Archive image from page 66 of Development and evaluation of an. Development and evaluation of an even- and uneven-aged ponderosa pine/Arizona fescue stand simulator . developmentevalu267hann Year: 1980 The differences in the size of coefficients between the uncut and cut data sets were then modeled as a function of time-since-last-cutting (TIME), expressed as the number of 5-year intervals since the last cutting. The uncut virgin data sets were given a value of 60 (or 300 years). I hypothesized that the transition from 'cut' regression coefficients to 'uncut' regression coefficients would be
Archive image from page 66 of Development and evaluation of an. Development and evaluation of an even- and uneven-aged ponderosa pine/Arizona fescue stand simulator . developmentevalu267hann Year: 1980 The differences in the size of coefficients between the uncut and cut data sets were then modeled as a function of time-since-last-cutting (TIME), expressed as the number of 5-year intervals since the last cutting. The uncut virgin data sets were given a value of 60 (or 300 years). I hypothesized that the transition from 'cut' regression coefficients to 'uncut' regression coefficients would be along a smooth path, and therefore three sigmoidal curve forms were developed using MATCHACURVE techniques (Jensen and Homeyer 1970). All three curves are zero when TIME is zero and one when TIME equals 60. The differences in the three curves are their forms (fig. 9), and they were chosen more or less subjectively. Because the TIME data fell at both extremes, I felt that a more objective approach was not possible. The three equations are: - + (-( - )2) A = + 1. 095336EXP(-(1. 25 - 0. 020833TIME) '+) A - + (-( - )) where TIME = time-since-last-cutting expressed in number of 5-year growth periods. Time Since Cutting in Five Year Growth Periods Figure sigmoidal curves. 59
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