WERA1022: Irrigation Technologies and Scheduling for Water Conservation and Water Resources Management

(Multistate Research Coordinating Committee and Information Exchange Group)

Status: Active

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SAES-422 Reports

Annual/Termination Reports:

[03/29/2025] [12/19/2025]

Date of Annual Report: 03/29/2025

Report Information

Annual Meeting Dates: 08/14/2024 - 08/15/2024
Period the Report Covers: 10/01/2023 - 09/30/2024

Participants

Brief Summary of Minutes

There no official minutes taken.

Accomplishments

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.   Arizona In support of Objective 1, we evaluated the accuracy of satellite-based actual evapotranspiration models, OpenET, beginning with a 103-acre alfalfa field, which resulted in an extension publication through the University of Arizona (UA) Cooperative Extension. Data collection was subsequently expanded to a 35-acre cotton field, and a manuscript was submitted to a peer-reviewed journal. In 2024, we initiated a project to provide growers with soil moisture sensors for improved irrigation management. This project involves supplying three Sentek sensors per farm on a three-year subscription basis, along with ongoing technical support. Furthermore, we assessed the efficiency and durability of a gravity drip irrigation system for cotton over two growing seasons (2023 and 2024), leading to a conference paper and an extension publication in 2024. Further irrigation experiments were conducted to compare pressurized drip, center pivot with overhead sprinklers, and traditional flood irrigation under varying irrigation rates to assess their impact on irrigation water use and crop productivity. These trials also tested soil amendments. In 2024-2025, the experiment focused on cantaloupe and broccoli, with silage corn recently planted. A broccoli extension publication was submitted to the UA Cooperative Extension Journal. Findings from all projects were shared through various extension events, including workshops, meetings, and field day events.   California UCR, Verdi Water Management Group During the review period, we worked on multiple projects focused on landscape irrigation management and water conservation in Southern California. Science-based irrigation scheduling recommendations using soil moisture and evapotranspiration (ET) data, implemented through smart controllers, were developed for several plant species. We also assessed the negative impacts of water conservation measures on the evaporative cooling benefits provided by irrigated landscapes. In addition, we conducted multiple studies on the safe use of recycled water for irrigation, aiming to minimize the uptake and accumulation of chemicals of emerging concern in plants. The collaborative nature of these projects enabled us to investigate the efficacy of novel treatment technologies and the impact of irrigation management—including deficit irrigation—on crop growth and health, yield, and irrigation water use efficiency. We also conducted a series of studies focused on the accurate characterization of soil hydraulic properties, which are critical for efficient irrigation management. In particular, we examined the impact of tire wear microplastics on soil water retention curves and conducted a comprehensive study on the measurement and modeling of soil water retention using soil moisture sensors, laboratory techniques, and AI-based pedotransfer function models.   Louisiana During the 2024 reporting year, most efforts supported the public release of the Drought Irrigation Response Tool (DIRT) that provides irrigation scheduling decision support for furrow-irrigated corn, cotton, soybean, grain sorghum, and sugarcane. This was the first crop season that the tool was available to the public. Trainings and extension materials were provided throughout the state to introduce the tool in both use and functionality to limit technological barriers to adoption. Additionally, seven on-farm demonstrations of soil moisture sensors placed in diverse fields across crop production areas were used to validate DIRT. Of these seven installations, four locations provided enough high quality data to evaluate the webtool resulting in validation for corn and soybean only. While cotton and grain sorghum still need to be evaluated, recently collected sensor data in sugarcane indicated that its perennial nature requires some significant updates to the model for consistently successful use. Publications supporting this work are planned for 2025.    Minnesota UMN, Sharma Irrigation Group In support of objective 1, we worked on multiple projects related to irrigation water management and precision irrigation. To name a few, one of our efforts was focused on the applicability of gridded weather datasets over point-based weather data from Mesonets for irrigation management or irrigation scheduling. We compared API-accessible gridded datasets from GEMS Exchange to MESONET data from the Minnesota Department of Agriculture (MDA). We evaluated the data sources directly for goodness-of-fit for solar radiation, temperature (min and max), dew point, and wind speed, as well as downstream predictions of Reference ET (ETref) and GDD. Our findings show that gridded data, despite its tendency to overestimate solar radiation, does not significantly impact the accuracy of ET (R2= 0.92; RMSE=0.02 for both 2022 and 2023) or GDD predictions (R2= 0.98 for both 2022 and 2023; RMSE=0.94 (2022), RMSE=1.26 (2023). This suggests that API-based gridded data, accessible for all locations, can be reliably used for ETref and GDD modeling for decision support and complements MESONET measures by providing developers with standard software interfaces for real-time weather information. Our paper is accepted for publication in Agrosystems, Geosciences & Environment. Another study under objective 1 is focused on remote sensing-based estimation of Crop Coefficients (Kc) for precision irrigation systems in Minnesota. This project improves actual evapotranspiration (ETa) estimation for corn and soybean in Minnesota using satellite-based data from OpenET and CropScape. Monthly ETa from OpenET was combined with reference evapotranspiration (ETo) from Climate Engine to calculate region-specific crop coefficients (Kc), which will be integrated into the Irrigation Management Assistant (IMA) tool to provide site-specific irrigation guidance.   Nebraska A study was conducted in south-central Nebraska in 2024 to evaluate the performance of a commercial irrigation scheduling tool and to compare it with the performance of Watermark soil tension sensors that have been historically used by local growers. Three commercial (Com.) scheduling treatments and two Watermark (WM) treatments were studied (5 total), with each approach including one deficit irrigation treatment. Full irrigation treatments included Com-1, Com-3, and WM-1, while deficit irrigation treatments were Com-2 and WM-2. For the full irrigation treatments, Com-1 and Com-3 resulted in 8.15 and 7.60 inches of seasonal irrigation application, respectively, smaller than the average irrigation for WM-1 (8.75 in). Com-2, which was designed to apply 70% of full irrigation, resulted in 5.65 inches of seasonal irrigation, while WM-2 (the other deficit irrigation treatment) resulted in 4.38 inches of total irrigation. Grain yields for the three commercial scheduling treatments ranged from 251.2 to 251.6 bu/ac. This suggests that the 70% irrigation applied to Com-2 was adequate. The yield of the WM-1 was 254.4 bu/ac. The slightly larger yield than Aluvio treatments was not statistically significant. The average yield of similar corn hybrids planted within the 25-mile radius of the study site and reported by Bayer Crop Science was similar, confirming that our yield was comparable to what was achieved by local growers. The average yield of the WM-2 treatment, which received the smallest irrigation, was 239.5 bu/ac. The difference between this yield and the yield of Com-1, Com-3, and WM-1 was statistically significant. The results of this study were shared with local growers through field days and with the neighboring Natural Resource Districts who are planning to use the results in calibrating their anticipated restrictions on irrigation allocations. Oklahoma Oklahoma representative (Sumon Datta, Oklahoma State University) joined this multi-state hatch project, WERA1022, around September, 2023. 2024 activities primarily focused on development of irrigation scheduling techniques using in-situ sensors (e.g., soil moisture sensors, canopy temperature sensors) and models (e.g., pyfao56, HYDRUS). 2024 summer was the first field data collection season which focused on collection of multi-depth soil moisture (SM), canopy temperature (CT), and water application data from 3 irrigated cotton fields on a 15-min basis. Such data collected between 2014 and 2023 were utilized to kickstart two projects: (1) evaluate OpenET’s feasibility in irrigation scheduling, and (2) data-driven modeling of CT for irrigation management. On the first project, daily crop evapotranspiration (ET) from OpenET was extracted from ~30 irrigated fields where in-situ multi-depth SM data was available. Following methodology laid out in Kettner et al. (2025), the SM values were converted to ET assuming SM losses from different layers of the soil equated to ET loss from the soil and plant surfaces to atmosphere and compared against ET from OpenET on a daily basis. The objectives for this project were two-fold: a) finding if ET from OpenET is responding to watering events properly, and b) the comparative difference among ET from OpenET and SM-based ET estimation approach, and ET from Eddy-covariance towers. Further analysis is ongoing for this project. On the second project, efforts focused on evaluating five commercially available machine learning (ML) approaches in modeling CT. This project used measured CT data from 11 irrigated fields to train the models, resulting in root mean square errors (RMSE) ranging from 1.2 to 2.3 °C. Several manuscripts are in preparation on these projects and expected to be published in 2025/2026.   USDA-ARS Texas Upland cotton (Gossypium hirsutum L.) production requires less irrigation compared with other crops and thus provides an opportunity to reduce risk and maintain profitability in areas where water is limited. Water use, canopy temperature, lint yield, and crop water productivity were evaluated for four early to medium maturity upland cotton cultivars under three levels (100%, 66%, and 33%) of alternate furrow subsurface drip irrigation (SDI) in a thermally limited environment (Schwartz et al., 2024). Crop evapotranspiration (ET) across cultivars and years averaged 627, 547, and 467 mm for the 100%, 66%, and 33% irrigation levels, respectively, and did not differ among cultivars (P > 0.05). Crop water use during boll maturation, as inferred from the developed crop coefficient curve, was considerably less than reported by other studies, signifying that irrigation could be terminated earlier without reducing lint yield. The cultivar effect on lint yield was significant in all study years (P < 0.001), but only at the 66% and 100% irrigation levels, with one cultivar exceeding the average yields of all evaluated cultivars by 13% across the three study years. Medium maturity cultivars usually yielded less than early maturity cultivars, especially for a year with less accumulation of thermal energy. Crop selection and late season irrigation water management were both key to improving cotton water productivity. The fitted FAO-56 crop coefficient for the initial stage (Kc-ini) under SDI was 0.38) and similar to values reported for surface drip irrigation. In this experiment, much of the benefit of SDI was lost for the alternate furrow SDI because of the need to wet the seed zone and establish the crop with a considerable amount of irrigation in most growing seasons. This result is in contrast to other studies at Bushland for flat planted cotton with SDI in alternate inter-rows, where Kc-ini values were much smaller, leading to the observation that bed and furrow cultivation is unnecessary and counterproductive for SDI. The fitted mid-season crop coefficient (1.19) was similar to the tabulated FAO-56 value; however, the duration of this period was short (20 d) and began well after first flower, extending from a couple days before cutout to 18 days after cutout. The late season crop coefficient converged to values near zero and was considerably smaller than reported by other studies or the FAO-56 tabulated value. This suggests that water savings could be achieved by reducing irrigation during boll maturation.   Objective 2.  Coordinate efforts to increase the efficient design and operation of irrigation systems including the use of add-on or independent technologies.   Louisiana In 2024, two research projects were initiated at the Red River Research Station in Bossier City, LA to evaluate and demonstrate alternatives to the typical gravity-fed systems used by 90% of farmers in Louisiana. The first project utilizes a semi-temporary drip irrigation system for small plots (100 ft x 40 ft). The uniqueness of a semi-temporary system involves using a thin walled drip line that was installed in the row during formation that will stay in place for up to 4 years, which aligns with the cultural practices of mid-South farmers. As a companion project to a colleague’s research, this project will evaluate rice production from both water efficiency and greenhouse gas emission standpoints. Efforts were focused on installing the irrigation system during the 2024 crop season due to frequent torrential rainfall that delayed all field work until late June. The second project will evaluate the use of tile drains for both irrigation and drainage applications. This project will compare sub-irrigation and drainage to other water management options such as rainfed, drainage only, furrow irrigation with drainage, and furrow irrigation without drainage. Just as the first project was delayed, installation of the tile occurred in July 2024 and will be utilized for the 2025 crop season. Both projects were cover-cropped for the winter months and the process to initiate treatments began in March 2025.   Nebraska Nebraska researchers and extension specialists worked with Nebraska NRCS and local growers on evaluating and improving the design of irrigation systems for urban and small-scale production systems across the state. Since large-scale production systems with thousands of acres have received most of the technical and financial support in improving their irrigation systems and management, small-scale production, which is expanding in the state, is in need of technical assistance with efficient design and operation of the wide range of irrigation systems available for these types of productions. Together with our local partners we established two demonstration sites at Lincoln and North Platte, where different types of microirrigation systems are used for vegetable production. Our team also offered audits of commercial operations, where we learned a lot about common mistakes in the design of small-scale irrigation systems and were able to help growers with their irrigation challenges.   USDA-ARS Texas USDA ARS scientists collaborated with University of Nevada Reno to develop a method to forecast spatially variable canopy temperatures using machine learning (Andrade et al., 2023a); and the team reported on their sensor-based decision support system (ISSCADA) for variable rate irrigation (Andrade et al., 2023b). USDA ARS scientists also collaborated with University of Nebraska to test the ISSCADA system there with a focus on crop water stress index and soil water balance inputs from automated systems (Bhatti et al., 2023). USDA ARS scientists collaborated with scientists nationally and internationally to test 41 maize (grain corn) models for their ability to estimate crop ET and yield (Kimball et al., 2023). Findings were that these crop models moderately to severely underpredict corn ET. The ARS team also collaborated with Washington State University to develop a machine learning model of reference ET that could lessen weather data input requirements (Kiraga et al., 2023). The OPENET system (<a href="https://etdata.org/">https://etdata.org/</a>) now offers ET data on a 30-m resolution for the western USA. USDA ARS scientist collaborated to provide weighing lysimeter ET data for calibration of the six satellite remote sensing-based ET models that make up the OPENET suite of models (Volk et al., 2023a,b). Marek et al. (2023) reported on a combined ARS-Texas A&M AgriLife experiment to evaluate conventional TDR sensors, neutron probe, and a downhole TDR-based sensor system in field plots with three levels of irrigation. They found that the conventional TDR sensors and neutron probe provided equivalent profile water content data while the downhole TDR sensor underestimated profile water content in most conditions and could overestimate immediately following irrigation. O’Shaughnessy et al. (2023a) reported on improved cotton water productivity when using mobile drip irrigation technology compared with low elevation spray application. They also reported on using the ISSCADA system to irrigation cotton in two years. In 2021, the irrigation savings was a minimum of 20%, while in 2022, the minimum savings was 16% without reducing yield (O’Shaughnessy et al. (2023b). Soto et al. (2024) also reported positive results when using mobile drip irrigation, this time with watermelon. Watermelon had more fruits per plant (1.9) than thus under LESA (1.4), and also greater yield and crop water productivity.   Objective 3.  Coordinate efforts to promote efficient irrigation through the development of a series of multi-state extension materials.   Minnesota UMN, Sharma Irrigation Group We successfully delivered the fourth season of the Minnesota Irrigator Program (MIP) in 2024. The overall goal of this program is to provide intensive training for irrigators and agricultural professionals on irrigation practices that can conserve water and limit irrigation’s impact on groundwater and surface water quality. We provide this training through an annual educational course consisting of three non-consecutive training days in late spring every year. For 2024, we delivered a three-day program at Central Lakes College, Staples, Minnesota, in March 2024. We saw 100% attendance from 25 participants (From MN and ND). We also formed an advisory committee (20-30 individuals) from key stakeholder groups, including the Minnesota Department of Agriculture, NRCS, Irrigator Association of Minnesota, Irrigation industry professionals, etc., to gather inputs on the course content and speakers. We collaborated with MDA’s Minnesota Agricultural Water Quality Certification Program (MAWQCP). The participants from the MIP can earn an “Irrigation Endorsement” for the MAWQCP, and the endorsement will help them apply for implementation funding with MDA. In addition, we have received funding from the Minnesota Department of Agriculture to execute this program. The post-event survey indicated a substantial increase in knowledge after the course attendance and indication of practice implementation. The post-event survey showed that the participants either helped manage or directly managed a minimum of 27,000 irrigated acres, with 87% of participants responding to change some of their irrigation practices based on what they learned in the program. We also hosted a summer field day at the Sand Plain Research farm on July 16th, 2024. Approximately 35 people (farmers, students, post-docs, extension educators, and state agency personnel) attended the field day, where 8 speakers from the University of Minnesota and University of North Dakota. This event aimed to provide a platform for growers, consultants, state agencies, industry, and researchers to come together and learn about cutting-edge research happening at the University and ask the researchers questions directly. Through the post-event surveys, it was evident that attendees had a great experience, participated in the discussions, and became aware of efficient agricultural management practices that have the potential to improve crop production and, at the same time, prevent agriculture-induced environmental pollution. About 50% of the respondents indicated changing some of their farming practices based on the knowledge they gained through this program.   USDA-ARS Texas The Bushland large weighing lysimeter ET and corresponding weather data sets are widely used for model development. The USDA-ARS team at Bushland, Texas, previously developed quality assurance (QA) and QC procedures for weighing lysimeter data from the four large weighing lysimeters at Bushland, as well as for research weather data compiled from a grassed research weather station, four large weighing lysimeters and a U.S. Weather Service station at Bushland. We applied these procedures to produce 32 years of quality 15-minute, 365-day weather and lysimeter ET data that were shared on the USDA National Agriculture Library Ag Data Commons. Infrared thermometers (IRTs) are now commonly used to monitor crop water status, and Colaizzi et al. (2023) reported methods for IRT data quality control.

