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

Calhoun, F.G.; Smeck, N.E.; Slater, B.L.; Bigham, J.M.; Hall, G.F.

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

2001

Calhoun, F.G. | Smeck, N.E. | Slater, B.L. | Bigham, J.M. | Hall, G.F.

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 (Db) 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 Db Three primary models were developed. The Lab Model, composed entirely of continuous variables, accounted for 56% of the variability in Db 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 Db for a subset of 402 horizons.

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