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

The study, published in the open-access journal Discover Soil, addresses a fundamental quantity in agricultural hydrology known as soil moisture retention. This property describes the relationship between the volume of water held in soil and the suction with which that water is bound to soil particles. Two reference points matter most. Field capacity marks the maximum water a soil retains after gravity has drained away the excess, conventionally measured at a pressure of −0.33 bar. The permanent wilting point marks the dryness at which plant roots can no longer extract water fast enough to survive, measured at −15 bar. The difference between the two is available water, the share of soil moisture genuinely accessible to crops, and it is the number on which irrigation schedules, hydrological models, and crop choices ultimately depend.

Measuring these constants traditionally requires a pressure plate apparatus, a device in which saturated soil samples sit on porous ceramic plates inside an airtight chamber. Increasing air pressure forces pore water through the plate until equilibrium is reached, a process that takes roughly a week across the full range of suction levels. The method is reliable but slow, costly, and demanding of technical expertise, which is precisely why characterisations of field capacity and permanent wilting point remain unavailable for many of the world’s agricultural regions. Punjab Agricultural University’s soil testing laboratory receives samples from farmers across the state, yet the sheer scale of demand far outstrips what pressure plate analysis can deliver.

The researchers, led by Swati Kashyap together with Bharat Bhushan Vashisht, Harsimran Kaur and Mohit Arora, took the modelling route instead. They assembled nine well-known pedotransfer functions, equations that translate easily measured soil properties such as sand, silt, clay content, soil organic carbon and bulk density into estimates of water retention. Pedotransfer functions were first proposed in the late 1980s as a way of adding value to routine soil survey data, and they have since been tested from the Congo basin to the Mekong Delta. The catch is that an equation calibrated on Ugandan ferrallitic soils or Brazilian Amazonian clays does not necessarily perform well on the alluvial sandy loams of north-western India, so local evaluation is essential.

To provide that evaluation, the team collected around 200 surface soil samples from 0 to 15 centimetres depth across Punjab’s different agroclimatic zones, drawing on farmer submissions held by the university’s Soil Testing Laboratory and on research fields. Seventy-eight samples were used to calibrate the candidate equations and forty independent samples were reserved for validation. Each sample was analysed for particle size distribution by the pipette method, organic carbon by wet oxidation, and water retention by the pressure plate apparatus itself, giving the researchers ground truth against which every model prediction could be scored. Performance was judged with three statistical indicators: root mean square error, which penalises large deviations; the index of agreement, which ranges from zero to one; and mean absolute error, which measures average prediction offset.

The results were strikingly clear. For field capacity, an equation published by J. D. Pidgeon in 1972 for ferrallitic soils in Uganda outperformed the field, achieving a root mean square error of 0.05 cubic centimetres of water per cubic centimetre of soil on validation, an index of agreement of 0.72 and a mean absolute error of 0.049. For the permanent wilting point, the 1979 equation of S. Gupta and W. E. Larson, built on particle size distribution, organic matter and bulk density, proved best, with an RMSE of 0.048, an index of agreement of 0.77 and a mean absolute error of 0.041. Critically, the researchers found that raw application of these imported equations systematically over- or under-estimated water contents. By adding a simple bias correction factor derived from the calibration data, −0.018 for the Pidgeon model at field capacity and +0.008 for the Gupta–Larson model at the wilting point, prediction accuracy improved markedly, shifting predicted values visibly closer to the one-to-one line when plotted against observations.

The physics behind the correlations is instructive. Silt, clay and organic carbon all correlated positively with water content at field capacity, while sand content correlated negatively with both constants. Clay governed retention at the wilting point more strongly than at field capacity, whereas organic carbon mattered more at field capacity. This makes sense because water held at low suction depends on the architecture of pore spaces, which organic matter helps build, while water held near the dry end is governed by adsorption forces on particle surfaces, a function of texture. Bulk density, meanwhile, increased retention at −15 bar, echoing earlier Indian studies on the influence of compaction on dry-end moisture.

