Peak functions for modeling high resolution soil profile data

Myers, D. Brenton; Kitchen, Newell R.; Sudduth, Kenneth A.; Miles, Randall J.; Sadler, E. J.; Grunwald, Sabine

doi:10.1016/j.geoderma.2011.07.014

Cited by 27 publications

(17 citation statements)

References 34 publications

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“…Traditionally, it is assumed that the horizon value of a particular attribute represents its average value over that horizon (Malone et al , ). In Luvisols that developed in thick loess deposits, soil properties including SOC content are expected to vary continuously with depth, except at the transition zone below a mixed plough layer (Minasny et al , ; Myers et al , ). This is confirmed by the resampled cores that do not display SOC vertical patterns of abrupt transitions between horizons (Doetterl et al , ).…”

Section: Methodsmentioning

confidence: 99%

Regional‐scale characterization of the geomorphic control of the spatial distribution of soil organic carbon in cropland

Stevens

Bogaert

Oost

et al. 2014

European J Soil Science

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The heterogeneity of the spatial distribution of soil organic carbon (SOC) at the landscape scale is generally not considered in regional or national SOC dynamics models. In cropland this heterogeneity is controlled largely by topography, which influences the distribution of water, energy and sediments, and thus the SOC dynamics. Sediment redistribution rates have increased strongly since the mechanization of agriculture. The over-simplification of landscape processes in regional models of C dynamics may add to the uncertainty in C balances. Therefore, a better characterization of the importance of landscape-scale effects on the SOC distribution throughout a region is needed. This study characterized the relative importance of geomorphology in the SOC horizontal and vertical variability across croplands in the Belgian loess belt region. A large legacy dataset of soil horizons was exploited together with 147 recently sampled profiles. Mean SOC depth profiles for different soil types w...

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Section: Methodsmentioning

confidence: 99%

Regional‐scale characterization of the geomorphic control of the spatial distribution of soil organic carbon in cropland

Stevens

Bogaert

Oost

et al. 2014

European J Soil Science

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“…This function has been found in weathering profile results from diffusion processes (Kirkby, ), depletion of leachable elements or minerals (Brantley et al ., ), depth distribution of soil horizons (Beaudette et al ., ) and gleyed horizons (Leblanc et al ., ). Peak functions can show accumulation (maxima) of some soil properties such as clay content with depth because of eluviation and illuviation processes, or in situ formation or discontinuity in soil parent materials. Peak functions can also indicate compaction and anthropogenic influences can create variation such as multiple peaks (Minasny, ; Myers et al ., )…”

Section: Pedological Knowledge and Dsmmentioning

confidence: 99%

Pedology and digital soil mapping (DSM)

Minasny

Malone

et al. 2019

European J Soil Science

168

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Pedology focuses on understanding soil genesis in the field and includes soil classification and mapping. Digital soil mapping (DSM) has evolved from traditional soil classification and mapping to the creation and population of spatial soil information systems by using field and laboratory observations coupled with environmental covariates. Pedological knowledge of soil distribution and processes can be useful for digital soil mapping. Conversely, digital soil mapping can bring new insights to pedogenesis, detailed information on vertical and lateral soil variation, and can generate research questions that were not considered in traditional pedology. This review highlights the relevance and synergy of pedology in soil spatial prediction through the expansion of pedological knowledge. We also discuss how DSM can support further advances in pedology through improved representation of spatial soil information. Some major findings of this review are as follows: (a) soil classes can be mapped accurately using DSM, (b) the occurrence and thickness of soil horizons, whole soil profiles and soil parent material can be predicted successfully with DSM techniques, (c) DSM can provide valuable information on pedogenic processes (e.g. addition, removal, transformation and translocation), (d) pedological knowledge can be incorporated into DSM, but DSM can also lead to the discovery of knowledge, and (e) there is the potential to use process‐based soil–landscape evolution modelling in DSM. Based on these findings, the combination of data‐driven and knowledge‐based methods promotes even greater interactions between pedology and DSM. Highlights Demonstrates relevance and synergy of pedology in soil spatial prediction, and links pedology and DSM. Indicates the successful application of DSM in mapping soil classes, profiles, pedological features and processes. Shows how DSM can help in forming new hypotheses and gaining new insights about soil and soil processes. Combination of data‐driven and knowledge‐based methods recommended to promote greater interactions between DSM and pedology.

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“…Maquere et al (2008) adopt a parametric form with modified exponential decay, whereas Myers et al (2011) use an approach based on asymmetric peak functions. Whilst capturing the non-linear form of the soil depth profile, neither exponential decay nor polynomial methods adequately handle the associated uncertainty and hence confidence intervals, with the method in Maquere et al (2008) assuming a t distribution and the method in Myers et al (2011) failing to produce confidence envelopes at all. Regression-based approaches similar to the popular GAM method have also been adopted using multiple covariates to account for any non-linearity (Indorante et al, 2013) and fitting cubic splines directly (Wendt and Hauser, 2013).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2016

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Abstract. 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 nonparametric 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.

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Peak functions for modeling high resolution soil profile data

Cited by 27 publications

References 34 publications

Regional‐scale characterization of the geomorphic control of the spatial distribution of soil organic carbon in cropland

Regional‐scale characterization of the geomorphic control of the spatial distribution of soil organic carbon in cropland

Pedology and digital soil mapping (DSM)

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

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