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ARS Home » Plains Area » El Reno, Oklahoma » Grazinglands Research Laboratory » Agroclimate and Natural Resources Research » Research » Research Project #432323

Research Project: Towards Resilient Agricultural Systems to Enhance Water Availability, Quality, and Other Ecosystem Services under Changing Climate and Land Use

Location: Agroclimate and Natural Resources Research

2019 Annual Report


Objectives
Objective 1: Quantify states, fluxes, and cycling of water, carbon, and hydrologic constituents within the soil-plant-hydrologic-atmospheric systems of selected landscapes, watersheds, and agricultural systems of the Southern Great Plains. Objective 2: Develop tools and techniques for the selection, placement, and evaluation of conservation and agricultural practices to improve watershed integrity and ecosystems services. Objective 3: As part of the LTAR network, and in concert with similar long-term, land-based research infrastructure in the region, use the Little Washita River/Fort Cobb Reservoir Experimental Watersheds 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 Southern Plains 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.


Approach
The project builds upon the prior 5-year project and is structured around three inter-related research objectives that: 1) develop, maintain, and expand long-term observational research infrastructure and databases to elucidate water-related agroecosystem processes for agricultural systems, 2) conducts studies that help understand processes and improve algorithms of commonly used hydrologic and water quality models, and 3) develops tools and techniques for the selection, placement, and evaluation of conservation and agricultural practices to improve watershed integrity and ecosystems services. Our long-term objective is to elucidate key hydrologic and agroecosystem processes and to bridge the gap between farm management goals and landscape or watershed goals that are shared across farms and communities, using long-term research sites and research watersheds as the primary outdoor laboratories to address these issues of global relevance. Research approaches include field studies, remote sensing analyses, mathematical and statistical assessment of climate, farm to watershed scale process modeling, and development of integrative optimization tools. This research will assist farmers, land owners, governmental action agencies, and residents to contribute to more resilient mixed land-use watersheds, in part by providing tools that help them evaluate and optimize multiple management objectives for mixed-enterprise agricultural systems.


