Location: Northwest Irrigation and Soils Research
2024 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, titled, “Improving Water Productivity and Quality in Irrigated Landscapes of the Northwestern United States”, which started in November 2021.
In support of Objective 1, ARS researchers in Kimberly, Idaho, continued data collection and sample analyses with the aim of improving knowledge on barley water productivity under deficit irrigation. A manuscript incorporating findings from this research is near completion and will be submitted this year. This research will guide irrigation management practices for growers and industry when water availability is reduced. Additional yield and chemical property data have been collected on sorghum-sudangrass data to evaluate the performance of this drought resistant crop as a forage alternative in arid and semi-arid dairy-producing regions. Dairy production has a large footprint in arid regions such as Idaho, California, New Mexico, and Texas where droughts are a frequent threat to crop production.
The second year of a subordinate project to evaluate methods of establishing alfalfa was completed. Interseeding alfalfa into silage corn at the same time that corn is planted had greater combined dry matter yield over two years than planting alfalfa alone or planting alfalfa the year following corn silage. Corn silage yield was about 10% less when alfalfa was interseeded with the corn compared to corn grown alone. When alfalfa was established by interseeding with corn, the alfalfa yield the following year was two times greater than alfalfa that was seeded in the spring following corn.
The development of the Infrared Temperature (IRT) sensor network for plant water stress estimation continued. Commercial components for the IRT sensor network have been selected and radio communication components of the network have been tested in the laboratory. The IRT network uses a master-slave architecture to collect data from remote field nodes. The field nodes use a low-power microcontroller equipped with a modern low-power long range radio communication protocol (Lora). Initial power consumption estimates suggest that the assembled system can leverage unique deep sleep features to provide more than five months of operation on a commonly available 1000 milliamp hour lithium battery pack. The master controller retrieves data from fields nodes and sends it to temporary cloud storage using cellular communications.
In support of Objective 2, ARS researchers 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 third year. The data collection process was streamlined with the integration of low-cost radio communication modules to send sensor data to a cloud platform from which data are populated in a database in near real time. Early data suggest that, compared to alternative practices including no till treatment or cover crop, soil temperature under business-as-usual management (conditionally tilled and no cover crop) was on average warmer (+0.73 degrees C) and dryer at 30cm depth during the period of active plant water demand. Soil water potential data suggest that no-till plots stayed on average consistently wetter than the tilled plots, irrespective of the presence of cover crop. Field observations point to greater ability of the no-till plots to infiltrate irrigation water, especially at the beginning of the irrigation season when vegetative cover is low on tilled plots.
Efforts continued to develop a furrow irrigation erosion prediction model. The process-based approach to furrow irrigation erosion prediction was successfully evaluated on 128 furrow sections. 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. Findings of this model development are currently being incorporated in a peer-reviewed manuscript to be submitted this year. Progress was also made in the effort to develop a machine learning approach for furrow irrigation soil loss prediction. 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 Resource Conservation Service (NRCS) irrigation specialists and is currently available for their use. A manuscript describing the spreadsheet model has also been prepared.
Research also continued to develop a soil parameterization database for a center pivot infiltration model. Simulated sprinkler irrigation was applied to bare soil in the laboratory to investigate the effect that water application rate pattern has on infiltration. Soil columns were subjected to different application rate patterns with equal depth of water applied, duration, and total applied droplet kinetic energy using a Cornell infiltrometer. Four application rate patterns were evaluated: constant, triangular, decreasing with time and increasing with time. There was no measurable difference in infiltrated volume between the application rate patterns. The results are consistent with a process-based infiltration model that includes soil surface sealing from droplet impact. The basic elements of the infiltration model have been reprogramed to function in a macro enabled Excel spreadsheet for easier use.
Collaboration with researchers at the University of Idaho has continued to evaluate the impact of variable soil depth on water balance and nutrient leaching. Data collected at the Center for Agriculture, Food and the Environment (CAFE) were synthesized by university collaborators into a peer-reviewed manuscript dealing with relationship between soil depth and water and nutrient dynamics. A field campaign was also conducted at CAFE to determine the feasibility of Ground Penetrating Radar (GPR) as a technology for soil depth estimation at resolution and accuracy suitable for field-scale management decision. GPR data were collected and processed by an ARS-funded collaborator at the University of Nevada, Reno. Preliminary GPR data analysis revealed some inherent challenges of this technology to resolve field-scale variations in soil depth.
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. Additional flow injection experiments were conducted at water quality ponds along irrigation return flow channels to estimate water and solute retention time in these water quality improvement structures. This research is expected to yield more accurate information on the efficiency of these structures at reducing return flow phosphorus concentration. Progress was also made towards applying artificial intelligence (AI) to develop irrigation methods maps of irrigated regions in this watershed. The initial deep learning approach has been presented in a peer-reviewed manuscript. Additional refinements to the methodology are being conducted as part of the dissertation of the University of Virginia PhD student who received ARS AI Center of Excellence support last year to work on this project. Maps of irrigation methods of major irrigated areas of the Upper Snake/Rock watershed are expected to be produced within the next few months in collaboration with an ARS-funded post-doctoral researcher at the University of Idaho. In collaboration with USDA-NRCS, sediment and agricultural samples were compiled, analyzed, and were incorporated into a nationwide USDA-ARS dataset. This data will be submitted later in this year. Data from this work are especially critical as semi-arid regions, such as Idaho, have few soil characteristics in common with Midwestern and Eastern United States production. Thus, this data will increase the accuracy of estimates in these highly productive agricultural regions. Results will be used to improve phosphorus-loss modelling tools such as Soil and Water Loss Assessment Tool (SWAT) and annual phosphorus loss estimator (APLE).
Accomplishments
Review Publications
Koehn, A.C., Bjorneberg, D.L., Ma, L., Leytem, A.B., Malone, R.W., Nouwakpo, S.K., Qi, Z. 2024. Climate change in a semi-arid environment: Effects on crop rotation with dairy manure applications. Journal of the ASABE. 66(6):1449-1468. https://doi.org/https://doi.org/10.13031/ja.15661.
King, B.A., Tarkalson, D.D., Bjorneberg, D.L. 2024. Evaluation of canopy temperature based crop water stress index for deficit irrigation management of sugar beet in semi-arid climate. Applied Engineering in Agriculture. 40(1):95-110. https://doi.org/10.13031/aea.15822.