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ARS Home » Midwest Area » Columbia, Missouri » Cropping Systems and Water Quality Research » Research » Publications at this Location » Publication #200491
Title: A QUANTITATIVE PEDOLOGY APPROACH TO CONTINUOUS SOIL LANDSCAPE MODELS

Author
item MYERS, D - UNIVERSITY OF MISSOURI
item Kitchen, Newell
item Sudduth, Kenneth - Ken
item MAYHAN, B - UNIVERSITY OF MISSOURI
item MILES, R - UNIVERSITY OF MISSOURI
item Sadler, Edward

Submitted to: ASA-CSSA-SSSA Annual Meeting Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: 11/14/2006
Publication Date: 11/14/2006
Citation: Myers, D.B., Kitchen, N.R., Sudduth, K.A., Mayhan, B.D., Miles, R.J., Sadler, E.J. 2006. A quantitative pedology approach to continuous soil landscape models [abstracts]. ASA-CSSA-SSSA Annual Meeting. Paper No. 193-4.

Interpretive Summary:

Technical Abstract: Continuous representations of soil profiles and landscapes are needed to provide input into process based models and to move beyond the categorical paradigm of horizons and map-units. Continuous models of soil landscapes should be driven by the factors and processes of the soil genetic model. Parametric soil depth functions can be combined with spatial pedogenic factor data (e.g. ClOrPT) to map soil profile properties across the landscape. Loess-derived soils in upland landscapes of Northern Missouri provided a case study for this investigation. A large database of horizon-sampled soil profile data was converted to continuous pedometric depth functions which handle the vertical anisotropy of pedogenesis. This was achieved via a clay-maximum depth estimation procedure, and coherent depth translation. The transformed soil profile data was fitted to peak and rational functions having parameters that reflect specific features of the soil property distribution curve such as peak amplitude, amplitude location, skew, attack, and decay. The parameters of these pedometric functions are analogous to the soil property maximum, depth to maximum, abruptness, and profile anisotropy. The function parameters were then predicted across regional soil landscapes, using spatial genetic factor data such as climate and paleoclimate information, loess thickness, and digital elevation models. These continuous models of soil landscapes are important for applications where the soil map-unit scale is insufficient, such as in precision agriculture, high-resolution hydrology and plant modeling, and intensive land-use planning.