Multiscale Soil Investigations: Physical Concepts and Mathematical Techniques

Logsdon, S. D.; Perfect, Edmund; Tarquis, Ana M.

doi:10.2136/vzj2007.0160

Cited by 20 publications

(10 citation statements)

References 30 publications

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“…Actually, such kind of underlying dynamic process of self-similarity properties, which is ubiquitous in nature, characterizes the spatial distribution patterns at multiple scales. As Logsdon et al (2008) indicated, soils can also be regarded as the result of spatial variation operating over several scales, indicating that factors influencing spatial variability differ with scales, and have been generally studied by multiscale methods from different aspects. Therefore, the multifractal distribution patterns discovered in this paper may provide more new insights into the study of dynamic transport behaviors in nature, such as the geochemical distributions of sediments of different orders and the evolution properties of soils from different horizons.…”

Section: Discussionmentioning

confidence: 99%

Geochemical multifractal distribution patterns in sediments from ordered streams

et al. 2010

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Section: Discussionmentioning

confidence: 99%

Geochemical multifractal distribution patterns in sediments from ordered streams

et al. 2010

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“…In the past, the monofractal approach which assumes that the property of interest can be uniquely characterized by a single fractal dimension, D, has been widely used but more recently the interest has turned to multifractal analysis of soil properties (Tarquis et al, 2003;Pachepsky et al, 2000Pachepsky et al, , 2006Logsdon et al, 2008). Fractal dimension characterizes average properties of a data set and might not always be sufficient to assess differences in geometry (Mandelbrot, 1983).…”

Section: Introductionmentioning

confidence: 99%

Assessing soil particle-size distribution on experimental plots with similar texture under different management systems using multifractal parameters

2010

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“…Soil variability has often been considered to consist of "functional" (explained) variations plus random fluctuations or noise (Goovaerts, 1997(Goovaerts, , 1998. However, the distinction between these two components is scale-dependent because increasing the scale of observation almost always reveals structure in the noise (Logsdon et al, 2008). Geostatistical methods and, more recently, multifractal/wavelet techniques have been used to characterize the scaling and heterogeneity of soil properties, among other approaches coming from complexity science (de Bartolo et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Scale and space dependencies of soil nitrogen variability

Tarquis

Castellanos

Cartagena

et al. 2017

Nonlin. Processes Geophys.

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Abstract. In this study, we use multifractal analysis, through generalized dimensions (D q ) and the relative entropy (E(δ)), to investigate the residual effects of fertigation treatments applied to a previous crop on wheat and grain biomass and nitrogen content. The wheat crop covered nine subplots from a previous experiment on melon responses to fertigation. Each subplot had previously received a different level of applied nitrogen (N app ), and the plants from the previous melon crop had already taken up part of it. Many factors affect these variables, causing them to vary at different scales and creating a non-uniform distribution along a transect. Correlations between the four variables and N app showed high volatility, although the relationships between grain weight and wheat weight versus wheat nitrogen content presented a statistically significant logarithmic trend.The D q values were used to study the relation between scales and E(δ) values, and their increments between scales were used to identify the scale at which the variable had the maximum structure and were compared with the scaling behaviour of the N app . E(δ) is particularly appropriate for this purpose because it does not require any prior assumptions regarding the structure of the data and is easy to calculate.The four variables studied presented a weak multifractal character with a low variation in D q values, although there was a distinction between variables related to nitrogen content and weight. On the other hand, the E(δ) and the increments in E(δ) help us to detect changes in the scaling behaviour of all the variables studied. In this respect, the results showed that the N app through fertigation dominated the wheat and grain biomass response, as well as the nitrogen content of the whole plant; surprisingly, the grain nitrogen content did not show the same structure as N app . At the same time, there was a noticeable structure variation in all the variables, except wheat nitrogen content, at smaller scales that could correspond to the previous cropping root arrangement due to uptake of the N app .

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Multiscale Soil Investigations: Physical Concepts and Mathematical Techniques

Cited by 20 publications

References 30 publications

Geochemical multifractal distribution patterns in sediments from ordered streams

Geochemical multifractal distribution patterns in sediments from ordered streams

Assessing soil particle-size distribution on experimental plots with similar texture under different management systems using multifractal parameters

Scale and space dependencies of soil nitrogen variability

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