Optimization of the sampling scheme for maps of physical and chemical properties estimated by kriging

Gener Tadeu Pereira,Daniel De Bortoli Teixeira,Rafael Montanari,Zigomar Menezes De Souza,José Marques Júnior

doi:10.1590/s0100-06832013000500002

Abstract

The sampling scheme is essential in the investigation of the spatial variability of soil properties in Soil Science studies. The high costs of sampling schemes optimized with additional sampling points for each physical and chemical soil property, prevent their use in precision agriculture. The purpose of this study was to obtain an optimal sampling scheme for physical and chemical property sets and investigate its effect on the quality of soil sampling. Soil was sampled on a 42-ha area, with 206 geo-referenced points arranged in a regular grid spaced 50 m from each other, in a depth range of 0.00-0.20 m. In order to obtain an optimal sampling scheme for every physical and chemical property, a sample grid, a medium-scale variogram and the extended Spatial Simulated Annealing (SSA) method were used to minimize kriging variance. The optimization procedure was validated by constructing maps of relative improvement comparing the sample configuration before and after the process. A greater concentration of recommended points in specific areas (NW-SE direction) was observed, which also reflects a greater estimate variance at these locations. The addition of optimal samples, for specific regions, increased the accuracy up to 2 % for chemical and 1 % for physical properties. The use of a sample grid and medium-scale variogram, as previous information for the conception of additional sampling schemes, was very promising to determine the locations of these additional points for all physical and chemical soil properties, enhancing the accuracy of kriging estimates of the physical-chemical properties.

Highlights

An insufficient sampling intensity can be an important limiting factor for precision agriculture and has been the subject of several studies (Atkinson & Lloyd, 2007; Montanari et al, 2012)
A regular grid is typically recommended for result optimization (Vašát et al, 2010), it does not take regionalized variables into account and the area where sampling optimization firstly occurs (Christakos & Olea, 1992)
According to Warrick & Nielsen (1986), the coefficient of variation (CV) obtained for the properties pH, CEC, clay content, silt content, and ST can be classified as low, for the properties OM, H + Al, V, MS, FS and VFS as average; and the properties P, K, Ca, Mg, SB, and AG, as high. These same authors observed that when dealing with natural data, the theoretical distribution adjustment is only approximate

Summary

Introduction

An insufficient sampling intensity can be an important limiting factor for precision agriculture and has been the subject of several studies (Atkinson & Lloyd, 2007; Montanari et al, 2012). 80 to 85 % of the total errors in liming and fertilizer recommendations can be attributed to the peculiarities of soil sampling (Siqueira et al, 2010). The sampling scheme influences research efficiency and cost of the spatial variability of soil properties. Sparse sampling schemes are cheaper, they might fail to capture some spatial dependence aspects (Groenigen et al, 1999; Groenigen, 2000). Geostatistics plays an important role in optimal sampling studies of regionalized variables (Kerry & Oliver, 2007; Pang et al, 2009; Baume et al, 2011; Montanari et al, 2012)

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