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abstract-horizonation.txt
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abstract-horizonation.txt
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Aggregate representation of genetic soil horizons via proportional-odds logistic regression.
D.E. Beaudette, P. Roudier, J.M. Skovlin
Published soil survey reports typically describe soils in terms of aggregate information: soil properties, interpretations, and limitations that are based on a collection of field-described soil profiles. While aggregate soil properties are readily estimated via standard statistical functions (mean, median, etc.), an aggregate representation of horizonation (e.g. genetic or functional horizon designation and depth) is typically difficult to construct. Variation in horizon designation among different soil scientists and different soil description systems, changes in horizon designation standards over time, variable depths at which horizons occur, and the various uncertainties associated with these are all factors that complicate the process of delivering an aggregate representation of horizonation.
In this paper we propose alternatives to the typical "representative profile"-- e.g. the selection of a single soil profile to represent a collection. Two possible methods for aggregating a collection of soil profiles into synthetic profiles are presented, describing depth-wise probability functions for each horizon. Both methods rely on an expert-guided description of generalized horizon designation (e.g. a subset of horizon designation labels that convey a reasonable "morphologic story") along with associated rules (regular expression patterns) used to correlate field-described to generalized horizon designation. The first method is based on (1-cm interval) slice-wise evaluation of generalized horizon designation; the second is based on a proportional-odds logistic regression model fit to depth-slices. These methods are demonstrated using USDA-NRCS soil survey data (USA), based on genetic horizons, and on S-Map soil survey data (NZ), based on functional horizons.