Spatial variability of erosion and soil organic matter content estimated from 137Cs measurements and geostatistics

Mabit, Lionel; Bernard, Claude; Makhlouf, Mohsen; Laverdière, M. R.

doi:10.1016/j.geoderma.2008.03.013

Cited by 108 publications

(69 citation statements)

References 21 publications

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“…It was signifi cantly correlated (r 2 = 0·63; p < 0·001) with the 137 Cs soil surface inventories (Mabit et al, 2008b). In the present study the experimental variograms for SOM were fi tted to theoretical models using their average semi- Stable estimates of the variogram and a good spatial autocorrelation were obtained with a number of pairs greater than 30 at each lag distance, a nugget variance (integrating the various sources of unexplained error and uncertainty) close to the origin and a high coeffi cient of correlation of 0·95.…”

Section: Resultsmentioning

confidence: 89%

“…The soil texture is a sandy loam (a well developed Podzol) and the fi eld was cropped to barley and corn in rotation. Forty-two composite soil samples were collected in the fi eld, following a sampling strategy and protocol described in detail in previous papers Mabit et al, 2008b). Soil surface samples (0-20 cm) were oven dried for 48 hours at 70 °C, sieved at 2 mm and homogenized.…”

Section: Methodsmentioning

confidence: 99%

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Spatial distribution and content of soil organic matter in an agricultural field in eastern Canada, as estimated from geostatistical tools

Mabit

Bernard

2009

Earth Surf Processes Landf

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Soil erosion induces soil redistribution within the landscape and thus contributes to the spatial variability of soil quality. This study complements a previous experimentation initiated by the authors focusing on soil redistribution as a result of soil erosion, as indicated by caesium-137 ( 137 Cs) measurements, in a small agricultural fi eld in Canada. The spatial variability of soil organic matter (SOM) was characterized using geostatistics, which consider the randomized and structured nature of spatial variables and the spatial distribution of the samples. The spatial correlation of SOM (in percentages) patterns in the topsoil was established taking into account the spatial structure present in the data. A signifi cant autocorrelation and reliable variograms were found with a R 2 ≥ 0·9, thus demonstrating a strong spatial dependence. Ordinary Kriging (OK) interpolation provided the best cross validation (r 2 = 0·35). OK and inverse distance weighting power two (IDW2) interpolation approaches produced similar estimates of the total SOM content of the topsoil (0-20 cm) of the experimental fi eld, i.e. 211 and 213 tonnes, respectively. However, the two approaches produced differences in the spatial distribution patterns and the relative magnitude of some SOM content classes.The spatialization of SOM and soil redistribution variability -as evidenced by 137 Cs measurements -is a fi rst step towards the assessment of the impact of soil erosion on SOM losses to recommend conservation measures.

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Section: Resultsmentioning

confidence: 89%

Section: Methodsmentioning

confidence: 99%

Spatial distribution and content of soil organic matter in an agricultural field in eastern Canada, as estimated from geostatistical tools

Mabit

Bernard

2009

Earth Surf Processes Landf

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“…However, the method has also some pitfalls (Mabit et al, 2008b(Mabit et al, , 2013Parsons and Foster, 2011). Assessment of soil redistribution rates is commonly based on a comparison of the 137 Cs inventory (areal activity density) at individual points in the landscape with that of "stable" landscape position (also termed reference site), where neither erosion nor deposition has occurred.…”

Section: K Meusburger Et Al: Stable Isotopes and Fallout Radionuclimentioning

confidence: 99%

Combined use of stable isotopes and fallout radionuclides as soil erosion indicators in a forested mountain site, South Korea

et al. 2013

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Abstract. The aim of this study is to assess and to validate the suitability of the stable nitrogen and carbon isotope signature as soil erosion indicators in a mountain forest site in South Korea. Our approach is based on the comparison of the isotope signature of "stable" landscape positions (reference sites), which are neither affected by erosion nor deposition, with eroding sites. For undisturbed soils we expect that the enrichment of δ15N and δ13C with soil depth, due to fractionation during decomposition, goes in parallel with a decrease in nitrogen and carbon content. Soil erosion processes potentially weaken this correlation. The 137Cs method and the Revised Universal Soil Loss Equation (RUSLE) were applied for the soil erosion quantification. Erosion rates obtained with the 137Cs method range from 0.9 t ha−1 yr−1 to 7 t ha−1 yr−1. Considering the steep slopes of up to 40° and the erosive monsoon events (R factor of 6600 MJ mm ha−1 h−1 yr –1), the rates are plausible and within the magnitude of the RUSLE-modeled soil erosion rates, varying from 0.02 t ha−1 yr−1 to 5.1 t ha−1 yr−1. The soil profiles of the reference sites showed significant (p < 0.0001) correlations between nitrogen and carbon content and its corresponding δ15N and δ13C signatures. In contrast, for the eroding sites this relationship was weaker and for the carbon not significant. These results confirm the usefulness of the stable carbon isotope signature as a qualitative indicator for soil disturbance. We could show further that the δ15N isotope signature can be used similarly for uncultivated sites. We thus propose that the stable δ15N and δ13C signature of soil profiles could serve as additional indicators confirming the accurate choice of the reference site in soil erosion studies using the 137Cs method.

