We reviewed the use of the van Genuchten–Mualem (VGM) model to parameterize soil hydraulic properties and for developing pedotransfer functions (PTFs). Analysis of literature data showed that the moisture retention characteristic (MRC) parameterization by setting shape parameters m = 1 − 1/n produced the largest deviations between fitted and measured water contents for pressure head values between 330 (log10 pressure head [pF] 2.5) and 2500 cm (pF 3.4). The Schaap–van Genuchten model performed best in describing the unsaturated hydraulic conductivity, K The classical VGM model using fixed parameters produced increasingly higher root mean squared residual, RMSR, values when the soil became drier. The most accurate PTFs for estimating the MRC were obtained when using textural properties, bulk density, soil organic matter, and soil moisture content. The RMSR values for these PTFs approached those of the direct fit, thus suggesting a need to improve both PTFs and the MRC parameterization. Inclusion of the soil water content in the PTFs for K only marginally improved their prediction compared with the PTFs that used only textural properties and bulk density. Including soil organic matter to predict K had more effect on the prediction than including soil moisture. To advance the development of PTFs, we advocate the establishment of databases of soil hydraulic properties that (i) are derived from standardized and harmonized measurement procedures, (ii) contain new predictors such as soil structural properties, and (iii) allow the development of time‐dependent PTFs. Successful use of structural properties in PTFs will require parameterizations that account for the effect of structural properties on the soil hydraulic functions.

No abstract

long been routinely used in soil mapping (Northcote, 1954). Geomorphometry was proposed as a data source Digital elevation models were proposed and used as a data source to predict soil properties (Moore et al., 1993; McKenzie to estimate soil properties. This study evaluated variability of texture and water retention of soils for a gently sloping 3.7-ha field located and Austin, 1993; McSweeney et al., 1994). in the long-term precision farming research site at the Beltsville Ag-Two basic approaches to relate soil properties to landricultural Research Center, MD. The specific objectives of this rescape position have been suggested to date. The first is search were (i) to characterize variability of water retention across based on separating hillslopes into distinct sections, i.e., the hillslope, and (ii) determine and describe any correlations of soil summit, upper and lower interfluve, shoulder, backwater retention with soil texture and surface topography. Soil was slope, upper and lower linear, footslope, toeslope, etc. sampled along four 30-m transects and in 39 points within the study It has been shown that soil properties within a section area. Textural fraction contents, bulk density, and water retention at vary much less than between sections, so that distinct 0, 2.5, 5.0, 10, 33, 100, 500, and 1500 kPa were measured in samples values of soil properties can be assigned to each section taken from 4-to 10-cm depth. A 30-m digital elevation model (DEM) (Ovalles and Collins, 1986). Section-specific regression was constructed from aerial photography data. Slopes, profile curvatures, and tangential curvatures were computed in grid nodes and equations can also be developed to correlate soil properinterpolated to the sampling locations. Regressions with spatially cor-ties (Brubaker et al., 1994). related errors were used to relate water retention and texture to The second approach to relate soil properties to landcomputed topographic variables. Sand, silt, and clay contents descape positions is to use topographic variables, or terrain pended on slope and curvatures. Soil water retention at 10 and 33 attributes, i.e., mathematical characteristics of the land kPa correlated with sand and silt contents. The regression model surface shape, such as slope, profile, plan and tangential relating water retention to the topographic variables explained more curvatures, and aspect (Evans, 1980; Mitá sova and Hothan 60% of variation in soil water content at 10 and 33 kPa, and only fierka, 1993; Shary, 1995). These variables can be com-20% of variation at 100 kPa. Increases in slope values and decreases in puted directly at the nodes of a grid and used for statistitangential curvature values, i.e., less concavity or more convexity cal correlation with soil properties at these nodes across the slope, led to the decreases in water retention at 10 and 33

