Abstract
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.
Highlights
Spatial analysis of a georeferenced variable using geostatistical models enables measuring the spatial dependence degree among samples within a determined area, describing its spatial dependence structure (Guedes et al, 2018)
The spatial dependence structure of a certain georeferenced variable should be described considering a stochastic process, whose data are expressed by Z(s1), Z(s2), ..., Z(sn), which are known in n sites si (i = 1, ..., n), being that si = (xi, yi)T is a two-dimensional vector
The worst results were obtained in systematic sampling, where the estimated values were more distant from the nominal value of this parameter (Table 1)
Summary
Spatial analysis of a georeferenced variable using geostatistical models enables measuring the spatial dependence degree among samples within a determined area, describing its spatial dependence structure (Guedes et al, 2018). The georeferenced variable can be expressed by a Gaussian spatial linear model: Z(si) = μ(si) + (si) (Uribe-Opazo et al, 2012), in which μ(si) = μ is the deterministic term, μ is a constant, and (si) represents the stochastic term with mean zero, i.e., E[ (si)] = 0; and variation between points in space separated by an Euclidean distance hij = ||h||, so that h = si - sj (i, j = 1, ..., n) is determined by a covariance function: C(hij) = cov[ (si), (sj)] = σij, which depends only on h.
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