Scott B. Jones

The soil water retention curve (SWRC) governs water storage and redistribution in the vadose zone and is a key input for hydrologic and agroecosystem models. Laboratory evaporation methods provide high‐quality SWRCs but are destructive and represent static conditions that may not reflect in situ soil behavior under natural field conditions. Co‐located field sensors measuring volumetric water content ( θ ) and matric potential ( h ) offer a continuous, nondestructive alternative for estimating field‐based SWRCs. In this study, the extent to which SWRCs can be derived from field‐installed sensors by directly comparing in situ θ – h relationships with laboratory evaporation measurements was evaluated. Six parametric retention models (Brooks–Corey, Fredlund–Xing, Kosugi, van Genuchten‐mnvar, van Genuchten [ m = 1 − 1/ n ], and van Genuchten‐PDI) were fitted to laboratory and field datasets in silt‐loam soil. Model performance and parameter stability were assessed using root mean square error of water content (RMSE_ θ ), and corrected Akaike information criterion (AICc). Field capacity (FC), permanent wilting point (PWP), and plant‐available water (PAW) were also calculated. Field‐derived θ _s was consistently lower than laboratory‐derived θ _s , indicating effective saturation in field conditions than complete saturation in laboratory conditions. No single retention model consistently provided the best fit across all laboratory and field datasets. The FC was relatively consistent across models, but PWP and PAW were more sensitive to model structure and extrapolation for θ _r . These results indicate that co‐located θ–h sensors can help field‐based SWRC estimation when model selection accounts for the limited suction domain. Additional high‐suction observations would improve constraint of dry‐end parameters and derived hydraulic indicators.

Accurate characterization of the effective DC electrical conductivity of surface-conducting soils is essential for translating non-invasive hydrogeophysical measurements into precise estimates of soil water content and salinity. Traditional single-stage mixing models often fail to capture the complex topological transitions and surface conduction mechanisms inherent to unsaturated porous media. This study presents the MG-sEMA model, a novel two-step hierarchical framework coupling a core-shell Maxwell Garnett (MG) mixing law with a symmetric Effective Medium Approximation (sEMA). The micro-scale MG stage utilizes an inside-out configuration where a highly conductive diffuse double layer (DDL) envelopes an insulating air-solid core within a free-water background. Crucially, this formulation honors actual pore drainage mechanisms, ensuring that free water drains completely before the DDL begins to shrink. The macro-scale sEMA stage subsequently governs phase connectivity, symmetrically blending conductive and insulating spheroidal domains to naturally establish percolation thresholds. The model's computations demonstrate its capability in reproducing complex effective conductivity-soil solution conductivity-water content relationships, seff(sw, q). The model successfully reconstructs the expected negative curvature of seff(sw), the positive curvature of seff(q), and a distinct percolation threshold (qc). This is achieved using a minimal set of four parameters: two structural parameters defining the spheroid aspect ratios at each mixing step, and two electrochemical parameters describing the dependence of the DDL thickness and its electrical conductivity on the soil solution electrical conductivity. At low water contents, the model dictates a higher tortuosity than usually observed, i.e., a larger positive curvature in seff(q), identifying a specific topological limitation of the static geometric assumption and providing a clear trajectory for future refinement. Ultimately, the decoupled MG-sEMA framework provides a rigorous, physically grounded approach for evaluating surface and bulk conduction in unsaturated soils.

