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Precision application of agrichemicals requires an accurate assessment of the spatial structure of soil properties. Spatial structure analysis of soil properties could be influenced by sampling design especially on highly variable alluvial derived soils. The objective of this research was to compare spatial structure analysis between grid-point and grid-cell type sampling for alluvial derived soils, which vary more in one dimension than a second. Soil samples (0- to 0.15-m depth) were taken along two transects in an irrigated cotton (Gossypium hirsutum L.) field located in the Lower Mississippi Valley flood plain and were analyzed for total C and N, extractable Ca, Mg, K, Na, Zn, and P, and pH. Moran's I autocorrelation coefficient was computed at preselected lag distances and correlograms were plotted to examine trends in autocorrelation. A correlation range of near 300 m appeared to be associated with sampling across two soil mapping units, while a shorter range as well as a cyclic spatial structure was likely influenced by alternating soil mapping units. Similarity in autocorrelation trends calculated using grid-point and grid-cell sampling designs suggests spatial pattern detection for soils of the Mississippi Delta can be achieved by either sampling methodology with a sampling resolution of approximately 46 m or less. Grid-point sampling compared with grid cell is more easily facilitated and requires less labor and time, but could be implemented more efficiently by using a grid with reduced sampling intensity in areas with a high probability of long-range autocorrelation. Reprinted by permission of the publisher.
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