Location: Agricultural Water Efficiency and Salinity Research Unit
2022 Annual Report
Objectives
Objective 1: Evaluate the effects of degraded irrigation waters on crop water use and yield at commercial production scales.
Subobjective 1A: Evaluate the impact of salinity on crop water use and productivity by observing evapotranspiration and carbon fluxes in commercial almond and pistachio orchards exhibiting a range of salinities.
Subobjective 1B: Develop quantitative relationships between remotely-sensed plant canopy observations and measured crop water use and productivity.
Objective 2: Develop an innovative, open informatics platform for disseminating information, tools, and recommendations for the management of marginal quality irrigation and artificial recharge waters.
Subobjective 2A: Develop a web-based platform for disseminating information, tools, and recommendations for evaluating and managing saline irrigation waters.
Subobjective 2B: Develop improved models to support managed aquifer recharge (MAR) treatment of alternative water resources for irrigation.
Objective 3: Develop a set of sensing technologies that measure soil and solution properties relevant to the use of low quality waters for irrigation, including salinity, sodicity, clay content, aluminum, iron oxides, organic matter, and soil solution boron concentration. Sensor technologies will include near-infrared (NIR), mid-infrared (MIR), and x-ray fluorescence (XRF) spectroscopy.
Objective 4: Develop and evaluate an integrated system of tools for site-specific irrigation management to control soil salinity and related adverse conditions when using degraded waters. The integrated multiple-sensor system will combine the use of geospatial apparent soil electrical conductivity (ECa), y-ray spectrometry, and multi-spectral imagery.
Subobjective 4A: Develop and evaluate an integrated multiple-sensor system (1) to delineate matric and osmotic stress patterns at field scale and (2) to enhance the robustness of regional-scale salinity assessment modeling.
Subobjective 4B: Develop a set of integrated tools to diagnose and manage infiltration problems due to sodic conditions by modeling the chemical effects on infiltration reduction and quantifying soil sodicity.
Approach
Drought, climate change, and competition for resources are reducing the availability of irrigation water and farmland in arid and semi-arid regions. One strategy for maintaining or enhancing productivity in the face of diminished resource availability is to make greater use of marginal lands and alternative water sources, both for irrigation and for recharging depleted aquifers. Sustainable use of low-quality waters requires soil, water, and crop management practices that optimize crop production and aquifer recharge while minimizing the degradation of natural resources by salts and other contaminants. Advanced multi-sensor technologies, models, and decision-support tools are needed to evaluate alternative management practices and to assist growers and water managers in satisfying increasingly stringent regulations.
In this project, we propose a combination of field studies and laboratory experiments designed to develop knowledge and technologies needed to enable optimal use of fresh, degraded, and recycled waters for irrigation and recharge. In the laboratory, we undertake a series of experiments to test the hypothesis that portable near-infrared (NIR), mid-infrared (MIR), and x-ray fluorescence (XRF) sensors can be calibrated to measure soil chemical properties, and ultimately can be used in the field to observe changes in soil properties and guide management. The influences of soil texture, mineralogy, EC, pH, ESP, water content, and surface roughness on sensor calibration and performance will be assessed. A second group of experiments will evaluate the effects of irrigation water quality (SAR, pH, EC) on the long-term impact of irrigation and rainfall on the infiltration capacity of soils of varying textures.
Two major field campaigns are planned. In the first, we field-test a multi-sensor platform for delineating field-scale spatial variations in soil salinity and texture and identifying associated matric and osmotic stress patterns. The platform consists of gamma ray (y-ray) and electromagnetic induction (EMI) instrumentation in combination with Landsat 7 multi-spectral imagery. In the second campaign, we use micro-meteorological methods to evaluate field-scale crop productivity and water-use across a network of research sites in commercial orchards exhibiting a range of soil salinities and irrigation water qualities.
Finally, we develop modeling tools focusing on two problems associated with alternative waters and managed aquifer recharge operations: (i.) decreasing infiltration due to soil clogging by colloids; and (ii.) infiltration depths and setback distances required to ensure microbial safety at groundwater extraction points. And lastly we develop an open, web-based informatics platform for disseminating information, models, and decision-support for the use of saline irrigation waters.
The project should lead to improved recommendations for managing alternative water resources for irrigation and recharge, and produce new capabilities for predicting the effects of management decisions on crop yields and on soil and water quality.
Progress Report
This is the final report for project 2036-61000-018-000D, Sustaining Irrigated Agriculture in an Era of Increasing Water Scarcity and Reduced Water Quality, which has been replaced by new project 2036-61000-019-000D, Water Management for Crop Production in Arid and Semi-Arid Regions and the Safe Use of Alternative Water Resources.
