TABLE 4. — Constrained-regression coefficients (b = ratio of bulk depth to high-intercept depth) 

 for skidding, species, and age groups 



Type of cutting 



Dominant 

 : species 



: Age of 

 ■ debris 

 : (years) 





Areas included 



b-coeff icient 

 Average Standard 

 deviation 



Ground- lead harvest 



DF 



0- 



•1 



iy /b- 



7 t n it i 7 1? iji 7 /-\ 

 -2,10,11,12,15,14,20 



0.610 



0.030 



Ground- lead harvest 



ur 



3- 



4 



iy /b- 



7 c -7 o n 



■ 5, b , / , o, y 



.599 



.031 



Ground- lead harvest 



I D 



Lr 



3 





iy / b- 



77 A 7C 7£ 



•25 , 24 , 2b , 2o 



.596 



.079 



Ground- lead harvest 



T D 



1 





i y / d- 



7 ^ 77 1/1 7/1 



-o,4,2o,z4,o0 



.698 



.036 



Precommercial thinning 



DD 



rr 



4 





iy /t>- 



in 1 7 17 1 C 1A 



-10, 12, lo, lb, ID 



.592 



.107 



Precommercial thinning 



LP 



3- 



4 



i y /o- 



i o r ^ -7 i "7 

 -l,2,b,D, / ,17 



.653 



.066 



Preconunercial thinning 



nc 

 Ur 



X. 



• 4 



iy /b- 



/I 1 C 1 C 1 7 lO in 71 77 



-4, lb, lb, l/,lo,iy, 21, 22 



. 575 



. 092 



Preconunercial thinning 



DD 



rr 



1 





iy / o- 



Q 11 9 7 7Q 



- y , 1 1 , 2 / , 20 



.713 



.059 



Precommercial thinning 



LP 



1 





1976- 



■29,31 



.689 



.086 



Skyline harvest 



Mixed 



1 





1976- 



-14,18,19,20,21,22,25,26 



.673 



.036 



Helicopter harvest 



Mixed 



1 





1976- 



-35,36,37,38 



.631 



.048 



Effect of Lopping 



The effect of lopping was evaluated by combining bulk depths determined before and 

 after lopping, as a data pair for each study area and computing a linear regression 

 constrained through the origin. The resulting equation is plotted over the scatter 

 diagram shown in figure 6. Similarly, the data from all precommercial thinning areas 

 were combined for regression (as shown in fig. 7). The quality of the relationships 

 expressed by "s" values of 0.91 and 0.69, respectively, was good. Note that the ratio 

 of the regression coefficients in table 3 for lopped and unlopped high-lead harvest 

 debris (3.60/4.60 = 0.78) is quite close to our result of 0.83 (fig. 6). 



HAZARD Model Application 



To illustrate how this study has been utilized, sample printouts of fuel inputs 

 (fig. 8) and fire behavior predictions (fig. 9) are shown for the slash HAZARD model 

 now in use by USDA Forest Service Northern Region. This model affords managers the 

 opportunity to assess the fire implications of tree cutting activities before debris is 

 put on the ground. The fuel loadings required to run the HAZARD model are generated 

 from tree inventories processed through a debris prediction model. 5 The debris pre- 

 diction model provides total potential debris for all trees on a site. Managers then 

 tailor the total potential debris to specific cutting prescriptions and submit the 

 data for processing by the HAZARD model. To ease checking of the transferred data, 

 the HAZARD model prints out the input data (fig. 8). 



See footnote 4. 



13 



