Understanding the spatial variability of soil texture within field soils is important due to its influence on a large number of soil and plant related processes and for site-specific application of inputs that are crucial to crop production. It remains a problem to obtain a reliable clay content map based on a limited number of sampling locations. The objective of this study was to identify spatial variability of soil clay content and the behavior of the estimation result for different spatial resolutions of measured clay content (0-20 cm depth) in combination with a coregionalization approach using apparent electrical conductivity (ECa). In a silty loam soil, soil clay content was measured at 96 points in a 50-m by 50-m grid within an agricultural field. ECa, was measured using a contact sensor Veris 3150. Data were analyzed with ordinary kriging and cokriging while using ECa, at a shallow depth. We analyzed different sampling scenarios based on clay subsamples of 48, 24, and 12 data points distributed over the 27-ha field. In all scenarios investigated here, the RMSE stayed in the range of 3 to 4% by using different validations, with cokriging performing constantly better than ordinary kriging. Clay content maps estimated with cokriging maintained a satisfactory precision when the sampling density was reduced to one sample per two hectares, a result that leads to the conclusion that electrical conductivity in combination with spatial coregionalization demonstrated to be a promising tool to estimate the spatial variation of clay content even at a low clay sampling density.

Crop irrigation which results in high water use efficiencies typically uses science-based irrigation scheduling tools to determine irrigation application timing and quantities. Although a large variety of sensors are available for measuring soil moisture status, there are a few easy-to-use irrigation scheduling tools which provide a yes/no irrigation decision or recommend how much water should be applied to return the soil profile to an optimal soil moisture condition. The work described here developed a method which uses soil water tension data from soil moisture sensors and the van Genuchten model to provide irrigation scheduling recommendations. The strength of the method is that it can use data readily available from USDA-NRCS soil surveys to predict soil water retention curves and calculate the volumetric water content and soil water tension of a soil at field capacity. Those parameters are then used to translate measured soil water tension into irrigation recommendations which are specific to the soil moisture status of the soil. The method was validated by comparing its results to other published methods and with continuous soil water tension data with multiple wetting and drying cycles from six fields in southern Georgia, USA. Finally, the model was incorporated into a web-based irrigation scheduling tool and used in conjunction with a wireless soil moisture sensing system to schedule irrigation in a large commercial field during 2015. By the van Genuchten model, we used about two thirds of the irrigation water and produced about the same yields as a commonly used yes/no irrigation decision tool. The presented method can be used to build resiliency to climate variability because it provides growers with data which they can use to make informed decisions about managing their water resources. Published by Elsevier B.V.

"Field capacity" is the most frequently cited soil physical quantity and also the most ambiguous one. Its true assessment involves an internal drainage experiment in the absence of evaporation and transpiration, together with the establishment of a negligible drainage rate. For practical reasons however, estimating field capacity using an arbitrary value of pressure head is common practice. The objective of this study was to assess field capacity based on a fixed bottom flux or on a fixed time. This goal should be achieved through numerical simulations of internal drainage experiments using experimentally determined soil hydraulic properties on 46 locations on a 50-m transect in a layered soil. Flux-based estimates of field capacity exhibited high correlation to unsaturated hydraulic conductivity at the lower profile boundary, hydraulic gradients ranging from 0.25 to almost 1. Considering the 46 locations, bottom flux at a fixed time varied over one order of magnitude, whereas the time to reach a predetermined bottom flux associated to field capacity also showed a variation of about an order of magnitude. By setting hydraulic conductivity equal ( or slightly higher to compensate for a smaller hydraulic gradient) to a pre-established bottom flux, the pressure head or water content of field capacity was then assessed. Based on the flux criterion of 1 mm d(-1), field capacity corresponded to a pressure head of about -0.55 m in the evaluated soil; the corresponding time of drainage to establish this bottom flux was 4 d when considering only the top 0.15 m to almost 2 wk for a 0.75-m profile depth.

Qualitative analyses of physical, chemical, or biological variables are difficult and often ambiguous. Soil physical quality (SPQ) indices are not an exception to this rule. There is no unique revealing parameter or index that enables evaluating soil structure. In high-intensity biomass production systems, SPQ indices are useful tools for management decisions because they indicate the sustainability of soil organic matter dynamics, drainage, infiltration, heat transfer, and storage processes. This work examines five energy parameters and two hydraulic energy functions for evaluating physical quality in terrestrial vegetative ecosystems. These indices are based on numerical integration of the soil water retention curve (SWRC) and on soil water content at field capacity, and they manifest the absolute aeration energy, the absolute water retention energy, the relative aeration energy, the relative water retention energy, and the relative air-water energy. This integration technique includes the use of all points of the SWRC. A script for fitting the parameters of the van Genuchten equation and for solving all the presented indices in this work was developed and made available. The energy indices and hydraulic functions were derived and validated for SWRCs, from several German, US, and Brazilian soils under different management, comparing them with other previously published SPQ indices. Our findings reveal that the energy indices and functions can be applied to assess the energy associated with the soil physical structure.

