Relationship Between Sample Design and Geometric Anisotropy in the Preparation of Thematic Maps of Chemical Soil Attributes

Guedes, Luciana Pagliosa Carvalho; Uribe-Opazo, Miguel Ángel; Ribeiro, Paulo Justiniano; Dalposso, Gustavo Henrique

doi:10.1590/1809-4430-eng.agric.v38n2p260-269/2018

Cited by 5 publications

(1 citation statement)

References 18 publications

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“…Spatial analysis of a georeferenced variable using geostatistical models enables measuring the spatial dependence degree among samples within a determined area, thus describing its spatial dependence structure (Guedes et al, 2018). The spatial dependence analysis, mainly of soil chemical properties in farmlands, allows to know their values in subregions (management zones) within the area of interest, which, in turn, enables the application of agricultural inputs at specific points (Gazolla-Neto et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Nugget Effect Influence on Spatial Variability of Agricultural Data

2020

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Spatial variability description of soil chemical properties by thematic maps depends substantially on suitable geostatistical models. One of the parameters composing a geostatistical model is nugget effect. This study aimed to evaluate the simultaneous influence of nugget effect and sampling design on geostatistical model estimation and estimation of soil chemical properties at unsampled sites, considering simulated data. Our results will be used as scientific basis for spatial variability analyses of soil chemical properties in agricultural areas. Given the simulation results and agricultural data, we concluded that the high nugget effect values obtained here reduced spatial estimation efficiency. Moreover, a systematic sampling design promoted the least accurate estimates of geostatistical model and at non-sampled sites. Despite that, these nugget effect estimates should be kept in the analysis. However, further studies will be needed to investigate which factors are responsible for such high nugget effect values.

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Section: Introductionmentioning

confidence: 99%

Nugget Effect Influence on Spatial Variability of Agricultural Data

2020

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Spatial patterns of magnetic susceptibility optimized by anisotropic correction in different Alisols in southern Amazonas, Brazil

et al. 2021

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Directional Differences in Thematic Maps of Soil Chemical Attributes with Geometric Anisotropy

Ribeiro,

Maltauro,

Guedes

et al. 2024

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In the study of the spatial variability of soil chemical attributes, the process is considered anisotropic when the spatial dependence structure differs in relation to the direction. Anisotropy is a characteristic that influences the accuracy of the thematic maps that represent the spatial variability of the phenomenon. Therefore, the linear anisotropic Gaussian spatial model is important for spatial data that present anisotropy, and incorporating this as an intrinsic characteristic of the process that describes the spatial dependence structure improves the accuracy of the spatial estimation of the values of a georeferenced variable in unsampled locations. This work aimed at quantifying the directional differences existing in the thematic map of georeferenced variables when incorporating or not incorporating anisotropy into the spatial dependence structure through directional spatial autocorrelation. For simulated data and soil chemical properties (carbon, calcium and potassium), the Moran directional index was calculated, considering the predicted values at unsampled locations, and taking into account estimated isotropic and anisotropic geostatistical models. The directional spatial autocorrelation was effective in evidencing the directional difference between thematic maps elaborated with estimated isotropic and anisotropic geostatistical models. This measure evidenced the existence of an elliptical format of the subregions presented by thematic maps in the direction of anisotropy that indicated a greater spatial continuity for greater distances between pairs of points.

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Relationship Between Sample Design and Geometric Anisotropy in the Preparation of Thematic Maps of Chemical Soil Attributes

Cited by 5 publications

References 18 publications

Nugget Effect Influence on Spatial Variability of Agricultural Data

Nugget Effect Influence on Spatial Variability of Agricultural Data

Spatial patterns of magnetic susceptibility optimized by anisotropic correction in different Alisols in southern Amazonas, Brazil

Directional Differences in Thematic Maps of Soil Chemical Attributes with Geometric Anisotropy

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