Publications

Impact Statements

Date of Annual Report: 12/19/2025

Report Information

Annual Meeting Dates: 08/13/2025 - 08/14/2025
Period the Report Covers: 10/01/2024 - 09/30/2025

Participants

Brief Summary of Minutes

Although no official minutes were taken, the agenda can serve as a basis for what was reported and the meeting's flow.

2025 Annual Meeting for WERA1022

Project title: Irrigation Technologies and Scheduling for Water Conservation and Water
Resources Management

NIMMS Link for Project: https://nimss.org/projects/view/mrp/outline/18933

Zoom link: https://okstate-edu.zoom.us/j/93880512298 & Meeting ID: 938 8051 2298

Meeting Agenda

Date: August 13-14, 2025

Location: 319 Agricultural Hall, Oklahoma State University, Stillwater, OK 74078

August 13 (Wednesday)
All times are in Central Time Zone

8:30 am Meet at 319 Agricultural Hall – Introductions

9:00 am – 12:00 pm State Reports and Discussions

15 mins per State (12 mins presentation + 3 mins Q&A)

10:15 – 10:30 am Break (15 mins)

12:00 – 1:00 pm Lunch

1:00 – 5:00 pm State Reports and Discussions (contd.)

3:00 – 3:15 pm Break (15 mins)

6:00 pm Dinner on your own

August 14 (Thursday)

All times are in Central Time Zone

8:00 – 11:30 am Continue State/Participants’ Reports and Discussions

Collaboration Opportunities to Discuss Future Projects

9:45 – 10:00 am Break (15 mins)

