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Ademola K. Braimoh and Paul L. G. Vlek

explanatory variables. Essentiality implies that a variable is required in the model at a given scale, whereas importance refers to the quantification of the relationship between the variable and cropland change. The statistical significance ( p value) of a regression analysis is the probability that the observed relationships between variables in a sample occurred by pure chance. The smaller the p value, the larger (stronger) the confidence we can attach to the relationship between two variables. Thus

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Deborah A. McGrath, Jonathan P. Evans, C. Ken Smith, David G. Haskell, Neil W. Pelkey, Robert R. Gottfried, Charles D. Brockett, Matthew D. Lane, and E. Douglass Williams

( Draper, 1999 ). Despite evidence of increasing harvest and pine conversion rates in this region, the Tennessee Division of Forestry recently reported that the state's forests were improving in condition since the 1950s, largely because of statewide increases in forest cover and growth rates. These observations were based upon a regional U.S. Forest Service Assessment [the Forest Inventory and Analysis (FIA)], which is a valuable tool for examining changes in forest cover on a statewide basis. However

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Jeffrey A. Hicke, David B. Lobell, and Gregory P. Asner

fractional increase in P is the sum of the fractional increase in area plus the fractional increase in NPP plus an interaction term. We used 1972 and 2001 as the 2 times in the equations, and the results from the linear regression equations to specify the values used at these times. 2.2. Limitations of NASS The NASS database does not include all crops in all years. Of particular concern is that crop information is available for some years but not for others since this will affect our trend analysis

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In-Young Yeo, Steven I. Gordon, and Jean-Michel Guldmann

analysis of field data to determine the rainfall–runoff processes. The curve number is developed to link the impacts of on-site land uses and soil types to the storm runoff. Due to its simplicity and good accuracy, this model has often been used. The conventional SCS curve number models yield lumped effects of land-use changes in a watershed, a subwatershed, or a field, but cannot account for the spatial effects of land-use changes [ McCuen, 1982 ; (United States Department of Agriculture) USDA, 1986

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