Influence of Mine Soil Properties on White Oak Seedling Growth: A Proposed Mine Soil Classification Model
Authors: Showalter, Julia M.; Burger, James A.; Zipper, Carl E.; Galbraith, John M.; Donovan, Patricia F.
Source: Southern Journal of Applied Forestry, Volume 31, Number 2, May 2007 , pp. 99-107(9)
Publisher: Society of American Foresters
Abstract:Appalachian landowners are becoming increasingly interested in restoring native hardwood forest on reclaimed mined land. Trees are usually planted in topsoil substitutes consisting of blasted rock strata, and reforestation attempts using native hardwoods are often unsuccessful due to adverse soil properties. The purpose of this study was to determine which mine soil properties most influence white oak (Quercus alba L.) seedling growth, and to test whether these properties are reflected adequately in a proposed mine soil classification model developed for application in field assessments of mine soil suitability for reforestation. Seventy-two 3-year-old white oaks were randomly selected across a reclaimed site in southwestern Virginia that varied greatly in spoil/site properties. Tree height was measured and soil samples adjacent to each tree were analyzed for physical, chemical, and biological properties. Our proposed mined land classification model used rock type, compaction, and slope aspect as mapping criteria. Tree height, ranging from 15.2 to 125.0 cm, was regressed against mine soil and site properties. Mapping units were not well correlated with differences in tree height. Microbial biomass, pH, exchangeable potassium, extractable inorganic nitrogen, texture, aspect, and extractable phosphorous accounted for 52% of the variability in tree growth. The regression model shows that white oaks were most successful on northeast-facing aspects, in slightly acidic, sandy loam, fertile mine soils that are conducive to microbial activity. Nutrient availability, although found to be highly influential on tree growth, was not adequately represented in the classification model. We recommend that pH be included as a classification criterion, because it was correlated with all nutrient variables in the regression model.
Document Type: Research Article
Publication date: 2007-05-01
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