The time consumption and productivity of harvesting are dependent on stand conditions, the operators' skills, working techniques and the characteristics of the forestry machinery. Even if the basic methods and machine types of the cut-to-length harvesting system have not changed significantly in 10 to 15 years, improvements in the operators' competence, technical solutions in forest machinery and changes in the working environment have undoubtedly taken place. In this study, the objective was to discover the special characteristics in the time consumption of mechanized cutting and forest haulage in Finnish conditions. The empirical time study was conducted with professional operators and medium-sized single-grip harvesters and forwarders in final fellings and thinnings in easy terrain in central Finland. The models for effective time consumption in the work phases and total productivity were formed. Stem size, tree species and bucking affected the cutting, whereas timber density on the strip road, the average driving distance, load capacity, wood assortment and the bunching result of the harvester operator had an effect on the forest haulage performance. The results may be used in simulations, cost calculations and education.

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Accurate timber assortment information is required before cuttings to optimize wood allocation and logging activities. Timber assortments can be derived from diameter-height distribution that is most often predicted from the stand characteristics provided by forest inventory. The aim of this study was to assess and compare the accuracy of three different pre-harvest inventory methods in predicting the structure of mainly Scots pine-dominated, clear-cut stands. The investigated methods were an area-based approach (ABA) based on airborne laser scanning data, the smartphone-based forest inventory Trestima app and the more conventional pre-harvest inventory method called EMO. The estimates of diameter-height distributions based on each method were compared to accurate tree taper data measured and registered by the harvester’s measurement systems during the final cut. According to our results, grid-level ABA and Trestima were generally the most accurate methods for predicting diameter-height distribution. ABA provides predictions for systematic 16 m × 16 m grids from which stand-wise characteristics are aggregated. In order to enable multimodal stand-wise distributions, distributions must be predicted for each grid cell and then aggregated for the stand level, instead of predicting a distribution from the aggregated stand-level characteristics. Trestima required a sufficient sample for reliable results. EMO provided accurate results for the dominating Scots pine but, it could not capture minor admixtures. ABA seemed rather trustworthy in predicting stand characteristics and diameter distribution of standing trees prior to harvesting. Therefore, if up-to-date ABA information is available, only limited benefits can be obtained from stand-specific inventory using Trestima or EMO in mature pine or spruce-dominated forests.

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The supply chain of the forest industry has increasingly been adjusted to the customer's needs for precision and quality. This has changed the operative environment both in the forest and on the roads. As the total removal of timber is increasingly divided into more log assortments, the lot size of each assortment decreases and the time consumed in sorting the logs increases. In this respect, the extra assortments have made harvesting work more difficult and affected the productivity of both cutting and forest transport; this has thus increased the harvesting costs.An activity-based cost (ABC) management system is introduced for timber harvesting and long-distance transport, based on the cut-to-length (CTL) method, in which the logistic costs are assigned to timber assortments and lots. Supplying timber is divided into three main processes: cutting, forest transport, and long-distance transportation. An ABC system was formulated separately for each of these main operations. Costs were traced to individual stands and to timber assortment lots from a stand. The cost object of the system is thus a lot of timber that makes up one assortment that has been cut, forwarded, and transported from the forest to the mill. Application of the ABC principle to timber harvesting and trucking was found to be relatively easy. The method developed gives estimates that are realistic to actual figures paid to contractors. The foremost use for this type of costing method should be as a tool to calculate the efficiency of an individual activity or of the whole logistic system.

Soil rutting caused by forest operations has negative economic and ecological effects and thus limits for rutting are set by forest laws and sustainability criteria. Extensive data on rut depths are necessary for post-harvest quality control and development of models that link environmental conditions to rut formation. This study explored the use of a Light Detection and Ranging (LiDAR) sensor mounted on a forest harvester and forwarder to measure rut depths in real harvesting conditions in Southern Finland. LiDAR-derived rut depths were compared to manually measured rut depths. The results showed that at 10-20 m spatial resolution, the LiDAR method can provide unbiased estimates of rut depth with root mean square error (RMSE) < 3.5 cm compared to the manual rut depth measurements. The results suggest that a LiDAR sensor mounted on a forest vehicle can in future provide a viable method for the large-scale collection of rut depth data as part of normal forestry operations.