With validated equations in hand, the team scaled up. Using soil maps covering 520 pedons, the basic mapping units of soil classification, compiled by the Department of Soil Science at Punjab Agricultural University, they extracted sand, silt, clay and organic carbon values for every pedon and predicted field capacity and permanent wilting point state-wide. Texture-specific bulk density values, ranging from 1.70 grams per cubic centimetre for sandy soils to 1.30 for clay loams, completed the input set. The predictions were then loaded into QGIS, the open-source geographic information system, and symbolised in graduated classes across four agroclimatic zones: the sub-mountain undulating region, the undulating alluvial plain, the central plain and the western alluvial plain.

The maps reveal a state with substantial but uneven water-holding wealth. Field capacity across Punjab soils ranges from 0.131 to 0.387 cubic centimetres per cubic centimetre, with roughly two-thirds of the land falling in a good band of 0.200 to 0.300. Permanent wilting point values span 0.009 to 0.228, with about 65 percent of soils in the 0.050 to 0.150 interval. Available water ranges from 0.113 to 0.183, and fully 93 percent of the state sits in the 0.120 to 0.160 band, a limited-to-good status in which ideal conditions are notably absent. The driest retention profiles appear in the arid western zone, where sandy loam and loamy sand textures combine with low organic carbon and clay. Intriguingly, the finest-textured clay loams, despite holding the most total water, show reduced availability, because water molecules bond tightly to negatively charged clay surfaces and resist extraction by roots.

For a state where rice and wheat consume some 61 percent of total water demand and unregulated groundwater extraction has created genuine scarcity, the practical implications are considerable. A farmer or irrigation planner equipped with these maps and a basic soil test can now estimate plant-available water for a specific field without ever touching a pressure plate, and schedule irrigation to match what the soil can actually store. The authors suggest the calibrated equations could be extended under different management systems for crop-specific water budgeting under a changing climate. More broadly, the study is a demonstration of a quiet but powerful idea in soil science: that the right simple model, rigorously calibrated and validated against local ground truth, can democratise information that expensive instruments have long reserved for a privileged few. In Punjab’s water-stressed fields, that democratisation may arrive just in time.

It is worth noting that the predictive skill reported in the study, while respectable, still leaves room for uncertainty. An index of agreement near 0.75 indicates that the calibrated equations capture the broad pattern of retention across Punjab’s soils but not every local deviation, so the mapped values are best treated as planning-grade estimates rather than substitutes for direct measurement where high-stakes decisions depend on precise water budgets.

The regional context also matters. Punjab’s soils are dominated by Inceptisols and Entisols developed on alluvial plains under a hyperthermic temperature regime, with annual rainfall between 400 and 1300 millimetres concentrated in the July-to-September monsoon. In such settings, sandy loam textures prevail, and coarse particles paired with low organic carbon naturally depress both field capacity and wilting point, which is consistent with the drier retention profiles the maps show in the arid western zone.

The approach also fits a wider trend in soil science toward digital soil mapping, where sparse laboratory measurements are extrapolated through pedotransfer functions and geographic information systems to produce continuous property surfaces. Because the underlying inputs, particle size distribution and organic carbon, are already collected routinely by soil testing laboratories, the framework could be updated cheaply as management practices change, and adapted to neighbouring alluvial regions facing similar groundwater stress.

Subject of Research: Modelling and mapping of soil moisture retention characteristics of Punjab soils using pedotransfer functions

Article Title: Modelling and mapping of soil moisture characteristics of the Punjab soils

Article References: Kashyap, S., Vashisht, B. B., Kaur, H., & Arora, M. (2026). Modelling and mapping of soil moisture characteristics of the Punjab soils. Discover Soil, 3(1), Article 153. https://doi.org/10.1007/s44378-026-00307-9

Image Credits: AI Generated

DOI: 10.1007/s44378-026-00307-9

Keywords: soil moisture retention, field capacity, permanent wilting point, available water, pedotransfer functions, Punjab, QGIS mapping, soil organic carbon, bulk density, irrigation scheduling, agricultural water management, soil texture