Spatial distribution of soil penetration resistance as affected by soil compaction: The fractal approach

Usowicz, B.; Lipiec, Jerzy

doi:10.1016/j.ecocom.2009.05.005

Cited by 34 publications

(24 citation statements)

References 24 publications

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“…Usowicz and Lipiec, 2009;Pérez et al, 2010). Furthermore, multifractal analysis has been used to assess patterns of spatial variation of penetrometric data sets measured along transects (Folorunso et al, 1994) or on planes (Roisin, 2007), providing valuable information to better understand the inner structure of PR for horizontal soil layers.…”

Section: G M Siqueira Et Al: Multifractal Analysis Of Soil Prmentioning

confidence: 99%

Multifractal analysis of vertical profiles of soil penetration resistance at the field scale

Siqueira

Silva

Montenegro

et al. 2013

Nonlin. Processes Geophys.

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Abstract. Soil penetration resistance (PR) is widely used as an indirect indicator of soil strength. Soil PR is linked to basic soil properties and correlated to root growth and plant production, and as such it is extensively used as a practical tool for assessing soil compaction and to evaluate the effects of soil management. This study investigates how results from multifractal analysis can quantify key elements of depth-dependent soil PR profiles and how this information can be used at the field scale. We analysed multifractality of 50 PR vertical profiles, measured from 0 to 60 cm depth and randomly located on a 6.5 ha sugar cane field in northeastern Brazil. The scaling property of each profile was typified by singularity, and Rényi spectra estimated by the method of moments. The Hurst exponent was used to parameterize the autocorrelation of the vertical PR data sets. The singularity and Rènyi spectra showed that the vertical PR data sets exhibited a well-defined multifractal structure. Hurst exponent values were close to 1, ranging from 0.944 to 0.988, indicating strong persistence in PR variation with soil depth. Also, the Hurst exponent was negatively and significantly correlated to coefficient of variation (CV), skewness and maximum values of the depth-dependent PR. Multifractal analysis added valuable information to describe the spatial arrangement of depth-dependent penetrometer data sets, which was not taken into account by classical statistical indices. Multifractal parameters were mapped over the experimental field and compared with mean and maximum values of PR. Combination of spatial variability survey and multifractal analysis appear to be useful to manage soil compaction.

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Section: G M Siqueira Et Al: Multifractal Analysis Of Soil Prmentioning

confidence: 99%

Multifractal analysis of vertical profiles of soil penetration resistance at the field scale

Siqueira

Silva

Montenegro

et al. 2013

Nonlin. Processes Geophys.

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“…It influences (directly and indirectly) many soil properties such as hydrological properties (Jadczyszyn and NiedŸwiecki, 2005;Toth et al, 2006;Tramontini et al, 2012), air-water conditions (Makó and Elek, 2006;Skierucha et al, 2006), thermal conductivity (Usowicz et al, 2008), microbial activity Fr¹c et al, 2012;Nosalewicz and Nosalewicz, 2011) or gas-exchange (Brzeziñska et al, 2011a;W³odarczyk et al, 2011;Woliñska et al, 2011). PSD can be the basis for calculation of other soil characteristics such as fractal dimension (Bieganowski et al, 2013;Gunal et al, 2011;Usowicz and Lipiec, 2009) or modelling (Lamorski et al, 2008;Wei Shangguana et al, 2012). It can be used for characterization of soil and geomorphological processes (D³ugosz et al, 2009;Dobrowolski et al, 2012;Kabala and Zapart 2012;Józefaciuk et al, 2006;Waroszewski et al, 2013).…”

Section: Particle Size Distributionmentioning

confidence: 99%

Database of Polish arable mineral soils: a review

Bieganowski¹,

Witkowska-Walczak²,

Gliński³

et al. 2013

International Agrophysics

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The database of Polish arable mineral soils is presented. The database includes a lot of information about the basic properties of soils and their dynamic characteristics. It was elaborated for about 1 000 representative profiles of soils in Poland The database concerns: particle size distribution, organic carbon content, acidity-pH, specific surface area, hydrophobicity - solidliquid contact angle, static and dynamic hydrophysical properties, oxidation-reduction properties and selected biological (microbiological) properties of soils. Knowledge about soil characteristics is indispensable for description, interpretation and prediction of the course of physical, chemical and biological processes, and modelling these processes requires representative data. The utility of simulation and prediction models describing phenomena which take place in the soil-plant-atmosphere system greatly depends on the precision of data concerning characteristics of soil. On the basis of this database, maps of chosen soil properties are constructed. The aim of maps is to provide specialists in agriculture, ecology, and environment protection with an opportunity to gain knowledge of soil properties and their spatial and seasonal variability.

