Project : USDA ARS

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

Location: Great Plains Agroclimate and Natural Resources Research

2022 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: Data collection from our long-term watershed sites, several eddy covariance flux sites on the Grazinglands Research Laboratory (GRL) grounds, a network of soil moisture sensors deployed around a COsmic-ray Soil Moisture Observing System (COSMOS), and four other COSMOS sites is ongoing. Soil moisture sensors and ancillary equipment have been purchased to establish an automated measurement network on the Watershed Runoff and Erosion (WRE) watersheds to support experiments from point to watershed scales and to improve satellite soil moisture products. Collaborated with Oklahoma State University (OSU) on irrigation system efficiency and management and peer-reviewed manuscripts. Findings were disseminated to stakeholders via field days, local meetings, and online videos. This research led to a new cooperative agreement with OSU to develop applied research and extension activities focused on better quantitative understanding of the climate risks for agricultural water availability and viable adaptive management strategies in the Southern Great Plains (SP). Manuscripts associated with this agreement have been published and a postdoc has been recruited to work on improving irrigation simulation routines in the Soil and Water Assessment Tool (SWAT) model. Submission of a proposal to SCINET funded a postdoctoral fellow to develop a drought prediction tool with early warning capabilities for agricultural production decision-making across the Southern Plains through the Oak Ridge Institute for Science and Education. Initiation of research with ARS in Kimberly, Idaho, and University of Idaho to test current and improved irrigation routines in SWAT. Volumetric sedimentation rates for 12 reservoirs were determined based on the bathymetric survey and related to the landscape, climate, and land use variables. Data from this project is being used to evaluate a Geographic Information System version of the Revised Universal Soil Loss Equation model to correlate potential soil erosion to measured reservoir sedimentation rates. Objective 2: Thirty-two climate and evapotranspiration (ET) datasets were compiled and processed for use with the coupled SWAT- Modular Three-Dimensional Finite-Difference Groundwater Flow model (MODFLOW) (SWATmf). SWATmf was modified to update to the latest versions of the two models to predictions associated with irrigation and water extraction volumes. New methods were developed to support data transformation from ET datasets to the format needed for SWATmf. An Unmanned Aerial Vehicle- Light Detection and Ranging (UAV-LiDAR) system was purchased to measure parameters associated with stream channel stability and to conduct rapid geomorphic assessments of stream corridors. UAV pilot and flight training completed by two technicians. Developed collaborative arrangements with Natural Resources Conservation Service (NRCS) and ARS Stillwater, Oklahoma, to accomplish objectives in the current and new research projects: 1) evaluate LiDAR data for assessment of selected flood plain characteristics (e.g., upstream and downstream stream sinuosity, degradation, and land use change) and impacts on selected conservation structures, and 2) to link LiDAR data with ARS’s Bank Stability and Toe Erosion Model (BSTEM) model to assess channel stream bank stability. A study site in central Oklahoma was identified. Preliminary data have been collected and evaluated. Scientists and postdocs completed development of software in support of a project funded by the USDA Office of Environmental Marketing to parameterize and validate the Agricultural Policy Environmental eXtender (APEX) model/Nutrient Tracking Tool. Along with collaborators, we developed SWAT- Landuse Update Tool (SWAT-LUT), a user-friendly graphical software for updating landuse in SWAT, thereby allowing land use changes in simulations. The tool, user manual, and pdf of the peer-reviewed paper are available on the Grazingland Research Laboratory (GRL) website. A collaborative project with ARS Kimberly, Idaho, SWAT-LUT will be used in the next research project to determine the impact of dynamic landuse on model performance and outputs. Objective 3: Experiments for the SP Long-Term Agroecosystem Research (LTAR) were performed in several sites. Long-term research sites were leveraged to support National Institute of Food and Agriculture (NIFA)-funded Grazing Coordinated Agricultural Project (CAP) research that focused on resilience of grazing systems to variable climate. Carbon, water, and energy fluxes were measured from perennial and annual grass sites. Nutrient use efficiency of cattle and impact of forage quality on enteric methane emissions were studied. The integrated 10-paddock crop-livestock Common Experiment study consisted of no-till and till treatments planted to cropping systems that changed over time. However, weather damaged some H-flumes and berms in 2019. Repairs have been delayed due to covid-19 and Section 106 permitting. We anticipate being at full experimental capacity by 2023. In collaboration with other ARS researchers in El Reno, Oklahoma, and the University of Oklahoma, eddy covariance systems were installed in 2018 on 8 of the 10 fields to measure fluxes of carbon dioxide (CO2), methane (CH4), and evapotranspiration (ET). Other measurements at these sites include biomass, leaf area index, chlorophyll concentration, and hyperspectral canopy reflectance. A new long-term weather station was established with data uploaded to the Ag Data Commons. Studies were conducted describing the seasonal and annual fluxes of ET and CO2 from winter wheat and canola under till and minimum till conditions. New water and soil health baselines describing diversified adaptive crop livestock system were developed, samplers were upgraded to collect surface runoff for water quality analyses; data were collected from various sections of each watershed to allow monitoring of biological, chemical, and physical indicators of soil health; and a class I soil survey and electromagnetic induction sensing were conducted on the WRE site. Additional equipment was purchased to measure water balance components. These data will be used for monitoring, parameterize and validate APEX simulations of alternative land management on water resources. A new long-term experiment addressing Environment x Rotation x Rhizobiome x Innoculants x Cultivar on Climatic Adaptation (ERRICCA) was initiated in 2020. Treatments consisting of various levels of fertilizers and bioinoculant were applied to winter wheat and cotton rotations. Soil and plant measurements include soil temperature and moisture, forage and fiber quality, biomass, yield, and soil nitrogen (N). Unit scientists with another ARS scientist measured reservoir sedimentation rates and developed methods to estimate reservoir sedimentation rates from physiographic and climate factors. Unit scientists with collaborators assessed the performance of soil moisture sensors for irrigation to determine the best ones to help producers efficiently use limited irrigation water resources for crop production. Unit scientists and collaborators used a modeling approach to quantify irrigation water components and determined that model outputs were comparable to field measurements from sensors. All above accomplishments are associated with Objective 1. With respect to Objective 2, Unit scientists with other ARS scientists developed a framework and associated software to parameterize and validate the APEX model to support nation-wide deployment of Nutrient Tracking Tool by USDA. Unit scientists with collaborators developed a user-friendly graphical interface for updating land use in SWAT. A unit scientist with collaborators developed a hydrologic modeling approach to evaluating the impacts of conservation practices. Unit scientists along with collaborators developed a methodology to identify areas along the riparian zone that are susceptible to erosion in to target conservation practices to protect stream channel networks. A unit scientist with collaborators developed a simulation framework to evaluate stabilization practices using hydraulic and sediment transport models, landowner preferences, construction costs, and effectiveness. Unit scientist led a group of scientists that published a special issue quantifying impacts of the first fifteen years of the watershed assessment studies of the Conservation Effects Assessment Project. With respect to Objective 3, laboratory scientists with other ARS scientists published a special issue on the SP LTAR grazing experiment to determine the impact of grazing management systems (continuous, vs rotational) on habitat for avian, terrestrial, and aquatic species, carbon (C) regulation, and hydrologic function. Unit scientists and collaborators evaluated changes in precipitation patterns from long-term ARS data records to inform development of strategies for sustainable/enhanced agricultural productivity. Unit scientists studied the impact of tillage operation on greenhouse gas (GHG) emissions and found that no-till reduced GHG emissions, resulting in soil C and nitrogen (N) conservation. Unit scientists developed a rapid and cost-effective method that reduces measurements needed to track stocks and flows of soil C and N in adaptive crop and land management systems. Unit scientists determined model parameters that affect ET and biomass prediction in the APEX model for winter wheat fields in conservation and conventional tillage systems and evaluated the ability of APEX to predict ET and daily biomass for these tillage systems in central Oklahoma. Unit scientists published twenty-three years of runoff water quantity and quality data essential for determining the impact of different agricultural management systems, understanding the processes related to hydrologic transport and water quality, and the development and validation of corresponding models.

