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Technical note: A bootstrapped LOESS regression approach for comparing soil depth profiles

Keith, Aidan M. ORCID: https://orcid.org/0000-0001-9619-1320; Henrys, Peter A.; Rowe, Rebecca L.; McNamara, Niall P. ORCID: https://orcid.org/0000-0002-5143-5819. 2016 Technical note: A bootstrapped LOESS regression approach for comparing soil depth profiles. Biogeosciences, 13 (13). 3863-3868. https://doi.org/10.5194/bg-13-3863-2016

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Abstract/Summary

Understanding the consequences of different land uses for the soil system is important to make better informed decisions based on sustainability. The ability to assess change in soil properties, throughout the soil profile, is a critical step in this process. We present an approach to examine differences in soil depth profiles between land uses using bootstrapped LOESS regressions (BLRs). This non-parametric approach is data-driven, unconstrained by distributional model parameters and provides the ability to determine significant effects of land use at specific locations down a soil profile. We demonstrate an example of the BLR approach using data from a study examining the impacts of bioenergy land use change on soil organic carbon (SOC). While this straightforward non-parametric approach may be most useful in comparing SOC profiles between land uses, it can be applied to any soil property which has been measured at satisfactory resolution down the soil profile. It is hoped that further studies of land use and land management, based on new or existing data, can make use of this approach to examine differences in soil profiles.

Item Type: Publication - Article
Digital Object Identifier (DOI): https://doi.org/10.5194/bg-13-3863-2016
UKCEH and CEH Sections/Science Areas: Parr
Shore
ISSN: 1726-4170
Additional Information. Not used in RCUK Gateway to Research.: Open Access paper - full text available via Official URL link.
NORA Subject Terms: Agriculture and Soil Science
Data and Information
Date made live: 07 Jul 2016 10:28 +0 (UTC)
URI: https://nora.nerc.ac.uk/id/eprint/513921

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