Over the life of the project, significant progress was made on all four objectives. The goal of Objective 1 was to evaluate the impacts of irrigation water quality on crop production at commercial production scales. Years of droughts in central California, the primary growing region for the state's $9 billion almond industry, have forced producers to draw ever deeper from aquifers to replace limited higher-quality surface water to quench thirsty trees. As groundwater levels have declined with pumping and drought, the quality of extracted groundwater in some areas also has declined as wells have had to reach deeper into levels with sediments of higher salinity. The higher salt content of this groundwater poses a significant threat to productivity with some almond farmers reporting yield losses of more than 30 percent. During Fiscal Year 2017, monitoring sites were established in five almond and pistachio orchards. The monitoring sites consisted of eddy covariance towers and instrumentation, plus soil monitoring instrumentation. In collaboration with cooperating University of California, Riverside, faculty (project 2036-61000-018-002S) and a graduate student, crop water-use and productivity were monitored over three growing seasons. The results showed that higher levels of salinity reduced productivity, but also that the effects of lower quality irrigation water may persist even after switching back to higher quality, less saline, water. Consequently, the use of lower quality water during times of water shortages potentially has longer range consequences. The monitoring data from these sites were also used in a multi-institution project assessing the performance of the OpenET tool for predicting and assessing crop water use. The results indicated that OpenET observed almond water use accurately, whereas there were substantial discrepancies between the measured pistachio evapotranspiration and OpenET assessments.
The focus of Objective 2 was the development of software and modeling tools for water and soil analyses and management. A public source code repository was established for software developed under this objective (https://github.com/usda-ars-ussl). Released software included Fluxpart, a Python module that processes high frequency eddy covariance data (https://github.com/usda-ars-ussl/fluxpart). The eddy covariance method is routinely used to measure gas fluxes over agricultural fields and other landscapes, providing essential data for many kinds of agronomic and climate research. However, greater insight into the functioning of agroecosystems is possible if measured gas fluxes can be separated into their constitutive components: the water vapor flux into transpiration and direct evaporation components, and the carbon dioxide flux into photosynthesis and respiration components. ARS researchers in Riverside, California, developed new mathematical results that facilitate partitioning analyses, and released new open-source software that processes large volume data streams that implement the flux variance similarity partitioning algorithm. The research and software are expected to benefit scientists, engineers, and irrigators seeking to monitor, understand, and optimize water use in agroecosystems. Users of the software include ARS researchers from El Reno, Oklahoma, and Stoneville, Mississippi.
Also released was a new implementation of the Rosetta pedotransfer function model. Rosetta is a machine learning (neural-network) model that predicts soil hydraulic parameters from more readily available soil characterization data. An updated Rosetta implementation has been among the most common requests received at Riverside, California, from the public and stakeholders, including especially the USDA Natural Resources Conservation Service (NRCS). The new implementation can be accessed as: (i.) a web application (https://www.handbook60.org/rosetta); (ii.) via a representational state transfer application programming interface (or REST API); or (iii.) a stand-alone Python application (https://github.com/usda-ars-ussl/rosettasoil). This work was done in close consultation with NRCS to ensure the products met their requirements.
In further support of Objective 2, considerable research was directed towards developing improved models to support managed aquifer recharge (MAR). Several models were developed and used to study the influence of high velocity regions on microbial transport from infiltration basins. Experimental studies of virus transport and fate under MAR conditions were also analyzed with a model, and the relative importance of various removal mechanisms was quantified. A novel model was constructed and used to simulate the transport and retention of stable and aggregating polydispersed colloidal suspensions. An interagency agreement (2036-61000-018-003I) was established between the U.S. Environmental Protection Agency and USDA, ARS to assess the performance of drywells for storm water capture and enhanced aquifer recharge. Experimental and mathematical modeling studies of drywell behavior at the Fort Irwin U.S. Army Base were initiated on this topic. Research into MAR has been picked up by researchers in Davis, California; see annual report 2032-13220-002-000D for further details.
The goal of Objective 3 was to develop a set of sensing technologies that measure soil and solution properties relevant to the use of low-quality waters for irrigation, including salinity, sodicity, clay content, aluminum, iron oxides, organic matter, and soil solution boron concentration. Sensor technologies were to include near-infrared, mid-infrared, and x-ray fluorescence spectroscopy. Progress on this objective was hindered by retirements and vacancies, with one position being abolished and the other being filled only in the fifth year at 0.5 full-time employee (FTE). Nevertheless, notable progress was made. Eight soils of varying texture and mineralogy were analyzed for texture and various soil chemical properties in the laboratory. They are to be used as standards in the evaluation of hand-held sensors that are used in the field to predict chemical and physical information relevant to mapping and management of saline soils. The measurement of chemical elements using a handheld portable x-ray fluorescence (PXRF) instrument was initiated. Using soils of known soil properties, the effect of water content and different soil textures, sodium adsorption ratio (SAR) and salinity on elemental composition without external calibration was evaluated. It was established that the sensor was affected by water content, with increased water content attenuating the signal for all elements. The instrument was relatively stable under low water content conditions, indicating it is suitable for field application using surface soils. It was determined that chloride (Cl-) content could be semi-quantitatively determined to Cl- concentrations of 20 milliequivalents per liter (Cl- concentration expressed in the standard format of saturation extract). This information is of direct use to researchers working on remote sensing of salinity and is expected to improve field scale diagnosis.