Progress Report
Objective 1: Our collaboration with sister ARS labs and National Aeronautics and Space Administration (NASA) continues, using ARS distributed soil moisture networks to improve satellite soil estimation procedures and corrections for biases in satellite soil moisture products. We have completed installation of an in situ soil moisture network around a COsmic-ray Soil Moisture Observing System (COSMOS) site for purposes of evaluating one-time vs. multi-temporal calibration. We are upgrading our in situ soil moisture network to automatically and remotely download data. Data from the COSMOS sites and the in situ soil moisture network are routinely manually downloaded and assembled for analysis. Research relating remotely sensed data to soil microbial biomass nitrogen and carbon continues. With respect to Sub-objective 1B, the second season of field evaluation of irrigation fluxes and efficiencies have been wrapped up and we have successfully initiated the third year of data collection. In this third year, four fields distributed across the Fort Cobb Reservoir Experimental Watershed (FCREW) have been instrumented. Due to very different rainfall amounts and patterns in the 2019 season, we expect that the data collected during this year could significantly enhance the comprehensiveness of analyses as a wide range of climatic conditions will be captured. The progress made towards achieving research objectives is demonstrated by one peer-reviewed journal publication and four conference presentations. The extension activities included one-on-one visits with several producers in the FCREW, as well as presenting the results at several field days and the annual Oklahoma Irrigation Conference. The 2020 Oklahoma Irrigation Conference is planned to take place in the FCREW, with an increased focus on disseminating the results of the present project to stakeholders. USDA ARS collaborators in Bushland, Texas, modified SWAT algorithms and added new input parameters that took irrigation system constraints and limited irrigation strategies into account. The findings of this reservoir sedimentation study were presented and well received at the Sedimentation and Hydrology 2019 Conferences on Sedimentation and Hydrologic Modeling in June 2019. A study on reservoir sedimentation as a function of land cover and climate is nearing completion. A study to determine the influence of reservoir sediment profile depth on carbon thermal stability and carbon functional groups as well nutrients is ongoing. Collaborators from Florida A&M University have taken sediment core and water samples. Analyses are completed. Objective 2: The study in Sub-objective 2A will be terminated due to retirement of SY and change of position for the collaborators in addition to lack of programming personnel due to the departure of a postdoc. The study site mentioned in Research Goal 2B.1 will be shifted from the LWREW and FCREW to landscapes located on site, and the Light Detection and Ranging (LiDAR) data source will be shifted from that acquired by USDA Natural Resources Conservation Service (NRCS) to that acquired using an Unmanned Aerial Vehicle (UAV)-mounted LiDAR that was purchased in late FY18. We have planned UAV pilot and flight protocol training for the summer/fall of 2019 and will collect our first data sets thereafter. All research related Objective 2C were completed. The Soil and Water Assessment (SWAT) land use update tool documentation was completed. The tool and documentation are available on the USDA ARS GRL website for SWAT users. Objective 3: Several scientists, two technicians, and one Post-Doc attended and participated in the annual Long-Term Agroecosystem Research (LTAR) meeting in Lincoln, Nebraska. LTAR site lead changed due to the retirement of the laboratory director. We are currently upgrading our LTAR phenocam sites and meteorological station to enable automated download of data. Our LTAR data manager continues to work on LTAR data issues and we continue participating in the LTAR wind erosion network. Weather impacted the wheat and canola harvest in this final year of our planned crop rotation treatment. No canola was harvested and many of the wheat fields could not be fully harvested. Yield data for the 2018/2019 harvest season were collected. Weather damaged some H-flumes and berms, which will be repaired in late summer and early fall of 2019. A full second year of eddy covariance flux and rain gage data been collected and will be combined with other data sets for analysis. A new warm-season grass prescribed burn experiment has been designed and was implemented in early 2019. This study will evaluate the effects of different burn frequencies on the fluxes of evapotranspiration and carbon dioxide (CO2), on soil microbial communities, and on selected components of the hydrologic budget. Research associated with LTAR grassland and croplands was continued in the GRL Grazinglands Research on agroEcosystems and the ENvironment (GREEN) Farm, tallgrass prairie field site and the Old World Bluestem Monoculture pasture. Automated in situ soil moisture measurement sites will be installed late 2019 on the GREEN Farm. Biweekly greenhouse gas (GHG) and monthly measurements were taken. Dissolved organic carbon (C)/nitrogen (N), ammonium, nitrate, soil water content, bulk density, pH, total organic carbon, inorganic carbon, microbial biomass carbon/nitrogen, plant residue, biomass, soil texture and total carbon and nitrogen of the soil and forage soil indices were also collected. Microbial biomass was taken three times a year concurrently with polylipid fatty acid (PLFA) profiling for use locally with GHG information and within the LTAR network. PLFA data from at least eight LTAR sites were processed and analyzed. In a perennial Old World Bluestem field, an intensive nitrogen study was conducted to assess nitrogen loss through GHG emissions post-fertilizer application, and the effects of nitrogen application on microbial communities. Microbial data was utilized for a cross analysis with the Florida LTAR site. A portion of the microbial information associated with this project was presented at a professional meeting in an invited speaker symposium. A pulse intensive study was conducted to further assess microbial biomass in perennial grasslands and to better understand the remote sensed data. A weeklong period was used to train teachers from the Southern Plains region in soil science principles, specifically soil microbiology. The teachers then wrote soil science units to take back to their classrooms. Research continued on the Watershed Runoff and Erosion (WRE) site. We are currently establishing new water and soil health baselines that will integrate a diversified adaptive crop livestock system. The landform complexes serve as replicates within and among the eight 1.6 ha sized watershed treatments that serve as paddocks allowing us to monitor biological, chemical, and physical indicators of soil health at field and watershed scales. Activities on the WRE included a class I soil survey in conjunction with electromagnetic induction sensing (EMS), baseline measurements of soil quality, and land management that includes adaptive use of forage cover crops as a means to address the impacts of climatic variations. The data generated from these studies will be used for monitoring, as well as to parameterize and validate the Agricultural Policy/Environmental eXtender model to predict the effects of alternative land management on water resources. A portion of this information was presented in two talks at the 2019 annual Soil and Water Conservation Society meeting.


Accomplishments
1. Parameterizing and validating APEX model for nutrient trading. The Agricultural Policy Environmental eXtender (APEX) model is the scientific basis for the Nutrient Tracking Tool (NTT), which is a user-friendly web-based computer program developed to estimate reductions in nutrient losses to the environment, associated with alternative practices. Open-source, user-friendly software was developed to automate parameterization and model evaluation of the APEX model to enable deployment of NTT nationwide by USDA Office of Environmental Markets (OEM). The initial release of NTT by USDA OEM that is now online, includes validated parameters for the Ohio and the Western Lake Erie Basin that were developed in collaboration with OEM as part of these efforts. The software was used in other project-related studies and can be used for other hydrologic and water quality modeling studies using APEX around the globe. Recommendations of data needs, appropriate methods to use within APEX, and processes in APEX model that need improvements were published and communicated to the developers. This research will help producers determine alternative agricultural production systems with least impacts on soil and water resources.

