Location: Northwest Irrigation and Soils Research
2025 Annual Report
Objectives
The research in this project includes a series of studies conducted under three broad objectives of improving water use efficiency and water quality in irrigated crop production. Water use efficiency research focuses on a variety of crops and conditions that occur in the northwestern U.S. Much of the water quality research focuses on the Upper Snake Rock (USR) watershed which is part of the ARS Conservation Effects Assessment Project (CEAP).
Objective 1: Characterize plant-climate-management interactions to optimize water productivity in intensively irrigated systems.
Subobjective 1A. Determine the effect of barley cultivar type (food and malt) on water use efficiency under full and deficit irrigation.
Subobjective 1B. Quantify the relationships between irrigation level and barley grain and straw yields under optimum and sub-optimum N supplies.
Subobjective 1C. Evaluate crop water use and agronomic response of sorghum-sudangrass hybrids under multiple management practices.
Subobjective 1D. Develop an IoT canopy temperature measurement system for crop stress monitoring.
Objective 2: Clarify climate and management impacts on water quantity and quality at the field edge and beyond the vadose zone.
Subobjective 2A. Evaluate the impact of tillage, cover crop, and fertilization management on surface and groundwater processes under linear-move irrigation system.
Subobjective 2B. Evaluate subsurface water quality dynamics under sprinkler and furrow irrigation at the field scale.
Subobjective 2C. Develop a furrow irrigation-induced erosion prediction tool.
Subobjective 2D. Develop a soil parameterization database for a center pivot infiltration model.
Subobjective 2E. Evaluate the impact of variable soil depth on water balance and nutrient leaching.
Objective 3: Identify environmental conditions and adaptive management strategies that improve water quality in surface and subsurface drainage networks in irrigated landscapes.
Sub-objective 3A: Develop a machine learning technique to detect and map in-field irrigation methods.
Sub-objective 3B: Evaluate the effect of long-term changes in irrigation methods and interannual variations in crop area on water availability and quality.
Sub-objective 3C: Evaluate the SWAT model for highly managed irrigated watersheds of the Northwest.
Sub-objective 3D: Determine P sorption capacity and equilibrium P concentration (EPC0) for a range of agricultural and canal soil/sediments in Idaho.
Approach
This project involves a combination of experimental field studies, watershed studies, model developments and tool development. The overall objective to optimize water productivity in intensively irrigated systems will be achieved through four field studies. A three-year study will measure the response of two barley cultivars to four irrigation levels ranging from full irrigation to 25% of full irrigation. A second study will further clarify the interrelation between irrigation level and barley straw and grain yield under optimum and sub-optimum nitrogen. This study will provide valuable data on evapotranspiration requirements under a variety of barley management scenarios. A third study will evaluate the performance and the viability of sorghum-sudangrass as an alternative forage crop while a fourth study will apply state-of-the-art sensing and wireless networking technologies (Internet-of-Things) to develop a canopy temperature measurement system to monitor crop water stress. This system will provide a practical canopy temperature measurement platform for the application of the crop water stress index (CWSI) to manage deficit irrigation for a wine grape cultivar.
In a second objective cover crop and no-till practices will be evaluated to devise sustainable and climate-resilient management systems by monitoring runoff, erosion, infiltration, soil water content and surface and groundwater quality on experimental fields. Another study will compare furrow irrigation to sprinkler irrigation on surface and subsurface water quantity and quality using field-installed lysimeters, soil moisture sensors and runoff measurements. A third study will develop a furrow irrigation soil erosion model by contrasting a machine learning approach with a process-based approach to erosion prediction in eroding furrows. In a fourth study, a database to parameterize an infiltration model for center pivot irrigation will be developed to more accurately account for surface sealing in infiltration prediction. A fifth study will apply a combination of field monitoring of soil processes, in-situ remote sensing, cutting-edge imaging and 3D reconstruction technologies including Ground Penetrating Radar to model soil depth and its impact on water and nutrient dynamics.
