Soil hydraulic properties are important for the movement and distribution of water in agricultural soils. The ability of plants to easily extract water from soil can be limited by the texture and structure of the soil, and types of soil amendments applied to the soil. Superabsorbent polymers (hydrogels) have been researched as potential soil amendments that could help improve soil hydraulic properties and make water more available to crops, especially in their critical growing stages. However, a lack of a comprehensive literature review on the impacts of hydrogels on soil hydraulic properties makes it difficult to recommend specific types of hydrogels that positively impact soil hydraulic properties. In addition, findings from previous research suggest contrasting effects of hydrogels on soil hydraulic properties. This review surveys the published literature from 2000 to 2020 and: (i) synthesizes the impacts of bio-based and synthetic hydrogels on soil hydraulic properties (i.e., water retention, soil hydraulic conductivity, soil water infiltration, and evaporation); (ii) critically discusses the link between the source of the bio-based and synthetic hydrogels and their impacts as soil amendments; and (iii) identifies potential research directions. Both synthetic and bio-based hydrogels increased water retention in soil compared to unamended soil with decreasing soil water pressure head. The application of bio-based and synthetic hydrogels both decreased saturated hydraulic conductivity, reduced infiltration, and decreased soil evaporation. Hybrid hydrogels (i.e., a blend of bio-based and synthetic backbone materials) may be needed to prolong the benefit of repeated water absorption in soil for the duration of the crop growing season.

The focus of this research was to evaluate the horizontal pressures on a cylindrical metal silo of corrugated walls and flat bottom with 1.82m diameter and 5.4m high, and to compare the values with those obtained theoretically by the ISO 11697, EP 433 and AS 3774 standards. The silo was symmetrically filled and constant speed with wheat cv. soft red for two different height/diameter ratios (H/D) and was unloaded through three orifices with a diameter of 71.6mm, one concentric and two eccentrics. Horizontal pressures were measured on the walls of the silo at three positions using hydraulic type pressure cells. The results showed that shortly after the start of the unloading, there was a mass flow above the quota of H/D = 1.2, whereas below this quota funnel flow occurred. It can be said that the EP 433 standard was more appropriate to predict horizontal pressures in silos in H/D ratio = 1.0, with eccentric unloading. For the H/D ratio = 1.5, AS 3774 standard was the one that produced values closer to the experimental.

Superabsorbent polymers (hydrogels) have been studied for their ability to influence soil hydraulic conductivity because they can store and release water due to their swelling properties. However, concerns related to the increased use of synthetic hydrogels necessitates a switch to bio-based hydrogels, which are renewable and more biodegradable in comparison to synthetic hydrogels. In this study, we synthesized a lignin-based hydrogel and amended a silt loam soil with it at concentrations of 0, 0.1, and 0.3% (w/w). A laboratory permeameter, double membrane tension infiltrometer, and evaporation method were used to measure the saturated (Ks), near saturated, and unsaturated hydraulic conductivity (K) of the samples, respectively. Saturated hydraulic conductivity was significantly decreased by the application of hydrogel at 0.1 and 0.3% (w/w) in comparison to the control treatment. The application of 0.3% (w/w) lignin-based hydrogel only significantly decreased hydraulic conductivity at −1 cm soil water pressure head. Hydraulic conductivity in the 0.1 and 0.3% (w/w) treatments increased along the K(θ) curve in the unsaturated zone (−750 cm < h < −10 cm) in comparison to the control treatment, which we hypothesized was due to bound water in the hydrogel being released and creating a wider path for the movement of water. The 0.1 and 0.3% hydrogel treatments also tended to store more water than the control treatment, especially after 24 h of evaporation. The implication of this study is that lignin-based hydrogels could swell and retain water in saturated soils and the bound water could be released to enhance the flow of soil water in unsaturated soil, thereby reducing the water stress of plants, which require less energy to move and absorb water.

Shelled yellow corn is commonly stored in concrete or corrugated steel bins. Granular materials compact under their own weight, primarily due to particle rearrangement, leading to an increase in bulk density and a change in volume when stored. Reliable grain pack factors are needed to estimate storage capacities and to accurately monitor grain inventories. A science-based model (WPACKING) of pack factors is available that uses the differential form of Janssen’s equation and takes into account the variation in density caused by pressure variation with height and moisture content of the grain and accounts for the effects of grain type, test weight, bin geometry, and bin material. However, this model needs to be compared to field data over a wide range of conditions to ensure robust prediction accuracy. The objective of this research was to determine the field pack factors and bin capacities for on-farm and commercial bins used to store corn in the U.S. and compare them to predictions of the WPACKING program. Bin inventory measurements were conducted in concrete bins with depths up to 31.4 m (114.8 ft) and corrugated steel bins with diameters up to 32.8 m (156 ft). These values were also compared to the techniques used by the USDA Risk Management Agency (RMA) and the USDA Farm Service Agency, Warehouse Branch (FSA-W). The differences between predicted and reported mass were -4.54% (maximum underprediction) to +4.53% (maximum overprediction) for WPACKING, -2.69% to 4.97% for the RMA method, and -3.33% to + 5.67% for the FSA-W method. The absolute average difference was lowest for the WPACKING model (0.90%) compared to the RMA and FSA-W methods (1.61% and 1.86%, respectively). WPACKING had less than half as many prediction differences above 1% (13 out of 51 bins) as did the RMA and FSA-W methods, which had 29 out of 51 and 33 out of 51, respectively. The RMA and FSA-W methods do not take into account the variations in pack factor due to bin type and moisture content of the stored grain.

Collecting remotely sensed spectral data under varying ambient light conditions is challenging. The objective of this study was to test the ability to classify grayscale targets observed by portable spectrometers under varying ambient light conditions. Two sets of spectrometers covering ultraviolet (UV), visible (VIS), and near−infrared (NIR) wavelengths were instrumented using an embedded computer. One set was uncalibrated and used to measure the raw intensity of light reflected from a target. The other set was calibrated and used to measure downwelling irradiance. Three ambient−light compensation methods that successively built upon each other were investigated. The default method used a variable integration time that was determined based on a previous measurement to maximize intensity of the spectral signature (M1). The next method divided the spectral signature by the integration time to normalize the spectrum and reveal relative differences in ambient light intensity (M2). The third method divided the normalized spectrum by the ambient light spectrum on a wavelength basis (M3). Spectral data were classified using a two−step process. First, raw spectral data were preprocessed using a partial least squares (PLS) regression method to compress highly correlated wavelengths and to avoid overfitting. Next, an ensemble of machine learning algorithms was trained, validated, and tested to determine the overall classification accuracy of each algorithm. Results showed that simply maximizing sensitivity led to the best prediction accuracy when classifying known targets. Average prediction accuracy across all spectrometers and compensation methods exceeded 93%.