Brief Summary of Minutes of Annual Meeting

Accomplishments

Objective 1.  Coordinate efforts to develop or improve the effectiveness and availability of irrigation scheduling techniques, tools, and resources that address limited water resource availability and water quality concerns.

List any technical activities, specific functions/events organized, duties carried out. Write brief summary of the results of your activities. Provide details on challenges faced, opportunities created/envisioned. If multiple projects, then list them one by one.

Arizona

The University of Arizona continued work to improve irrigation scheduling and crop water management under the limited and increasingly variable water supplies in Arizona. Interest from Arizona growers in using field-based data to support irrigation decisions led to expanded deployment and evaluation of soil moisture sensors on additional farms. Results demonstrated the value of soil moisture data for improving irrigation timing and managing crop water use. Arizona also expanded its evaluation of OpenET to include cotton and incorporated field based actual evapotranspiration sensors to evaluate the accuracy of OpenET estimates. Results from this work were disseminated through extension and peer-reviewed publications. The irrigation system comparison study also continued to generate data on crop response, water use, and irrigation management under Arizona conditions. These activities are particularly relevant to the challenges facing agriculture in the Colorado River Basin, where continued water shortages and reductions in available water require growers to make more informed irrigation decisions and produce crops with less water. Extension publications, presentations, and on-farm consultations helped make these tools and information available to growers and irrigation professionals.

Florida

Florida – Sandra Guzmán Smart Irrigation and Hydrology (SIAH): Research and development activities focused on improving the interoperability and usability of sensor-based irrigation decision support systems through the integration of soil moisture sensors, ET, weather data, satellite observations, and artificial intelligence tools into a unified platform for irrigation management. A major effort during the reporting period was the continued development and evaluation of the IrrigMonitor platform, a centralized decision-support system designed to standardize and communicate data from multiple sensor technologies and environmental data sources.

Commercial-scale validation was conducted in commercial sweet corn production systems in Central and South Florida. Irrigation forecasts generated by the platform were combined with targeted communication strategies delivered through Extension agents. Results demonstrated that growers who used forecast information alongside soil moisture sensor data achieved approximately 30% additional irrigation water savings compared with using soil moisture sensors alone, while maintaining crop productivity. These findings highlight the value of integrating data streams and translating complex sensor information into actionable recommendations for growers.

The platform was further expanded to incorporate satellite-derived information and predictive analytics to improve irrigation forecasting capabilities. Beta testing was conducted with Extension professionals and growers to evaluate usability, communication approaches, and adoption barriers. Feedback from users was systematically collected and incorporated into software interface improvements, dashboard visualization tools, and recommendation delivery formats. This work directly addresses challenges associated with disparate sensor platforms and data formats by advancing standardized approaches for integrating field-based and remotely sensed data into a common irrigation management framework

Results and demonstrations were presented at grower meetings, including the Florida Citrus Show, Citrus Expo, and multiple commodity-focused educational programs. One peer-reviewed publication was completed during the reporting period, and additional manuscripts evaluating relationships among technology adoption, irrigation management, crop yield, nutrient management, and water conservation are currently in preparation.

Kansas

Digital Agronomy Research Team (by Dr. Jha) in Kansas focused on a practical question: can precision irrigation technologies improve water use without compromising crop productivity under on-farm conditions? This is particularly important for the Great Plains, where irrigated agriculture is increasingly constrained by declining groundwater availability. We evaluated an AI-Radar autonomous center-pivot irrigation system against an existing subsurface drip irrigation (SSDI) system. Rather than using a fixed irrigation schedule, the AI-Radar system combines spatial soil properties, ground-penetrating radar measurements, weather information, and crop water demand to account for variability within the field and provide more targeted irrigation recommendations. UAV multispectral and thermal imagery was used throughout the season to quantify crop condition and spatial actual evapotranspiration (ETa), and ET was integrated with yield data to estimate field-scale water use efficiency.  The field results showed a clear agronomic response. The AI-Radar field maintained lower crop water stress during critical reproductive growth stages and greater spatial uniformity than the field managed with the existing SSDI settings. Mean soybean yield was approximately 3,189 kg ha⁻¹ under AI-Radar compared with 2,208 kg ha⁻¹ under SSDI, while UAV-derived field-scale water use efficiency increased from 5.70 to 7.17 kg ha⁻¹ mm⁻¹. OpenET provided a similar assessment, with estimated WUE of 6.66 and 5.06 kg ha⁻¹ mm⁻¹ for AI-Radar and SSDI, respectively.