11:30 am – 12:30 pm Lunch

1:00 pm Adjourn 2025 WERA1022 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. <h2>Arizona</h2> Comparative irrigation studies conducted at the University of Arizona Maricopa Agricultural Center demonstrated significant improvements in crop yield and water productivity across multiple irrigation systems. Subsurface drip irrigation consistently achieved the highest yields and water-use efficiency for broccoli and corn, while cantaloupe performance, initially reduced by high soil salinity, was successfully enhanced by increasing the leaching fraction to reduce salt accumulation, which improved soil structure and nutrient availability. In contrast, center pivot systems experienced challenges with soil crusting and poor germination under saline conditions. The project established a comprehensive framework for comparing irrigation systems under both full and deficit irrigation scenarios, generating crop-specific yield and water-use datasets and introducing practical salinity management strategies to optimize productivity under arid conditions. Integrating soil moisture sensors into irrigation scheduling significantly improved water management and crop performance. In 2024, a statewide deployment of soil moisture sensors led to strong grower participation and demonstrated measurable increases in water savings and yield efficiency. Another project evaluated OpenET, a satellite-based evapotranspiration (ET) tool, in comparison with the soil water balance method. Results showed strong alignment between the two approaches and were presented at professional meetings, ending in a peer-reviewed manuscript that was accepted for publication in Agricultural Water Management in October 2025. Extensive outreach and extension activities, including publications, conference presentations, workshops, and on-farm consultations, ensured broad dissemination of projects’ outcomes. In total, six publications were produced: one conference paper, one peer-reviewed journal article, and four University of Arizona Cooperative Extension publications, each detailing research findings, irrigation strategies, and grower recommendations. Supported by funding from the State of Arizona Water Irrigation Efficiency Program and collaboration with USDA researchers, this work has substantially advanced irrigation scheduling and water management in Arizona. By validating sensor- and satellite-based approaches, demonstrating cost-effective salinity mitigation, and quantifying the benefits of deficit irrigation, these projects have provided growers with practical tools to enhance water productivity and sustainability. These achievements, evident through expanded technology adoption, increased interest in OpenET data, directly support WERA 1022’s mission to strengthen agricultural water management and resilience across the western United States.   <h2>California</h2> The Verdi Water Management Group at UCR continued field irrigation trials to determine landscape coefficient (plant factor) values for drought-tolerant groundcover species. As part of this effort, a large-scale experiment with 144 fully automated research plots was conducted to evaluate the response of eight groundcover species to three irrigation rates. In addition, an easy-to-use webpage was developed (<a href="https://www.ucrwater.com/landscape-irrigation.html">https://www.ucrwater.com/landscape-irrigation.html</a>) to provide ET-based irrigation recommendations for more than 20 groundcover and turfgrass species in inland Southern California, based on research conducted by Dr. Verdi’s team over the past decade. The webpage also provides information on the relative evaporative cooling potential of the studied plant types under a wide range of irrigation management and water conservation scenarios. We also conducted a remote sensing study to assess the performance of drone-based thermal imagery for high-resolution mapping of actual ET in urban landscapes. The results were promising and highlight the potential of this approach for urban water management applications.   <h2>Colorado</h2>   <h2>Florida</h2> Florida - Bayabil’s Water Resource Research Lab: Coordinated research and extension activities were undertaken to advance UAV-based and AI-enhanced irrigation scheduling systems for specialty crop production in Florida. UAV RGB and thermal imagery were combined with canopy temperature measurements and machine learning models to estimate crop evapotranspiration (ET) with high spatial resolution, enabling more accurate determination of irrigation scheduling optimized for water conservation (Teshome et al., 2025). UAV-derived vegetation indices and machine learning algorithms were applied to predict leaf area index (LAI), which is widely used in crop simulation models. By integrating LAI with ET estimation, crop growth and water use patterns were tracked dynamically throughout the growing season, improving the temporal responsiveness of irrigation management (Hussain et al., 2025). A fully automated end-to-end UAV image processing pipeline, Agricultural Sensing and Artificial Intelligence (AgriSenAI), was developed to streamline thermal and multispectral dataset processing. Image stitching, georeferencing, and feature extraction were automated, after which AI models were applied to estimate ET. This automation was designed to reduce technical barriers to operationalizing advanced irrigation scheduling in production fields (Tulu et al., 2025). Furthermore, machine learning-based ET estimates were integrated with crop simulation models such as DSSAT, resulting in improved predictive performance and irrigation decision support under variable environmental conditions (Hailegnaw et al., 2024). These research activities were supported by a series of extension and outreach efforts through which the developed tools and workflows were demonstrated at research farms and grower field days. UAV sensing platforms, canopy temperature measurements were showcased, and practical discussions were facilitated on their integration with on-farm irrigation scheduling. Through these combined research and outreach efforts, a scalable and technically robust framework for real-time, data-driven irrigation scheduling was established. Data processing workflows were automated. Challenges were encountered with sensor calibration variability, UAV operational logistics, and data processing requirements. However, opportunities were created to integrate affordable sensing and AI technologies into practical irrigation scheduling systems and to foster broader adoption through targeted training and stakeholder engagement. <h2>Kansas</h2>   <h2>Louisiana</h2> With 91% of irrigated acreage utilizing gravity-fed irrigation strategies primarily from groundwater sources, Louisiana’s efforts to improve efficiency of agricultural irrigation are critically important to strengthening aquifer resilience. The combination of increased competition for water resources due to land use changes, recent droughts during the crop season, and record-breaking extreme temperatures have exacerbated the need for improvements to irrigation strategies. In response, the Drought Irrigation Response Tool (DIRT) was co-created with end users in 2024 as an integrated web-based platform that assists farmers in making irrigation scheduling decisions. This science-based method determines when to apply irrigation based on crop water needs; it was evaluated for functionality and predictive ability against soil moisture datasets collected in the last year from various crops. Results indicate acceptable agreement on selecting the best irrigation date in most cases. The lack of an infiltration model combined with imprecise satellite-based rainfall estimations can affect the accuracy of the tool, but the divergency was typically short-lived due to the naturally humid climate. Overall, the results were acceptable as a first step in creating cost effective decision support. The DIRT project highlighted the need for a reliable and accurate source of evapotranspiration and soil moisture data for use in Louisiana agricultural applications.  Thus, recent efforts have focused on establishing the Louisiana Climate and Digital Ag Network (LaCADIAN), which is a network of environmental sensing nodes in process of being installed across agricultural areas. The integrity of the measured incoming data is ensured with the implementation of benchmark routines, including sensor-specific calibration, standardized environmental thresholds, and cross-validation between variables. In addition to stabilizing DIRT’s long-term economic feasibility, 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. <h2>Michigan</h2> MSU, Dong Irrigation Lab Michigan State University has developed the MSU Irrigation Mobile App, which uses data from local MSU Enviroweather stations and the National Weather Service to provide site-specific irrigation recommendations. The first version of the MSU Irrigation Scheduler is now available on both the <a href="https://apps.apple.com/us/app/irrigation-app/id6477047162">Apple App</a> Store and <a href="https://play.google.com/store/apps/details?id=edu.msu.irrigmsu&hl=en_US">Google Play</a> Store. Currently, the app has 295 registered users, managing over 49,000 acres of corn, soybeans, vegetables, and fruit crops. To better serve diverse users, the app offers both English and Spanish language options to support Hispanic growers. In addition, the team is developing the MSU Berrigation App, a dedicated irrigation scheduling tool for blueberry production. Because many blueberry growers use drip irrigation systems, the app will prompt users to input their drip emitter spacing, flow rate, and whether their system has single or dual lines. Based on this information, it will generate irrigation recommendations in both run time and inches of water. The Berrigation App is scheduled for release in early 2026. <h2>Minnesota</h2> UMN, Sharma Irrigation Group In support of objective 1, we worked on multiple projects related to irrigation water management and precision irrigation. To name a few, one of our efforts was focused on the applicability of gridded weather datasets over point-based weather data from Mesonets for irrigation management or irrigation scheduling. We compared API-accessible gridded datasets from GEMS Exchange to MESONET data from the Minnesota Department of Agriculture (MDA). We evaluated the data sources directly for goodness- of-fit for solar radiation, temperature (min and max), dew point, and wind speed, as well as downstream predictions of Reference ET (ETref) and GDD. Our findings show that gridded data, despite its tendency to overestimate solar radiation, does not significantly impact the accuracy of ET (R2= 0.92; RMSE=0.02 for both 2022 and 2023) or GDD predictions (R2= 0.98 for both 2022 and 2023; RMSE=0.94 (2022), RMSE=1.26 (2023). This suggests that API-based gridded data, accessible for all locations, can be reliably used for ETref and GDD modeling for decision support and complements MESONET measures by providing developers with standard software interfaces for real-time weather information. Our paper is published in Agrosystems, Geosciences & Environment. Another study under objective 1 is focused on remote sensing-based estimation of Crop Coefficients (Kc) for precision irrigation systems in Minnesota. This project improves actual evapotranspiration (ETa) estimation for corn and soybean in Minnesota using satellite-based data from OpenET and CropScape. Monthly ETa from OpenET was combined with reference evapotranspiration (ETo) from Climate Engine to calculate region-specific crop coefficients (Kc), which will be integrated into the Irrigation Management Assistant (IMA) tool to provide site-specific irrigation guidance.   <h2>Montana</h2> A newly established (started in Fall 2024) Sapkota’s Precision Crop, Soil, and Water Management Group at Montana State University initiated multiple research projects on irrigation management that align with the objectives of WERA 1022. We studied irrigation management in spring wheat and established an experimental field with four different irrigation treatments (Rainfed, 25%, 50%, and 100% ET-based irrigation) and three replications in Creston, MT. The effects of different rates of irrigation on crop morphological and physiological parameters, along with soil respiration, were measured during the growing period. Additionally, crop yield and protein content as affected by irrigation treatments were assessed. We also used drone-acquired multispectral and thermal images to estimate the actual evapotranspiration from spring wheat in Montana. Additionally, continuous measurements of canopy temperature data are being used to establish crop water stress index (CWSI) thresholds for irrigation scheduling of spring wheat. This project aims to optimize irrigation water use, develop remote sensing approaches and thresholds for ET and CWSI estimation, evaluate the effects of irrigation on soil respiration, and enhance spring wheat yield and protein content. We have submitted two abstracts to present results from this first year of experiments at the ASA-CSSA-SSSA Annual Meeting in Utah, USA (Adhikari et al., 2025a; Adhikari et al., 2025b). The effect of deficit irrigation on alfalfa yield and crude protein content was also evaluated at a grower’s field. Two 10-degree sectors were used for the irrigation experiment, where one 10-degree sector received irrigation based on the grower’s standard practice and the other 10-degree sector received 25% less than the grower’s standard practice. We hypothesized that deficit irrigation in alfalfa would have no significant impact on yield and forage protein content. Results from this deficit irrigation study will help growers better optimize irrigation rates. We plan to continue these experiments in the 2026 field season. In 2026, we will also initiate a new study to evaluate the potential of OpenET for precision irrigation in Montana. We will conduct a comparative study to evaluate the effects of different irrigation scheduling approaches on crop yield and growth.   <h2>Mississippii</h2>   <h2>Nebraska</h2> In Abia’s Irrigation Water Management Lab: A Crop2Cloud platform was developed to support Agricultural Water Management in terms of water stress monitoring and application of data-driven irrigation decisions based on various methods including fuzzy-logic that use individual water stress parameters as data inputs, including Crop Water Stress Index (CWSI), Soil Water Stress Index (SWSI), and reference Evapotranspiration (ETr). The platform was designed using Internet of Things products (i.e., LoRAWAN) and in-field sensors to collect multiple data sources such as soil moisture and canopy temperature, all stored in Google Cloud and to be specific in the BigQuerry database. Weather data was also used to estimate crop evapotranspiration. Thereafter, real-time data and irrigation recommendations from each method are displayed on a dashboard.  The designed platform was installed in corn and soybeans and then used for irrigation management for 2024 and 2025 growing seasons, while comparing fuzzy-logic approach as the multi-data method to individual single-data methods - Crop Water Stress Index (CWSI), Soil Water Stress Index (SWSI), and ET-Model. Results in 2024 indicated that the fuzzy-logic model applied less irrigation amounts against other methods without significant difference in yields.  A paper has been published in Smart Agricultural Technology that describes the development and application of the Crop2Cloup platform. Saleh Taghvaeian’s lab During the reporting period, a field experiment and demonstration at the South Central Agricultural Laboratory (SCAL) near Clay Center, Nebraska compared two corn irrigation scheduling strategies: sensor-based (Watermark) and a commercially-available satellite-guided approach. The findings demonstrated that deficit irrigation strategies using soil moisture sensors could reduce applied water by nearly 50% with minimal yield loss (6%), underscoring opportunities for more efficient irrigation management under new groundwater allocation limits. The results were shared with stakeholders during a field day at SCAL on August 7, 2025, which attracted producers, Natural Resource District (NRD) staff, and extension educators, highlighting practical scheduling methods and water-saving technologies. Ongoing collaboration with the Little Blue NRD, following the district’s 2025 groundwater allocation mandate, has strengthened university-NRD partnerships and created new opportunities to develop standardized, data-driven irrigation decision-support frameworks. Challenges included limited access to NRD irrigation data due to privacy concerns and the need for formalized data-sharing agreements. However, these discussions have initiated promising pathways toward trusted, collaborative frameworks that will expand data accessibility and accelerate the adoption of science-based irrigation scheduling practices statewide. <h2>Oklahoma</h2> Oklahoma State University - Sumon Datta, Irrigation Research Laboratory, The newly developed “Oklahoma Agricultural Scientific Irrigation Scheduler – OASIS” tool is being developed at OSU by Dr. Datta based on pyfao56, a python implementation of daily dual-crop coefficient-based soil water balance model. This model follows procedures laid out by Food and Agriculture Organization Irrigation and Drainage Paper No. 56, FAO-56 (Allen et al., 1998) and its revision – Manual of Practice 70 – MOP-70. The tool is designed to provide robust, flexible, automated, and user-friendly irrigation scheduling support for farmers, based on well-established soil–plant–atmosphere water balance methods. The development integrates multiple data sources—soils, crops, and weather—into an automated system that can forecast irrigation needs and provide dynamic decision support. The tool integrates crop growth, plant characteristics, and soil data to generate site-specific irrigation schedules. Crop growth parameters—such as stage durations and basal crop coefficients—are based on FAO-56 and MOP-70 but can be adjusted using local field observations and pilot data (2022–2024). Plant parameters, including management allowed depletion, rooting depth, and measured plant height, refine daily water balance estimates. OASIS combines real-time and historical Oklahoma Mesonet data with NOAA forecasts to compute reference evapotranspiration (ETo) using ASCE standards. A custom soil data pipeline retrieves SSURGO-based texture data (sand, silt, clay) via API, processed using Rosetta and van Genuchten equations to estimate field capacity and wilting point. The integrated system automatically updates evapotranspiration, runoff, and irrigation recommendations daily, providing seven-day forecasts through a robust, web-deployed platform that supports adaptive, data-driven water management across Oklahoma’s diverse cropping systems. The modeling framework simulates soil water balance processes including crop evapotranspiration (ET) translated to irrigation scheduling. The tool is available to Oklahoma producers at the following website: <a href="https://irrigation.okstate.edu">https://irrigation.okstate.edu</a>. Due to universal suitability of this tool’s framework, we already have state-sponsored investments to expand this tool to Kansas and Texas by the end of 2027. The tool will enter beta-testing at the field level with 15 producers in 2026 summer growing season in Oklahoma. The project is funded by Oklahoma Mesonet.   <h2>Tennessee</h2> The University of Tennessee (UT) Irrigation Specialist (Brian Lieb) has already developed MoisturePlus.Net, which has been tested and is currently used by several row crop growers across the state of Tennessee. The newly established Tennessee State University (TSU) Irrigation Program (Behnaz Molaei) focused on orchards and fruit crop producers, as the majority of orchards in Tennessee have not been irrigated in the past but are now beginning to consider irrigation systems. Our efforts are aimed at promoting drip irrigation and implementing irrigation scheduling practices for these orchards. Creating a weather network for promoting irrigation scheduling: Tennessee currently lacks an agriculture-based mesonet, and data from the UT weather stations are not publicly available. Therefore, promoting the irrigation scheduling app requires providing All-in-One ATMOS-41 weather stations in farmers’ fields which is the additional cost for each site. Over the past two years, TSU has installed ATMOS-41 weather stations across farmers’ fields in Tennessee and established one research-grade Campbell Scientific weather station at the TSU Research Farm as a reference site. We now have a shared network of weather stations (UTK with 13 weather stations, and TSU with 11 weather stations), and under the new USDA-NIFA CBG grant, we plan to develop an open-access Tennessee Agricultural Mesonet. This network will be available to all growers and stakeholders, enabling them to connect to the MOISTURE Plus app and use its irrigation scheduling tools. The TSU Irrigation Research Team is currently focused on evaluating a drone-based METRIC model (UAS-METRIC) for the hot and humid climate of Tennessee, as well as developing methodologies to improve the satellite-based METRIC model for the southern region of the United States. These research studies are funded by a USDA-NIFA-AFRI in 2024 and represent a collaborative effort among Tennessee State University, USDA-ARS Bushland (Texas), and Mississippi State University. Some of the results will be presented at CANVAS 2025. We anticipate that the success of this project will enhance site-specific irrigation management and large-scale water use estimation for various crops across Tennessee. Furthermore, it will serve as a complementary tool to the existing irrigation scheduling app, enabling spatially resolved crop water use estimation. <h2>Texas</h2> Texas A&M AgriLife Research at Amarillo, Crop Physiology Group, Q. Xue Irrigated Corn- Corn is the major irrigated crop in the North Texas High Plains, and the irrigation uses 53% of the entire water budget annually (1.43 million ac-ft , 1 ac-ft = 325,851 gallons) in the region. However, the declining water table along irrigation pumping restrictions by water districts will challenge the sustainable corn production. Field experiments are continuously conducted in different hybrids under various irrigation conditions from full irrigation to limited irrigation. In the 2024 and 2025 corn study, we started a new project entitled “Enhancing Corn Water Use Efficiency through Integration of Sensor, Crop Model, and Machine Learning-based Approaches” funded by Texas Water Resources Institute. Corn was grown at two irrigation levels (100% and 50% ET requirements). Field data collection included field agronomy, water use, yield, yield components, soil moisture, sap flow, and canopy temperature. Part of field data were reported in a poster at the ASA-CSSA-SSSA International Annual Meeting (November 10-13, 2024) in San Antonio, TX. The field data will be used for calibrating crop models and machine learning tools. Field data from 2025 will be presented in a poster at the ASA-CSSA-SSSA International Annual Meeting (November, 2025) in Salt Lake City, UT. Canola- This is a multi-year project funded by NIFA to evaluate canola as an alternative crop for marginal lands irrigated with brackish waters in the Southern Great Plains region. One objective is to evaluate salinity tolerance among canola during emergence and at seedling stage. We conducted growth chamber and greenhouse studies in 2023 and 2024 in 24 canola genotypes, including some new Roundup Ready spring canola genotypes. Again, genotypes were evaluated under six levels of salinity (deionized water (control), 2, 4, 6, 8, and 10dS/m). We have identified some genotypes that showed superior performance under salinity. The information is very important for producers to select salt tolerant cultivars. In the greenhouse study in 2025, we specifically investigated the  physiological responses of canola genotypes with contrast differences in salt tolerance. Sorghum – Two sorghum projects were started in the 2024 summer growing season. One project is entitled “A reappraisal of sorghum growing degree days: a regional approach”, and we collected phenological and yield data in four grain sorghum hybrids planted in two planting dates. The data will be used to calibrate growing degree day (GDD) models for predicting grain sorghum phenological stage at regional scale from Nebraska and South Dakota to Texas High Plains and southern Texas. This information is particularly important for developing irrigation schedules in sorghum based on GDD models across the Great Plains. In another project, we investigate the grain sorghum and forage sorghum performance under organic production system. Grain sorghum was growing in sorghum-cotton rotation and forage sorghum was growing in sorghum-corn rotation system. Field data collections included growth dynamics, yield, and soil nutrient status and soil health parameters. Texas A&M AgriLife Research Station at Halfway, Nakabuye Irrigation Group To meet the WERA objective I, we worked on various field  projects aimed at investigating  the role of data driven irrigation water management and climate resilient practices on irrigation water use efficiency, and crop yields in cotton-forage sorghum rotation cropping systems. Cotton: Specifically, in the 2024 and 2025 growing seasons, studies were  started to evaluate the response of different cotton varieties to deficit irrigation with the aim of developing data informed region specific crop water stress indicators and irrigation trigger thresholds. The irrigation treatments included 20%, 40%, 60%, 80% of the crop evapotranspiration requirement as well as a non irrigated, dry land treatment. Data types collected from the experimental plots included soil moisture measurements, canopy temperature, photosynthetic gas exchange and chlorophyll a fluorescence, as well as crop agronomic data including leaf area index, growth stage parameters, and plant height. To further inform these field research efforts, a meta-analysis evaluating the effect of deficit irrigation on water use efficiency and water productivity in cotton cropping systems across the United States is being conducted and preliminary results of this synthesis will be presented at the the ASA-CSSA-SSSA International Annual Meeting (November 8-13, 2025) in Salt Lake City, UT (Yupanqui et al., 2025). Forage sorghum: In 2025, with funding from the Ogallala Aquifer Program, a new study titled smart forage sorghum: precision sensing for optimized irrigation water management was established to identify