Brief Summary of Minutes of Annual Meeting

There no official minutes taken.

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 (https://etdata.org/) 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.