improve cropland/pasture/rangeland parameters within the Agricultural Land Management Alternative with Numerical Assessment Criteria (ALMANAC) simulation model. All these advancements greatly benefit Conservation Effects Assessment Porject (CEAP) efforts to simulate conservation processes and practices by more accurately simulating plant growth and crop yields in excessively wet and dry environments. Several components of CEAP relate to soil erosion, water quality, and water quantity. These components are strongly related to plant cover, plant water use, and plant nutrient uptake. Thus, accurate simulation of the plant components contributing to these is vital to the assessments.
Williams, A.S., Mushet, D., Lang, M., McCarty, G.W., Shaffer, J.A., Kahara, S.N., Johnson, M.V., Kiniry, J.R. 2020. Improving the ability to include freshwater wetland plants in process-based models. Journal of Soil and Water Conservation. 75(6):704-712. https://doi.org/10.2489/jswc.2020.00089.
Kim, S., Jeong, J., Kahara, S.N., Kim, S., Kiniry, J.R. 2020. APEX simulation: Water quality of Sacramento Valley wetlands impacted by waterfowl droppings. Journal of Soil and Water Conservation. 75(6):713-726. https://doi.org/10.2489/jswc.2020.00117.
Williams, A.S., Kim, S., Kiniry, J.R. 2021. Advances in application of a process-based crop model to wetland plants and ecosystems. Wetlands. 41. Article 18. https://doi.org/10.1007/s13157-021-01416-7.
Yildirim, T., Zhou, Y., Flynn, K.C., Gowda, P.H., Ma, S., Moriasi, D.N. 2021. Evaluating the sensitivity of vegetation and water indices to monitor drought for three Mediterranean crops. Agronomy Journal. 113:123-134. https://doi.org/10.1002/agj2.20475.
Baez-Gonzalez, A.D., Fajardo-Diaz, R., Padilla-Ramirez, J.S., Osuna-Ceja, E.S., Kiniry, J.R., Meki, M.N., Acosta-Diaz, E. 2020. Yield performance and response to high plant densities of dry bean (Phaseolus vulgaris L.) cultivars under semi-arid conditions. Agronomy. 10(11). Article 1684. https://doi.org/10.3390/agronomy10111684.
Kim, S., Meki, M.N., Kim, S., Kiniry, J.R. 2020. Crop modeling application to improve irrigation efficiency in year-round vegetable production in the Texas Winter Garden Region. Agronomy. 10(10). Article 1525. https://doi.org/10.3390/agronomy10101525.
Bailey, R.T., Bieger, K., Arnold, J.G., Bosch, D.D. 2020. A new physically-based spatially-distributed groundwater flow module for SWAT+. Hydrology. 7(75). Article 7040075. https://doi.org/10.3390/hydrology7040075.
Kim, S., Ofekeze, E., Kiniry, J.R., Kim, S. 2020. Simulation-based capacity planning of a biofuel refinery. Agronomy. 10(11). Article 1702. https://doi.org/10.3390/agronomy10111702.
Flynn, K.C., Lee, T., Endale, D.M., Franzluebbers, A.J., Ma, S., Zhou, Y. 2021. Assessing remote sensing vegetation index sensitivities for tall fescue (Schedonorus arundinaceus) plant health with varying endophyte and fertilizer types: A case for improving poultry manuresheds. Remote Sensing. 13(3). Article 521. https://doi.org/10.3390/rs13030521.
Ha, M., Wu, M., Tomer, M.D., Gassman, P.W., Isenhart, T.M., Arnold, J.G., White, M.J., Parish, E.S., Comer, K.S., Beldan, B. 2020. Biomass production with conservation practices for two Iowa watersheds. Journal of the American Water Resources Association. 56(6):1030-1044. https://doi.org/10.1111/1752-1688.12880.
Druille, M., Williams, A.S., Torrecillas, M., Kim, S., Meki, N., Kiniry, J.R. 2020. Modeling climate warming impacts on grain and forage sorghum yields in Argentina. Agronomy. 10(7). Article 964. https://doi.org/10.3390/agronomy10070964.
Chaganti, V.N., Ganjegunte, G., Niu, G., Ulery, A., Encisco, J.M., Flynn, R., Meki, N., Kiniry, J.R. 2021. Yield response of canola as a biofuel feedstock and soil quality changes under treated urban wastewater irrigation and soil amendment application. Industrial Crops and Products. 170. Article 113659. https://doi.org/10.1016/j.indcrop.2021.113659.
Crow, S.E., Wells, J., Sierra, C.A., Youkhana, A.H., Ogoshi, R.M., Richardson, D., Glazer, C.T., Meki, M.N., Kiniry, J.R. 2020. Carbon flow through energycane agroecosystems established post-intensive agriculture. Global Change Biology Bioenergy. 12:806-817. https://doi.org/10.1111/gcbb.12713.
Flynn, K.C., Frazier, A.E., Admas, S. 2020. Nutrient prediction for tef (Eragrostis tef) plant and grain with hyperspectral data and partial least squares regression: Replicating methods and results across environments. Remote Sensing. 12(18). Article 2867. https://doi.org/10.3390/rs12182867.
Witt, T.W., Flynn, K.C., Zoz, T., Monteiro, E.B. 2020. Site suitability analysis incorporating disease prediction in castor (Ricinus communis L.) production. Springer Nature Applied Sciences. 2:1820. https://doi.org/10.1007/s42452-020-03602-4.
Baath, G.S., Flynn, K.C., Gowda, P.H., Kakani, V.G., Northup, B.K. 2021. Detecting biophysical characteristics and nitrogen status of finger millet at hyperspectral and multispectral resolutions. Frontiers in Agronomy. 2. Article 604598. https://doi.org/10.3389/fagro.2020.604598.
Admas, S., Tesfaye, K., Haileselassie, T., Shiferaw, E., Flynn, K.C. 2021. Phenotypic variability of chickpea (Cicer arietinum L.) germplasm with temporally varied collection from the Amhara Regional State, Ethiopia. Cogent Food & Agriculture. 7(1). Article 1896117. https://doi.org/10.1080/23311932.2021.1896117.
Jacot, J., Kiniry, J.R., Williams, A.S., Coronel, A., Su, J., Miller, G.R., Mohanty, B., Saha, A., Gomez-Casanovas, N., Johnson, J.M., Browning, D.M. 2021. Use of PhenoCam measurements and image analysis to inform the ALMANAC process-based simulation model. Journal of Experimental Agriculture International. 43(4):120-144. https://doi.org/10.9734/jeai/2021/v43i430684.
Chaganti, V., Ganjegunte, G., Meki, N.N., Kiniry, J.R., Niu, G. 2021. Switchgrass biomass yield and composition and soil quality as affected by treated wastewater irrigation in an arid environment. Biomass and Bioenergy. 151. Article 106160. https://doi.org/10.1016/j.biombioe.2021.106160.
Jacot, J., Williams, A.S., Kiniry, J.R. 2021. Biofuel benefit or bummer? A review comparing environmental effects, economics, and feasibility of North American native perennial grass and traditional annual row crops when used for biofuel. Agronomy. 11(7). Article 1440. https://doi.org/10.3390/agronomy11071440.