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.
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 (FY)20, operation of the monitoring sites established in FY17 was largely completed, but was not finished due to the maximized telework. (Sub-objective 1A). The monitoring sites consist of eddy covariance (ECV) towers and instrumentation, plus soil monitoring instrumentation. Data analysis for ECV sites has continued, and preliminary data has been provided to an established repository (Ameriflux) for archiving. A cooperating graduate student from University of California, Riverside, (project 2036-61000-018-02S) has continued incorporating the soil water content and electrical conductivity observations in her modeling research and is working on completing her dissertation. Data from the monitoring sites has been used to validate multiple satellite evapotranspiration algorithms in irrigated, perennial, agriculture in California. 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, we released several updates to Fluxpart (current version 0.2.10), a computer program that processes data from eddy covariance monitoring stations to determine water vapor and carbon dioxide fluxes in agricultural fields and other landscapes. Water and carbon dioxide flux data provide information about plant growth and water use that is essential in many kinds of agronomic and climate research, including our field work under Objective 1. The software updates include some minor bug fixes, new capabilities for handling alternative data file formats, and, more significantly, a new optimization method for estimating leaf-level water use efficiency. A joint project with ARS-El Reno, Oklahoma and ARS-Stoneville, Mississippi, is currently testing some of these new software features. Also, in FY20, coding began on a pedotransfer model for estimating soil hydraulic parameters from basic soil characterization data. This work is progressing with some coordination with USDA-National Resources Conservation Service, who are looking for a simpler alternative to their current method of estimation. Under Sub-objective 2B, progress was made in understanding and evaluating various Managed Aquifer Recharge strategies. Numerical experiments were conducted to compare the infiltration, vadose zone storage, and groundwater recharge from infiltration basins and drywells under various subsurface heterogeneity conditions. Results show that five drywells, with a very small surface footprint, can infiltrate and recharge much more water than a 70 m diameter infiltration basin. These benefits were most pronounced during shorter time scales, but also held under steady-steady flow conditions for 20 years. Other numerical simulations studied the fate of viruses in drywell source water under steady-state flow conditions. Results demonstrated that the current standard of a 3 m setback distance from the bottom of the drywell to the top of the water table may frequently be inadequate to ensure 6 log removal of viruses. This research was partially supported by an interagency agreement with the Environmental Protection Agency. Objective 3 seeks to develop a set of sensing technologies that measure soil and solution properties. Progress on Objective 3 in FY2020 was negatively impacted by critical multi-year vacancies and maximum telework restrictions. Consequently, progress was minimal, with essentially no new data collected. Objective 4 aims to develop and evaluate an integrated system of tools for site-specific irrigation management. Progress has been made in Sub-objective 4A(1) in developing an integrated multiple-sensor system to delineate matric and osmotic stress patterns at field scale. A mobile platform and data post-processing algorithm was developed to map soil. The platform includes a gamma-ray spectrometer for improved accuracy in soil mapping and monitoring via sensor data fusion with apparent soil electrical conductivity. The combination of the two sensors better delineates osmotic stress patterns and enables better characterization of soil properties in micro-irrigated systems. The platform was tested on a 0.4 hectare citrus orchard to illustrate sensor data acquisition, data processing, and sensor-directed sampling to delineate matric and osmotic stress patterns. A manuscript has been prepared for publication. This platform is the foundation upon which a multi-spectral imagery sensor will be added in the future to create an integrated multi-sensors system that further enhances the ability to map soil properties influencing crop yield. Solid progress was also made in Sub-objective 4A(2) by reaching one of four stated goals for enhancing the robustness and credibility of the regional-scale salinity model development. The achieved goal was the evaluation of a hybrid model that combines the annual integral approach of Zhang et al.2015 with the multi-year approach of Lobell et al. (2010). A regional-scale soil salinity modeling approach using plant-performance metrics was proposed by Zhang et al. (2015) for farmland in the Yellow River Delta, China, a region with a humid continental/subtropical climate. The one-year integral of temporally interpolated Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) time series data were proposed as an explanatory variable for agricultural soil salinity modeling. This methodology was tested in California’s San Joaquin Valley, United States, a region with a semi-arid Mediterranean climate. Time series of Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI), and Canopy Response Salinity Index (CRSI) were created for the 2007–2013 period. Seventy-three Moderate Resolution Imaging Spectroradiometer (MODIS) pixels surveyed for 0–1.2-m soil salinity in 2013 were used as the ground-truth dataset. Results validated the approach: the 2013 integral of CRSI (best performing index) had a Pearson correlation coefficient (r) of -0.699 with salinity. Results obtained using temporally integrated data were almost always better than those obtained using individual data. Furthermore, the methodology was shown to be improved by the use of multi-year data.
1. Multi-sensor platform to delineate matric and osmotic stress patterns. As micro-irrigation systems become more prevalent in water scarce agricultural areas such as the San Joaquin Valley, California, producers will need accurate high-resolution salinity maps to manage the spatially complex soil salinity with minimal water usage while maintaining crop productivity. Current maps of soil salinity derived from geospatial measurements of soil electrical conductivity are most reliable when obtained in uniformly wet fields. However, soil moisture in micro-irrigated (e.g., drip and micro-sprinklers) orchards or vineyards is non-uniform with soil moisture decreasing with distance from the drip line. A mobile platform was developed to improve accuracy in soil mapping and monitoring, particularly for fields under micro-irrigation systems, by combining information from gamma-ray spectrometer and apparent soil electrical conductivity measurements. The combination of the two sensors better delineates water and salinity patterns providing producers with detailed information for site-specific irrigation management.
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