English

Abstract

Methods and procedures to evaluate and predict the productivity potential of disturbed soils are of great interest to soil scientists, land planners, and environmental specialists, especially in reclamation and restoration of disturbed landscapes. This study is based upon data collected by the Natural Resource Conservation Service in Grand Traverse County, Michigan. The procedure employs Principal Component Analysis to develop a latent dimension vegetation variable from corn (Zea mays L.), silage corn (Zea mays L.), oat (Avena sativa L.), winter wheat (Triticum aestivum L.), grass and legume mixtures, eastern red cedar (Juniperus virginiana L.), white spruce (Picea glauca L.), red pine (Pinus resinosa Ait.), eastern white pine (Pinus strobus L.), amur maple (Acer ginnala Maxim.), green ash (Fraxinus pennsylvanica Marsh.), siberian peashrub (Caragana arborescens Lam.), and lilac (Syringa vulgaris L.). Soil factors examined in the study include: topographic position, % slope, % rock fragments, % clay, bulk density, hydraulic conductivity, available water holding capacity, soil reaction, % organic matter. A predictive equation for evaluating and reconstructing soil profiles in reclamation applications and in assessing other forms of soil disturbance was generated (p<0.0001), explaining 73.72% of the variance.