Water table contribution to plant water use is a significant element in improving water use efficiency (WUE) for agricultural water management. In this study, lysimeter experiments were conducted in a controlled greenhouse environment to investigate the response of soybean water uptake and growth parameters under four different water table depths (WTD) (30, 50, 70, and 90 cm). Soybean crop water use, WUE, and root distribution under the different WTD were examined. For 30, 50, 70, and 90 cm of WTD treatments, the average water table contributions were 89, 83, 79, and 72%; the grain yields were 15.1, 10.5, 14.1, and 17.2 g/lys.; and the WUEs were 0.22, 0.18, 0.25, and 0.31 g/lys./cm, respectively. Further analysis of the root mass and proportional distribution among the different soil layers illustrated that the lysimeters with 70 and 90 cm WTD had greater root mass with higher root distribution at 40–75 cm of the soil layer. The results indicated that 70 and 90 cm of constant WTD can yield higher grain yield and biomasses with greater WUE and better root distribution than the irrigated or shallow WTD treatments.

Predicting surface runoff and flooding in seasonally frozen areas such as the Red River of the North Basin (RRB) in USA is a challenging task. It depends on the knowledge of the complex process of infiltration in frozen soil, such as phase changes of water, ice content and distribution in the infiltration zone (the top 0–30 cm of the soil profile), soil pore size distribution, soil temperature and freeze–thaw cycles. In this study, the infiltration rates into frozen soil (Colvin silty clay loam according to the United States Department of Agriculture (USDA) Classification, and Chernozem according to Food and Agriculture Organization of the United Nations (FAO) international soil Classification) were measured at three different initial water contents: permanent wilting point (PWP), θ_pwp; field capacity (FC), θ_fc; and between FC and PWP, θ_mid. Laboratory infiltration experiments were conducted using a Cornell sprinkle infiltrometer with three replications for each initial water content. Volumetric soil water content (θ_v) and soil temperature at three depths were also continuously monitored using sensors. The average infiltration rates were 0.66, 0.38, and 0.59 cm/min for three initial water contents (θ_pwp, θ_mid, and θ_fc, respectively). Initial infiltration into frozen soil occurred quickly in the soil with θ_pwp because the soil was dry. Melted ice water contributed to the total soil water content over time, so it made the initial infiltration comparatively slower in the soil with θ_mid. Initial infiltration was also slower in the soil with θ_fc because the wet soil had very small pore space, so the soil rapidly reached its saturation after the infiltration started. The Horton infiltration equation was fitted with the observed infiltration rates for the soils with three initial water contents, and the goodness of fit was evaluated by using the coefficient of determination (R²) and the root-mean-square error (RMSE). The final infiltration rates from the fitted Horton equations were 0.060, 0.010, and 0.027 cm/min for the initial water contents (θ_pwp, θ_mid, and θ_fc, respectively). The soil water content along the soil profile changed with the amount of infiltrating water over time. However, the initial soil water content and melt water from ice resulting from soil temperature rise regulated the change in soil water content. The amount of ice melt water contribution to soil water content change varied among the soils with different initial water contents (θ_pwp, θ_mid, and θ_fc, respectively). The θ_v changed gradually in the θ_pwp soil, rapidly at 0 °C in the θ_mid soil, and less in the θ_fc soil. The change in pore distribution due to freeze–thaw cycles and soil packing altered the soil hydraulic properties and the infiltration into the soil. This study can provide critical information for flood forecasting model and subsurface drainage design in the RRB.

Comparison of soil water sensors for irrigation scheduling requires an accurate reference measurement. An Acclima TDR-310H soil water content sensor was calibrated and validated for sand, loamy sand, clay loam, sandy clay loam, and sandy loam soils. In 2021, sensor readings and soil samples were collected in the same sensor volume at depths of 15 and 46 cm at three irrigated field sites with five stations each. Gravimetric water content, bulk density, and volumetric water content (θ_v) were determined in the lab. The root mean square error was 0.032 cm³ cm⁻³, which was within acceptable limits. The sensor underestimated θ_v with a mean bias error of −0.025 cm³ cm⁻³. The linear field calibration equation was θ_v-Sample = 0.9498 θ_v-TDR + 0.0357 with R² = 0.8984. t-tests showed that the 95% confidence intervals (CIs) were 0.82 < slope < 1.08 and 0.0072 < intercept < 0.0642. Since the field calibration result was different from the factory calibration (intercept CI not straddling 0), use of the field calibration is recommended. A validation dataset collected in 2023 confirmed the calibration equation. The calibrated sensor can be used to evaluate other θ_v sensors for irrigation scheduling in the soil types in this study.

With the increasing availability of soil water sensing technologies for irrigation management, it is essential to compare sensor options in production field settings. In this study, four soil water sensors—CropX, FieldNet with Watermark sensors, Valley Aqua Trac Lite using Sentek probe, and Aqua Trac Pro with Watermark sensors—were compared with Acclima TDR‐310H (where TDR is time domain reflectometry) at four depths during the 2021–2023 growing seasons in three production fields in southeastern North Dakota. The Acclima sensor was calibrated in the same fields where the sensors were installed and was considered the reference standard for comparison with the other sensors. Soil water characteristic curves were developed to convert soil water potential from Watermark sensors from FieldNet and Valley Aqua Trac Pro systems to volumetric water content for comparison to other sensors. Overall, Aqua Trac Lite and CropX had mixed results at all depths (root mean square error [RMSE] typically above 0.05 cm ³ cm ⁻³ ). Lindsay and Aqua Trac Pro had better (smaller) RMSE across depths at sites 1 and 3 compared to CropX and Aqua Trac Lite. As expected, measured soil water contents varied over time for all sensors (i.e., significant main effect for timestamp [ p < 0.05]). However, the sensor type's effect on θ _v was consistent across all 3 years (i.e., main effect for sensor type [ p < 0.05] but there was no interaction with timestamp [ p > 0.05]). These results will help farmers and crop consultants understand sensor performance when selecting soil water sensors for irrigation management.