2. Performance of soil moisture sensors for irrigation assessed. Agricultural producers are increasingly using commercial soil moisture sensors to manage irrigation scheduling. There are different types of commercially available soil moisture sensors but their performance in soils with different levels of salinity and clay content has not been evaluated. The performance of Time Domain Reflectometry (TDR315), Campbell Scientific (CS655), METER Group Sensor (GS1), Spectrum (SM100), and CropX commercial soil moisture sensors at factory settings was evaluated for their accuracy in two irrigated cropping systems, one each in central and southwest Oklahoma with variable levels of soil salinity and clay content. It was determined that only the CS655, TDR315, and GS1 sensors measured soil moisture accurately at the site with lower levels of salinity and clay, while none of them performed satisfactorily at the site with higher levels of salinity and clay. In addition, a wide range of accuracies was noted among soil moisture sensors and methods for determining soil moisture thresholds, thus making it difficult to utilize soil moisture sensors for irrigation scheduling applications without being tested and customized. Therefore, we recommend that studies like this need to be conducted under variable field conditions to evaluate the performance of new sensors being developed in order to provide guidelines on how they can be used for irrigation scheduling purposes. Determination of appropriate soil moisture sensors will help producers efficiently use limited irrigation water resources for crop production.

3. Soil disturbance increased greenhouse gas emissions. A research study found that chisel plow tillage negatively impacted greenhouse gas (GHG) emissions. Following tillage, carbon dioxide (CO2) flux from the soil was doubled and remained elevated for one week. Nitrous oxide (N2O) flux from soil also increased after chisel plow tillage while methane assimilation decreased after chisel plow tillage. This study confirmed that water-filled pore space (WFPS) was an important driver of CO2 emissions. Mechanical disturbance through tillage appeared to be the biggest driver of N2O efflux and produced a flush of soil nitrogen (N). However, soil N availability, as a result of tillage, caused a transition of assimilation to efflux for methane that was not affected by WFPS. No-till practices reduced GHG efflux, resulting in soil carbon and nitrogen conservation. Findings from this study can be used to identify or improve land management practices to reduce greenhouse gas emissions from agricultural lands, which should result in improved environmental conditions.

4. Southern Plains LTAR Grazing Experiment. The prairie ecosystems of the Southern Great Plains are important for livestock grazing and provide benefits that include habitat for avian, terrestrial and aquatic species, carbon regulation, and hydrologic function. The impact of grazing management systems (continuous (C), vs rotational (R) stocking) on many of these functions is unknown and the results reported in the literature are often contradictory. Results from a multi-year grazing study revealed that microbial biomass in the soil surface layer decreased in the C treatments but increased in R treatments; that there were no treatment differences in total, particulate, microbial mass, and mineralizable carbon and nitrogen fractions between treatments; individual calf weaning weights were higher in C than in R; available plant biomass did not differ between treatments, but concentrations of nitrogen and in vitro true digestibility were higher and concentrations of acid detergent fiber and neutral detergent fiber were lower in R than in C; and that phenology and gross primary productivity of tallgrass pastures were similar between treatments and that both treatments were resilient to drought. Findings from this study contribute directly to the goals of LTAR (i.e., development of tools and information to establish resilient and sustainable agricultural systems) to meet growing demands for feed and fiber, and improve rural prosperity.


