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Ten soil profiles were selected from the major soil bodies under development for irrigated agriculture in Western Kern County for examination of properties and soil-forming processes in relation to crop capability and pretreatment needs. The soils were carefully examined in the field and sampled by genetic horizons for detailed examination of chemical-mineralogy properties. Unusually high amounts of salinity, alkali, and boron were found in subsurface horizons of practically all soils. Saturation extracts of some soils contained also unusually large concentrations of copper, iron, molybdenum, zinc, lithium, and selenium. The clays were found to be almost exclusively montmorillonitic. All soils contained lime and gypsum. Boron sorption-desorption characteristics were associated with hydroxy iron compounds rather than with aluminum. The relation between sorbed and soluble boron was adequately described mathematically with a Langmuir isotherm equation.

A large computer and multivariate statistics were used to select a minimal number of soil properties with the greatest prediction value for use in soil classification. Key communality cluster analysis and principal axis factor analysis with Varimax rotation were applied to six sets of soil data containing from 21 to 66 profile characteristics. The analyses selected from four to seven factors or dimensions which were highly independent of each other and contained from two to four highly correlated soil properties. These factors accounted for 100% of the communality and 90% of the raw variance in all properties used in each set. In spite of the diversity of the soils (220 and 620 California, 59 and 86 World, and 148 Ohio soils) the same factors appeared repeatedly; i.e., properties related to reaction, hue and chroma, texture, color value and mottling, and profile differentiation in five analyses and solum thickness in four. Composite factor scores computed for each soil were also used for Numerical Taxonomy. The groupings formed were sufficiently similar to those of the New Classification System of the USDA to suggest that these methods may be very useful in soil classification. The use of these methods for the development of a coordinate system of soil classification is discussed.

One hundred ninety-five soil horizons from 50 benchmark soil profiles in California were analyzed for total aluminum, magnesium, manganese, nickel, cobalt, copper, iron, molybdenum, and zinc by acid decomposition, resin column separation, and colorimetric methods. Half of the soil series contain low and possibly deficient levels of one or more of the following essential elements: cobalt, copper, molybdenum, manganese, and zinc. Four soil series profiles identified with alkali basins contain relatively high, and probably toxic, levels of molybdenum. Statistical analyses of the data show that soil series can be grouped on the basis of horizon development, differences between soil profiles or parent material and stratification, or combinations of these controlling influences. In most soils, two distinct groups of elements occur that are negatively correlated between groups and highly correlated positively within groups. Elements which appear most frequently in the same groups together are cobalt, copper, iron, magnesium, and often zinc. Molybdenum is generally negatively correlated with this group of elements.

Publisher Summary This chapter discusses the objectives and problems of soil classification. The classification of soil is done to understand the relationships among soils. It is assumed that soils fall into natural clusters or groups that can then be ordered into a classification. The chapter discusses the numerical taxonomy or cluster analysis of soils. Numerical taxonomy is the grouping by numerical methods of taxonomic units into taxa based on their character states. An approach is discussed for the examination of taxonomic structure—that is, ordination, which is normally used when the distribution of individuals in n-space tends to be continuous rather than in distinct clusters. Two kinds of ordination have been used in soil studies: Q-type ordination and R-type ordination. The chapter presents three statistical methods for comparing soil classification, including cophenetic correlation, coefficient of association, and Wilk's criterion. Cluster analysis of soils is a most effective means of classifying soils when the number of distinct soils or soil groups is limited as within a relatively small land area.