Skip to main content
ARS Home » Pacific West Area » Riverside, California » Agricultural Water Efficiency and Salinity Research Unit » Research » Publications at this Location » Publication #383466

Research Project: Sustaining Irrigated Agriculture in an Era of Increasing Water Scarcity and Reduced Water Quality

Location: Agricultural Water Efficiency and Salinity Research Unit

Title: A system for concurrent on-the-go soil apparent electrical conductivity and gamma-ray sensing in micro-irrigated orchards

item SCUDIERO, ELIA - University Of California
item Corwin, Dennis
item MARKLEY, PAUL - University Of California
item POURREZA, ALIREZA - University Of California, Davis
item ROUNSAVILLE, TAIT - University Of California
item Skaggs, Todd

Submitted to: Soil and Tillage Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/12/2023
Publication Date: 1/1/2024
Citation: Scudiero, E., Corwin, D.L., Markley, P.T., Pourreza, A., Rounsaville, T., Skaggs, T.H. 2024. A system for concurrent on-the-go soil apparent electrical conductivity and gamma-ray sensing in micro-irrigated orchards. Soil and Tillage Research. 235:105899.

Interpretive Summary: a) Problem Statement: Sensors measuring soil apparent electrical conductivity or ECa, paired with a geo-referencing system (e.g., GPS), can be used to map soil properties, such as soil moisture and texture. Micro-irrigated orchards are often characterized by small scale heterogeneity of soil moisture: soil is very wet along the tree rows and much drier in the alleys between tree rows. The use of ECa in such heterogeneous systems is under investigated. b) Accomplishment: Our research shows that ECa measurements are remarkably different when taken in wetter vs. drier locations in micro-irrigated fields. The ECa measurements should be taken over wet soil profiles to increase their accuracy. Therefore, we devised a novel field vehicle that allows taking field measurements along the micro-irrigation emitters. Additionally, the vehicle was equipped with a gamma-ray spectrometer, which is known as a reliable predictor for soil texture and can be used to improve the accuracy of soil moisture and texture maps based on the ECa measurements. c) Contribution: The accomplishments of this work will benefit growers, agricultural technology industry, and soil scientists by increasing the reliability of ECa technology for soil mapping and irrigation management in micro-irrigated orchards.

Technical Abstract: 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. ECa maps and surveys are most useful and reliable when obtained in uniformly wet fields. However, soil moisture in micro-irrigated (e.g., drip or micro-sprinklers) orchards is typically non-uniform, with moist soil along tree and irrigation lines, and dry soil between tree rows. We developed a mobile platform and data post-processing algorithm to facilitate geospatial ECa measurements along or near driplines. Gamma-ray spectrometry is commonly used for clay content and type mapping. Fusion between ECa and gamma-ray is often reported to increase the accuracy of field-scale soil maps. However, contrarily to ECa, gamma-ray spectrometry is best suited for sensing soils in dry conditions. Micro-irrigated orchards are ideal environments for the combined application of these two sensor technologies. The fusion of topsoil (top 0.5 m) ECa (measured along the driplines) and gamma-ray total counts (TC) (measured between the tree rows) data was tested at a 0.4-ha sandy loam citrus orchard in Southern California. Here, we discuss sensor data acquisition, data processing, sensor-directed sampling scheme delineation, and characterization of field-scale soil particle size fraction (0–0.4 m soil profile) spatial variability. Pearson correlation coefficients between sand and silt content with both ECa and TC were significant (p < 0.05). A principal component analysis biplot suggested strong positive relationship with TC and clay content. Backwards stepwise multiple linear regression predicted sand content using TC, elevation, and spatial coordinates as explanatory variables with mean absolute error (MAE) of 3.06 %. Silt content was predicted (MAE=1.55 %) using ECa, elevation, and spatial coordinates. The development of this platform enables better characterization of soil properties in micro-irrigated orchard systems using on-the-go sensing technology.