Abstract
Knowledge of the relationship between soil sampling density and spatial autocorrelation with interpolation accuracy allows more time- and cost-efficient spatial analysis. Previous studies produced contradictory observations regarding this relationship, and this study aims to determine and explore under which conditions the interpolation accuracy of chemical soil properties is affected. The study area covered 823.4 ha of agricultural land with 160 soil samples containing phosphorus pentoxide (P2O5) and potassium oxide (K2O) values. The original set was split into eight subsets using a geographically stratified random split method, interpolated using the ordinary kriging (OK) and inverse distance weighted (IDW) methods. OK and IDW achieved similar interpolation accuracy regardless of the soil chemical property and sampling density, contrary to the majority of previous studies which observed the superiority of kriging as a deterministic interpolation method. The primary dependence of interpolation accuracy to soil sampling density was observed, having R2 in the range of 56.5–83.4% for the interpolation accuracy assessment. While this study enables farmers to perform efficient soil sampling according to the desired level of detail, it could also prove useful to professions dependent on field sampling, such as biology, geology, and mining.
Highlights
Spatial interpolation of soil chemical and physical properties is necessary to model its continuous distribution from discrete geo-referenced soil samples, which in this form do not exhibit a representative state of the agricultural land [1]
Based on the research of the influence of spatial autocorrelation and sampling density on the interpolation accuracy using ordinary kriging (OK) and inverse distance weighted (IDW), it was determined that: 1. Interpolation accuracy primarily increases with the sampling density, having R2 produced by linear regression in the range of 56.5–83.4%
Spatial autocorrelation indicated a lower impact on the interpolation accuracy but has potentially higher applicability in cases of lower spatial autocorrelation; 2
Summary
Spatial interpolation of soil chemical and physical properties is necessary to model its continuous distribution from discrete geo-referenced soil samples, which in this form do not exhibit a representative state of the agricultural land [1]. Detection of input parameters for spatial interpolation, such as sampling density and method as well as terrain heterogeneity, enables more economical and efficient soil sampling by adjusting the sampling plan to accommodate these factors [3]. The influence of these segments with varying intensity affects the heterogeneity of agricultural land and can be divided into micro- and macrolevel [4]. Determining the level of impact of soil sampling density is important for both levels of research, given the high cost and time inefficiency of conventional field sampling and laboratory soil analysis [7]
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