Modeling energy inputs to predict pedogenic environments using regional environmental databases

We present a model for prediction of pedogenic environments and soil properties based on energy input to the soil system. The model estimates rates of precipitation and net primary production (NPP) energy input using the Parameter-Regression Independent Slope Model (PRISM) climate data, and a parent material index (PMI). Soil order, soil C, and clay data from the State Soil Geographic (STATSGO) database were compared with rates of NPP and precipitation energy input for major geographic regions of the continental USA, including California, Oregon, Washington, Texas, North Dakota, Alabama, Pennsylvania, and New Hampshire. Soil orders in all states show differences in total energy input (E(in), kJ m(-2)yr(-1)) and the percentage of E(in) from NPP (%E(npp)) (e.g., Ultisols E(in) = 29 915, %E(npp) = 49%; Mollisols E(in) = 5880, %E(npp) = 90%). Using linear regression models, rates of NPP estimated (R2 = 0.82***) trends in soil C content in western states, but failed to estimate soil C in other geographic areas. Parent material index adjusted energy flux estimated soil clay content for the majority (99.5%) of igneous parent materials in California and Oregon (R2 = 0.67**), the only states with digital geologic data. The model underestimated clay content in steeply sloping Inceptisols and Andisols (0.5% of igneous land area). Results suggest that rates of NPP may be used to estimate soil C for climate regimes with steep environmental gradients. Landscape age and stability components might improve clay prediction in young and erosive landscapes. Modeled energy input provides a tool for estimating pedogenic environments, soil order, and soil properties. Energy input parameters may aid efforts to pre-map broad landscape units for soil survey.