Palygorskite is the dominant clay mineral in a petrocalcic horizon of the Rotura soil series (a coarse‐loamy, mixed, thermic, Typic Haplargid), a soil of middle Pleistocene age in southern New Mexico. The purpose of this study was to determine if palygorskite is eolian in origin, inherited from its alluvial parent material, or neoformed in the soil. The origin of palygorskite was determined by examining clay mineral distribution, soil‐water extract chemistry, and palygorskite fiber morphology. An eolian origin of palygorskite is unlikely because it is absent in upper soil horizons and no palygorskite was captured in dust traps during a 10‐yr study. An inherited origin is also unlikely because palygorskite is not an inherent component of Rio Grande alluvium. Scanning electron microscopy revealed that delicate palygorskite fibers radiate perpendicularly into pore spaces, which indicates neoformation. The petrocalcic horizon may foster palygorskite neoformation because it is the site of high Mg content and abundant sand‐ and silt‐grain dissolution.
The iron oxides and clay mineralogy were characterized for several soils of Venezuela which contain plinthite and pseudo‐plinthite. Molar ratios of SiO2/Al2O3 (Ki ratios) of the plinthite and pseudo‐plinthite zones range from below 2 to above 4 suggesting a wide range in mineral composition. Cation exchange capacity for these zones ranged from 14.6 to 65.6 meq/100 g of clay suggesting that these mottled zones are not always associated with low activity clays. Kaolinite dominated the clay minerals. Montmorillonite was identified in a few pedons.Goethite was the dominant iron oxide present in the plinthite. The percentage of iron was determined by extracting the soil with citrate‐bicarbonate‐dithionite and by extracting with ammonium oxalate. These two extractions were ratioed (OX/CBD) yielding values of >0.05 for pseudo‐plinthite and values of <0.05 for plinthite in most cases. Soils with plinthite contained most of the iron in the sand‐ and silt‐size fraction following one drying cycle while soils with pseudo‐plinthite contained most of the iron in the clay‐size fraction.
The application of geostatistical analysis to evaluate a soil transect was explored by measuring soil properties in four equal depths (0–0.3, 0.3–0.6, 0.6–0.9, and 0.9–1.2 m), at 30‐m intervals along a transect of 1800‐m length in southern New Mexico. Semivariograms were computed for clay, sand, coarse fragments, and CaCO3 percent for each depth along the transect. The horizontal semivariograms were combined to form one semivariogram to account for the anisotropy in the measurement procedure. A similar procedure was used for vertical semivariograms to account for the anisotropy in the measurement units. The combination of the equations describing the horizontal and vertical semivariograms was used in a kriging program. The kriging program was used to estimate values of soil properties at 2245 grid points from the 240 original measurements. Two‐dimensional, kriged contour maps of clay, sand, coarse fragments, and CaCO3 percent were constructed. It appears the kriging technique has excellent potential in preparation of two‐dimensional vertical contour maps of soil properties.
Locating the exact boundaries of soil map units is one of the primary objectives for soil surveyors. Statistical methods were used to assure the most accurate location. Soil spatial variability, autocorrelation function, and soil boundary locations were examined along a 2700‐m transect in southern New Mexico. Eighty‐nine observation points were equally spaced along the transect. Selected physical and chemical characteristics through the transect were determined. A multivariate method of principal‐component analysis was used to produce one set of data. These data were first inspected for stationary manner, i.e., that the mean and variance of each property remain fairly constant for each data set. Log‐normal transformation was used to detrend the data. The stationary manner of autocorrelations was tested with semivariograms. The range of dependence obtained from the autocorrelations and semivariograms was used in a squared‐Euclidean‐distance procedure to locate the soil boundaries. These boundaries were compared with those obtained by conventional soilsurvey methods. Some of the calculated boundaries agreed with those obtained by conventional soil survey. The latter method is more economical and more productive than the statistical method.
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