Future International Space Station (ISS) mini-gardens must provide fresh produce repeatedly while maintaining water and nutrient conditions within a small, reusable root-zone volume. This study evaluated the operational performance of staggered planting and selective harvesting over four months in a ground-based Utah Reusable Root Module (URRM) simulator operated as a zero-discharge system. Five independent root modules (RMs), providing a total planted area of approximately 0.40 m², were planted with leafy greens, bonsai basil, and dwarf tomato. The system produced 4,299 g of fresh edible biomass, with produce available on 77 of the 100 days within the harvest period. Daily water use remained below approximately 2 L d⁻¹ for most of the trial, and peak demand occurred at different times among RMs. RMs containing established tomato plants behind newly planted leafy greens showed lower water-use peaks and biomass production, suggesting incomplete use of module capacity. Lettuce performed well during the first cycle but was less consistent in subsequent cycles, whereas mizuna and kale provided more consistent production under reused-media conditions. Bulk electrical conductivity (bulk EC) declined during periods of rapid crop growth, indicating that nutrient depletion, rather than salinity accumulation, was the main root-zone risk under zero-discharge operation. These results indicate that ISS mini-garden operations can distribute harvests and water demand over time, but crop calendars should coordinate planting dates, crop architecture, harvest timing, residue management, reused-media constraints, and root-zone nutrient monitoring across independently managed modules.

Time domain reflectometry (TDR) is widely used and provides accurate estimates of soil moisture when sensor rods are fully inserted into the soil. However, automation of sensor insertions, especially in field conditions, is hindered by inaccuracies caused by possible incomplete sensor rod insertion, which distorts waveforms and leads to inaccurate travel‐time readings. We address this challenge by introducing a depth‐aware multi‐linear regression model that incorporates sensor insertion depth to predict soil permittivity under partial insertion conditions accurately. The goal is to bridge the gap between lab‐grade or manually inserted TDR measurements and the practical constraints of autonomous, mechanized field sensing, particularly for robotic systems in heterogeneous soils. Our approach estimates permittivity in two steps. First, a depth‐based linear model was used for predicting the amplitudes corresponding to the time at which the pulse reflects from the tip of the metallic probe for each depth. Second, the linear model was intersected with a polynomial function fitted to the deformed waveform, allowing for the accurate estimation of the amplitude at which reflection occurs at the probe tip ( t ₃ ). The model was trained on curated datasets of TDR waveforms and validated against lab and manual insertion depths ranging from 2.5 to 5.0 cm. The proposed method demonstrated high predictive accuracy, with an R ² value of 0.82, mean absolute error of 71.55 ps, and a root mean square error of 98 ps. The results confirm the method's effectiveness in overcoming the TDR limitations under partition insertion, providing a low‐complexity and robust solution for automated soil moisture sensing in environmental and agricultural monitoring applications.

Nighttime evapotranspiration (ETₙ) has been widely reported across plant species, yet its magnitude and physiological significance in controlled-environment crop production remain poorly understood. This study quantified ETₙ in lettuce (Lactuca sativa L. cv. Outredgeous) grown under controlled-environment conditions and evaluated nighttime water flux and water-use efficiency (WUE). Plants were grown in peat-based root modules operated as zero-drainage systems, allowing ET to be quantified from continuous mass loss of individual nutrient reservoirs. Two surface treatments were compared: uncovered growth media and growth media covered with flexible silicone-rubber sheets. ET was partitioned into evaporation (E) and transpiration (T) components using the fraction of canopy cover derived from digital image analysis. During early development, E was reduced by 87% in covered modules. After canopy closure (23–28 days after sowing), the nighttime-to-daytime ET ratio was 0.46 for the uncovered treatment and 0.42 for the covered treatment (p < 0.001), indicating reduced nocturnal water loss. Nighttime stomatal conductance remained high, averaging 84% (536 daytime; 450 nighttime mmol m−2 s−1) of daytime values in the uncovered treatment and 77% (615 daytime; 476 nighttime mmol m−2 s−1) in the covered treatment. Silicone-rubber covers reduced cumulative water use by 32% without affecting biomass production, increasing WUE from 2.5 to 3.8 g L−1. The higher WUE in the covered treatment resulted from both E suppression and reduced nighttime water flux. These results demonstrate that substantial ETₙ persisted after canopy closure due to sustained stomatal opening, while surface covering improved WUE by reducing both E and nighttime water loss.