Review Publications
Nelson, A.M., Moriasi, D.N., Talebizadeh, M., Steiner, J.L., Gowda, P.H., Starks, P.J., Tadesse, H.K. 2018. Use of soft data for multicriteria calibration and validation of agricultural policy environmental eXtender: impact on model simulations. Journal of Soil and Water Conservation. 73(6):623-636. https://doi:10.2489/jswc.73.6.623.
Sarker, N.C., Borhan, M., Fortuna, A., Rahman, S. 2019. Understanding gaseous reduction mechanisms in swine manure resulting from nanoparticle treatments under anaerobic storage conditions. Journal of Environmental Science. https://doi.org/10.1016/j.jes.2019.03.005.
Datta, S., Taghvaeian, S., Ochsner, T.E., Moriasi, D.N., Gowda, P.H., Steiner, J.L. 2018. Performance assessment of five different soil moisture sensors under irrigated field conditions in Oklahoma. Sensors. 18(11): 1-17. https://doi:10.3390/s18113786.
Zou, C.B., Twidwell, D., Bielski, C.H., Fogarty, D.T., Mittelstet, A.R., Starks, P.J., Will, R., Zhong, Y., Acharya, B. 2018. Impact of eastern redcedar proliferation on water resources in the Great Plains USA – current state of knowledge. Water. 10(12). https://doi:10.3390/w10121768.
Joshi, S., Garbrecht, J.D., Brown, D.P. 2019. Observed spatiotemporal trends in intense precipitation events across United States: applications for stochastic weather generation. Climate. 7(3): 36. https://doi.org/10.3390/cli7030036.
Garbrecht, J.D., Zhang, X.J., Brown, D.P., Busteed, P.R. 2019. Generation of synthetic daily weather for climate change scenarios and extreme storm intensification. Environment and Natural Resources Research. 9(2). https://doi.org/10.5539/enrr.v9n2p1.
Talebizadeh, M., Moriasi, D.N., Steiner, J.L., Gowda, P.H., Tadesse, H.K., Nelson, A.M., Starks, P.J. 2018. APEXSENSUN: An open-source package in R for sensitivity analysis and model performance evaluation of APEX. Journal of the American Water Resources Association. https://doi.org/10.1111/1752-1688.12686.
Neel, J.P., Moriasi, D.N., Brown, M.A., Belesky, D.P. 2019. Model predicted DMI, nitrogen (N) excretion and N use efficiency utilizing plasma urea nitrogen (PUN) versus values estimated in conjunction with viable dry matter intake estimates in lambs grazing pasture. Journal of Animal Science and Research. 3(1). https://doi.org/10.16966/2576-6457.123.
Nelson, A.M., Moriasi, D.N., Talebizadeh, M., Tadesse, H.K., Steiner, J.L., Gowda, P.H., Starks, P.J. 2019. Comparing the effects of inputs for NTT and ArcAPEX interfaces on model outputs and simulation performance. Water. 11:554-580. https://doi.org/10.4236/jwarp.2019.115032.
Maina, C.W., Sang, J.K., Raude, J.M., Mutua, B.M., Moriasi, D.N. 2019. Sediment distribution and accumulation in Lake Naivasha, Kenya over the past 50 years. Lakes and Reservoirs. 24:162-172. https://doi.org/10.1111/lre.12272.
Talebizadeh, M., Moriasi, D.N., Steiner, J.L., Gowda, P.H., Tadesse, H.K., Nelson, A.M., Starks, P.J. 2019. A parallel computation tool for dynamic sensitivity and model performance analysis of APEX: Evapotranspiration modeling. Journal of the American Water Resources Association. https://doi.org/10.1111/1752-1688.12758.
Tadesse, H.K., Moriasi, D.N., Gowda, P.H., Steiner, J.L., Talebizadeh, M., Nelson, A.M., Starks, P.J., Marek, G.W. 2019. Comparison of evapotranspiration simulation performance by APEX model in dryland and irrigated cropping systems. Journal of the American Water Resources Association. https://doi.org/10.1111/1752-1688.12759.
Franzluebbers, A.J., Starks, P.J., Steiner, J.L. 2019. Conservation of soil organic carbon and nitrogen fractions in a tallgrass prairie in Oklahoma. Agronomy. 9(4):204. https://doi.org/10.3390/agronomy9040204.
Ma, S., Zhou, Y., Gowda, P.H., Chen, L., Steiner, J.L., Starks, P.J., Neel, J.P. 2019. Evaluating the impacts of continuous and rotational grazing on tallgrass prairie landscape using high spatial resolution imagery. Agronomy. 9(5):238. https://doi.org/10.3390/agronomy9050238.
Northup, B.K., Starks, P.J., Turner, K.E. 2019. Stocking methods and soil macronutrient distributions in southern tallgrass paddocks: Are there linkages? Agronomy. 9(6):281. https://doi.org/10.3390/agronomy9060281.
Northup, B.K., Starks, P.J., Turner, K.E. 2019. Soil macronutrient responses in diverse landscapes of southern tallgrass to two stocking methods. Agronomy. 9(6):329. https://doi.org/10.3390/agronomy9060329.
Starks, P.J., Steiner, J.L., Neel, J.P., Turner, K.E., Northup, B.K., Gowda, P.H., Brown, M.A. 2019. Assessment of the standardized precipitation and evaporation index (SPEI) as a potential management tool for grasslands. Agronomy. 9(235). https://doi.org/10.3390/agronomy9050235.
Wang, J., Xiao, X., Bajgain, R., Starks, P.J., Steiner, J.L., Doughty, R.B., Chang, Q. 2019. Estimating leaf index and aboveground biomass of grazing pastures using sentinel-1, sentinel-2 and landsat images. Journal of Photogrammetry and Remote Sensing. 154:189-201. https://doi.org/10.1016/j.isprsjprs.2019.06.007.
Zwieback, S., Bosch, D.D., Cosh, M.H., Starks, P.J., Berg, A. 2019. Vegetation-soil moisture coupling metrics from dual-polarization microwave radiometry using regularization. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2019.111257.
Peterson-Munks, B.L., Starks, P.J., Sadowsky, C., Scott, T. 2018. Using canopy hyperspectral reflectance to predict root biomass carbon and nitrogen content. Environment and Natural Resources Research. 8(1):84-93. https://doi.org/10.5539/enrr.v8n1p84.
Starks, P.J., Brown, M.A. 2018. Estimation of dry-matter intake in lambs via field-based NIR proximal sensing. Grass and Forage Science. https://doi.org/10.1111/gfs.12381.