The third objective will be accomplished through watershed research. Irrigation water diverted into the 82,000 ha Upper Snake Rock watershed and water returning to the Snake River will be monitored for water quantity and quality to determine water, sediment, and nutrient balances for the watershed. Watershed research will evaluate potential associations between the extent of specific crops in a watershed and water quality outcomes. A methodology will be developed by applying deep learning techniques and computer vision to map the types of irrigation used on agricultural fields of the watershed. One study will parameterize the SWAT model for highly managed irrigated areas and evaluate improved irrigation routines developed for this model. A final study will use carefully designed benthic sediment sampling strategies to determine equilibrium phosphorus concentration in irrigation return flow drainage systems.
Progress Report
This report documents FY 2025 progress for project 2054-13000-010-000D, “Improving Water Productivity and Quality in Irrigated Landscapes of the Northwestern United States”, which began in November 2021.
In support of Objective 1, data collection and sample analyses continued with the aim to improve knowledge on crop productivity in highly managed irrigated systems. Work continued to help understand water and nutrient dynamics of barley production under deficit irrigation. Research findings on malt-barley water stress response under deficit irrigation were published in a peer-reviewed manuscript. Predictive models using infrared canopy temperature sensors and neural networks were developed to compute barley-specific crop water stress index (CWSI), offering timely irrigation decision support in semi-arid regions. All agronomic field data were collected to evaluate the interactive effects of irrigation level and nitrogen supplies on barley straw and grain yield. A manuscript is being developed with these new data with submission anticipated by fall 2025. This work will further inform irrigation management strategies under full and deficit irrigation scenarios. Work on the evaluation of sorghum-sudangrass as an alternative forage crop in water-limited regions also continued. Initial analysis of crop water use data for sorghum-sudangrass was completed and more detailed investigation is ongoing to assess this crop’s potential in drought-resilient forage systems. Work on the development of the Infrared Temperature (IRT) sensor network for plant water stress estimation was delayed by recent challenges in purchasing, personnel, and logistical support.
In support of Objective 2, research continued to evaluate no-till and cover crop as management alternatives to improve plant water availability and water quality in irrigated systems. Data collection continued for a fourth year. Treatment effects observed on soil moisture by year 3 persisted in the fourth year. Compared to alternative practices including no till treatment or cover crop, soils under business-as-usual management (conventionally tilled and no cover crop) were on average more than 30 percent drier at 30cm depth during the growing season. Field observations point to greater ability of the no-till plots to infiltrate irrigation water that is applied with high application rate from a linear-move irrigation system, especially at the beginning of the irrigation season when vegetative cover is low on tilled plots. Agronomic data and field observations need to be investigated in-depth to verify that soil moisture benefits of cover crop and no till treatments benefit crop yield. A manuscript is under development that presents results from this study. Another manuscript is under development to present the suite of technologies that were specifically developed to measure key hydrologic processes at the field scale where commercial out-of-the-box options are unavailable.
Efforts continued to develop a furrow irrigation erosion prediction model. Results of the process-based approach have been published in a peer reviewed journal. A power relationship between furrow flow hydraulics and soil detachment rate was found to fit the observed data better than the linear relationship typically used in concentrated flow erosion models. Results of the machine learning approach have also been published in a peer-reviewed manuscript. The transfer learning model developed to predict sediment loss from furrow irrigation was programmed to function as a macro-enabled Excel spreadsheet. The spreadsheet-based model was shared with USDA Natural Resources Conservation Service (NRCS) irrigation specialists and is currently available for their use. Research also continued to develop a soil parameterization database for a center pivot infiltration model. The center pivot infiltration model was populated with sprinkler specific information needed to model the effect of water droplet impact on infiltration rate. A sub-model to predict soil water relations based on soil texture was added to the infiltration model to provide soil specific information needed to execute the infiltration model. The model was shared with NRCS personnel as a macro-enabled Excel spreadsheet to solicit their input. The model is under technical review by NRCS personnel and a sprinkler manufacturer stakeholder. The effect of conservation practices on predicted infiltration rate is being evaluated for incorporation in the final model.
