A single irrigation sensor can mean the difference between a thriving field and a thirsty one, but new research suggests that the same sensor reading may tell two entirely different stories depending on the soil it is buried in. In a study published in the journal Discover Soil, researchers José O. Payero and Selvaraj Selvalakshmi of Clemson University systematically calibrated two widely used soil moisture sensors across all twelve USDA soil textural classes, from pure sand to heavy clay, and found that soil texture fundamentally reshapes the relationship between how much water a soil holds and how hard plants must work to extract it. The findings deliver a pointed warning for precision agriculture: irrigation thresholds cannot be universally applied across contrasting soil textures without risking wasted water or stressed crops.
The study tackles a distinction that is easy to overlook but central to soil physics. Volumetric soil water content, symbolized as θv, measures the sheer quantity of water stored in the soil, expressed as a percentage of soil volume. Soil water potential, denoted Ψ, measures something subtly different: the energy status of that water, or how much suction a plant root must exert to pull it out. Two soils can hold identical amounts of water while offering dramatically different availability to crops, because the force binding water to soil particles depends on pore size, and pore size depends on texture. Sand, with its large macropores, releases water readily but drains quickly. Clay, packed with micropores, clings to water tenaciously even when quantities look abundant.
To untangle these relationships, the team conducted an outdoor experiment at the Edisto Research and Education Center in Blackville, South Carolina, from late November 2017 to early February 2018. Rather than digging up twelve naturally occurring soils, they constructed the textural spectrum themselves, mixing commercially available sand, silt, and clay in precise proportions defined by the USDA classification system. This yielded twelve soil mixtures representing sand, loamy sand, sandy loam, loam, silt loam, silt, sandy clay loam, clay loam, silty clay loam, sandy clay, silty clay, and clay. Each soil was packed into replicate plastic containers, each holding 700 cubic centimeters, and instrumented with two affordable and widely deployed sensors: the Decagon EC-5, a capacitance-based device that estimates volumetric water content by measuring dielectric permittivity at 70 MHz, and the Watermark 200SS, a granular matrix sensor that gauges soil water potential through electrical resistance within a hydrated gypsum matrix.
The experimental protocol was elegantly simple. The researchers saturated each container with water, then let the soil dry naturally under ambient outdoor conditions while recording sensor outputs and total system weight every morning at nine. Because the container weights were known when dry and wet, the team could compute gravimetrically determined water content at every time point, providing a trusted reference against which to judge both sensors. Particle-size distributions were verified with the hydrometer method, and bulk density was calculated from the oven-dry mass packed into each known volume. Field capacity, permanent wilting point, and available water were then estimated for each texture using the generalized soil water characteristic equations of Saxton and colleagues.
The drying patterns that emerged were starkly texture-dependent. Clay-rich soils began the experiment holding enormous quantities of water, with clay at roughly 50 percent volumetric water content, silty clay at 47 percent, and sandy clay at 45 percent. Sand, by contrast, started at only about 15 percent and loamy sand at 20 percent. As drying progressed, fine-textured soils retained residual water contents of 8 to 12 percent while coarse soils fell to just 2 to 5 percent, a direct consequence of pore-size distribution. The Watermark sensors told the complementary energy story: near saturation, all soils read close to minus 10 kilopascals, but sandy soils plummeted rapidly toward minus 150 to minus 200 kilopascals, the sensor’s practical detection limit, while clay and silty clay lingered between minus 40 and minus 60 kilopascals far longer, releasing their water grudgingly.
Perhaps the most practically valuable result came from the team’s use of segmented regression, a statistical technique that locates breakpoints in nonlinear relationships. Applied to the drying curves, this analysis identified threshold soil water potential values, the points beyond which a small loss of water content triggers a steep drop in water potential and a corresponding crash in plant availability. Across all textures, average thresholds landed at approximately 40 kilopascals for the gravimetric-Watermark pairing and 44 kilopascals for the EC-5-Watermark pairing, but individual textures ranged widely, from minus 18 to minus 52 kilopascals in the gravimetric comparisons. These breakpoints, the authors argue, offer texture-specific reference points for irrigation scheduling that a single universal threshold simply cannot provide.