The broader impact of this work is that precision irrigation should not be evaluated simply by how much water is applied or saved. The more meaningful measure is whether water is delivered at the right place and time to prevent damaging crop stress while maintaining or improving productivity. These results demonstrate the potential for integrating field sensing, UAV observations, and satellite ET products to evaluate irrigation performance at commercial-field scale and provide producers with information that is directly relevant to management. We are now expanding this work to evaluate OpenET, Eddy Covariance, LI-710, and AquaCrop across crop phenological stages. Initial corn results showed seasonal ET estimates of 517 mm from AquaCrop, 576 mm from OpenET, and 647 mm from LI-710. OpenET followed the seasonal AquaCrop trend relatively well, whereas LI-710 captured short-term ET dynamics but showed greater variability. The next phase includes validation against eddy covariance measurements across Kansas, Oklahoma, and Texas, with the longer-term goal of developing scalable approaches for field-level irrigation assessment and decision support across the Great Plains.

Louisiana

This year’s efforts have focused on creating procedures, knowledge, and resource availability for the Louisiana Climate and Digital Ag Network (LaCADIAN). LaCADIAN is a newly operational network of environmental sensing nodes installed across agricultural areas of Louisiana. Procedures to maintain the integrity of the measured incoming data were enacted that include benchmark routines, sensor-specific calibration, standardized environmental thresholds, and cross-validation between variables. In addition to stabilizing the long-term economic feasibility of Drought Irrigation Response Tool, LaCADIAN provides reliable, high-resolution environmental data critical for advancing future soil-climate modeling, flood-drought predictive capabilities, irrigation optimization, and land-atmosphere interaction studies. Additionally, several presentations were given to educate potential end users on LaCADIAN’s utility and how they can integrate the data into their practices that can benefit from real-time weather data.

Michigan

MSU, Dong Irrigation Lab

Michigan State University continues to advance irrigation decision support through the MSU Irrigation Mobile App, which integrates weather data from MSU Enviroweather stations and the National Weather Service to deliver site-specific irrigation recommendations. As of 2026, the platform has grown to 412 registered users managing 431 fields across 61,823 acres of corn, soybean, vegetable, and fruit crops. The app is available in both English and Spanish, increasing accessibility for diverse agricultural audiences. The team also launched the MSU Berrigation App, a crop-specific irrigation scheduler for blueberry production that converts irrigation recommendations into both application depth and system run time based on user-defined drip irrigation parameters. These tools provide growers with actionable information that enhances irrigation efficiency, conserves water resources, and improves crop management across Michigan agriculture

Nebraska

In Abia’s Irrigation Water Management Lab:

The program continues to evaluate individual data-driven irrigation management approaches and their integration, including soil moisture sensing, canopy temperature monitoring, and weather- and evapotranspiration (ET)-based irrigation scheduling for corn and soybean production. In-field sensors were deployed to collect soil and crop data, which were processed through the Crop2Cloud platform to develop and evaluate multiple water-stress indicators, including the Crop Water Stress Index (CWSI), Soil Water Stress Index (SWSI), and a Combined Water Stress Index that integrates both soil- and crop-based indicators. The 2025 growing season provided an important opportunity to evaluate these approaches under relatively wet conditions. Most of the precipitation occurred during June and July, substantially reducing the need for supplemental irrigation during critical periods. Consequently, there were no significant differences in observed crop yields or irrigation water savings among the data-driven treatments compared with the fully irrigated and rainfed treatments. Although the wet conditions limited the ability to distinguish treatment effects, the field evaluation provided valuable experience in integrating multiple data streams and identifying practical challenges associated with deploying sensor-based irrigation technologies under real-world farm conditions.

