Partitioning Variability of Soil Properties Affecting Solute Movement with Soil Taxonomy
- M. S. Seyfried *,
- A. G. Hornsby and
- P. V. Rao
Soil taxonomy provides a classification framework for large amounts of data collected in conjunction with soil survey work. This framework may be very useful in parameterizing models of solute movement. An important criteria for usefulness is that the classification groupings reduce sample variability. We used nested analysis of variance to assess the effectiveness of the USDA soil taxonomy in describing sample variability. Soil characterization data from a total of 689 pedons sampled by horizon in Florida were analyzed. Eleven different soil physical and chemical properties important to the modeling of solute movement in soils were selected. Depth effects were incorporated by using surface horizon and depth-weighted average values to depths of 50, 100, and 150 cm. We found that the USDA soil taxonomy very effectively partitioned variance, as evidenced by average R2 values of 0.80 for surface samples and 0.90 for depth-weighted averages. Effectiveness improved with depth of consideration, reflecting the importance of subsoil characteristics in the USDA soil taxonomy. In general, the most effective taxonomic levels were the order, great group, and subgroup. This indicates that it is reasonable to lump or extrapolate data within the family or subgroup levels to obtain better estimates of soil property values in Florida. We believe that these results are probahly qualitatively applicable to other soil properties and locations, hut that quantitative extrapolation should not be done without further study.Please view the pdf by using the Full Text (PDF) link under 'View' to the left.
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