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a USDA Forest Service, Northern Research Station, c/o Baltimore Ecosystem Study, 5200 Westland Blvd., Univ. of Maryland-Baltimore County, Baltimore, MD 21227
b Center for Urban Environmental Research and Education, Univ. of Maryland-Baltimore County, Baltimore, MD 21250
c Dep. of Mathematics and Statistics, Univ. of Maryland-Baltimore County, Baltimore, MD 21250
* Corresponding author (rpouyat{at}fs.fed.us).
We investigated the effects of land use and cover and surface geology on soil properties in Baltimore, MD, with the objectives to: (i) measure the physical and chemical properties of surface soils (010 cm) by land use and cover; and (ii) ascertain whether land use and cover explain differences in these properties relative to surface geology. Mean and median values of each variable measured across all plots showed that soil properties varied considerably. Chemical properties generally varied more than physical properties. A subset of the variables measured showed a pattern with land use and cover. Potassium, P, and bulk density were the most discerning variables differentiating forest cover from land uses dominated by turfgrass cover. Soil pH differentiated residential land use and cover from the other turfgrass types. This separation may reflect differences in management, e.g., additions of fertilizer, although additional research is needed to assess the importance of management on soil properties. Differences in surface soil properties among land use and cover types could be useful when conducting urban soil surveys, at least to spatially differentiate remnant soils from highly disturbed and managed soils. Other soil properties (Al, Mg, V, Ti, Mn, Fe, Ni, and soil texture) were related to surface geology and thus unique to the Baltimore region. The importance of surface geology was contrary to our expectation that urban factors would be more important in determining the distribution of surface soil characteristics. Heavy metal concentrations did not differentiate land use and cover, suggesting that these elements are more related to other factors.
Abbreviations: CDA, canonical discriminant analysis CT, commercial or transportation F, forest IU, industrial or urban open I, institutional MANOVA, multivariate analysis of variance P, park or golf course R, residential
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