Accomplishments
1. Assessment of long-term shifts in agroecosystems processes. A gap exists in inventorying, monitoring, parameterizing, and validating the impact of land use and management on natural resources. Indicators of soil health were used as tools to evaluate land use and conservation management. ARS researchers at El Reno, Oklahoma, used baseline measures of soil health from the Water Resources and Erosion (WRE) unit source watersheds as tools to determine changes in agroecosystems processes. Soil health indicators varied significantly due to climate, managements, terrain, and depth. Soil carbon (C) and nitrogen (N) were used to parameterize the spatial variability of the watersheds and to determine effects of land management on soil health (Fortuna et al., 2022). Soil C fractions were used to monitor changes in soil organic carbon (SOC), biologically available C, and resistant C. Measuring soil C is time consuming and expensive. Remote sensing techniques and applications can reduce the number of samples required by about a tenth relative to a number of applications. The technology uses electromagnetic spectrum consisting of visible (350–780 nm), near-infrared (780–2500 nm), and optical radiation (100–1000 nm) ranges to predict chemical bonds and elements that a heterogeneous material such as C contains. Soil organic C represents a gradient of compounds derived from plant, microbial, and recalcitrant humified constituents that contain different spectral information. We used less expensive hyperspectral radiometers to predict biologically active and resistant stocks of SOC. Our findings compared well to baseline results acquired previously using a full spectrum (350-2500 nm) radiometer that correlated laboratory measurement of SOC stocks. Based on our study, we concluded that satisfactory to good prediction of soil C stocks can be made from spectral reflectance data (over the 400-1000 nm wavelength region) of soils acquired with relatively inexpensive radiometers.

