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Predicting bulk density of Ohio Soils from Morphology, Genetic Principles, and Laboratory Characterization Data
Author(s) -
Calhoun F.G.,
Smeck N.E.,
Slater B.L.,
Bigham J.M.,
Hall G.F.
Publication year - 2001
Publication title -
soil science society of america journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.836
H-Index - 168
eISSN - 1435-0661
pISSN - 0361-5995
DOI - 10.2136/sssaj2001.653811x
Subject(s) - soil water , loess , characterization (materials science) , soil science , field (mathematics) , data set , horizon , environmental science , mathematics , geology , statistics , geomorphology , materials science , geometry , pure mathematics , nanotechnology
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.