A major component of the program is identifying and addressing barriers that limit farmers’ ability to use irrigation data effectively. During interactions with cooperating farmers, several producers identified challenges with data visualization and interpretation associated with some commercial soil moisture sensing technologies. For example, Sentek commercial probes provide detailed soil moisture information through the IRRiMax platform, including moisture levels at multiple depths and the movement of applied irrigation water through the active root zone. However, farmers reported that the platform can be difficult to use for rapid and actionable irrigation decision-making because it presents large amounts of information primarily as soil moisture trends across multiple sensor depths. While valuable for technical analysis, the presentation can make it challenging for producers to quickly determine how much water is available, how much has been depleted, and when irrigation should be initiated. To address this farmer-identified need, the program developed the UNL Mobile Irrigation Testing Lab dashboard (irrigationtest.com), which retrieves data from Sentek sensors through the IRRiMax/Sentek API and translates complex sensor information into simplified, decision-oriented metrics. The dashboard presents information such as current available soil water (inches), soil water depleted or used (inches), and the actual irrigation trigger level (inches). These metrics are calculated based on the field capacity (FC) and permanent wilting point (PWP) established for the soil and management conditions in each field. This approach transforms detailed sensor outputs into information that farmers can more readily interpret and use when making day-to-day irrigation decisions. Farmers also expressed interest in comparing soil water availability with crop water use to provide an additional basis for irrigation decisions. In response, the dashboard was expanded to incorporate estimated crop ET from multiple sources, including nearby Mesonet weather stations, NOAA weather data, and OpenET. Incorporating OpenET also provides an important extension opportunity to introduce farmers to the practical use of satellite-based remote sensing and ET products on their own farms. By viewing soil water dynamics alongside estimated crop water use, farmers can better understand the relationship between water stored in the root zone, crop water consumption, and irrigation requirements.

The program continues to encounter practical challenges associated with technology deployment, particularly sensor failures and unreliable network connectivity. Sensor failures can interrupt data collection and often require component replacement or field maintenance. Similarly, poor cellular or network connectivity can disrupt the transmission of data from field sensors to cloud-based platforms, limiting access to real-time information through APIs and decision-support dashboards. These challenges have been addressed through regular system monitoring, troubleshooting, maintenance, and replacement of failed components, while also providing an important learning opportunity for evaluating the reliability and scalability of data-driven irrigation technologies under actual farm conditions.

Oklahoma

Oklahoma State University - Sumon Datta, Irrigation Research Laboratory

The Oklahoma Agricultural Scientific Irrigation Scheduling (OASIS) tool became fully operational during this reporting period and is now in routine use by Oklahoma producers. OASIS integrates in-field soil moisture measurements, Oklahoma Mesonet weather data, and crop-specific water use estimates to generate site-specific irrigation recommendations delivered through a web-based dashboard. Following the 2025 beta-testing season, the tool was released to growers across the state's principal irrigated regions. During the 2026 season, approximately 10 producers used OASIS to guide in-season irrigation decisions on their fields. Grower feedback collected throughout the season was used to refine the recommendation interface, soil-water reporting, and irrigation alert functions, and to correct sensor-to-dashboard data handling issues identified under field conditions. Expansion of OASIS to Kansas and Texas is underway. Two awards received during the reporting period support this work: a grant from the USDA-ARS Ogallala Aquifer Program, and a NASA A.8 Water Resources grant, which together fund adaptation and deployment of the tool for both states. Activities to date include compiling soils, weather-network, and crop coefficient data layers for the Kansas and Texas service areas, identifying cooperating producers and demonstration sites in each state, and coordinating with irrigation specialists at partner institutions to align recommendation logic with regional crop mixes and management practices.

Challenges encountered during the reporting period included the spatial density and representativeness of publicly available weather station data in parts of western Oklahoma, which limits reference ET accuracy at the field scale; variability in soil moisture sensor installation quality and calibration across cooperator fields; intermittent cellular connectivity at remote sites, which delayed real-time data delivery; and the need to adapt soil, crop, and weather data sources for Kansas and Texas, where mesonet coverage and cropping systems differ from Oklahoma. Sustained grower engagement also remains a challenge — producers enroll readily but require continued extension support to act on the recommendations consistently through the season. Next steps are to complete integration of the Kansas and Texas weather and soils data layers and release regional versions of OASIS for beta testing in both states; begin systematic collection of key performance indicators (irrigation water applied, yield, and crop water productivity) from Oklahoma users to quantify the tool's impact; extend crop coefficient support to additional crops; deliver in-person and virtual training for producers, crop consultants, and extension educators in all three states; and pursue additional funding to sustain the platform and the supporting sensor network beyond the current award periods.