The laser diffraction method (LDM) has been increasingly applied for quantifying soil particle size distribution (PSD), owing to its advantages of rapid analysis, high reproducibility, and continuous PSD measurement for a wide range of size fractions. However, some ambiguities exist regarding the comparability of results with those obtained using other classical methods. The objective of the current study was to evaluate LDM-derived PSDs via comparisons with PSDs obtained with the standard sieve-pipette method (SPM) and from the absolute method of microscopy. A total of 277 soil samples were collected at different soil depths in a typical cropland in the northeast mountainous region of Beijing and analyzed with both SPM and LDM. Due to time and labor constraints, scanning electron microscopy (SEM) was performed on 100 samples randomly selected for the PSDs within the clay fraction withdrawn by SPM. The results manifested on the average 18.9% underestimation of clay content and 25.3% overestimation of silt content by LDM compared to SPM. These disagreements directly caused the shifts of soil texture class in 44.8% of the soil samples. Significant linear regression equations were generated to convert LDM-derived sand and silt contents to SPM-derived ones (p < 0.01). The linear conversions for the clay content were only significant for the calibration samples, but possessed negative coefficients of determination for the validation set. According to SEM, silt-sized particles were wrongly included in the clay fraction identified by SPM. Eliminating such particles, the clay contents corrected by SEM were significantly lower when assuming the shape of clay particles < 2 mu m as plates or discs with constant thickness-diameter ratio of 1/10, and higher when considering the clay particles as spheres for volume calculation, in contrast to those measured by LDM (P < 0.01). Detailed volume-based PSDs within the clay fraction were further compared between SEM and LDM, revealing dissimilar PSD patterns but statistically similar median particle diameters. These findings suggest the effectiveness of LDM in soil PSD determination. Future work is needed to systematically quantify the impact of other possible factors such as clay mineralogy and refractive index on LDM-derived PSDs.

Saturated hydraulic conductivity (K-s) is a crucial hydraulic property for assessing soil water dynamics. Understanding the spatial variability of K-s in a field is important for site-specific resource management. However, direct measurement of hydraulic conductivity K as a function of soil water pressure head h [K(h)] is time consuming and laborious. Alternatively, pedotransfer functions (PTFs) have been developed to predict K-s indirectly based on more easily measurable soil properties. Although PTFs have been used for decades, their validity for estimating the field-scale spatial variability of K-s remains unclear. The objectives of this study were to characterize the spatial pattern of K(h) at and near saturation in an agricultural field by a coregionalization technique, and in comparison, to evaluate the performance of ROSETTA PTF in characterizing the spatial variability of K(h) at the field scale. Surface soil (7-13 cm) K(h) in the vertical direction was measured at 48 locations in a 71-m by 71-m grid within a no-till farmland. Apparent electrical conductivity was densely measured using a contact sensor Veris 3150 and used as ancillary variable in a coregionalization approach. Experimental semivariograms and cross semivariograms were derived and applied in cokriging to generate K(h) maps. Geostatistical analysis presented similarities in maps of measured K(h) with ROSETTA-predicted K(h) data for a matric potential of - 10 cm. However, the strong spatial heterogeneity of measured K-s, which was caused by macropores, observed in the field was not captured by ROSETTA estimates. The results indicated that texture dominated PTFs like ROSETTA, in which soil structure is not considered, might be useful in characterizing the spatial pattern of unsaturated hydraulic conductivity rather than K-s. Field scale K-s maps based on PTF estimates should be evaluated carefully and handled with caution.

A precise description of saturated (K-s) and near-saturated hydraulic conductivity (K-10) and their spatial variability is important for understanding water/solute transport in the vadose zone. However, it is laborious to measure K directly. Alternatively, K could be predicted from easily measurable soil properties using pedotransfer functions (PTFs). Because PTFs ignore the spatial relationships and covariance between soil variables, they often perform unsatisfactorily when field-scale estimations of K are needed. Therefore, the objective of this study was to improve the estimation of K at field scale through consideration of spatial dependences between soil variables. K was measured at 48 locations in a 71 x 71-m grid within a farmland under no-till. An autoregressive state-space approach was used to quantify the spatial relations between K and soil properties and to analyze the spatial variability of K in the field. In comparison, multiple linear regression (MLR) was used to derive PTFs for K estimation. Using various combinations of variables, state-space analysis outperformed PTFs in estimating spatial K distribution across the field. While state-space approach explained 69%, MLR method explained only 6% of the total variation in K-s. For K-10, the best state-space model included silt, clay, and macroporosity and performed almost perfectly (R-2 >95%) in characterizing the spatial variability of K-10. In that case, the best MLR-type PTF explained only 60% of the variation. The results indicate that, by considering the spatial relations between soil variables, state-space approach is an effective tool for analyzing the spatial variability of K at field scale.

