Abstract

Spatial variability has a profound influence on a variety of landscape-scale agricultural issues including solute transport in the vadose zone, soil quality assessment, and site-specific crop management. Directed soil sampling based on geospatial measurements of apparent soil electrical conductivity (ECa) is a potential means of characterizing the spatial variability of any soil property that influences ECa including soil salinity, water content, texture, bulk density, organic matter, and cation exchange capacity. Arguably the most significant step in the protocols for characterizing spatial variability with ECa-directed soil sampling is the statistical sampling design, which consists of two potential approaches: model- and design-based sampling strategies such as response surface sampling design (RSSD) and stratified random sampling design (SRSD), respectively. The primary objective of this study was to compare model- and design-based sampling strategies to evaluate if one sampling strategy outperformed the other or if both strategies were equal in performance. Using three different model validation tests, the regression equation estimated from the RSSD data produced accurate and unbiased predictions of the natural log salinity levels at the independently chosen SRSD sites. Design optimality scores (i.e., D-, V-, and G-optimality criteria) indicate that the use of the RSSD design should facilitate the estimation of a more accurate regression model, i.e., the RSSD approach should allow for better model discrimination, more precise parameter estimates, and smaller prediction variances. Even though a model-based sampling design, such as RSSD, has been less prevalent in the literature, it is concluded from the comparison that there is no reason to refrain from its use and in fact warrants equal consideration.

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