In support of Objective 3, irrigation return flow continues to be monitored at 30 sites in the Upper Snake/Rock watershed for the Conservation Effects Assessment Project (CEAP) and in cooperation with the Twin Falls Canal Company (TFCC). Overall trends indicate that water quality has stabilized after substantial improvements occurred in the past 20 years from converting furrow irrigated fields to sprinkler irrigation and installing water quality ponds. Data analysis began on flow injection experiments conducted at water quality improvement ponds along irrigation return flow channels. These experiments aimed to estimate water and solute retention time in these water quality improvement structures. Preliminary results indicate that despite a short residence time (less than two hours) at many of these structures, suspended sediment concentration was reduced by a factor of 2 and total phosphorus by a factor of 1.6 while no effect was noted on dissolved phosphorus. Progress was also made towards applying artificial intelligence (AI) to develop irrigation methods maps of irrigated regions. Two peer-reviewed manuscripts have been submitted by a university collaborator. Maps of irrigation methods of major irrigated areas of the Upper Snake/Rock watershed are expected to be produced within the next few months. Research related to the determination of phosphorus sorption capacity and equilibrium concentration in soils and sediments in Idaho was published in collaboration with a national Legacy Phosphorus project. This research resulted in a generalized model that was able to predict the phosphorus concentration that soils and sediments can maintain in water based on physical and chemical properties. The model is a major step forward as it allows prediction across diverse landscapes and regions. Data from this research will be used to guide management practices and to inform water quality models such as the Soil Water Assessment Tool (SWAT).
Accomplishments
1. Using sensors to sustain barley yield and quality with less water. Barley, mainly for malt, is a nearly $6 billion dollar industry in the United States with much of this grown under irrigation in drought prone regions of the west where water availability remains a constant concern. ARS reserchers in Kimberly, Idaho, investigated malt barley response to reduced irrigation and evaluated sensor-based strategies to improve irrigation management. Results indicated that water savings of up to 25% would largely retain yield and quality at acceptable levels. Further, an AI-driven model to predict the crop water stress index (CWSI) from infrared canopy temperature sensors and meteorological data was found to be an effective tool to optimize irrigation while maintaining barley yield and quality. These results allow both more accurate prediction for harvest decisions as well as the potential to more accurately schedule irrigation which are critical factors for ensuring the long-term profitability of malting barley in the largest production areas of the United States.
2. A deep learning tool to map on-farm irrigation methods in the western United States. Many arid and semi-arid regions of the western United States rely on irrigation to maintain some of the most productive agriculture in the nation. The type of irrigation system used on farms, i.e. flood, sprinkler or micro-irrigation systems, has profound implications on irrigation efficiency, water availability and water quality. ARS researchers in Kimberly, Idaho, applied a deep learning computer model to publicly available satellite data products to identify and map irrigation methods used across agricultural regions of the Northwest. The model mapped irrigation methods with 78% accuracy. The model is currently being applied to assess the impacts of changing irrigation methods on water use so limited water resources can be more effectively managed in southern Idaho.
3. Predictive model for determining phosphorus equilibrium concentrations with water to improve water quality across the United States. Concerns over phosphorus inputs into surface waters date back decades and continue even though current conservation practices have reduced phosphorus losses from agricultural fields. ARS researchers in Kimberly, Idaho, collaborated on a nationwide project with other ARS and university scientists to evaluate soil/sediment relationships with water. A complex predictive model was developed using common soil and chemical properties that allows for the first time, phosphorus prediction across diverse watersheds. This work will play a critical role in improving large-scale watershed models (e.g., SWAT) and phosphorus-transport models used to predict legacy phosphorus effects, ensuring appropriate practices are used to maintain the long-term water quality and agricultural production.
4. Soil loss from furrow irrigated fields can now be predicted with improved accuracy. Water flowing in irrigation furrows transports large quantities of sediment, yet no satisfactory soil loss prediction model exists for furrow-irrigated fields. The only available tool has limited applicability due to the simplistic modeling approach and limited source data. A new model developed by ARS researchers in Kimberly, Idaho, expands the data with over 2000 field measurements and leverages advances in machine learning to predict soil loss from eroding furrows with a 16% margin of error. A spreadsheet version of this model is available for widespread use and is applicable to a much wider range of conditions than the original tool. USDA NRCS is using this model to determine the reduction in soil loss when furrow irrigated fields are converted to sprinkler irrigation or other conservation practices are applied.