Predicting bulk density of Ohio Soils from Morphology, Genetic Principles, and Laboratory Characterization Data

Abstract

A 937‐horizon data set composed of site characteristics, morphology, and laboratory characterization data for soils of Ohio was used to develop soil bulk density (D b ) prediction models. We tested the hypothesis that using a combination of continuous variables (laboratory data) and nominal variables (site/state factor and morphological class descriptors) would enable the development of improved Pedo‐Transfer Functions (PTFs) for D b Three primary models were developed. The Lab Model, composed entirely of continuous variables, accounted for 56% of the variability in D b Using only state factors and morphology as nominal variables, the Field Model explained 69%. A combined Field + Lab Model accounted for 72%. Restricting the data set to samples derived from loess and glacial till generated a Field + Lab Model that explained nearly 80% of the variability in D b for a subset of 402 horizons.