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“…Various prediction or interpolation methods have been applied in the digital mapping of soil properties, especially statistical methods such as multiple linear regression (Hengl et al, 2007;Mabit et al, 2008;Ciampalini et al, 2012), geostatistical approaches such as ordinary kriging (Bishop & McBratney, 2001;Grunwald et al, 2008) and co-kriging (Ersahin, 2003;Rivero et al, 2007), and hybrid techniques such as regression-kriging (Bishop & McBratney, 2001;Sun et al, 2012). …”

Section: Introductionmentioning

confidence: 99%

Evaluation of statistical and geostatistical models of digital soil properties mapping in tropical mountain regions

Carvalho

Chagas

Lagacherie

et al. 2014

Rev. Bras. Ciênc. Solo

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SUMMARYSoil properties have an enormous impact on economic and environmental aspects of agricultural production. Quantitative relationships between soil properties and the factors that influence their variability are the basis of digital soil mapping. The predictive models of soil properties evaluated in this work are statistical (multiple linear regression-MLR) and geostatistical (ordinary kriging and co-kriging). The study was conducted in the municipality of Bom Jardim, RJ, using a soil database with 208 sampling points. Predictive models were evaluated for sand, silt and clay fractions, pH in water and organic carbon at six depths according to the specifications of the consortium of digital soil mapping at the global level (GlobalSoilMap). Continuous covariates and categorical predictors were used and their contributions to the model assessed. Only the environmental covariates elevation, aspect, stream power index (SPI), soil wetness index (SWI), normalized difference vegetation index (NDVI), and b3/b2 band ratio were significantly correlated with soil properties. The predictive models had a mean coefficient of determination of 0.21. Best results were obtained with the geostatistical predictive models, where the highest coefficient of determination 0.43 was associated with sand properties between 60 to 100 cm deep. The use of a sparse data set of soil properties for digital mapping can explain only part of the spatial variation of these properties. The results may be related to the sampling density and the quantity and quality of the environmental covariates and predictive models used.Index terms: multiple linear regression, kriging, Co-Kriging.(1) Received for publicationon July 9, 2013 andapprovedonFebruary 27, 2014. RESUMO: AVALIAÇÃO DE MODELOS ESTATÍSTICOS E GEOESTATÍSTICOS NO MAPEAMENTO DIGITAL DE PROPRIEDADES DOS SOLOS, EM REGIÕES TROPICAIS MONTANHOSASAs propriedades dos solos têm grande impacto sobre aspectos econômicos e ambientais da produção agropecuária. As relações quantitativas entre as propriedades dos solos e os fatores que condicionam sua variabilidade são a base do mapeamento digital de solos. Os modelos preditivos de propriedades dos solos avaliados neste trabalho são os estatísticos (Regressão Linear Múltipla-RLM) e geoestatísticos (krigagem ordinária e cokrigagem). Este estudo foi desenvolvido para o município de Bom Jardim, RJ, e usou um banco de dados de solos com 208 pontos amostrais. Foram avaliados modelos preditivos para as frações areia, silte e argila, pH em água e carbono orgânico para seis profundidades, de acordo com as especificações do consórcio de mapeamento digital de solos em nível global (GlobalSoilMap). Utilizaram-se covariáveis preditoras contínuas e categóricas, estas últimas para avaliar suas contribuições ao modelo. Apenas as covariáveis ambientais elevação, aspecto, índice de potência de fluxo (SPI), índice de umidade (SWI), índice de vegetação por diferença normalizada (NDVI) e relação entre bandas b3/b2 apresentaram correlação significativa com as propriedades d...

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Spatial variability of erosion and soil organic matter content estimated from 137Cs measurements and geostatistics

Cited by 108 publications

References 21 publications

Spatial distribution and content of soil organic matter in an agricultural field in eastern Canada, as estimated from geostatistical tools

Spatial distribution and content of soil organic matter in an agricultural field in eastern Canada, as estimated from geostatistical tools

Combined use of stable isotopes and fallout radionuclides as soil erosion indicators in a forested mountain site, South Korea

Evaluation of statistical and geostatistical models of digital soil properties mapping in tropical mountain regions

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