Objective 2.  Coordinate efforts to increase the efficient design and operation of irrigation systems including the use of add-on or independent technologies.

List any technical activities, specific functions/events organized, duties carried out. Write brief summary of the results of your activities. Provide details on challenges faced, opportunities created/envisioned. If multiple projects, then list them one by one.

 Arizona

The irrigation system comparison experiment provided additional information on the performance and operation of different irrigation systems and their effects on crop production and water application. Soil moisture sensors and actual evapotranspiration (ET) measurements were incorporated as corresponding technologies to improve irrigation scheduling, system operation, and the precision of water application. Arizona also initiated a new cotton irrigation experiment to evaluate how different irrigation systems affect crop growth, yield, and fiber quality, providing data on system selection and management under limited water conditions. The guayule irrigation study continued to evaluate irrigation strategies under deficit conditions, providing additional information on how irrigation systems can be operated to manage limited water supplies.

These activities contributed to WERA-1022 Objective 2 by evaluating irrigation system performance and operation. The work provided practical information to support the improved design, management, and adoption of irrigation systems and technologies by Arizona growers facing increasing water limitations.

Florida

Florida – Sandra Guzmán, Smart Irrigation and Hydrology (SIAH): Efforts focused on evaluating and integrating emerging sensing technologies into automated irrigation management systems. Soil moisture sensors, weather station networks, satellite data products, and wireless communication technologies were evaluated, and most of them were integrated into the IrrigMonitor platform to enhance system interoperability and operational efficiency. Technical activities included evaluating data acquisition protocols, sensor communication standards, cloud-based data transfer, and integration of forecasting algorithms for irrigation scheduling.

The platform was also tested to include satellite-based indicators of crop water status, enabling supplemental irrigation recommendations where field sensor coverage was limited. Challenges included harmonizing data from different sensor manufacturers and ensuring consistent communication among data collection, storage, and visualization components. However, these activities created opportunities to develop unified protocols that support the broader implementation of sensor-based irrigation management systems across diverse production environments.

Kansas

N/A

Louisiana

Within the last year, a project titled “Urban Water Management BMPs from a Landscape Resiliency Perspective,” was installed and in the process of becoming fully functional. A USDA funded seed grant (Award No. 2024-67020-42657), this project’s purpose is to develop Louisiana-specific, science-based landscape best management (BMP) practices that reduce freshwater demand, protect runoff water quality, and improve resilience to drought, heat, hurricanes, and flooding while keeping functionality and attractiveness. Four urban plots were installed at LaHouse Research and Education Center, Baton Rouge, LA that demonstrate combinations of turfgrass and mixed-ornamentals with both spray and drip irrigation options. This project was delayed by almost a year for various procedural reasons and suffered from inflation that reduced the project size. However, the 500 ft2 plots are now being used to estimate the entire water cycle including rainfall, irrigation, soil moisture, evapotranspiration, runoff, and deep percolation. All landscapes are evaluated for landscape quality to ensure any potential water savings does not result in a decline in health or attractiveness. This project transitioned from installation to data collection at the end of this reporting year; thus, no field results are available to-date. However, progress was made on determining public perceptions on urban water management BMPs, which has informed the development of a full needs assessment. This assessment was deployed in September 2026 and will stay open for a minimum of six months.

Artificial drainage technologies, such as tile drainage, have not been evaluated as part of agricultural water management (AWM) practices in Louisiana. Tile systems create channelized flows that can alter hydrological, ecological, nutrient, and sediment dynamics of the watershed and increase pollution potential if not managed correctly. The long-term goal of this project is to evaluate new strategies for increasing resilience in AWM for Louisiana cropping systems. A privately funded three-plot tile drainage system was installed in 2024 so that five treatments can be initiated: 1) controlled drainage with freshwater irrigation, 2) controlled drainage only, 3) controlled drainage with drainage water recycling, 4) freshwater irrigation only, and 5) rainfed. Additionally, an on-farm comparison will be monitored to compare a free-draining tiled field to a similar non-tiled field. Efforts continue to focus on obtaining external funding to initiate this research. For this reporting period, a baseline estimate of unirrigated soybeans is being collected to better understand the treatment outcomes once they are initiated.

