| The proliferation of mined landscapes and concern for the environmental impacts associated with these lands have led to an increased interest in developing empirical predictive models to quantitatively assess the vegetative productivity potentials of reconstructed soils (neo-sols). This research presents equations for a northern Michigan mining region based on data derived from the National Resources Conservation Service. We employed principal component analysis to develop models to predict the vegetative productivity of corn, corn silage, oats, alfalfa/hay, Irish potatoes, red maple (Acer rubrum L.), white spruce (Picea glauca [Moench] Voss), red pine (Pinus resinosa Aniton), eastern white pine ( Pinus strobus L.), jack pine (Pinus banksiana Lamb.), and lilac (Syringa vulgaris L.). Soil attributes that were examined in this research include: available water holding capacity, moist bulk density, % clay, % rock fragments, hydraulic conductivity, % organic matter, soil reactivity, % slope, and topographic position. Five predictive equations based on land use have been developed and are described as an all woody and crop equation, a xeric equation, an equation specific to jack pine, and two semi-wet equations of varying conservativeness. The models were highly significant (p<0.0001) and explained 87.93%, 74.52%, 65.33%, 91.79% and 87.68% of the variation in site productivity of the respective land uses. These equations are intended to be used in efforts to assess the vegetative productivity potentials of reconstructed soils on post-mined landscapes. |
