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Soil erosion is a major constraint to agricultural sustainability. Land management that protects soil from water erosion can be assessed from the fraction of aggregates stable upon water immersion. Conservation management with minimal soil disturbance is known to enhance water‐stable aggregation, but how annual vs. perennial vegetative cover affects soil biological activity and aggregation from soils varying in texture remains relatively unexplored. This study assessed the effects of long‐term conventional‐till and no‐till cropland, grassland, and woodland on dry‐stable and water‐stable mean‐weight diameter (MWD) from 25 research stations across North Carolina. Soil clay concentration varied from 36 to 279 g kg–1 and sand concentration varied from 392 to 904 g kg–1 (5–95% distribution). Across locations, water‐stable MWD followed the order: conventional‐till cropland (0.64 mm) < no‐till cropland (0.87 mm) < woodland (1.12 mm) = grassland (1.15 mm). Soil stability index (i.e., water‐stable MWD/dry‐stable MWD) followed a similar order: conventional‐till cropland (0.59 mm mm–1) < no‐till cropland (0.76 mm mm–1) < grassland (0.92 mm mm–1) = woodland (0.96 mm mm–1). Although water‐stable MWD was positively associated with clay concentration (r =.50, p <.001, n = 310), soil‐test biological activity had the strongest association with water‐stable MWD (r =.80). Soil‐test biological activity reflected biological manipulation of surface‐concentrated organic matter, as mediated by conservation land uses. This study revealed the large impact of surface organic C and N resources on water‐stable aggregation through soil biological manipulations interacting across a textural gradient. Core Ideas: Conservation land uses of no‐till cropland, grassland, and woodland improved soil aggregation characteristics.Soil stability index discerned conservation land use impacts across a soil texture gradient.Soil aggregation characteristics were not different between grasslands and woodlands.Soil‐test biological activity and sand were strongly associated with soil aggregation characteristics. [ABSTRACT FROM AUTHOR]

This study developed a comprehensive database of soil physical properties to expand the potential applications of soil moisture observations across the Kansas Mesonet environmental monitoring network. The database comprises 14 site‐ and depth‐specific soil hydraulic properties for 40 stations monitoring soil water content. The measured soil hydraulic properties include sand, clay, and silt contents; bulk density; particle density; total porosity; effective saturation; saturated hydraulic conductivity; and water retention at six matric potentials. The soil database spans eight soil textural classes, with silty clay loam, silt loam, and silty clay soils dominating the fine‐textured soils, and sandy loam and sandy clay loam soils comprising the only coarse‐textured soils. All the measured soil hydraulic properties showed low coefficient of variation (CV ≤10%) within soil textural classes, except for the saturated hydraulic conductivity, which varied from a median of 0.520 ± 308 cm d–1 in the clay soils to 47.1 ± 119 cm d–1 in the sandy loam soils. The uncertainty of the Kansas Mesonet soil water content observations was reduced by at least 29% using laboratory sensor calibration equations instead of the factory default sensor equation. Our soil physical property database offers new prospects to use historical and near real‐time in situ soil water content observations across the Kansas Mesonet for diverse agricultural and hydrological applications, including drought monitoring, wildfire preparedness, and estimating potential drainage and groundwater recharge rates. Core Ideas: This study developed a comprehensive soil physical property database for the Kansas Mesonet.The database comprises 14 site‐ and depth‐specific soil hydraulic properties for 40 stations.The soil database is dominated by fine‐textured soils with 92% of the soils having >20% clay content.Laboratory sensor calibration equations reduced the uncertainty of the water content measurements by at least 29%. [ABSTRACT FROM AUTHOR]