Michigan

MSU, Dong Irrigation Lab

The focus on irrigation system efficiency has expanded significantly with the launch of the MSU Mobile Irrigation Lab (MIL) in late 2025, establishing a dedicated program to help Michigan growers evaluate, improve, and modernize irrigation systems. Through on-farm assessments, the MSU Irrigation Team conducts irrigation system uniformity testing, pressure evaluations, visual inspections, and assessments of electrical panels and grounding systems to identify opportunities for improved performance and reduced operating costs.

Working with cooperating growers across Michigan, the Mobile Irrigation Lab has demonstrated substantial opportunities to improve water and energy efficiency, particularly in aging center pivot systems. Previous evaluations and sprinkler package upgrades showed an average 20% increase in irrigation uniformity, resulting in water savings of 1.2 to 3.3 inches per acre per season. For a typical 100-acre irrigated field, this equates to approximately 3 to 9 million gallons of water saved annually and $420 to $1,617 in reduced energy costs.

In addition to irrigation system evaluations, the Mobile Irrigation Lab has introduced Variable Rate Irrigation (VRI) technology to Michigan growers through field demonstrations, educational programs, and on-farm consultations. By applying water according to spatial variations in soil and crop water needs, VRI technology has the potential to improve water-use efficiency, reduce unnecessary irrigation applications, and enhance crop productivity. Through these efforts, the MSU Mobile Irrigation Lab is helping growers adopt advanced irrigation technologies and management practices that support long-term agricultural sustainability and water conservation in Michigan. 

Nebraska

In Abia’s Irrigation Water Management Lab:

Through extension efforts associated with his signature outreach program, the Mobile Irrigation Testing Lab (MITLab), irrigation systems primarily center pivots are being evaluated to assess application efficiency and identify opportunities to improve irrigation uniformity and water use. The program combines traditional catch-can testing with Unmanned Aerial Vehicle (UAV)-based remote sensing to provide a more comprehensive assessment of sprinkler performance and crop response. In 2026, MITLab worked with four cooperating farmers to evaluate center pivot systems planted exclusively to soybeans. Results showed that approximately 95% of the evaluated pivot systems had application efficiencies greater than 90%, while 3% ranged from 85–90% and 2% were below 85%. Pivots with efficiencies greater than 85% generally applied water depths close to the depths set on the pivot control panel. In contrast, systems with lower efficiencies were found to have a combination of clogged, damaged, or poorly functioning sprinklers along the pivot spans. The evaluation also highlighted the importance of maintaining and periodically updating sprinkler packages. Farmers operating highly efficient systems were generally proactive in maintaining and updating their sprinkler packages. While systems with lower performance often had sprinkler packages that were approximately 8–10 years old, indicating that replacement or substantial maintenance was needed to restore system performance. Furthermore, UAV imagery was used to derive Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red Edge (NDRE) values, which were compared with catch-can measurements to examine the relationship between sprinkler performance and crop response. The results showed a strong relationship between the remotely sensed vegetation indices and catch-can measurements, demonstrating the potential for UAV-based sensing to complement traditional irrigation system evaluations across the field. To translate the evaluation results into actionable recommendations, system evaluation report cards were developed and provided to cooperating farmers. The report cards summarized system performance, identified potential problem areas, and provided specific recommendations for corrective action. Farmers with poorly performing systems were advised to repair or replace faulty sprinklers and, where appropriate, install new sprinkler packages. This approach allows farmers to move beyond simply identifying irrigation-system problems toward implementing practical solutions that can improve water application and irrigation efficiency.  One of the primary challenges encountered was evaluating center pivots planted to corn, particularly later in the growing season when crop height becomes substantial. Because many pivot sprinklers are positioned above the crop canopy, tall corn can intercept water before it reaches the catch cans, resulting in less reliable measurements and potentially inaccurate estimates of application performance.

Oklahoma

N/A

Objective 3.  Coordinate efforts to promote efficient irrigation through the development of a series of multi-state extension materials.

List any technical activities, specific functions/events organized, duties carried out. Write brief summary of the results of your activities. Provide details on challenges faced, opportunities created/envisioned. If multiple projects, then list them one by one.

