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.
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 on Objective 1 of this project involves monitoring and modeling water use and crop productivity in five commercial almond and pistachio orchards using irrigation waters of varying quality. During fiscal year 2021 (FY21), removal of the monitoring sites established in FY17 was completed (Sub-objective 1A). The monitoring sites consisted of eddy covariance (ECV) towers and instrumentation, plus soil monitoring instrumentation. Data analysis for ECV sites has continued, and additional data have been provided to the Ameriflux repository for archiving. A cooperating graduate student from the University of California, Riverside, continued to incorporate the soil water content and electrical conductivity observations in her modeling research and completed her dissertation (project 2036-61000-018-02S). Multiple publications from this student’s work are now under revision for peer-reviewed journals. For Sub-objective 1B, data from the monitoring sites have been used to validate multiple satellite evapotranspiration algorithms in irrigated, perennial agriculture in California and is currently being used to validate regional hydrologic and crop production models. The focus of Objective 2 is the development of informatics and modeling tools for salt-affected irrigated agricultural systems and for managed aquifer recharge operations. Under Sub-objective 2A, in FY21 we developed 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 from the public and stakeholders, including especially, the USDA, Natural Resources Conservation Service (NRCS). The new implementation can be accessed as (1.) a web application (https://www.handbook60.org/rosetta), (2.) via a representational state transfer application programming interface (or REST API), or (3.) a stand-alone Python application (https://github.com/usda-ars-ussl/rosetta-soil). This work was done in close consultation with USDA-NRCS to ensure the products met their requirements. The scientist leading the work under Sub-objective 2B left our research unit last year, and is now Research Leader of the Sustainable Agricultural Water Systems unit in Davis, California. That Unit is continuing work on managed aquifer recharge. Progress on Objective 3 and Sub-objective 4B has been limited due to the retirements of the leads for those objectives/sub-objectives in 2017 and 2018. The positions were not immediately filled due to the vacant Research Leader position, laboratory re-organization via a Program Decision Adjustement Item (PADI), and various hiring freezes and delays. One scientist position was filled in June 2021. The other is proposed for abolishment due to a budget shortfall. Under Sub-objective 4A, strides were made toward enhancing the robustness and credibility of the regional-scale salinity model developed for the San Joaquin Valley, including: (1.) incorporation of orchards and vineyards into the model and (2.) validation of the model with an independent data set for a variety of crops (e.g., orchards, vineyards, alfalfa, cotton, vegetables, etc.). Early in this project, preliminary ground truth soil salinity data collected from orchards and vineyards revealed that our salinity assessment protocols developed for flood and sprinkler irrigated fields were not applicable to micro-irrigated fields due to a complex three-dimensional local-scale spatial variability in soil salinity and water content. This problem needed to be corrected before orchards and vineyards could be incorporated into the regional-scale salinity model and necessitated pushing back the timetable for some milestones and goals. In FY19 and FY20, 630 soil samples were collected for model validation and modified protocol development. Extensive analyses of these samples were performed in FY21. Measured data included apparent soil electrical conductivity (ECa), salinity, water content, bulk density and saturation percentage. An intensive quality assurance/quality control (QA/QC) analysis unfortunately revealed significant problems with the obtained lab and field data. For a portion of the samples, it was possible to reanalyze and correct the data, but another portion had to be discarded. A new sensor survey and 210 associated soil samples were collected. A revised set of protocols for cropped fields under micro-irrigation has been developed and will be validated once the new soil samples have been analyzed and gone through a QA/QC analysis.
1. New release of the Rosetta model for estimating soil hydraulic parameters. Rosetta is a widely used machine learning model that predicts soil hydraulic parameters from more readily available soil characterization data. ARS scientists in Riverside, California, released a new implementation of Rosetta called “rosetta-soil”. The new implementation can be accessed (1) as a web browser application (https://www.handbook60.org/rosetta), (2) via a representational state transfer application programming interface (REST API), or (3) as a stand-alone Python application (https://github.com/usda-ars-ussl/rosetta-soil). “rosetta-soil” provides a valuable new set of tools for researchers and engineers who require rapid estimates of soil hydraulic properties.
2. Almonds have higher salinity tolerance than previously believed. Almonds are a major crop in California, contributing over $9 billion U.S. dollars per year to the state’s economy, and consuming a significant amount of water. However, almonds have been viewed as a salt-sensitive crop. This salt sensitivity presents a major irrigation challenge for California growers (especially in the Western San Joaquin Valley) who may need to irrigate with higher salinity groundwater during drought periods when lower salinity surface water is unavailable. ARS researchers at Riverside, California, evaluated the impact of irrigating with lower quality groundwater via a comparison study with less saline orchards with better groundwater. They found that current, commercial, almond salinity tolerance was significantly higher than previously reported. This discovery provides a tool for growers to temporarily use higher salinity groundwater temporarily to mitigate the lack of surface water during a drought and provides additional potential for increased and sustainable almond production in the Western San Joaquin Valley.
3. New satellite algorithm provides improved evapotranspiration for crop water use and irrigation management. Recent droughts in the western United States have put tremendous strain on water resources, and there is increased pressure on the agricultural community to improve irrigation efficiency. However, established satellite evapotranspiration (ET) algorithms (and particularly thermal algorithms that work well in the western United States) have higher latency and longer revisit times that limit their utility for managing irrigation in specialty vegetable crops. ARS researchers at Riverside, California, and Maricopa, Arizona, have evaluated a new thermal satellite ET algorithm based on the international space station (ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station – ECOSTRESS) that overflies farms once every four days (instead of ~16 days for the Landsat satellite). They found that ECOSTRESS works very well for measuring crop water use in the western United States. This discovery provides another tool that could be adapted to help farmers improve irrigation scheduling.
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