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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

View larger version (13K): [in a new window]   Fig. 1. Example of soil sample locations (solid circles) overlaid with a simulated experiment in one of the studied field sites.

View larger version (14K): [in a new window]   Fig. 2. Example of sample variograms for total soil C from three of the studied field sites and the variogram models fitted to the sample variograms by restricted maximum likelihood method in PROC MIXED (SAS). Model parameters are shown in Table 1.

View larger version (20K): [in a new window]   Fig. 3. Relationships between the number of replications and the difference in total soil C (%) between the treatments that can be detected as statistically significant via F-test for treatment effects with power of 0.80 and of 0.05 using standard randomized complete block design (RCBD) analysis and random field analysis with correlated errors based on plot data (SA1) analysis with spatial correlation ranges of 15, 25, and 35 m.