Location: Northwest Irrigation and Soils Research
2022 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 progress for project 2054-13000-010-000D, “Improving Water Productivity and Quality in Irrigated Landscapes of the Northwestern United States,” which started November 2021 and continues research from expired project 2054-13000-009-000D, “Improving Water Use Efficiency and Water Quality in Irrigated Agricultural Systems.”
In support of Sub-objective 1A, research was conducted to evaluate malt and human-food barley lines under both full and deficit irrigation. Barley plots were planted and samples were collected for grain yield and quality. Barley was also analyzed for malt quality by ARS collaborators in Wisconsin. Early results indicate severe yield and quality losses with low irrigation but barley yield and quality may possibly be maintained with moderate irrigation reductions. Additional research on barley under Sub-objective 1B aimed to study the response of barley grain and straw yield to nitrogen supply under a range of irrigation levels. For this study, barley plots were planted in spring 2022, soil water measurement instrumentation was installed, and irrigation and nitrogen treatments were implemented as planned. Research on sorghum-sudangrass was conducted under Sub-objective 1C. For this research, plant biomass and forage quality were analyzed and water use was measured. Preliminary data indicated that single cuttings and narrower row spacing increased yields compared to multiple cuttings and wide row spacings. A cooperative study synergistic with Objective 1 was initiated in 2020 to compare different methods for establishing alfalfa. Initial results indicate that interseeding alfalfa with silage corn reduced corn silage yield about 10% but alfalfa yield the following year was almost double the yield from treatments where alfalfa was planted after the corn was harvested.
In support of Sub-objective 2A, experimental plots were established to evaluate an Aspirational (ASP) management system combining cover crop and no-till in relation to a Business as Usual (BAU) alternative consisting of conventional tillage without cover crop. Runoff monitoring systems were developed, tested, and deployed to the experimental plots along with other sensors to monitor rainfall/irrigation amounts, soil moisture, and soil temperature. Porous cup lysimeters were also installed to sample soil pore water. Monitoring records have captured all irrigation events of Year 1 and a few natural precipitation events. All experimental plots at Year 1 had the same initial treatment (i.e., freshly tilled and planted with barley after sugar beet was grown in 2021). Runoff was measured from some plots during a 25 mm irrigation event suggesting that treatment effects on runoff may be measurable as no-till and cover-crop treatments are implemented in subsequent years. Surface and subsurface water quality samples have been collected and analyzed for sediments and major nutrients including nitrates, ammonium, and phosphorus. Nitrate and phosphorus concentrations were consistent with observed subsurface concentration of these nutrients in nearby experimental sites.
Sub-objective 2B aims to evaluate the effect of conversion from furrow to sprinkler irrigation, which is one the main conservation practices implemented in the Upper Snake Rock watershed in southern Idaho. For this study, leaching and irrigation efficiency for furrow and sprinkler irrigation continue to be measured on plots where pan lysimeters have been installed. Initial data indicate that similar or greater amounts of water leached with sprinkler irrigation compared to furrow irrigation. Nitrate-nitrogen concentrations decreased with time for both sprinkler and furrow irrigation.
In support of Sub-objective 2C, historical published and unpublished data sets containing measurements of furrow irrigation sediment loss in the western United States were assembled into a furrow sediment loss data set comprising over 2000 measurements. The collective data set included nine common hydraulic and field condition variables: freshly cultivated or previously eroded by prior irrigation; compacted by wheel traffic or uncompacted; irrigation duration (hr); furrow length (m); furrow inflow rate (L min-1); furrow slope (%); soil sand and clay fractions (%); and furrow sediment loss (kg). The data were used in a Kohonen self-organizing map (KSOM) model. The inherent variability in measured furrow sediment loss limited the ability of a KSOM model to reliability predict furrow sediment loss because the model was clustering the data based on input parameters defining the hydraulic and soil conditions. This outcome was used to develop a transfer learning approach where a KSOM model was used to cluster data records with similar hydraulic and soil conditions. Mean measured sediment loss and furrow flow rate of each cluster were then determined. Furrow sediment loss can then be predicted by identifying the cluster that most closely matches the input information and the mean measured sediment loss associated with the identified cluster. Additional work under Sub-objective 2C identified that the dynamic concentrated flow erosion routine used in the Rangeland Hydrology and Erosion Model (RHEM) could be a framework for a process-based furrow erosion model.
In support of Sub-objective 2D, an application rate versus hydraulic head calibration curve was developed for the Cornell sprinkle infiltrometer, an easy-to-use device for measuring soil infiltration. The calibration curve was linear with a coefficient of determination of 0.99. Drop size and velocity produced by the infiltrometer at various fall heights were measured in the laboratory using a laser disdrometer. Droplet kinetic energy varies with fall height and application rate, allowing the Cornell sprinkle infiltrometer to simulate various types of center pivot sprinklers. Multiple field soils have been collected for laboratory testing of the effects of sprinkler droplet energy on infiltration.
Water quality and quantity monitoring in the Upper Snake Rock watershed under the Conservation Effects Assessment Project (CEAP) continues to drive a significant portion of the studies under Objective 3. Twenty monitoring sites were added in 2021 in cooperation with the Twin Falls Canal Company (TFCC) in southern Idaho. In support of Sub-objective 3A, a machine learning methodology was developed to map irrigation methods used across agricultural fields in southern Idaho. In 2022, the deep learning model has been trained using the Utah Water-Related Land Use dataset. This dataset provided irrigation methods for agricultural fields in the state of Utah for many years. The trained model correctly classified surface (flood and furrow) and sprinkler irrigation more than 70% of the time in Utah. When the trained model was applied to the TFCC irrigation tract, irrigation methods maps obtained were consistent with known spatial patterns of furrow and sprinkler irrigation adoption across the basin. The proportions of agricultural area irrigated by each of these irrigation practices was estimated using the developed machine learning model for years 2003 to 2016 and results were also consistent with previously conducted surveys.
In collaboration with researchers at the University of Idaho, the Soil and Water Assessment Tool (SWAT) was used to model hydrologic processes in sub-watersheds of the TFCC irrigation project. This collaborative research effort advances Sub-objective 3C to evaluate the SWAT model for highly managed irrigated watersheds. Early tests suggest that successful model calibration is possible when model assumptions are informed by an accurate understanding of irrigation water management, irrigation methods and farmers’ decision making in the watershed. Currently available irrigation routines in SWAT based mainly on soil moisture status may need to be updated to reflect supply-based irrigation decision making used when irrigation water is provided by an off-farm source.
Channel sediment samples were collected at 20 sites for initial analysis of phosphorus concentration. This research supporting Sub-objective 3D has expanded through participation in a legacy phosphorus project with seven other ARS locations.
Accomplishments