The aim of Objective 4 was to develop and evaluate an integrated system of tools for site-specific irrigation management to control soil salinity and related adverse conditions when using degraded waters. On-the-go soil apparent electrical conductivity (ECa) sensors are great tools for mapping and monitoring soil properties such as water content, texture, and salinity. Researchers in Riverside, California, developed a mobile platform that combines geospatial ECa measurements and gamma ray (y-ray) spectrometry, which is commonly used for clay content and type mapping. The new platform and data post-processing software (https://github.com/usda-ars-ussl/sensoff) are especially useful for taking sensor readings along or near driplines in orchards, vineyards, and other micro-irrigated fields. Soil moisture in micro-irrigated orchards is typically non-uniform, with moist soil along tree and irrigation lines, and dry soil between tree rows. The non-uniformity of such systems has been a challenge to mapping salinity in drip-irrigated fields. A comprehensive review conducted in fiscal year (FY) 2021 and FY 2022 of ECa, soil salinity, water content, bulk density, and saturation percentage led to the development of modified ECa-directed soil sampling protocols specifically for mapping salinity in drip-irrigated tree crops. Field surveys using the new protocols are underway, with the goal being to incorporate orchards into the ARS-Riverside San Joaquin Valley regional-scale salinity model, which currently includes only annual row and pasture crops. For more information, please review the annual reports for project number 2036-61000-019-000D.
Progress on this project was frequently delayed and some work left undone due to maximized telework and multiple retirements, vacancies, and position abolishments. Nevertheless, this project can point to numerous successes and accomplishments across all objectives. Much of the unfinished work has been incorporated into the new project, 2036-61000-019-000D.
Accomplishments
1. New guidelines for mapping soil salinity in orchards. Climate change and related water conservation efforts in the San Joaquin Valley, California, have led to an expansion of drip irrigation in high value orchards and vineyards. Monitoring soil salinity in drip irrigated systems using sampling protocols developed for flood and sprinkler irrigation is not possible due to the complex three-dimensional local- and field-scale variations in water content and salinity that exist under drip irrigation. Based on an extensive three-dimensional data set of proximal sensor and salinity measurements, an ARS researcher in Riverside, California, developed a set of protocols and guidelines to map soil salinity for drip-irrigated tree crops. These protocols fill a knowledge gap that will significantly improve guidelines for locating and taking soil core samples, characterizing average root zone salinity in a drip-irrigated tree crops, and mapping complex local- and field scale variations in soil salinity under drip irrigation systems.
Review Publications
Semiz, G.D., Suarez, D.L., Lesch, S.M. 2022. Electromagnetic sensing and infiltration measurements to evaluate turfgrass salinity and reclamation. Scientific Reports. 12. Article 5115. https://doi.org/10.1038/s41598-022-09189-7.
Corwin, D.L., Scudiero, E., Zaccaria, D. 2022. Modified ECa – ECe protocols for mapping soil salinity under micro-irrigation. Agricultural Water Management. 269. Article 107640. https://doi.org/10.1016/j.agwat.2022.107640.
Ghanbarian, B., Skaggs, T.H. 2021. Soil water retention curve inflection point: Insight into soil structure from percolation theory. Soil Science Society of America Journal. 86(2):338-344. https://doi.org/10.1002/saj2.20360.
Schmidt, M.P., Mamet, S.D., Senger, C., Schebel, A., Ota, M., Tian, W., Aziz, U., Stein, L.Y., Regier, T., Stanley, K., Peak, D., Siciliano, S.D. 2022. Positron-emitting radiotracers spatially resolve unexpected biogeochemical relationships linked with methane oxidation in Arctic soils. Global Change Biology. 28(13):4211-4224. https://doi.org/10.1111/gcb.16188.
Helalia, S.A., Anderson, R.G., Skaggs, T.H., Jenerette, D., Wang, D., Šimunek, J. 2021. Impact of drought and changing water sources on water use and soil salinity of almond and pistachio orchards: 1. Observations. Soil Systems. 5(3). Article 50. https://doi.org/10.3390/soilsystems5030050.
Helalia, S.A., Anderson, R.G., Skaggs, T.H., Šimunek, J. 2021. Impact of drought and changing water sources on water use and soil salinity of almond and pistachio orchards: 2. Modeling. Soil Systems. 5(4). Article 58. https://doi.org/10.3390/soilsystems5040058.
