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Spatial Variability

Using Information about Spatial Variability to Improve Estimates of Total Soil Carbon

^a Dep. of Crop and Soil Sciences, Michigan State Univ., East Lansing, MI 48824-1325
^b W.K. Kellogg Biological Station and Dep. of Crop and Soil Sciences, Michigan State Univ., Hickory Corners, MI 49060-9516

^* Corresponding author (kravche1{at}msu.edu)

Received for publication November 10, 2005. Changes in soil C as a result of changes in management are relatively slow, and several years of experimentation are needed before differences in management practices can be detected using traditional statistical procedures such as randomized complete block design (RCBD). Using spatial analyses (SA) that take into account spatial variability between plots has a potential for faster and more efficient detection of soil C differences. We hypothesize that for variables with strong spatial continuity, such as total soil C, accurate spatial variability assessment can be obtained even in relatively small experiments. Thus, SA can significantly improve the statistical efficiency of even these experiments. The objective of this study is to test this hypothesis by comparing performances of RCBD analysis and SA for simulated small-sized experiments where soil C is the response variable. Total soil C data collected from 11 field sites at the Long-Term Ecological Research (LTER) experiment in Michigan were used as an input for simulated experiments. Performance of SA depended on the strength of spatial correlation in soil C and was found to be related to topographical diversity of the experimental sites. In the sites with more diverse topography and stronger spatial correlation of soil C the SA produced lower standard errors for treatment means than those of the RCBD analysis (8 out of 11 sites). In two sites with the flattest topography and weak spatial correlation, SA did not have advantages over RCBD.

Abbreviations: AIC, Akaike Information Criterion • LTER, Long-Term Ecological Research site • RCBD, randomized complete block design • SA, spatial analyses • SA1, random field analysis with correlated errors based on plot data • SA2, random field analysis with trend and spatially correlated residuals • SA2a, random field analysis with trend and correlated errors based on subsample data