Efficient water management in closed-loop plant production systems requires accurate characterization of evapotranspiration (ET) under defined condensation-recovery benchmarks. Day–night ET dynamics were quantified in the Engineering Development Unit of the Utah Reusable Root Module, a zero-discharge system for controlled-environment crop production. A condensation recovery capacity of 3 L d⁻¹ was used as an operational benchmark during rapid vegetative growth.Volumetric water content was monitored at high temporal resolution using two electromagnetic sensors installed at different depths within five independent root modules. Hourly ET was derived from water-content depletion and cross-checked against irrigation inputs. Following seedling emergence, system ET increased from <1 L d⁻¹ to approximately 3 L d⁻¹ by day 12. During rapid growth, ET differed significantly among daytime, nighttime, and transition intervals, with daytime ET highest, nighttime ET lowest, and transition intervals intermediate.Although the cumulative daily nighttime-to-daytime ET ratio was 0.30 under a 16/8 h photoperiod, time-normalized analysis yielded a mean hourly ETₙ/ETd ratio of 0.53, indicating substantial nocturnal water flux that is not captured by daily totals. Scenario analysis suggested that reducing the photoperiod from 16/8 to 8/16 h could decrease daily ET by up to 19%, although these projections do not account for potential impacts on biomass production or system performance. These results highlight the importance of accounting for nighttime water loss when evaluating ET in controlled environments. Incorporating time-resolved ET partitioning into irrigation scheduling and environmental control strategies may improve water-use management in closed-loop and other resource-constrained production systems.

Nighttime scheduling of sprinkler irrigation is a widespread strategy to conserve water by reducing wind drift and evaporation losses (WDEL). Water intercepted by a canopy and evaporated during daytime irrigation events, however, may decrease gross water loss and ease heat stress, while microclimate changes from nighttime irrigation may be less consequential. The objectives were to determine whether water savings exist when comparing daytime versus nighttime irrigation with two urban crops by measuring (1) field water- and energy-balances to quantify WDEL, crop evapotranspiration (ET _c ), and changes in soil moisture storage; (2) the response of microclimate and evaporative demand; and (3) crop response. A field study at the Greenville Research Farm in North Logan, Utah (41.77° N, 111.81° W), tested two sprinkler irrigation timings (daytime, 14:00–17:00, and nighttime, 02:00–05:00) with Poa pratensis L. ‘Kentucky bluegrass’ turfgrass and Zinnia elegans L. ‘Benary’s Giant Salmon Rose’ cut flowers, in triplicate from 2019–2021, resulting in 126 paired events. The mean WDEL were 7% greater with daytime irrigation than with nighttime irrigation across both crops. Using the field water-balance method, the mean total seasonal ET _c, was 13 mm greater with nighttime irrigation than with daytime irrigation for both crops. There were no significant differences in ET _c using the energy-balance method, nor was the response of either crop significantly different by day/night irrigation timing. Though recommendations may vary by climate and sprinkler design, landscape irrigation should avoid hours with wind speeds greater than 3 m·s ^− 1 and not rely on nighttime irrigation as a sole water conservation approach.

This study presents a novel method for automatic soil texture classification that combines frequency domain dielectric spectroscopy (2–500 MHz) with supervised Machine Learning (ML). Broadband dielectric spectra were measured across five soil textures within a range of controlled water content- and temperature-conditions using a coaxial cavity probe and vector network analyzer. Multiple supervised ML models and spectral feature configurations were evaluated. The ensemble subspace discriminant (ESD) model achieved the highest performance, with up to 90% classification accuracy under random cross-validation. Support vector machines (SVM) and artificial neural networks (ANNs) also performed strongly, particularly with reduced or engineered feature sets. To evaluate model generalization, we implemented a group-aware cross-validation approach that ensured all spectra from a given soil and moisture condition were assigned exclusively to either training or test sets, avoiding overlap of similar samples. This more conservative strategy yielded accuracies up to 77% when dry or water saturated samples were excluded from the test set. Classification performance was highest under moist, unsaturated conditions, where dielectric dispersion most clearly reflected textural differences. These findings demonstrate that ML models can extract soil texture-related patterns from dielectric spectra, offering a viable method for real-time, in situ soil classification with potential benefits for precision irrigation, high-resolution soil mapping, and dielectric soil moisture sensor accuracy.