2. Optimal placement of crop management systems under no-till system. No-till farming has many benefits including reducing soil erosion. However, as farmers adopt the recommended no-till farming system there is a need for knowledge on how best to use cropland area to minimize environmental impacts on soil and water quality resources while maximizing crop yields. ARS researchers at El Reno, Oklahoma, used the Multi-objective Evolutionary Algorithm for Soil and Water Assessment Tool (SWAT-MEA) to determine optimal spatial placement of soybeans, winter wheat, grain sorghum, upland cotton, and peanuts cropping systems under no-till in the Fort Cobb Reservoir watershed located in southwest Oklahoma. Results showed that under optimal placement, converting to no-till reduced nitrogen, phosphorus, and sediments by 45%, 32%, and 65%, respectively, while maintaining yields. These results also showed that the SWAT-MEA has potential for use as a decision-making tool to determine optimal land use and management to minimize environmental impacts while maintaining yields. These results contribute to the goals of the Conservation Effects Assessment Project (CEAP) and Long-Term Agroecosystem Research (LTAR) network USDA initiatives.

3. Evaluating uniformity of center-pivot irrigation systems. Improvement of the performance of irrigation systems is critical to addressing declining groundwater resources and ensuring environmental integrity in Oklahoma. In this study, ARS researchers at El Reno, Oklahoma, conducted irrigation audits to investigate the application uniformity and conveyance efficiency of center pivots in western Oklahoma. The results showed wide ranges of irrigation uniformities and conveyance efficiencies for the tested center pivots. About one-third of the tested center pivots performed below acceptable levels of irrigation uniformity. The average water conveyance efficiency was 93%, indicating that 7% of pumped water was lost before reaching the soil surface. Water losses were mainly caused by pipe leakages in the systems and wind drift. The irrigation nonuniformities and loss of water via conveyance caused large deep percolation fluxes and nutrient losses in areas that were over irrigated, which can lead to contamination of downstream water resources. Furthermore, irrigation nonuniformities had negative impacts on crop yield. These results highlight the need for improving irrigation uniformity of center pivots in western Oklahoma. In addition, these results will likely motivate producers to improve performance of their irrigation systems in order to conserve the limited water resources and reduce their pumping energy costs while keeping the environment clean.

4. Evaluation of effects of variable input data on the outputs of web-based irrigation scheduling tools. With recent advances in web-based irrigation scheduling tools and mobile applications and the possibility of using more complex modeling approaches, it is important to evaluate the effects of variable input data on the output of these tools and models. Two types of input data that are highly variable across irrigated fields and soil profiles are soil textural data and root water uptake distribution (RWUD). In this study, ARS researchers at El Reno, Oklahoma, used a subsurface model to determine the impact of using freely available web soil survey (WSS) and labor intensive measured soil texture in combination with three RWUDs (constant, linear, sensor-based) on predicted soil water content (swc). On average, % sand particles based on WSS was about half of the measured amount, resulting in a large difference in estimated soil water flow properties and thresholds. Sensor-based data revealed that RWUDs varied greatly, with more than 60% of water extraction occurring in the top 30 cm of the root zone. Model results indicated that measured data led to the smallest errors in predicted swc, which were 33% lower than those predicted using freely available data. The predicted swc data were translated to actionable end-user variables of irrigation trigger and soil water depletion, which determine the timing and the amount of irrigation application, respectively. Results showed that relying on freely available data led to more frequent and more amount of irrigation application than when measured soil data were used, which can lead to wasting water and increasing pumping costs. Therefore, it is critical to use accurate input data for irrigation tools and models used to ensure accurate outputs needed by producers to manage irrigation water.