The Use of Cokriging with Limited Field Soil Observations

Vauclin, M.; Vieira, Sidney Rosa; Vachaud, G.; Nielsen, D. R.

doi:10.2136/sssaj1983.03615995004700020001x

Cited by 281 publications

(152 citation statements)

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“…Models considered included linear, exponential, circular, spherical, tetraspherical, pentaspherical, and Guassian. Final variogram model selection was based on cross-validation (Vauclin et al, 1983;Warrick et al, 1986). In a cross-validation, each point in the sampling scheme is removed singly and its value is predicted based on kriging the remaining data.…”

Section: Geostatistical Data Analysismentioning

confidence: 99%

“…In a cross-validation, each point in the sampling scheme is removed singly and its value is predicted based on kriging the remaining data. The resulting root mean square error (RMSE) of the cross-validation process was examined, and the variogram model with the lowest RMSE was selected (Vauclin et al, 1983;Heisel et al, 1999). In all cases either exponential or spherical models were selected and final selected model r 2 values were as large as 0.95.…”

Section: Geostatistical Data Analysismentioning

confidence: 99%

“…To apply cokriging, it was necessary to model semivariograms for each soil variable separately as well as cross-variograms for all pairs of soil EC and soil variable measured at the same location (McBratney and Webster, 1983a;McBratney and Webster, 1983b;Vauclin et al, 1983;Triantafilis et al, 2001). The correlations TARR ET AL. shown in Table 2 were also examined (Chien et al, 1997).…”

Section: Geostatistical Data Analysismentioning

confidence: 99%

“…This relationship can subsequently be used to improve the prediction variance associated with the soil variable of interest. For example, Vauclin et al (1983) incorporated secondary information on the spatial variability of sand content to help predict values of two primary variables of interest, available water content and water stored at 33 1/3 kPa. McBratney and Webster (1983a) estimated topsoil silt content utilizing its spatial interrelationship with subsoil sand and subsoil silt as secondary variables.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Improving Map Accuracy of Soil Variables Using Soil Electrical Conductivity as a Covariate

Tarr¹,

Moore

Bullock

et al. 2005

Precision Agric

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Section: Geostatistical Data Analysismentioning

confidence: 99%

Section: Geostatistical Data Analysismentioning

confidence: 99%

Section: Geostatistical Data Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Improving Map Accuracy of Soil Variables Using Soil Electrical Conductivity as a Covariate

Tarr¹,

Moore

Bullock

et al. 2005

Precision Agric

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“…According to Martinho (2000), owing to the soil physical changes implied to the soil surface layer in the no tillage system, it is necessary to study the spatial variabil-ity in order to adequately characterize the environment. The spatial variability can be analyzed using geostatistics that involves a sequence of procedures for the construction of contour maps (Campbell, 1978;Burgess & Webster, 1980;Vieira et al, 1981;Vauclin et al, 1983;Vauchaud et al, 1985;Wendroth et al, 1997;Vieira, 2000;Grego & Vieira, 2005).…”

Section: Introductionmentioning

confidence: 99%

Geostatistical analysis for soil moisture content under the no tillage cropping system

Grego

Vieira

Antonio

et al. 2006

Sci. agric. (Piracicaba, Braz.)

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Experiments in agriculture usually consider the topsoil properties to be uniform in space and, for this reason, often make inadequate use of the results. The objective of this study was to assess the variability for soil moisture content using geostatistical techniques. The experiment was carried out on a Rhodic Ferralsol (typic Haplorthox) in Campinas, SP, Brazil, in an area of 3.42 ha cultivated under the no tillage system, and the sampling was made in a grid of 102 points spaced 10 m × 20 m. Access tubes were inserted down to one meter at each evaluation point in order to measure soil moisture contents (cm 3 cm -3 ) at depths of 30, 60 and 90 cm with a neutron moisture gauge. Samplings were made between the months of August and September of 2003 and in January 2004. The soil moisture content for each sampling date was analyzed using classical statistics in order to appropriately describe the central tendency and dispersion on the data and then using geostatistics to describe the spatial variability. The comparison between the spatial variability for different samplings was made examining scaled semivariograms. Water content was mapped using interpolated values with punctual kriging. The semivariograms showed that, at the 60 cm depth, soil water content had moderate spatial dependence with ranges between 90 and 110 m. However, no spatial dependence was found for 30 and 90 cm depths in 2003. Sampling density was insufficient for an adequate characterization of the spatial variability of soil moisture contents at the 30 and 90 cm depths.

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Use of Auxiliary Data for Spatial Interpolation of Ozone Exposure in Southeastern Forests

et al. 1997

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The Use of Cokriging with Limited Field Soil Observations

Cited by 281 publications

References 0 publications

Improving Map Accuracy of Soil Variables Using Soil Electrical Conductivity as a Covariate

Improving Map Accuracy of Soil Variables Using Soil Electrical Conductivity as a Covariate

Geostatistical analysis for soil moisture content under the no tillage cropping system

Use of Auxiliary Data for Spatial Interpolation of Ozone Exposure in Southeastern Forests

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