multiple site-specific water stress indicators for forage sorghum based off soil moisture, canopy temperature, and weather data as well as to quantify economic feasibility of transitioning to irrigated forage sorghum production in water limited environments. The data collected from the field plots included crop agronomic parameters, soil moisture, vegetation indices, plant photosynthetic activity as well as yield and assessment for forage nutritive value and digestibility. The study was conducted under two irrigation levels and also included a dry land treatment. Preliminary results indicate higher range nitrate levels in the dry land and deficit irrigated treatments limiting the use of this forage to 35-40% of ration dry matter and the forage within this quality range is not recommended in expecting animals. With the growth of the dairy industry in the Texas High Plain region, this research will fill the knowledge gap of forage sorghum response to water stress conditions as measured across a spectrum of water stress indicators including soil moisture deficit, crop canopy temperature, and environment driven crop water demand. <h2>US Bureau of Reclamation</h2>   <h2>Utah</h2> Subsurface Drip Irrigation Water Use: Three studies of subsurface drip irrigation (SDI) water use continued in 2024-2025. One is nearing completion using a soil water balance to compare yield, evapotranspiration (ET), and applied irrigation water between SDI- and conventional- (sprinkler or surface) irrigated fields in the Great Salt Lake Basin of Northern Utah. Preliminary results are forthcoming. A second study is also nearing completion in Northern Utah comparing ET and yield from a commercial SDI and a commercial wheel line field. In this study, eddy covariance and remote sensing models are being used to quantify ET. Preliminary results indicate greater ET in SDI that the wheel line. A third, more controlled study is being conducted in the Colorado River Basin in Eastern Utah comparing applied irrigation, ET, deep percolation, and yield from SDI and a center pivot. Results are forthcoming for that project. Wheel Line Irrigation: A study of irrigation water use, yield, and ET for wheel line irrigation systems has been conducted in 2024 and 2025 at nine locations throughout Utah. Preliminary results suggest that irrigation performance is similar between impact and rotator sprinklers. Wobbler sprinklers require significant adjustments to management to be effective. Yield results indicate no significant differences in yield. Grapes: Work continues investigating wine grape water use and water management in Utah. This is a relatively new crop (last 10+ years) in Utah. It is unclear how well management methods form other locations translate to the high-elevation climates of Utah. Water Management Outreach to Farmers: The AG-DRIP (Ag water Demonstration, Research, and Implementation Program), which began in 2023 has expanded to over 80 enrolled fields (<a href="https://extension.usu.edu/ag-drip/">https://extension.usu.edu/ag-drip/</a>). Participants receive a flow meter, soil moisture sensors, an irrigation evaluation, a weather station, and funds for an alternative crop seed. They must develop and follow up on an irrigation water management plan for five years as part of the program. Many participants report that the measurements have been beneficial for their operations. Water Balance Monitoring Network: A pilot has been initiated for the Utah Ag WaTER Net (Ag Water Teaching, Research, and Extension Network; <a href="https://extension.usu.edu/irrigation/ag-water-net">https://extension.usu.edu/irrigation/ag-water-net</a>). The goal is to establish several long-term observatories for full field water balance with data updated in near real time. Each site will include two or three production-scale management treatments. Water Optimization Research: Work continues on long-term plot research investigating combinations of land management (cover crops, etc.), crop types (alfalfa, maize, alternatives), center pivot sprinkler methods (e.g., LEPA vs. mid-elevation sprinklers), and irrigation rates (full vs. deficit) at three sites throughout Utah. Results have been published in several journal manuscripts. One current focus is on alternative forage crops (e.g., small grains, teff, etc.). Utah Water Manager Opinions: Work on the attitude of Utah irrigation canal company managers regarding water markets has recently been published. This provides insight to state agencies regarding impacts and challenges regarding water management at the middle scale (between the State and the farm). <h2>Washington</h2>   <h2>USDA-ARS-Texas</h2> Upland cotton (Gossypium hirsutum L.) production requires less irrigation compared with other crops and thus provides an opportunity to reduce risk and maintain profitability in areas where water is limited. Water use, canopy temperature, lint yield, and crop water productivity were evaluated for four early to medium maturity upland cotton cultivars under three levels (100%, 66%, and 33%) of alternate furrow subsurface drip irrigation (SDI) in a thermally limited environment (Schwartz et al., 2024). Crop evapotranspiration (ET) across cultivars and years averaged 627, 547, and 467 mm for the 100%, 66%, and 33% irrigation levels, respectively, and did not differ among cultivars (P > 0.05). Crop water use during boll maturation, as inferred from the developed crop coefficient curve, was considerably less than reported by other studies, signifying that irrigation could be terminated earlier without reducing lint yield. The cultivar effect on lint yield was significant in all study years (P < 0.001), but only at the 66% and 100% irrigation levels, with one cultivar exceeding the average yields of all evaluated cultivars by 13% across the three study years. Medium maturity cultivars usually yielded less than early maturity cultivars, especially for a year with less accumulation of thermal energy. Crop selection and late season irrigation water management were both key to improving cotton water productivity. The fitted FAO-56 crop coefficient for the initial stage (Kc-ini) under SDI was 0.38) and similar to values reported for surface drip irrigation. In this experiment, much of the benefit of SDI was lost for the alternate furrow SDI because of the need to wet the seed zone and establish the crop with a considerable amount of irrigation in most growing seasons. This result is in contrast to other studies at Bushland for flat planted cotton with SDI in alternate inter-rows, where Kc-ini values were much smaller, leading to the observation that bed and furrow cultivation is unnecessary and counterproductive for SDI. The fitted mid-season crop coefficient (1.19) was similar to the tabulated FAO-56 value; however, the duration of this period was short (20 d) and began well after first flower, extending from a couple days before cutout to 18 days after cutout. The late season crop coefficient converged to values near zero and was considerably smaller than reported by other studies or the FAO-56 tabulated value. This suggests that water savings could be achieved by reducing irrigation during boll maturation. Marek et al. (2023) reported on a combined ARS-Texas A&M AgriLife experiment to evaluate conventional TDR sensors, neutron probe, and a downhole TDR-based sensor system in field plots with three levels of irrigation. They found that the conventional TDR sensors and neutron probe provided equivalent profile water content data while the downhole TDR sensor underestimated profile water content in most conditions and could overestimate immediately following irrigation. Forage sorghum was planted on four large, precision weighing lysimeters and surrounding four 4.4-ha fields in 2025, two of which were irrigated with subsurface drip irrigation (SDI) and two of which were irrigated using low elevation spray application (LESA). Irrigation management aimed to fully irrigate the crop, which resulted in differing amounts of irrigation due to decreased evaporative losses with SDI. Crop coefficients and crop water productivity metrics will be developed once the crop is harvested and data are analyzed. Evett et al. (2025) and Colaizzi et al. (2025) developed alternative methodologies for analysis and quality control of weighing lysimeter data to develop accurate ET data at daily and sub-daily time steps. Evett et al. (2022b; 2023a,b; 2024b) provided crop growth, yield, and water use data from weighing lysimeter experiments with full and deficit irrigation for cotton, sorghum, and maize on the USDA ARS NAL Ag Data Commons internet site. Also provided was a spreadsheet for lysimeter data analysis with advanced algorithms for reduction of lysimeter water storage data to ET values (Evett et al., 2024a).       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. <h2>Arizona</h2>   <h2>California</h2>   <h2>Colorado</h2>   <h2>Florida</h2> Florida - Bayabil’s Water Resource Research Lab: Variable rate irrigation technologies integrated with UAV-based monitoring, AI-driven decision support, and sensor-based automation were systematically evaluated. Advanced soil moisture sensors capable of simultaneously measuring volumetric moisture content and soil water potential, thus enabling the development of soil moisture characteristic curves, were tested in collaboration with a start-up company. These state-of-the-art sensors feature fully wireless connectivity and provide continuous monitoring of volumetric moisture content, water potential, electrical conductivity, and soil temperature. UAV platforms equipped with RGB and thermal sensors were deployed to characterize field-scale variability in crop water stress. Technical activities included UAV data acquisition campaigns, sensor calibration and deployment, algorithm development, and the testing of automated control workflows. Demonstration events and hands-on sessions with collaborators and stakeholders were conducted. <h2>Kansas</h2>   <h2>Louisiana</h2> The primary goal of this project is to evaluate the water conservation potential and economic impacts of transitioning furrow-irrigated rice (i.e., row rice) toward drip-irrigated rice while maintaining or improving yield. Demonstration to irrigators of drip technology for use in production agriculture is the secondary goal. To best integrate with mid-South production systems, drip lines were placed on top of the soil surface but built into each row prior to the 2025 crop season. These lines were connected to 11-row sub-mains to define each plot and included flow measurement, pressure regulation, and valve control to automate irrigation treatments. Each block contained four plots operated by the same riser and three risers operated off one water source. Each riser also utilizes flow measurement, pressure regulation, and filtration. Thus, the randomized block design included three replications of four treatments that were designed to apply 75%, 100%, and 125% of daily evapotranspiration rates compared to a generic timed treatment that applied 10 mm per day. While work was conducted to initiate this project, early drought conditions combined with time consuming infrastructural limitations resulted in ineffective weed control causing the project to be terminated early. While results could not be determined as designed, the installation has provided opportunities for education through extension programming and training during the crop season. Agricultural water management (AWM) best management practices (BMPs) can provide benefits to crop productivity, farm management, and downstream water quality by managing both irrigation and drainage to realize full agroecosystem benefits. Gravity-fed irrigation systems can create significant drainage volumes from both surface runoff and deep percolation. However, artificial drainage technologies, such as tile drainage, have not been evaluated as part of AWM 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 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. Both installations began in 2024 and infrastructure changes continued in 2025. Thus, treatments are expected to start in 2026. <h2>Michigan</h2> MSU, Dong Irrigation Lab The MSU Irrigation Team continues to focus on improving irrigation system efficiency, particularly for aged systems, through comprehensive system evaluations. These evaluations include uniformity testing, visual inspections, and assessments of the electrical panel and grounding. Working with five cooperating farmers, the team has demonstrated significant potential for water and energy savings by upgrading sprinkler packages. With support from MSU Project GREEEN, several center pivot irrigation systems were evaluated and retrofitted. The results showed an average 20% increase in the coefficient of uniformity, leading to water savings of 1.2–3.3 inches per acre per season. For a 100-acre irrigated field, this translates to an annual savings of 3–9 million gallons of water and $420–$1,617 in energy costs.   <h2>Minnesota</h2>   <h2>Montana</h2>   <h2>Nebraska</h2> In Abia’s Irrigation Water Management Lab: Through his extension efforts with the signature outreach program called Mobile Irrigation Testing Lab (MITLab), irrigation systems mainly center pivots are being tested and evaluated on the application efficiency using traditional catch-can test and Unmanned Aerial Vehicles (UAVs). In 2025, the program worked with five cooperating farmers whose center pivots that are only planted with soybeans were evaluated. The evaluation results indicated that 90% of pivots demonstrated efficiency above 90%, 2% ranged between 85 - 90%, and lastly, 8% were below 85%. Pivots with high efficiency (> 85%) had the set depth similar to the amount collected in the catch-cans and all the sprinkler packages were properly designed and none was damaged or clogged. While those with less efficiency (<85%) had old sprinkler packages or some sprinklers on some spans were either damaged or clogged which impacted the results. Furthermore, UAVs proved to be accurate when compared to catch-can tests using vegetation indices that are sensitive to crop water stress or crop growth or performance like Normalized Difference Red Edge (NDRE) and Normalized Vegetation Index (NDVI). Farmers with lower performing efficiency have been advised to repair the faulty sprinklers or get new sprinkler packages to be retrofitted on their systems. We would have evaluated many pivots but some of them were planted with corn and this makes it a challenge to collect accurate results in the season due to high interceptions by the crop’s canopy since the majority of the sprinklers are placed on top of the pivots. <h2>Oklahoma</h2> N/a <h2>Tennessee</h2>   <h2>Texas</h2> N/A for Xue N/A for Nakabuye <h2>US Bureau of Reclamation</h2>   <h2>Utah</h2>   <h2>Washington</h2>   <h2>USDA-ARS-Texas</h2> USDA ARS scientists collaborated with University of Nevada Reno to further develop a sensor-based decision support system (ISSCADA) for variable rate irrigation (Andrade et al., 2023b), with two major improvements: 1) Making the system work with VRI systems from all manufacturers; and 2) Improving and developing sensor systems for GPS positioning and plant growth. Work with the University of Nebraska demonstrated the useful combination of the ISSCADA system with a spatial ET modeling interface using soil water sensing for management of variable rate maize and soybean irrigation in Nebraska (Bhatti et al., 2023). Soto et al. (2024) evaluated three irrigation application methods for watermelon production over three years in the semi-arid environment of Bushland, Texas, and found that mobile drip irrigation (MDI) produced greater yields and crop water productivity than did low elevation spray application (LESA) or surface drip irrigation under plastic mulch. Watermelon had more fruits per plant (1.9) under MDI than under LESA (1.4). USDA ARS scientists collaborated with scientists nationally and internationally to evaluate multi-model averaging approaches for ensemble ET and yield from maize models (Nand et al., 2025). The OPENET system (<a href="https://etdata.org/">https://etdata.org/</a>) now offers ET data on a 30-m resolution for the western USA. USDA ARS scientists evaluated OPENET data for 2022 and 2023 using weighing lysimeter ET data and found overall underestimation of corn and cotton ET by OPENET. USDA ARS scientist collaborated with Washington State University to compare ET estimated from crop model data-fusion and satellite data-based ET models with weighing lysimeter ET (Stöckle et al., 2025) under both SDI and LESA irrigation. They found that a mechanistic modeling approach using local weather data was superior to the remote sensing-based models, which included OPENET. Collaboration with the Agricultural Model Intercomparison and Improvement Project (AgMIP) Maize modeling group showed that 41 maize crop growth and water use models tended to underestimate maize ET over several seasons at Bushland, Texas (Kimball et al., 2023); and further comparison of 33 models showed that soil temperature was better predicted by models with a mechanistic solution of the soil physics (Kimball et al., 2024). Collaboration with the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat group involved comparing ET estimates from 13 unique models with three seasons of winter wheat ET data from the Bushland weighing lysimeters under deficit and full irrigation and eddy covariance ET data from a 12-year crop rotation in Avignon, France (Webber et al., 2025). The model median underestimated ET in both the Bushland and Avignon semi-arid environments. Results indicated that atmospheric ET demand simulation must be improved in order to properly assess impacts of drought or water resource curtailments. O’Shaughnessy et al. (2023a) reported on improved cotton water productivity when using mobile drip irrigation technology compared with low elevation spray application. They also reported on using the ISSCADA system to irrigation cotton in two years. In 2021, the irrigation savings was a minimum of 20%, while in 2022, the minimum savings was 16% without reducing yield (O’Shaughnessy et al. (2023b).       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. <h2>Arizona</h2>   <h2>California</h2>   <h2>Colorado</h2>   <h2>Florida</h2> Florida - Bayabil’s Water Resource Research Lab: Extension activities were carried out to promote efficient irrigation through the development and dissemination of multi-state educational materials focused on variable rate irrigation (VRI) and precision irrigation technologies. As part of this effort, comprehensive fact sheets were prepared to explain variable rate technology (VRT) and its applications in irrigation, fertilizers, and other inputs across different production systems (<a href="https://edis.ifas.ufl.edu/publication/AE607">https://edis.ifas.ufl.edu/publication/AE607</a>). A step-by-step extension guide was also developed to support the practical implementation of VRI systems in the field (<a href="https://edis.ifas.ufl.edu/publication/AE609">https://edis.ifas.ufl.edu/publication/AE609</a>). These fact sheets were adapted and shared across multiple states to ensure consistency of messaging while allowing for region-specific tailoring based on soil, crop, and irrigation system characteristics. Webinars, training sessions for extension agents, and grower field days were organized to present the concepts of map-based and sensor-based VRT, management zone delineation, prescription map development, and system operation. <h2>Kansas</h2>   <h2>Louisiana</h2> N/A <h2>Michigan</h2> MSU, Dong Irrigation Lab The Michiana Irrigated Corn and Soybean Conference is an annual effort between MSU Extension and Purdue Extension to provide education and networking opportunities that address facets of growing corn and soybeans under irrigation. Counties along the Michigan Indiana border (referred to as Michiana) have a higher percentage of irrigated acres than counties in other parts of those states. In 2024, 53 attendees were from Indiana and 74 were from Michigan. The 2024 program included Extension faculty and educators from both MSU and Purdue, including Darcy Telenko, PhD (Purdue), Erin Burns, PhD (MSU), Chris Difonzo, PhD (MSU), Stephen Boyer (Purdue), Shan Casteel, PhD (Purdue), Jeff Burbrink (Purdue), Mike Staton (MSU), Lyndon Kelley (MSU/Purdue), and Younsuk Dong, PhD (MSU). The cost of the event is kept low due to the continued generous support from the corn and soybean checkoff programs of Indiana and Michigan. Certified crop adviser, Michigan restricted use pesticides, Indiana continuing certification hours and Indiana pesticide applicator recertification program credits were provided through the conference. Each year, a post-meeting survey is given to measure the impact of the education provided. Of the 68 people who returned the survey, 96% indicated their knowledge increased and 66% planned to change some aspect of their farm operation or recommendations to producers as a result of the information presented. These changes were estimated to impact 42,976 acres and result in $492,635 in increased revenue or savings, or an average of $7,245 per respondent. A 10-month follow-up survey is also sent to participants to see what changes were made during the growing season due to information provided at the meeting. Of the 33 surveys returned, 52% indicated they had changed some aspects of their operation or recommendations to producers during the growing season. These changes were estimated to impact 16,135 acres and result in $167,100 in increased revenue or savings, or an average of $5,064 per respondent. <h2>Minnesota</h2> UMN, Sharma Irrigation Group We successfully delivered the fourth season of the Minnesota Irrigator Program (MIP) in 2024, and the 2025 program is scheduled to be held on November 18 and 19, 2025.  The overall goal of this program is to provide intensive training for irrigators and agricultural professionals on irrigation practices that can conserve water and limit irrigation’s impact on groundwater and surface water quality. We provide this training through an annual educational course consisting of three non- consecutive training days in late spring every year. For 2024, we delivered a three-day program at Central Lakes College, Staples, Minnesota, in March 2024. We saw 100% attendance from 25 participants (From MN and ND). We also formed an advisory committee (20-30 individuals) from key stakeholder groups, including the Minnesota Department of Agriculture, NRCS, Irrigator Association of Minnesota, Irrigation industry professionals, etc., to gather inputs on the course content and speakers. We collaborated with MDA’s Minnesota Agricultural Water Quality Certification Program (MAWQCP). The participants from the MIP can earn an “Irrigation Endorsement” for the MAWQCP, and the endorsement will help them apply for implementation funding with MDA. In addition, we have received funding from the Minnesota Department of Agriculture to execute this program. The post-event survey indicated a substantial increase in knowledge after the course attendance and indication of practice implementation. The post-event survey showed that the participants either helped manage or directly managed a minimum of 27,000 irrigated acres, with 87% of participants responding to change some of their irrigation practices based on what they learned in the program. We also hosted a summer field day at the Sand Plain Research farm on July 16th, 2024. Approximately 35 people (farmers, students, post-docs, extension educators, and state agency personnel) attended the field day, where 8 speakers from the University of Minnesota and University of North Dakota. This event aimed to provide a platform for growers, consultants, state agencies, industry, and researchers to come together and learn about cutting-edge research happening at the University and ask the researchers questions directly. Through the post-event surveys, it was evident that attendees had a great experience, participated in the discussions, and became aware of efficient agricultural management practices that have the potential to improve crop production and, at the same time, prevent agriculture induced environmental pollution. About 50% of the respondents indicated changing some of their farming practices based on the knowledge they gained through this program.   <h2>Montana</h2> N/A <h2>Nebraska</h2> In Abia’s Irrigation Water Management Lab: Through his signature outreach program called Mobile Irrigation Testing Lab (MITLab), he is continuing to create awareness of collecting and using data on the farms to support effective irrigation-decision making. Additionally, the program is exposing farmers to advanced irrigation technology to improve irrigation management on the farms. Technology is installed and farmers are trained on how to use the technology to inform data-driven irrigation decisions. Through this process, the program has facilitated experiential learning  opportunities where farmers directly interact with extension educators and specialists to build relationships. As a result, this has helped farmers to build confidence and trust in using data for their irrigation decisions. Different technologies spanning from soil moisture sensors to ET-based irrigation decision support tools (apps/web-based dashboards) are being promoted in collaboration with industry companies and state water regulatory bodies - Twin Platte Natural Resource District (TPNRD). The program has so far engaged 20 farmers and over 15,000 irrigated acres for the first time have been managed with data. Farmers have shared their testimonies of cutting at least 2 inches of irrigation water after receiving the technology and are willing to adjust their irrigation practices. Of all the participating farmers, 98% have never used any technology and 2% tried but stopped because of less support from extension or industry. <h2>Oklahoma</h2> N/a <h2>Tennessee</h2> N/A <h2>US Bureau of Reclamation</h2>   <h2>Utah</h2>   <h2>Washington</h2>   <h2>USDA-ARS-Texas</h2> The USDA-ARS team at Bushland, Texas, developed quality assurance (QA) and QC procedures for weighing lysimeter data from the four large weighing lysimeters at Bushland, as well as for research weather data compiled from a grassed research weather station, four large weighing lysimeters and a U.S. Weather Service station at Bushland. We applied these procedures to produce 32 years of quality 15-minute, 365-day weather and lysimeter ET data that were shared on the USDA National Agriculture Library Ag Data Commons (Evett et al., 2022a). Infrared thermometers (IRTs) are now commonly used to monitor crop water status, and Colaizzi et al. (2023) reported methods for IRT data quality control.