The impacts of soils’ heterogeneity due to temporal (ET)- and seasonal (ES) changes from minute variations in precipitation (as rain, ice, and snow) and air temperatures (P+TA) on the ground-penetrating radar (GPR) responses (RGPR) are not rigorously tested. We aimed to understand how environmental changes (ET and ES) can be accountable to alter RGPR and help relate to real-time (TR) soil environmental status. Soil moisture (volumetric water content, VWC), temperature (TS), and electrical conductivity (EC) in every 30 min were recorded by soil sensors daily in different seasons to match the diurnal and seasonal changes. Readings of GPR alerts from six soil types of various GPR-suitability classes were recorded two to three times daily. Changes in P+TA, which changed physicochemical soil properties (PCSP) temporally and impacted RGPR up to 76%. The RGPR, which was negatively corelated with clay (R = –0.89) and organic matter (OM) (R = –0.91) percentages, remained low in soils when cation exchange capacity (CEC) is also high. The RGPR which were pronounced after rain, during fall, in sandy soils, and in case of neutral to basic pH impacted by narrow-range VWC variations on sandy but broad range on clay soils. Soils of same clay percent and similar OM acted differently if they have differential CEC and pH values. Ninety-six percent of RGPR, which is impacted by electric resistances and bonding nature of water and soils, were explainable if interactions of clay, OM, and VWC at TR are considered. Future insights on influences from freeze–thaw cycles and specific silica-mineral types of soils are required. [ABSTRACT FROM AUTHOR]

The extractable soil-K concentration, used for fertilizer-K recommendations, may be affected by soil drying. Although air or oven drying are the most common soil processing methods, K from field-moist soil has been documented to be a better predictor of soil-K availabilities and fertilizer-K needs for soybean [Glycine max. (L.) Merr.] grown on fertile silt loam to clayey soils. We evaluated the effect of four soil processing methods (field-moist [FM], air-dry [AD], air-dry followed by oven-dry [ADOD], and oven-dry [OD]) in predicting extractable soil-K availability for soybean production on less fertile Mid-Atlantic sandy-textured soils. Twelve soybean field trials were conducted in 2014 on Coastal plain and piedmont soils in Virginia and North Carolina. Soil K was extracted by Mehlich-1 with each soil processing method and correlated with soybean relative yield. Soil-K concentrations from each method were statistically similar and strongly correlated (r² = 0.94-0.98) with each other having intercept and slope coefficients that were not different from zero and one, respectively. Extractable soil-K concentrations from each method were also strongly correlated with soybean relative yield and explained 93 to 95% of the relative yield variation for FM soil, 95 to 96% for AD soil, 83 to 86% for ADOD soil, and 94 to 95% for OD soil. Results suggest that soil-K concentrations from FM, AD, and OD samples are similar in predicting K availability for soybean. Soil drying should not be an issue of concern in extracting soil K and recommending fertilizer-K rate for soybean production on Mid-Atlantic coarse-textured Coastal Plain and Piedmont soils. [ABSTRACT FROM AUTHOR]

This study examined the utility of nonlinear regression as a means of developing a statistically-based function for the estimation of particle density (Dp) of clay-rich mineral soils where the <0.002 mm fraction was mineralogically diverse. Physical and chemical soil property data (including semi-quantitative clay mineralogy) were analyzed for 54 mineral soil horizons sampled from 18 soil profiles at seven different locations across North America. Plausible estimates of particle density for the mineral component (2.72 Mg m-3), and particularly for the humic component (1.48 Mg m-3), were obtained (r² = 0.856, root mean square error [RMSE] = 0.035 Mg m-3 P < 0.0001) even though the calibration data set had a limited range of soil organic matter content (<23%). Particle density was shown to vary with clay content (P < 0.05), and Dp of different mineral particle-size fractions could also he distinguished statistically. The convenient and traditional assumption of Dp = 2.65 Mg m-3 for silicate-based soils in the absence of Dp measurements is challenged, given the considerable observed variability in Dp with changes in soil organic matter content and texture. [ABSTRACT FROM AUTHOR]

The objective of this work was to construct and validate a model that shows how the clay shrinkage curve, under the influence of a silt-sand admixture as well as an inter- and intraaggregate structure, is transformed to the soil shrinkage curve. To meet this objective, we investigated (i) the reference shrinkage curve, that is, one without cracks, because cracks lead to a multivalued shrinkage curve; (ii) the rigid superficial (interface) layer of aggregates, with changed pore-size range and distribution, compared with the intraaggregate matrix; and (iii) soils with sufficiently high clay content when large pores inside the intraaggregate clay (so-called lacunar pores) are nonexistent. The methodology is based on detail accounting for contributions of the interface aggregate layer and intraaggregate matrix to the soil volume and water content during shrinkage. The reference shrinkage curve is determined by six physical soil parameters: oven-dried specific volume; maximum swelling water content; mean solid density; soil clay content; oven-dried structural porosity; and the ratio of aggregate solid mass to solid mass of the intraaggregate matrix. Only the last parameter was fitted for lack of data and compared with an unfitted estimate. The model was validated using data for eight soils. The important new conclusion is that the mere existence of the rigid superficial aggregate layer leads to a soil reference shrinkage curve that is convex upward in the structural shrinkage area, unlike the shrinkage curve of the clay contributing to the soil. The model can have numerous applications. [ABSTRACT FROM AUTHOR]