The calibration performance of the sensors themselves also diverged by texture. The Decagon EC-5 showed outstanding agreement with gravimetric measurements, with coefficients of determination between 0.987 and 0.997 across all twelve soils, and root mean square errors from just 0.29 percent in sand to 4.94 percent in clay. Polynomial models, mostly quadratic or cubic, provided the best fit, including sand at R² of 0.994, sandy clay at 0.992, silty clay at 0.997, and clay loam at 0.995. The higher errors in clay-dominated soils reflect the greater variability in dielectric response that clay content introduces, reinforcing a theme from the broader sensor literature that soil-specific calibration beats factory defaults. Under extremely dry conditions, the EC-5 even produced slightly negative readings in sand, an artifact of diminished dielectric contrast and poor probe-soil contact in nearly waterless coarse material.
The Watermark sensor, meanwhile, proved more texture-sensitive. Its relationship with gravimetrically measured water content ranged from a moderate R² of 0.745 in sand to a strong 0.970 in clay and silty clay, consistent with earlier reports that granular matrix sensors struggle in low-water-retention sandy profiles. Yet comparisons between the Watermark’s potential readings and the EC-5’s content readings remained consistently strong across textures, with R² values from 0.896 to 0.978, suggesting the two sensing principles can be meaningfully linked once soil-specific calibration curves are in place. Such linkage matters because capacitance and resistance sensors answer different questions: one reports how much water is present, the other how available it is to roots.
The authors are candid about the study’s boundaries. The experiment used disturbed, prepared soil mixtures under outdoor container conditions, so real-world complications like soil structure, organic matter, root activity, and weather variability were not captured. The Watermark’s operating range of roughly 0 to minus 200 kilopascals also left the dry end of the retention curve, including the permanent wilting point near minus 1500 kilopascals, outside measurable reach, and hydraulic properties were estimated from texture rather than measured with pressure-plate apparatus. These constraints prevented fitting mechanistic models such as the van Genuchten equation. Still, the empirical relationships developed here, spanning all twelve USDA textural classes under a single unified framework, appear to be the first of their kind reported for South Carolina, a state where irrigated acreage is expanding rapidly across highly heterogeneous soils.
The practical message is resonating in an era when smart irrigation systems promise water savings through automation. As the study concludes, accurate interpretation of soil moisture data demands that soil texture and soil-specific relationships between water content and water potential be considered alongside sensor calibration. A grower reading minus 40 kilopascals in a loamy sand is witnessing a very different soil condition than one reading minus 40 kilopascals in clay, and irrigating both fields identically will inevitably overwater one and shortchange the other. The texture-specific thresholds and calibration curves published in this work offer a concrete starting point for building such nuance into irrigation decision tools, though the authors stress that field validation across diverse crops and climates is still required before widespread deployment.
Subject of Research: Empirical relationships between soil moisture and soil water potential across soil textural classes for irrigation management
Article Title: Influence of soil texture on soil moisture and soil water potential dynamics
Article References: Payero, J. O., & Selvalakshmi, S. (2026). Influence of soil texture on soil moisture and soil water potential dynamics. Discover Soil, 3(1), Article 152. https://doi.org/10.1007/s44378-026-00305-x
Image Credits: AI Generated
DOI: 10.1007/s44378-026-00305-x
Keywords: soil texture, soil moisture, soil water potential, irrigation management, sensor calibration, water retention, Decagon EC-5, Watermark 200SS, field capacity, available water, soil physics, precision agriculture
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Alan Morgan. (September 12, 2026). Soil Texture Emerges as the Hidden Variable Deciding When Crops Truly Need Water. Scienmag. https://scienmag.com/soil-texture-emerges-as-the-hidden-variable-deciding-when-crops-truly-need-water/
Alan Morgan. “Soil Texture Emerges as the Hidden Variable Deciding When Crops Truly Need Water.” Scienmag, 12 September 2026, https://scienmag.com/soil-texture-emerges-as-the-hidden-variable-deciding-when-crops-truly-need-water/. Accessed 12 September 2026.
Alan Morgan. “Soil Texture Emerges as the Hidden Variable Deciding When Crops Truly Need Water.” Scienmag. September 12, 2026. https://scienmag.com/soil-texture-emerges-as-the-hidden-variable-deciding-when-crops-truly-need-water/
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Tags: available waterDecagon EC-5differences in sand and clay soil moisture dynamicsfield capacityirrigation managementoptimizing water use in agricultureprecision agricultureprecision agriculture irrigation managementsensor calibrationsoil moisturesoil moisture sensors calibrationsoil physicssoil physics and plant water uptakesoil physics research on irrigation sensingsoil textureSoil texture and crop water requirementssoil texture impact on irrigation thresholdssoil water potentialsoil water potential and plant stresssustainable water management in farmingUSDA soil textural classesvolumetric soil water content measurementwater retentionWatermark 200SS