Publications

Attalah, S., Elsadek, E. A., Waller, P., Hunsaker, D. J., Thorp, K. R., Bautista, E., Williams, C., Wall, G. W., Orr, E. & Elshikha, D. E. M. (2025). Evaluation and Comparison of OpenET Models for Estimating Soil Water Depletion of Irrigated Alfalfa in Arizona. Agricultural Water Management, 320, 1 November 2025, 109850. <a href="https://doi.org/10.1016/j.agwat.2025.109850">https://doi.org/10.1016/j.agwat.2025.109850</a>  Bhatti, S., D. M. Heeren, S. A. O’Shaughnessy, C. M. U. Neale. J. LaRue, S. Melvin, E. Wilkening, and G. Bai. (2023) Toward automated irrigation management with integrated crop water stress index & spatial soil water balance. Precision Agriculture.<a href="https://doi.org/10.1007/s11119-023-10038-4"> https://doi.org/10.1007/s11119-023-10038-4</a> Colaizzi, P. D., O’Shaughnessy, S. A., Evett, S. R., Marek, G. W., Brauer, D., Copeland, K. S., & Ruthardt, B. B. (2023). Data quality control for infrared thermometers viewing crops. Appl. Eng. Agric. 39(4): 427-438.<a href="https://doi.org/10.13031/aea.15642"> https://doi.org/10.13031/aea.15642</a>.   Colaizzi, P. D., Marek, G. W., Evett, S. R., Copeland, K. S., & Ruthardt, B. B. (2025) Algorithms to process weighing lysimeter data. Journal of the ASABE (in press).<a href="https://doi.org/10.13031/ja.16404"> https://doi.org/10.13031/ja.16404</a> Evett, Steven R.; Copeland, Karen S.; Ruthardt, Brice B.; Marek, Gary W.; Colaizzi, Paul D.; Howell, Terry A., Sr.; Brauer, David K. (2022a). Standard quality controlled research weather data - USDA-ARS, Bushland, Texas. USDA ARS NAL Ag Data Commons.<a href="https://doi.org/10.15482/USDA.ADC/1526433"> https://doi.org/10.15482/USDA.ADC/1526433</a> Evett, S.R., Marek, G.W., Copeland, K.S., Howell, T.A. Sr., Colaizzi, P.D., Brauer, D.K. (2022b). Weighing Lysimeter Data for The Bushland, Texas, Cotton Datasets. Ag Data Commons. Dataset. <a href="https://doi.org/10.15482/USDA.ADC/25114670.v1">https://doi.org/10.15482/USDA.ADC/25114670.v1</a> Evett, S.R., Marek, G.W., Copeland, K.S., Howell, T.A. Sr., Colaizzi, P.D., Brauer, D.K. (2023a). Agronomic Calendars for the Bushland, Texas Cotton Datasets. Ag Data Commons. Dataset. <a href="https://doi.org/10.15482/USDA.ADC/1529368">https://doi.org/10.15482/USDA.ADC/1529368</a> Evett, S.R., Marek, G.W., Copeland, K.S., Howell, T.A. Sr., Colaizzi, P.D., Brauer, D.K. (2023b). Growth and Yield Data for the Bushland, Texas, Cotton Datasets. Ag Data Commons. Dataset. <a href="https://doi.org/10.15482/USDA.ADC/1529408">https://doi.org/10.15482/USDA.ADC/1529408</a> Evett, S. R., Marek, G. W., Colaizzi, P. D., Copeland, K. S., & Ruthardt, B. B. (2024a). Spreadsheet for lysimeter data analysis, Bushland, Texas. Ag Data Commons. Software. https://doi.org/10.15482/USDA.ADC/26898151.v1 <a href="https://doi.org/10.15482/USDA.ADC/26898151.v1">https://doi.org/10.15482/USDA.ADC/26898151.v1</a> Evett, S.R., Marek, G.W., Copeland, K.S., Howell, T.A., Colaizzi, P.D., Ruthardt, B.B. (2024b). Weighing lysimeter data for the Bushland, Texas, sorghum datasets. Ag Data Commons. https://doi.org/10.15482/USDA.ADC/25114610.v1. <a href="https://doi.org/10.15482/USDA.ADC/25114610.v1">https://doi.org/10.15482/USDA.ADC/25114610.v1</a> Evett, S. R., Colaizzi, P. D., Marek, G. W., Copeland, K. S., & Ruthardt, B. B. (2025a). Analysis and quality control of weighing lysimeter water storage data, Agricultural Water Management, 317,2025,109674,<a href="https://doi.org/10.1016/j.agwat.2025.109674"> https://doi.org/10.1016/j.agwat.2025.109674</a> Evett, S. R., Marek, G. W., Colaizzi, P. D., Copeland, K. S., Ruthardt, B. B. & Howell, T. A. Sr. (2025b). The Bushland, Texas, maize evapotranspiration, growth, and yield dataset Collection. Sci Data 12, 209 (2025).<a href="https://doi.org/10.1038/s41597-025-04539-2"> https://doi.org/10.1038/s41597-025-04539-2</a> Kimball, Bruce A., Kelly R. Thorp, Kenneth J. Boote, Claudio Stockle, Andrew E. Suyker, Steven R. Evett, et al. (2023). Simulation of evapotranspiration and yield of maize: An Inter-comparison among 41 maize models, Agricultural and Forest Meteorology, Volume 333, 2023, 109396, ISSN 0168-1923,<a href="https://doi.org/10.1016/j.agrformet.2023.109396"> https://doi.org/10.1016/j.agrformet.2023.109396</a>. Marek, G.W., Evett, S.R., Marek, T.H., Porter, D.O., Schwartz, R.C. 2023. Field evaluation of conventional and downhole TDR soil water sensors for irrigation scheduling in a clay loam soil. Applied Engineering in Agriculture. 39(5):495-507.<a href="https://doi.org/10.13031/aea.15574"> https://doi.org/10.13031/aea.15574</a> Nand, V., Qi, Z. Ma, L., Helmers, M. J., Madramootoo, C. A., Smith, W. N., Zhang, T., Weber, T. K. D., Pattey, E., Li, A., Wang, J., Jin, V. L., Jiang, Q., Tenuta, M., Trout, T. J., Cheng, H., Harmel, R. D., Kimball, B. A., Thorp, K. R., Boote, K. J., Stockle, C., Suyker, A. E., Evett, S. R., Brauer, D. K., Coyle, G. G., Copeland, K. S., Marek, G. W., Colaizzi, P. D., Acutis, M., Alimagham, A. M., Archontoulis, S., Babacar, F., Barcza, Z., Basso, B., Bertuzzi, P., Constantin, J., Migliorati, M. D. A., Dumont, B., Durand, J.-L., Fodor, N., Gaiser, T., Garofalo, P., Gayler, S., Giglio, L., Grant, R., Guan, K., Hoogenboom, G., Kim, S.-H., Kisekka, I., Lizaso, J., Masia, S., Meng, H., Mereu, V., Mukhtar, A., Perego, A., Peng, B., Priesack, E., Shelia, V., Snyder, R., Soltani, A., Spano, D., Srivastava, A., Thomson, A., Timlin, D., Trabucco, A., Webber, H., Willaume, M., Williams, K., van der Laan, M., Ventrella, D., Viswanathan, M., Xu, X., Zhou, W, 2025. Evaluation of multimodel averaging approaches for ensembling evapotranspiration and yield simulations from maize models, Journal of Hydrology, 2025, 133631,<a href="https://doi.org/10.1016/j.jhydrol.2025.133631"> https://doi.org/10.1016/j.jhydrol.2025.133631</a> O'Shaughnessy, S.A., Colaizzi, P.D. (2023a). Improving water productivity in cotton using mobile drip irrigation technology. Pp. 16-25 In Managing Limited Groundwater and Surface Water Supplies to Meet Irrigation Demands - Proceedings of the U.S. Committee on Irrigation and Drainage Conference, October 17-20, 2023, Ft. Collins, Colorado. US Committee on Irrigation and Drainage.<a href="https://www.uscid.org/_files/ugd/9996e6_4aa59ee97cfa4569ad02521d61b0a2b0.pdf"> https://www.uscid.org/_files/ugd/9996e6_4aa59ee97cfa4569ad02521d61b0a2b0.pdf</a> O'Shaughnessy, S.A., Colaizzi, P.D., Bednarz, C.W. (2023b). Sensor feedback system enables automated deficit irrigation scheduling for cotton. Frontiers in Plant Science. 14:1-14.<a href="https://doi.org/10.3389/fpls.2023.1149424"> https://doi.org/10.3389/fpls.2023.1149424</a>. Leiva Soto, A., Shrestha, R.,Xue, Q., Colaizzi, P., O’Shaughnessy, S., Workneh, F., Adhikari, R., & Rush, C. (2024). Evaluation of three irrigation application systems for watermelon production in the Texas High Plains. Agronomy Journal, 116 (5), 2535-2550.<a href="https://doi.org/10.1002/agj2.21653"> https://doi.org/10.1002/agj2.21653</a> Schwartz, R. C., Witt, T. W., Ulloa, M., Colaizzi, P. D., & Baumhardt, R. L. (2024). Irrigation response, water use, and lint yield of upland cotton cultivars. Journal of the ASABE. 67(2):421-437.<a href="https://doi.org/10.13031/ja.15868"> https://doi.org/10.13031/ja.15868</a> Stöckle, C. O., Liu, M., Kadam, S. A., Evett, S. R., Marek, G. W., & Colaizzi, P. D. (2025). Comparing evapotranspiration estimations using crop model-data fusion and satellite data-based models with lysimetric observations: Implications for irrigation scheduling. Agri. Water Manage. 311, 109372.<a href="https://doi.org/10.1016/j.agwat.2025.109372"> https://doi.org/10.1016/j.agwat.2025.109372</a> Subedi, S., Kechchour, A., Kantar, M., Sharma, V., & Runck, B. C. (2025). Can gridded real-time weather data match direct ground observations for irrigation decision-support? Agrosystems Geosciences and Environment, 8(2).<a href="http://dx.doi.org/10.1002/agg2.70100">  doi: 10.1002/agg2.70100</a> Webber, H., Cooke, D., Wang, C., Martre, P., Asseng, S., Ewert, F., Kimball, B., Hoogenboom, G., Evett, S., Chanzy, A., Garrigues, S., Olioso, A., Copeland, K.S., Steiner, J.L., Cammarano, D., Chen, Y., Crépeau, M., Ferrise, R., Manceau, L., Gaiser, T., Gao, Y., Gayler, S., Guarin, J.R., Hunt, T., Jégo, G., Padovan, G., Pattey, E., Ripoche, D., Rodríguez, A., Ruiz-Ramos, M.,, Shelia, V., Srivastava, A.K., Supit, I., Tao, F., Thorpe, K., Viswanathan, M., Weber, T. , White, J. (2025). Wheat crop models underestimate drought stress in semi-arid and Mediterranean environments. Field Crops Research, 332, 2025, 110032,<a href="https://doi.org/10.1016/j.fcr.2025.110032"> https://doi.org/10.1016/j.fcr.2025.110032</a>. Shrestha, R., S Thapa, Q Xue, J Bell, R Aiken, K Jessup, C Naylor, William Rooney, Thomas Marek. 2025. Biomass yield and water‐use efficiency in photoperiod‐sensitive sorghum genotypes in the US Southern Great Plains. Agrosystems, Geosciences & Environment, 8 (3), e70172. Shrestha, R., Q Xue, AL Soto, G Ganjegunte, SS Palmate, VN Chaganti, S Kumar, A L Ulery, S Zapata. 2025. Plant Traits in Spring and Winter Canola Genotypes Under Salinity. Agronomy 15 (7), 1657.  Zhang, L., G Bai, SR Evett, PD Colaizzi, Q Xue, G Marek, R Dhungel, H Zhao, N Wan, X Lin. 2025. Increased irrigation could mitigate future warming-induced maize yield losses in the Ogallala Aquifer. Communications Earth & Environment 6 (1), 483. Shrestha, R, Q Xue, A Leiva Soto, G Ganjegunte, SS Palmate, VN Chaganti, S Kumar, AL Ulery, RP Flynn, S Zapata. 2025. Seedling emergence in winter and spring canola genotypes under salinity stress. Crop Science 65 (2), e70011.  Ajaz, A, TA Berthold, Q Xue, S Jain, B Masasi, Q Saddique. 2024. Free weather forecast and open-source crop modeling for scientific irrigation scheduling: proof of concept. Irrigation Science 42 (2), 179-195. Verdi, A., Singh, A., Sapkota, A.,  Ghodsi, S. (2024). Assessing The impact of water conservation on cooling potential of two turfgrass species. Journal of the ASABE. 67(3): 749-759. Iradukunda, J.C., Verdi, A. (2024). Evaluating the Tradeoffs Between Water Conservation, Aesthetic Value, Evaporative Cooling and CO2 Emissions in St. Augustinegrass and Buffalograss. Agricultural Water Management, 305 (2024) 109117. Adhikari, S., Dalen, M., Torrion, J. A., & Sapkota, A. (2025a) Estimating Actual Evapotranspiration of Spring Wheat for Efficient Irrigation [Abstract]. CANVAS 2025, Salt Lake City, UT. https://scisoc.confex.com/scisoc/2025am/meetingapp.cgi/Paper/166472 Adhikari, S., Dalen, M., Torrion, J. A., & Sapkota, A. (2025b) Evaluating the Impact of Irrigation Rates on Soil Respiration and Physiology of Spring Wheat [Abstract]. CANVAS 2025, Salt Lake City, UT. https://scisoc.confex.com/scisoc/2025am/meetingapp.cgi/Paper/166456   Emerson, L., Guan, W., Camberato, J., Dong, Y., (2024). Effects of Irrigation and Fertigation on Seedless Watermelon Yield in Southern Indiana. HortTechnology. 34(5) 604-612.    Ali, N., Dong, Y., Lavely, E. (2024). Impact of irrigation scheduling on yield and water use efficiency of apples, peaches, and sweet cherries: a global meta-analysis. Agricultural Water Management. 306, 109148.  Kelley, B., Ali, N., Dong, Y., (2025). Methods to correct temperature-induced changes of soil moisture sensors to improve accuracy. MethodX. 14, 103100.  Rose, T., Ali, N., Dong, Y., (2025). Design and development of an IoT-based dendrometer system for real-time trunk diameter monitoring of Christmas trees. Smart Agricultural Technology. 10, 100765.  Ali, N., Dong, Y., Rouland, G., (2025). Estimation of Deep Percolation in Agricultural Soils Utilizing a Weighing Lysimeter and Soil Moisture Sensors. Science of The Total Environment. 969, 178974.  Wade, C., Check, J., Chilvers, M., Dong, Y., (2025). Monitoring leaf wetness dynamics in corn and soybean fields using an IoT (Internet of Things)-based monitoring system. Smart Agricultural Technology. 11, 100919.  Kelley, B., Dong, Y., Chilvers, M., Das, N., (2025). Understanding the Impact of Irrigation Scheduling on Water Use Efficiency in Corn and Soybean Production in Humid Climate: Insights from On-Farm Demonstration. Frontiers in Agronomy. 7, 1496198.  Rai, A., Dong, Y. (2025). A scoping review of irrigation scheduling methods in potato (Solanum tuberosum L.) production. American Journal of Potato Research. 102. 348-371.  Rai, A., Ali, N., Dong, Y., AquaCrop modeling for sustainable potato irrigation: trade-offs between yield and crop water productivity. Frontiers in Plant Science. 16. 24099.  Dong, Y., Tucker, S., Singh, G., Ali, N., Yazdanpanah, N., Vander Wide, J., Sears, M., (2025). Optimizing Soil Moisture Sensor Placement Through Spatial Variability Analysis in Orchards. Smart Agricultural Technology. 10. 101273. Yzdanpanah, N., Dong, Y., (2025). Calibration and performance evaluation of Internet of Things-based soil moisture sensors under saline irrigation conditions, Smart Agricultural Technology. 12. 101451.