The shrinkage geometry factor connects the changes of subsidence and the total crack volume at the shrinkage of a soil layer matrix. An available approach for estimating the factor does not account for the crack volume in samples, the stretching of a shrinking soil layer, and a possible change of the factor with water content. A recent new approach for estimation of the factor enables one to account for the factor's dependence on water content and the impact of cracks in a sample. The present work also accounts for the stretching of a shrinking soil layer. Corrected value of the factor is described in terms of the shrinkage curves of: a layer including unconnected solid cubes from the available approach, a stretched layer with cracks, a sample with cracks, and the soil matrix without cracks. This correction leads to changes in the soil subsidence and total crack volume compared with those obtained from the available approach. Two known physical conditions are used: the specific soil matrix volume without cracks is the same for the sample and the layer, and, the matrix deformations of an unlimited shrinking soil layer are longitudinal those of a thin quasi-elastic plate. Two available experimental examples are considered. The former relates to clay paste samples. The latter relates to an aggregated clay soil in situ and in samples. The geometry factor from the available approach, and that obtained after correction, differ significantly. The corrected factor also shows the appreciable change with water content. [ABSTRACT FROM AUTHOR]

Sodium affected rice (Oryza sativa L.)-cropped fields are very common. Due to their high exchangeable Na percentage, clay dispersion is one of the major risks for these soils when they are flooded. During flooding, Fe2+ may become a major cation due to reductive dissolution of Fe oxides, but the flocculation effect of Fe2+ is not known. In this paper, the effect of Fe2+ on clay flocculation is studied by establishing critical coagulation concentration (CCC) curves of clays extracted from a paddy soil of Northeast Thailand. The effect of Na-Fe2+ and Na-Ca electrolytes on the CCC values is compared for sodium adsorption ratio (SAR) values ranging from 0 to 40 and total electrolyte concentration (TEC) ranging from 0.5 to 10 mmol L-1. The extracted day is a mixture of kaolinite and smectite but only the smectite and some poorly ordered kaolinite could be dispersed. The CCC values largely reflected the behavior of smectite, in agreement with previous studies. The CCC values were equal for electrolytes with Fe2+ or Ca2+ cations, suggesting that Fe2+ strongly adsorbs on smectite exchange sites and behaves similarly to Ca2+. The Fe2+ concentration needed to flocculate the clays was at maximum 0.6 mmol L-1 for the most dispersive electrolytes, which is a common concentration in flooded rice fields. Reductive dissolution of Fe can protect sodic soils from clay dispersion upon flooded rice cropping. [ABSTRACT FROM AUTHOR]

Capacitance probe sensors are a popular electromagnetic method of measuring soil water content. However, there is concern about the influence of soil salinity on the sensor readings. In this study capacitance sensors are calibrated for a saline silty clay soil. An electric circuit model is used to relate the sensor's resonant frequency F to the permittivity (ε) of the soil. The circuit model is able to account for the effect of dielectric losses on the resonant frequency. Dielectric mixing models and empirical models are used to relate the permittivity to the soil water content (θ). The results show that the electric circuit model does not fit the F-ε(θ) data if the calibrated bulk electrical conductivity (EC) model is used. The dielectric losses are overestimated. Increasing the exponent c in the tortuosity factor of the bulk EC model and thereby lowering the bulk EC and the dielectric losses improves the performance of the model. Measured and calculated volumetric water contents compare reasonably well (R² = 0.884). However, only 73 out of 88 data points can be described. The rejected points are invariably at high water contents where the high dielectric losses result in the sensor frequency being insensitive to ε(θ). [ABSTRACT FROM AUTHOR]