Field Scale Variations in Soil Properties for Spatially Variable Control: A Review

It is known that field-scale variations in subsurface hydraulic characteristics are influenced, to a large extent, by soil properties. Limited information, however, exists on the sensitivity of hydraulic functions to field-scale variations in soil properties. The sensitivity of 4 soil water retention functions, θ(h), to variations in soil properties and changes in bulk density (ρ) across and within soils along a 500-m transect has been assessed in this study. The θ(h) functions compared are those of van Genuchten, Brooks and Corey, Campbell, and Gardner. Water retention characteristics for 7 soils, each packed to 2 relative ρ, were established for each function. The coefficient of determination, R 2 , for the best fit of water retention ranged from 0·79 to 0· 98 for the Gardner and Campbell functions, from 0· 92 to 0·99 for the Brooks and Corey function, and from 0·83 to 0·99 for the van Genuchten function. Simple linear regression analysis indicated the nonlinear slope parameters of the 4 functions were more strongly correlated with soil properties. However, only the van Genuchten slope parameters were sensitive to changes in ρ. No consistency existed between the sensitivity of the linear parameters of the 4 functions and soil properties, and none were sensitive to changes in ρ. Except for the a parameter in the van Genuchten function, all the parameters in this function can be predicted with satisfactory confidence from soil properties and ρ. The results indicate that, of the 4 functions assessed, the van Genuchten θ(h) function is the most sensitive to field-scale variations in soil properties along a transect in a landscape unit and to changes in ρ.

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Three-dimensional digital soil mapping of multiple soil properties at a field-scale using regression kriging

Geoderma ◽

10.1016/j.geoderma.2020.114253 ◽

2020 ◽

Vol 366 ◽

pp. 114253 ◽

Cited By ~ 2

Author(s):

Yakun Zhang ◽

Wenjun Ji ◽

Daniel D. Saurette ◽

Tahmid Huq Easher ◽

Hongyi Li ◽

...

Keyword(s):

Soil Properties ◽

Three Dimensional ◽

Soil Mapping ◽

Digital Soil Mapping ◽

Regression Kriging ◽

Field Scale

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Estimation of soil properties at the field scale from satellite data: a comparison between spatial and non-spatial techniques

European Journal of Soil Science ◽

10.1111/ejss.12202 ◽

2014 ◽

Vol 65 (6) ◽

pp. 842-851 ◽

Cited By ~ 27

Author(s):

F. Castaldi ◽

R. Casa ◽

A. Castrignanò ◽

S. Pascucci ◽

A. Palombo ◽

...

Keyword(s):

Soil Properties ◽

Satellite Data ◽

Field Scale

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Spatial Variability of Grapevine Bud Burst Percentage and Its Association with Soil Properties at Field Scale

PLoS ONE ◽

10.1371/journal.pone.0165738 ◽

2016 ◽

Vol 11 (10) ◽

pp. e0165738 ◽

Cited By ~ 2

Author(s):

Tao Li ◽

Xinmei Hao ◽

Shaozhong Kang

Keyword(s):

Spatial Variability ◽

Soil Properties ◽

Bud Burst ◽

Field Scale

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Enhanced A Horizon Framework and Field Form for detailed field scale monitoring of dynamic soil properties

Canadian Journal of Soil Science ◽

10.4141/cjss2013-079 ◽

2014 ◽

Vol 94 (2) ◽

pp. 189-208 ◽

Cited By ~ 2

Author(s):

Catherine A. Fox ◽

Charles Tarnocai ◽

Gabriele Broll ◽

Monika Joschko ◽

David Kroetsch ◽

...

Keyword(s):

Soil Properties ◽

Best Management Practices ◽

Management Practices ◽

Dynamic Properties ◽

Experimental Studies ◽

Density Level ◽

Field Scale ◽

Dynamic Soil Properties ◽

Field Form ◽

Fingerprint Code

Fox, C. A., Tarnocai, C., Broll, G., Joschko, M., Kroetsch, D. and Kenney, E. 2014. Enhanced A Horizon Framework and Field Form for detailed field scale monitoring of dynamic soil properties. Can. J. Soil Sci. 94: 189–208. Taxonomic protocols for A horizon description are limited when detailed monitoring of soil change in dynamic soil properties is required for determining the effectiveness of best management practices, remediation efforts, and assessing subtle impacts on soil properties from environmental and anthropogenic stressors. The A Horizon Framework was designed by consolidating protocols from national and international description systems and expert opinion to optimize descriptive capability through use of additional enhanced lowercase designators. The Framework defines new protocols and syntax resulting in a unique soil fingerprint code. Five levels of enhanced lowercase A horizon designators are defined: Level 1, Soil processes and environmental context; Level 2, Soil structure-bulk density; Level 3, Organic carbon; Level 4, pH and electrical conductivity; and, Level 5, Soil and landscape context (i.e., soil texture, surface conditions, current land use, slope character). An electronic Field Form based on the new Framework syntax automatically records the soil fingerprint code in an enhanced (all Levels included) and a minimum detail mode focused on the key dynamic properties. The soil fingerprint codes become a powerful tool by which to identify trends of soil change and small alterations in the dynamic soil properties. Examples of soil fingerprint codes from selected Canada and Germany long-term experimental studies are presented.

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Covariates selection assessment for field scale digital soil mapping in the context of precision fertilization management

10.5194/egusphere-egu2020-4904 ◽

2020 ◽

Author(s):

Nada Mzid ◽

Stefano Pignatti ◽

Irina Veretelnikova ◽

Raffaele Casa

Keyword(s):

Soil Properties ◽

Precision Agriculture ◽

Prediction Models ◽

Central Italy ◽

Soil Mapping ◽

Digital Soil Mapping ◽

Soil Maps ◽

Field Scale ◽

Umbria Region ◽

Digital Soil Maps

<p>The application of digital soil mapping in precision agriculture is extremely important, since an assessment of the spatial variability of soil properties within cultivated fields is essential in order to optimize agronomic practices such as fertilization, sowing, irrigation and tillage. In this context, it is necessary to develop methods which rely on information that can be obtained rapidly and at low cost. In the present work, an assessment is carried out of what are the most useful covariates to include in the digital soil mapping of field-scale properties of agronomic interest such as texture (clay, sand, silt), soil organic matter and pH in different farms of the Umbria Region in Central Italy. In each farm a proximal sensing-based mapping of the apparent soil electrical resistivity was carried out using the EMAS (Electro-Magnetic Agro Scanner) sensor. Soil sampling and subsequent analysis in the laboratory were carried out in each field. Different covariates were then used in the development of digital soil maps: apparent resistivity, high resolution Digital Elevation Model (DEM) from Lidar data, and bare soil and/or vegetation indices derived from Sentinel-2 images of the experimental fields. The approach followed two steps: (i) estimation of the variables using a Multiple Linear Regression (MLR) model, (ii) spatial interpolation via prediction models (including regression kriging and block kriging). The validity of the digital soil maps results was assessed both in terms of the accuracy in the estimation of soil properties and in terms of their impact on the fertilization prescription maps for nitrogen (N), phosphorus (P) and potassium (K).</p>

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