Apparent soil electrical conductivity and gamma-ray spectrometry to map particle size fraction in micro-irrigated citrus orchards in California

Authors: SCUDIERO, ELIA - University Of California, Riverside; Schmidt, Michael; Skaggs, Todd; Ferreira, Jorge; ZACCARIA, DANIELE - University Of California, Davis; POURREZA, ALIREZA - University Of California, Davis; Corwin, Dennis

Submitted to: Frontiers in Plant Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/19/2025
Publication Date: 3/10/2025
Citation: Scudiero, E., Schmidt, M.P., Skaggs, T.H., Ferreira, J.F., Zaccaria, D., Pourreza, A., Corwin, D.L. 2025. Apparent soil electrical conductivity and gamma-ray spectrometry to map particle size fraction in micro-irrigated citrus orchards in California. Frontiers in Plant Science. 16. https://doi.org/10.3389/fpls.2025.1512598.
DOI: https://doi.org/10.3389/fpls.2025.1512598

Interpretive Summary: Problem Statement Water and fertilizer management are critical challenges for farmers, especially in California, where water is scarce and the costs of agricultural inputs are rising. Efficient management depends on accurate soil maps for properties like sand, silt, and clay contents. These maps help farmers apply the right amount of water and fertilizer in the right places, reducing waste and saving money. However, creating these detailed maps is usually expensive and time-consuming because it requires extensive soil sampling and analysis. Accomplishment This research demonstrated that sensor technologies can make soil mapping faster, cheaper, and accurate. Specifically, a sensor that measures the soil electrical conductivity was highly effective at predicting sand and silt contents, even with just a small number of physical soil samples. Producing accurate soil maps with limited ground-truth measurements is a milestone achievement and a significant improvement compared to current recommended practices, which generally require collecting many soil samples. A second technology, gamma-ray spectrometry, also helped, though its results varied depending on local soil conditions. The key finding is that these tools can give farmers detailed soil maps without requiring extensive, labor-intensive soil sampling, allowing them to manage water and fertilizer more efficiently. Contribution This research has significant implications for American tree-crop farmers, particularly in water-scarce regions. By enabling more precise irrigation and fertilizer application, these technologies can help farmers save money, increase yields and reduce environmental impacts, like pollution from fertilizer leaching. If widely adopted, these sensor-based methods could benefit millions of acres of farmland, potentially saving thousands of dollars per field in reduced input costs. This research helps individual farmers and supports broader efforts to manage agricultural resources sustainably, benefiting rural communities.

Technical Abstract: In specialty crops, water and nutrient management may be optimized using accurate, high-resolution soil maps, especially in resource-constrained farmland, such as California. We evaluated the use of soil apparent electrical conductivity (ECa)and gamma-ray spectrometry (GRS) to map particle size fraction across three micro-irrigated non-saline citrus orchards in California. Our research showed that ECa was a reliable predictor of soil texture, particularly sand and silt contents, with Pearson correlation coefficients (r) as high as -0.92 and 0.94, respectively, at the field level. Locally-adjusted analysis of covariance (ANOCOVA) regressions using ECa data returned accurate sand, silt, and clay content estimations with mean absolute errors (MAE) below 0.06, even when calibrated with a limited dataset (n=5 per field). On the other hand, we observed mixed results with GRS. We observed negative correlations between GRS total counts and sand content over the entire dataset (r = -0.55). However, one site (Strathmore) showed a field-scale positive correlation (r = 0.88). Clay content significantly correlated with gamma-ray total counts (TC) over the entire dataset (r = 0.37) but not at the field scale. Additional soil data analyses using GRS radionuclide ratios and soil laboratory analyses using diffuse reflectance infrared Fourier transform spectroscopy and acid ammonium oxalate extractable elements indicated unique geochemical and mineralogical characteristics in Strathmore, suggesting that factors such as soil mineralogy influenced the GRS measurements. This inconsistency prevented the development of a multi-field GRS-based soil texture ANOCOVA model. These findings confirm that ECa is highly effective for soil texture mapping in non-saline soils using linear modeling, while GRS may require field-specific calibration due to variations in local mineralogy. Integrating multi-sensor data is a viable means for reducing ground-truthing requirements and related costs, and improving the quality and accuracy of soil maps in agriculture.