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“…For instance, soil compaction caused by tractors increased soil resistance to penetration (raPer, 2005;GaBriel Filho et al, 2007), which spatial variability has been previously observed (usowiCz and liPieC, 2009). The level of soil compaction caused by traffic increases with soil water content (riChard et al, 1999), thus it is important to consider this relation in order to diminish this problem.…”

Section: Introductionmentioning

confidence: 99%

“…In such cases, indicator kriging is considered an appropriate tool (motoyima et al, 2006) since it provides a binary transformation of the results, namely between 0 and 1, which is the estimatedvalues probability of being greater or lower than the threshold. Thus, the spatial of soil penetration resistance variability can be used to determine minimum number of penetrations and sampling positions for accurate evaluation of the effectiveness of agricultural practices and mechanical impedance for root growth (usowiCz and liPieC, 2009). The maps of spatial variability of soil penetration resistance obtained by kriging are valuable means in precision agriculture to establish adequate management measures (sirjaCoBs et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

Spatial variability of soil penetration resistance influenced by season of sampling

et al. 2010

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The aim of this work was to analyze the spatial distribution of soil compaction and the influence of soil water content on the resistance to penetration. The latter variable was described by the cone index. The soil at the study site was a Nitisol and the cone index data were obtained using a penetrometer. Soil resistance was assessed at 5 different depths, i.e. 0-10 cm, 10-20 cm, 20-30 cm, 30-40 cm and deeper than 40 cm, whereas soil water content was measured at 0-20 cm and 20-40 cm. Soil water conditions varied during the different samplings. Coefficients of variation for cone index ranged from 16.5% to 45.8% while those for soil water content varied from 8.96% to 21.38%. Results suggested a high correlation between soil resistance, as assessed by the cone index, and soil depth. However, the expected relation with soil water content was not observed. Spatial dependence was observed in 31 out of 35 data series, both cone index and soil water content. This structure was fitted to exponential models with nugget effect varying from 0 to 90% of the sill value. Four of the data series showed a random behaviour. Inverse distance technique was used in order to map the distribution of the variables when no spatial structure was observed. Ordinary kriging showed a smoothing of the maps compared to those from inverse distance weighing. Indicator kriging was used to map the cone index spatial distribution for recommendation of further soil management. O objetivo deste trabalho foi analizar a distribuição espacial da compactação do solo e a influência da umidade do solo na resistência à penetração. Esta última variável foi descrita pelo índice de cone. O solo estudado foi Nitossolo e os dados de índice de cone foram obtidos usando um penetrômetro. A resistência do solo foi avaliada a 5 profundidades diferentes, 0-10 cm, 10-20 cm, 20-30 cm, 30-40 cm e mais de 40 cm, porém o conteúdo de umidade do solo foi medido a 0-20 cm e 20-40 cm. As condições hídricas do solo variaram nas diferentes amostragems. Os coeficientes de variação para o índice de cone foram 16,5% a 45,8% e os do conteúdo de umidade do solo variaram entre 8,96% e 21,38%. Os resultados sugeriram elevada correlação entre a resistência do solo, estimada pelo índice de cone e a profundidade do solo. Sem embargo, a relação esperada com a umidade do solo não foi apreciada. Observouse dependência espacial em 31 de 35 séries de dados de índice de cone e umidade do solo. Esta dependência foi ajustada por modelos exponenciais com efeito pepita variável de 0 a 90% o valor do patamar. Em séries de dados o comportamento foi aleatório. Portanto, a técnica das distâncias inversas foi utilizada para cartografar a distribuição das variáveis que não tiveram estrutura espacial. Na krigagem constatou-se uma suavização dos mapas comparados com esses das distâncias inversas. A krigagem indicadora foi utilizada para cartografar a variabilidade espacial do índice de cone e recomendar melhor manejo do solo.Palavras-chave: índice de cone, krigagem indicadora, compactação do sol...

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Spatial distribution of soil penetration resistance as affected by soil compaction: The fractal approach

Cited by 34 publications

References 24 publications

Multifractal analysis of vertical profiles of soil penetration resistance at the field scale

Multifractal analysis of vertical profiles of soil penetration resistance at the field scale

Database of Polish arable mineral soils: a review

Spatial variability of soil penetration resistance influenced by season of sampling

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