Volume 15, Number 1, January 2004

EN:

An analysis of log-making (bucking) performance for five logging crews in southern Appalachian mixed-hardwood stands of Virginia and West Virginia was conducted. Cutting accuracy and value recovery were analyzed and compared to an optimal solution that was determined through the use of the HW-BUCK computer software. In total 148 trees were bucked into 510 logs and only 11 percent were cut accurately. Fifteen percent were under cut and 74 percent were over length. The crew with the best performance in length cutting accuracy also recorded the lowest value recovery loss. An average value loss of 20.7 percent was calculated for all five crews.

EN:

This paper describes a method for optimizing cable logging layouts using a heuristic network algorithm. A timber harvest unit layout is formulated as a network problem. Each grid cell containing timber volume to be harvested is identified as an individual entry node of the network. Mill locations or proposed timber exit locations are identified as destinations. Each origin will then be connected to one of the destinations through alternative links representing alternative cable corridors, harvesting equipment, landing locations, and truck road segments. A heuristic algorithm for network programming is used to solve the cost minimization network problem. A computerized model has been developed to implement the method. Logging feasibility and cost analysis modules are included in the model in order to evaluate the logging feasibility of alternative cable corridors and estimate yarding and transportation costs. The model was successfully applied to a harvest planning area to generate harvesting plans. This case study indicates that the planning method is best used for pre-planning since modeling assumptions with respect to tail spar availability and unconstrained road alignments may require modification of the plan before implementation.

EN:

In the computerized bucking to demand procedure bucking is done according to a given price list and demand matrix, which defines the demands for different log length-diameter class proportions. To achieve as good a log length-diameter distribution as possible, the computer compares demand and actual output to appropriately direct bucking. A comparison has been made with a variable called distribution level, which, however, is unable to distinguish between error that is close to the optimum log length-diameter class proportion and error that is further away. In addition, the distribution level does not distinguish between log length-diameter classes, even though error in one class can be far more undesirable than in another.

In this study, bucking to demand using the distribution level was compared to bucking to value and bucking to demand using the penalty segmented distribution level, squared distribution level, chi-square formula and flexible penalty segmented distribution level. The bucking outcome employing these various techniques was achieved by using a bucking simulator and artificially generated stand and stem data.

The results show that the best bucking outcomes were produced by methods with a squared error term, i.e. the squared distribution level, chi-squared formula and flexible penalty segmented distribution level. In addition, it was possible to direct error toward preferred log length-diameter classes without substantial loss in overall goodness of fit.

EN:

In this paper, the applicability of linear programming (LP) in management of seedling transportation was compared to that of mixed integer programming (MIP). In the LP model, presented in an earlier paper, a linear objective function was used as a surrogate for the actual objective function, which is intrinsically nonlinear. In the LP model, transportation costs were determined per seedling, whereas in the MIP model they were based on vehicle loads. When the number of transported seedlings within a certain period decreased, for instance, due to planting through the growth period, the computational accuracy of the LP model was clearly lower than that of the MIP model. Despite that, differences in allocation of orders between these two models were small. Thus, in the actual business situation of Finnish nursery companies, standard LP seems to be an adequate tool for management of seedling transportation. From the standpoint of cost-efficient seedling business, planting through the growth period increased optimal transportation costs markedly. In addition to the seedling business, these results can be utilized in other types of business dealing with analogous transportation problems.

EN:

Log damage was examined in terms of volume and value losses by harvesting system, function, tree species, and log size in four central Appalachian hardwood sites. Observations were made of all grade logs during the felling, skidding, decking/sorting, and loading operations. Sawlogs sustaining damage to the bark or cambium were recorded with additional information obtained for the location, dimensions, and type of damage. The data were analyzed statistically to determine significant differences of damage and to estimate the potential damage probability of a log given select operational variables. The results suggest that motor-manual harvesting systems caused more damage to logs than mechanized harvesting systems. Felling resulted in significantly more log damage when compared to skidding, decking, and loading operations. Results also suggest that the process of skidding, decking, and loading of logs has very little impact on damage levels. Volume and value losses of damaged logs were not sensitive to tree species and log size.

EN:

Three methods for measuring the density of a forest soil were compared: sampling with a traditional hand-held soil sampler and thin-wall (Shelby) tubes, and testing with a single-probe nuclear moisture-density gauge. An incremental approach was used when sampling with the nuclear gauge. Values of wet density for the soil layer between the source and the sensor were then recast for individual soil layers and converted to dry densities using the water contents provided by either the hand-held soil sampler or Shelby tubes. Shelby tube and nuclear gauge derived values of dry density were strongly related (R2 = 0.80), values derived from the hand-held soil sampler and nuclear gauge less so (R2 = 0.66). The nuclear gauge proved the most economical in use by a factor of about two, and data collection with the Shelby tube sampler and nuclear gauge was quicker when compared to the traditional hand-held soil sampler by a factor of about two. Sample compression associated with the Shelby tubes was corrected for when calculating the final density values. However, the degree of sample disturbance associated with the hand-held soil sampler was uncertain. The advantages and disadvantages of each method are discussed.