For long-term studies on spatiotemporal processes of soil water focusing on spatial variability at the field scale, appropriate calibration techniques for capacitance sensors are lacking. In an ideal case, soil water content samples would need to be taken in the close vicinity of the access tube, that is, within the physical sphere of influence, for sensor calibration. This destructive sampling procedure is prohibitive because the measurement site of that particular access tube would be destroyed for any further capacitance probe measurements. On the other hand, despite the advantage of controlled boundary conditions, it is difficult or impossible to conduct a lab calibration of the sensor that would be valid for the site specific conditions in a heterogeneous field soil. The objective of this study was to propose a sampling and data processing procedure to compare six calibration scenarios with respect to the RMSE values of their associated calibration functions and to describe the spatial series of calibration parameters in an autoregressive state-space model. All scenarios were based on a data set obtained during the installation of access tubes and four subsequent soil sampling campaigns in the vicinity of 1-m radius around the tube. Site- and depth-specific calibration yielded the best calibration results, ranging among the best calibration results presented in the literature. Layer-specific stratification of measurement points according to the soil clay content yielded RMSE values approximately twice as high as the site- and layer-specific calibration and ranged among many other published studies. The presented sampling and data-processing procedure in combination with site- and layer-specific calibration are critically discussed and show a promising way for field-scale studies focused on spatiotemporal processes of soil water content and underlying calibration parameters. This study shows that the spatial range of representativity can substantially exceed the small physical sphere of influence of the capacitance sensor and supports its calibration.

Widely popular Mehlich-3 phosphorus (P) was not used in the estimation of the intrinsic status of soil P. The objectives of this study were to develop the state-space model to determine the intrinsic soil P pools, a weakly adsorbed P pool, and a tightly adsorbed P pool, from the Mehlich-3 P. A linear relationship between Mehlich-3 P and the intrinsic soil P pools was derived from theoretical analysis of the extraction process of adsorbed P as competitive exchange by aqueous fluoride. Adsorption experiments of soil P were carried out to determine the dynamics of Mehlich-3 P after fertilizer phosphate was applied to soils. The results showed that state-space models of the two soil P pools could describe the dynamics of soil Mehlich-3 P in P adsorption experiments. Mehlich-3 P was a fraction of the weakly adsorbed P, and this fraction showed a linear relationship with soil P-sorption site density.

Although numerous soil-landform modeling investigations have documented the effects and importance of spatial autocorrelation (SAC), little is known about how to predict the magnitude of those effects from the degree of SAC in the model variables. In this study, we quantified the SAC inherent in soil and landform variables of four widely divergent pedogeomorphological systems around the world to examine general relationships between SAC and spatial regression model results. Spatial regressions were performed by incorporating spatial filters, extracted by spatial eigenvector mapping, into non-spatial models as additional predictor variables. Results indicated that incorporation of spatial filters improved the performance of the non-spatial regressions-increases in R-2 and decreases in both Akaike Information Criterion (AIC) and residual SAC were observed. More remarkable was that the degree of improvement was strongly and linearly related (i.e., proportional) to the level of SAC inherently possessed by each soil variable. Our findings show that spatial modeling outcomes are sensitive to the degree of SAC possessed by a soil property when treated as a response variable. Thus, the level of SAC present in a soil variable can serve as a direct indicator for how much improvement a non-spatial model will undergo if that SAC is appropriately taken into account.

Novel approaches for defining and measuring intrinsic P were developed based on the soil P adsorption reactions kinetics in tropical soils. Weakly and tightly adsorbed P pools were proposed as latent variables to describe the internal soil P status. The Olsen extractable P by a weak soil test contained only the weakly adsorbed P pool, and the modified Truog extractable P by a strong soil test contained both pools. The objectives of this study were to develop and test a new method that estimates the tightly adsorbed P pool using mathematical models derived from the state-space modeling technique. Five fertilizer phosphate (0-800 mg P kg(-1) soil) rates were applied to seven tropical soils. Soil P was then extracted with the Olsen and modified Truog methods at nine times during a 180 d incubation. Results showed that the extraction efficiencies by the modified Truog method decreased as soil P sorption site density increased. For the seven soils, 22% to 62% of the weakly adsorbed P pool was extracted by the modified Truog method, and it was 28.8% higher than the Olsen method. For soils with low P sorption capacity, 14% to 36% of the tightly adsorbed P pool was extracted by the modified Truog method whereas only small portions were extracted from soils with high P sorption capacity. This research can be used to develop a mechanistic approach for determining P recommendations designed to optimal yields and minimize the impact of agriculture on the environment.