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.

Key message: Rut depth in fine-grained boreal soils induced by an 8-wheeled forwarder is best predicted with soil moisture content, cumulative mass of machine passes, bulk density and thickness of the humus layer. Context: Forest machines are today very heavy and will cause serious damage to soil and prevent future growth if forest operations are carried out at the wrong time of the year. Forest operations performed during the wettest season should therefore be directed at coarse-grained soils that are not as prone to soil damage. Aims: The study aimed at investigating the significance of the most important soil characteristics on rutting and developing models that can be utilized in predicting rutting prior to forest operations. Methods: A set of wheeling tests on two fine-grained mineral soil stands in Southern Finland were performed. The wheeling experiments were conducted in three different periods of autumn in order to get the largest possible variation in moisture content. The test drives were carried out with an 8-wheeled forwarder. Results: Soil moisture content is the most important factor affecting rut depth. Rut depth of an 8-wheeled forwarder in fine-grained boreal soil is best predicted with soil moisture content, cumulative mass of machine passes, bulk density and thickness of the humus layer. Conclusion: The results emphasize the importance of moisture content on the risk of rutting in fine-grained mineral soils, especially with high moisture content values when soil saturation reaches 80%. The results indicate that it is of high importance that soil type and soil wetness can be predicted prior to forest operations.

The strength of soil is known to be dependent on water content but the relationship is strongly affected by the type of soil. Accurate moisture content – soil strength models will provide forest managers with the improved ability to reduce soil disturbances and increase annual forest machine utilization rates. The aim of this study was to examine soil strength and how it is connected to the physical properties of fine-grained forest soils; and develop models that could be applied in practical forestry to make predictions on rutting induced by forest machines. Field studies were conducted on two separate forests in Southern Finland. The data consisted of parallel measurements of dry soil bulk density (BD), volumetric water content (VWC) and penetration resistance (PR). The model performance was logical, and the results were in harmony with earlier findings. The accuracy of the models created was tested with independent data. The models may be regarded rather trustworthy, since no significant bias was found. Mean absolute error of roughly 20% was found which may be regarded as acceptable taken into account the character of the penetrometer tool. The models can be linked with mobility models predicting either risks of rutting, compaction or rolling resistance.

Precision forestry—i.e., the division of a stand to smaller units and managing of the stand at a micro-stand level—provides new possibilities to increase forest growth, arrange forest stand structure and enhance forest health. In the regeneration phase by adjusting the tree species selection, soil preparation, intensity of regeneration measures (method, planting density, and material), and young stand management procedures according to precise information on soil properties (e.g., site fertility, wetness, and soil type) and microtopography will inevitably lead to an increase in growth of the whole stand. A new approach to utilizing harvester data to delineate micro-stands inside a large forest stand and to deciding the tree species to plant for each micro-stand was piloted in central Finland. The case stands were situated on Finsilva Oyj forest property. The calculation of the local growth (m³/ha/year) for each 16 × 16-m grid cell was based on the height of the dominant trees and the stand age of the previous tree generation. Tree heights and geoinformation were collected during cutting operation as the harvester data, and the dominant height was calculated as the mean of the three largest stems in each grid cell. The stand age was obtained from the forest management plan. The estimated local growth (average of nine neighboring grid cells) varied from 3 to 14 m³/ha/year in the case stands. When creating micro-stands, neighboring grid cells with approximately the same local growth were merged. The minimum size for an acceptable micro-stand was set to 0.23 ha. In this case study, tree species selection (Scots pine or Norway spruce) was based on the mean growth of each micro-stand. Different threshold values, varying from 6 to 8 m³/ha/year, were tested for tree species change, and they led to different solutions in the delineation of micro-stands. Further stand development was simulated with the Motti software and the net present values (NPVs (3%)) for the next rotation were estimated for different micro-stand solutions. The mixed Norway spruce–Scots pine stand structure never produced a clearly economically inferior solution compared to the single species stand, and in one case out of six, it provided a distinctly better solution in terms of NPV (3%) than the single species option did. Our case study showed that this kind of method could be used as a decision support tool at the regeneration phase.

Fuel consumption is one of the key parameters in mechanised forest operations, particularly on lower bearing capacity soils, as wheel chains or bogie tracks can have a strong effect on it. This study aims to analyse the fuel consumption of several individual wheeled cut-to-length forwarder set-ups with different types of bogie tracks on peatland using automatic recording of data bus information. Two types of forwarders, 8-wheeled and 10-wheeled, and three types of tracks were tested on peatland in Eastern Finland. A mixed-model approach is the basis to study the fuel consumption as a function of the soil bearing capacity, the number of passes of the machine on the same soil, the section (curve or straight) and other variables related to the machine performance and set-up, for a total of N=27,928 fuel observations on three machines in 33 plots (trail segments). The model results in an R2=0.78; the number of passes increases the fuel consumption significantly, while the soil bearing capacity did not affect the fuel consumption. There are, however, important differences between the machines performance, which are addressed in the model. By contributing to the knowledge on the connection between operational conditions and fuel consumption, the study can contribute to the aim towards a sustainable forest operation through minimizing negative environmental impacts and providing the necessary tools for further research efforts.