Arizona

The University of Arizona contributed to WERA-1022 Objective 3 by developing research-based educational materials that provide information and resources that can serve as a foundation for future multi-state extension materials promoting efficient irrigation management. These materials address the need for improved water-use efficiency in irrigated agriculture and provide practical information that can be adapted for use across regions facing similar irrigation and water-management challenges. Topics include maintaining and calibrating field-installed soil and plant moisture sensors, using computer models and decision-support platforms for precision irrigation management, applying the OpenET platform and LI-710 sensor for irrigation scheduling, and evaluating irrigation scenarios to improve crop yield, water productivity, and quality.

Although these materials were developed for Arizona audiences, the research findings, methodologies, and management approaches have broader applicability to irrigated agriculture in other states. The publications provide research-based information that can be used as source material for developing coordinated multi-state extension resources and educational programs focused on irrigation efficiency, precision irrigation technologies, and data-driven water management.

Florida

Florida – Sandra Guzmán, Smart Irrigation and Hydrology (SIAH): Extension and outreach activities focused on promoting efficient irrigation management and accelerating adoption of data-driven irrigation technologies. Educational programs emphasized the use of interoperable sensor networks, irrigation decision support systems, and digital agriculture tools for improving irrigation scheduling.

An irrigation workshop was conducted on June 2, 2026, with approximately 50 participants, including growers, Extension personnel, consultants, and agency representatives. The workshop featured demonstrations of soil moisture sensors, decision support platforms, field-based monitoring technologies, and irrigation forecasting tools. Participants engaged in hands-on evaluations of the software and provided feedback regarding functionality, user interface design, and communication preferences.

Additional demonstrations and educational activities were conducted through commodity and stakeholder meetings, including:

Florida Citrus Show, Fort Pierce, FL (March 12, 2026): 50 participants in hands-on software demonstrations and approximately 60 participants in educational presentations.

Citrus Growers Institute, Avon Park, FL (April 14, 2026): 20 participants engaged in hands-on demonstrations of irrigation decision support technologies.

Citrus Expo (2025 and 2026): Demonstrations and educational sessions highlighting sensor interoperability, irrigation forecasting, and data-driven irrigation management.

These activities facilitated stakeholder feedback regarding data visualization, irrigation recommendation delivery, and technology adoption barriers. Educational materials and demonstrations emphasized the importance of integrating multiple data sources into standardized platforms that simplify irrigation management and improve technology transfer across specialty crop systems.

Kansas

N/A

Louisiana

N/A

Michigan

MSU, Dong Irrigation Lab

N/A

Nebraska

In Abia’s Irrigation Water Management Lab:

Through his signature outreach program, the Mobile Irrigation Testing Lab (MITLab), he is raising awareness about the value of collecting, interpreting, and using farm-level data to improve irrigation management and water conservation. The program provides farmers with hands-on exposure to advanced irrigation technologies, including soil moisture sensors and ET-based irrigation decision-support tools such as mobile apps and web-based dashboards. In collaboration with industry partners and water-management agencies, including the Twin Platte Natural Resources District (TPNRD), technologies are installed directly on participating farms, and farmers receive training and ongoing support to use the information to make data-driven irrigation decisions. By bringing technology and experiential learning directly to working farms, MITLab creates opportunities for irrigators to evaluate technologies under real-world conditions, build practical skills, and develop confidence in adopting new irrigation management strategies. The program also strengthens relationships among farmers, extension educators and specialists, industry partners, and water-management agencies, creating a collaborative environment for learning, technology evaluation, and adoption. This farmer-centered approach has helped build trust in using data to guide irrigation decisions and has encouraged producers to reconsider established irrigation practices. To date, MITLab has engaged 20 farmers, with more than 15,000 irrigated acres being managed using data generated from newly installed irrigation technologies for the first time. The program has also influenced irrigation management practices across more than 50,000 irrigated acres through changes in farmer behavior and decision-making. Participating irrigators have reported reducing irrigation by at least 2 inches after adopting the technologies and have expressed a willingness to continue adjusting their irrigation practices based on data and crop water needs. Importantly, 98% of participating farmers had never previously used irrigation-management technology, while the remaining 2% had previously tried such technologies but discontinued their use because of limited technical support from Extension or industry. MITLab addresses this critical adoption barrier by pairing technology deployment with hands-on training, technical support, and continued engagement. In doing so, the program is not only increasing access to irrigation technology but also helping farmers develop the knowledge, confidence, and support systems needed to sustain data-driven irrigation management and conserve groundwater resources.

Oklahoma

N/A

None reported