No abstract

the parameters control position and shape of the water retention equation. Two pedotransfer function (PTF) approaches can be used for ob-If both soil basic data and soil water retention infortaining the analytical expression of the whole retention curve: (i) soil basic data is used to estimate soil water retention at specific water mation are available for a set of samples, two approaches potentials; and then an analytical expression of the retention curve can be used to estimate the relationship between the is fitted to the estimated soil moisture values; and (ii) soil basic data parameters of an analytical equation of soil water retenis used for estimating the parameters of an analytical expression of tion and soil basic data: (i) fit the parameters of the water retention curves. The objective of this study was to compare analytical equation to measured values for each sample the performance of both techniques using data representing the main of the data set; (ii) build a table relating those parame-Brazilian soils. First, we derived PTFs for the parameters of van ters to their corresponding soil basic data; and (iii) de-Genuchten equation and for water contents at Ϫ6, Ϫ10, Ϫ33, Ϫ100, velop a relationship between fitted parameters and soil and Ϫ1500 kPa for the same development data set. Second, we combasic data. The second approach is: (i) build a relationpared the performance of both techniques for the same validation data set. The approach, based on the estimation of water contents at ship between water contents at selected soil water pospecific water potentials, provided better results: for the validation tentials and soil basic data; and (ii) fit the parameters of data set, this technique showed an average root mean squared error the analytical water retention equation to the estimated of 0.036 m 3 m Ϫ3 , compared with an averaged error of 0.098 m 3 m Ϫ3 water contents. In other words, the first approach fits of the technique based on the direct estimation of van Genuchten the analytical curve first, and uses PTF estimations later, parameters. A possible explanation for this result might be related whereas the second approach uses PTF estimation first to the fact that soil moisture is controlled by different independent and fits the analytical curve later. variables at different ranges of soil water potential, and those differ-Both approaches have been widely used for various ences are not directly related to the van Genuchten parameters.

Pedotransfer functions (PTFs; i.e., dependencies of soil water retention and soil hydraulic conductivity on basic soil parameters available from soil surveys) are widely used to predict soil functioning in agricultural and environmental systems. The reliability of PTFs needs to be assessed by examining the correspondence between measured and estimated data for data set(s) other than the one used to develop a PTF. Our objective was to see whether grouping according to taxonomic unit, soil moisture regime, soil temperature regime, and soil textural class would improve both PTF accuracy and reliability. We estimated soil water contents at matrix potentials of −33 kPa and −1500 kPa for the 447 soil samples from the Oklahoma National Resource Conservation Service database. Dry bulk density, the ratio of cation‐exchange capacity (CEC) to clay content, and contents of clay, sand, coarse fragments, and organic matter were used as predictors. The Group Method of Data Handling (GMDH) was used to develop PTFs. To assess accuracy and reliability of the PTFs, we used cross‐validation; i.e., repeated random splitting of the data set into subsets for development and validation. The PTF accuracy and reliability was quantified by the root mean square error in the development and validation data set, respectively. Grouping improved the accuracy of PTFs in most cases. None of the grouping criteria proved to be clearly superior. Although PTFs developed from the groups were more accurate than the PTFs developed from the whole database, they were not more reliable. Improving PTF reliability may be an issue distinctly different from improving PTF accuracy.

When a field or a small watershed is repeatedly surveyed for soil water content, sites often can be spotted where soil is consistently wetter or consistently dryer than average across the study area. The phenomenon has been called time stability, temporal stability, or temporal persistence in spatial patterns of soil water contents. Relatively less is known about temporal persistence of water content at various depths. The objectives of this work are to demonstrate the temporal persistence in soil water contents measured on a vertical two‐dimensional grid, and to propose a technique to use this persistence to remedy the effect of probe malfunctioning on the estimates of the average water content in the layer. Sixty time domain reflectometry (TDR) probes (two rods) were installed along the trench in loamy soil at 12 locations with 50‐cm horizontal spacing at five depths (15, 35, 55, 75, and 95 cm). The water content data were incomplete due to malfunctioning of connections in the automated measurement system. When all probes worked, some probes at a given depth consistently showed water contents below average whereas others showed water contents above the average. To quantify the persistence, we computed relative water contents as ratios of individual‐probe water contents to average water contents from the same depth. Average relative water contents were used in a technique we proposed to correct estimates of depth‐average water contents by accounting for missing data. A numerical experiment showed the efficiency of the proposed technique. Corrections for temporal persistence can be useful in estimating layer‐averaged water contents and their uncertainty.

The purpose of this study was to relate the temporal and spatial variability of corn (Zea mays L.) grain yield on a Typic Fragiochrept soil on a hillslope to soil properties and topographic features. Corn grain yields were sampled from a field that measured 280 by 150 m using a grid and five transects. One‐hundred forty yield measurements were taken on the grid (1983–1985) and 190 measurements on the transects (1984 and 1985) from plots 5.3 m long and two corn rows wide. Measurements of soil surface elevation, soil organic matter (OM), P, and K contents were also taken at the grid plot locations. These data were analyzed using the methods of spectral analysis. Yield spatial and temporal variability was strongly related to surface undulations and the value of surface curvature was found to be a useful parameter to quantify variations in topography. The intra‐annual differences in weather had the largest effect on grain yield at locations where the magnitude of curvature was large. Where the magnitude of curvature was small, the correlations of yield for the wet (1984) and dry (1985) years were highly significant. Yields correlated with soil P, K, and OM only in the dry year, 1985. Yields in the relatively dry years (1983 and 1985) correlated with depth to fragipan. Elevation data helped us interpret the spatial and temporal variability of grain yield by separation of areas with convex curvature from areas with concave curvature.