Impact Statements

University of Arizona research improved crop yields and water efficiency through advanced irrigation, salinity management, and precision tools. Statewide adoption of these strategies boosts sustainability, resilience, and supports WERA 1022’s mission in the western U.S. Extensive outreach and publications ensured growers can apply these practices effectively, enhancing long-term agricultural productivity.
The Verdi Water Management Group at UCR focuses on developing practical solutions and science-based tools to support sustainable water management in both urban landscapes and agricultural systems.
The Verdi Water Management Group at UCR focuses on developing practical solutions and science-based tools to support sustainable water management in both urban landscapes and agricultural systems.
Sapkota’s research group at Montana State University aims to promote the adoption of data-driven irrigation scheduling to optimize water use while enhancing crop yield, quality, and environmental sustainability. The research also focuses on evaluating emerging irrigation sensing technologies and developing innovative solutions for achieving optimal irrigation management.
Abia’s program continues to explore irrigation strategies that rely on data to improve water use efficiency on the farms while increasing farm yields and profits. Research and extension projects are focusing on developing and testing irrigation decision support tools that are applicable to farmers and can provide accurate information to reduce groundwater withdrawals from Ogallala aquifer to enhance water conservation.
Oklahoma State University - Sumon Datta, Irrigation Research Laboratory We have not measured the impact of OASIS tool yet as it is under beta-testing. We will collect KPI related to this tool from beta-users from next summer 2026.
The 32-years of Bushland large weighing lysimeter ET, crop yield, soil water content, and corresponding weather data sets are widely used for model development, intercomparison, and improvement. Examples are multiple papers from the Agricultural Model Intercomparison and Improvement Project (AgMIP) Maize modeling group and Winter Wheat modeling group, as well as multi-model efforts by collaborators at Washington State University and University of Nebraska. The AgMIP modeling groups comprise research institutions and universities across the US and from many other countries.
Multi-year demonstration of increased yield and crop water productivity for melon production using mobile drip irrigation (MDI) compared with low elevation spray irrigation (LESA) is an important outcome that can drive adoption of the MDI application method for specialty crops.