Variowin

Pannatier, Yvan

doi:10.1007/978-1-4612-2392-4

Cited by 210 publications

(6 citation statements)

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“…was fitted, with C 0 the nugget variance, C 0 + C 1 the sill and a the range of spatial autocorrelation. The best fit was obtained with the ‘Indicator Goodness of Fit’ parameter of Variowin (Pannatier, 1996) as a guide. The model parameters are given in Table 2, and both variograms are plotted in Figure 5.…”

Section: Geostatistical Analysismentioning

confidence: 99%

Could shelling in the First World War have increased copper concentrations in the soil around Ypres?

Meirvenne

Meklit

Verstraete

et al. 2008

European J Soil Science

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In the First World War, the region around Ypres (West-Flanders, Belgium) was a battlefield where millions of copper-containing shells were fired. To investigate the consequences of this shelling, we analysed statistically data on the copper (Cu) content in the topsoil (0-0.5 m) of the province of West-Flanders, an area of 3144 km 2 . The measurements had been made on the fine earth (< 2 mm) fraction of 2786 samples. A preliminary screening of the data revealed larger concentrations of Cu in a region of approximately 625 km 2 around Ypres. These concentrations were estimated by ordinary block kriging on the logarithms of the Cu concentration with separate variograms for the battlefield area and the rest of the province and mapped. The median concentration in the battlefield area was 18.0 mg Cu kg À1 compared with 12.0 mg Cu kg À1 elsewhere. We conclude that the current Cu enrichment in the soil around Ypres is the legacy of the millions of shells that were fired in the First World War.

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Section: Geostatistical Analysismentioning

confidence: 99%

Could shelling in the First World War have increased copper concentrations in the soil around Ypres?

Meirvenne

Meklit

Verstraete

et al. 2008

European J Soil Science

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“…All measured soil physical and chemical properties were checked for normality. Using Variowin (Pannatier, 1996) and ArcGIS geostatistical Analyst (ESRI, 2004), variograms of each soil physical property and cross-variograms were obtained. The spherical models were fitted to the variograms (Fig 2):…”

Section: Discussionmentioning

confidence: 99%

Variability of Soil Organic Carbon in Two Reclaimedsites in South Eastern Ohio

Shukla

Lal²

2005

JASMR

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Knowledge of spatial variability is essential for assessing the true soil organic carbon (SOC) content and the sequestration potential of reclaimed minesoils (RMS). Two experimental sites were selected for determining the spatial variability of some soil properties including soil organic C (SOC) stock. Both sites located in Guernsey County of Ohio were reclaimed in 1978 with topsoil application and are under grass and forest cover, respectively. Soil bulk density ( b), SOC, total nitrogen (TN) concentrations and stocks were determined for both sites for 0-15 cm, 15-30 cm, and 30-50 cm depths. In the Forest site, the statistical variability of  b was low in all three depths. The  b increased with depth and ranged from 0.88 Mg m-3 to 1.16 Mg m-3 for 0-15 cm, 0.91 Mg m-3 to 1.32 Mg m-3 for 15-30 cm, and 1.37 Mg m-3 to 1.93 Mg m-3 for 30-50 cm depths. The variability in  b was also low in Grass site and ranged from 0.82 Mg m-3 to 1.18 Mg m-3 for 0-15 cm, 1.04 Mg m-3 to 1.37 Mg m-3 for 15-30 cm, and 1.18 Mg m-3 to 1.83 Mg m-3 for 30-50 cm depths. The  b showed strong spatial dependence for 0-15 cm depth only in the Forest site (nugget:sill ratio = 20). The statistical variability of SOC concentrations and stocks were high for all depths in both sites (CV > 0.36). The SOC stocks also had strong spatial dependence for 0-15 cm and 30-50 cm depths (nugget:sill ratio < 17) and moderate to strong dependence for 15-30 cm depth in the Forest site (nugget:sill ration=24). In contrast, in Grass site,  b was weakly and SOC stocks moderately spatially dependent for all depths. Variability did not follow a consistent trend and both short (mostly) and long range variability were observed. These results suggest that the management effects are important and assessment of spatial variability is necessary for the correct assessment of SOC accretion in reclaimed minesoils.

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“…To determine the length, width and unitary surface area of the pruning residues, we used image j . We used variowin 2·21 (Pannatier, ) for variogram analysis and geoMS (Centre for Natural Resources and the Environment, ) for stochastic simulation. Finally, to visualize the maps and execute general operations, Arcgis 10·1 (Environmental Systems Research Institute, ) was employed.…”

Section: Methodsmentioning

confidence: 99%

Assessing Local Uncertainty of Soil Protection in an Olive Grove Area with Pruning Residues Cover: A Geostatistical Cosimulation Approach

et al. 2017

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The spatial characterization of soil protection is important for preventing land degradation. Mulch covers of pruning residues are increasingly used by farmers, but little is known regarding their spatial variability. Thus, field research was conducted in a 14·7‐ha traditional olive grove located in Córdoba (Spain), to evaluate the variability of the initial density of pruning (Dpo), tree crown volume (V) and N content in residues (Np). Dry pruning weight and Np were characterized in 49 and 47 samples, respectively, covering approximately half of the area. V was measured in 178 locations covering all of the study area. The spatial continuity of these variables was described by spherical variograms. Geostatistical simulation algorithms were employed. One hundred realizations were made for Np through direct sequential simulation. Because the association with V was large (rxy = 0·72, p‐value < 0·001), we ran a geostatistical cosimulation algorithm to obtain 100 maps of Dpo. The final density of pruning (FDp) was determined by applying a residue decomposition model for a 2‐year period to pairs of previously simulated maps of Np and Dpo. After setting soil protection cut‐off values for FDp, the soil protection probability was calculated for pruning strips of 2 and 4 m width. Finally, we converted the probability maps into binary maps and classified the farm into protected, unprotected and uncertain zones. V provided valuable input to evaluate soil protection. The application of stochastic simulations coupled with a decomposition model was useful to identify critical areas. This methodology can be easily applied to other crops. Copyright © 2017 John Wiley & Sons, Ltd.

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Variowin

Cited by 210 publications

References 0 publications

Could shelling in the First World War have increased copper concentrations in the soil around Ypres?

Could shelling in the First World War have increased copper concentrations in the soil around Ypres?

Variability of Soil Organic Carbon in Two Reclaimedsites in South Eastern Ohio

Assessing Local Uncertainty of Soil Protection in an Olive Grove Area with Pruning Residues Cover: A Geostatistical Cosimulation Approach

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