Optimal soil sampling design based on the maxvol algorithm

Spatial soil sampling is an integral part of a soil survey aimed at describing spatial variability in soil properties. We propose considering the soil sampling procedure as a task of optimal design. In practical terms, optimal experiments can reduce experimentation costs, as they allow the researcher to obtain one optimal set of points. We present a sampling design, based on the fundamental idea of selecting sample locations with the most significant dissimilarities. The proposed sampling design is founded upon an optimal design method called the maxvol algorithm. The sampling design is tested in three real cases differing in field data availability and compared to the popular sampling schemes - simple random sampling, conditional Latin Hypercube, Kennard-Stone and stratified random sampling based on complex geographical strata. It is shown that the maxvol-base algorithm has a high potential for practical usage. Our method outperforms popular sampling methods in soil taxa prediction based on topographical features of the site. The proposed algorithm can be especially beneficial for practical application because it can produce high-quality sampling scheme with very few points.

Automated summary

Spatial soil sampling is crucial for describing spatial variability in soil properties. Optimal experiments can reduce costs and select sample locations with significant dissimilarities. The maxvol algorithm-based sampling design outperforms popular methods in soil taxa prediction.