Current issue: 56(1)
Under compilation: 56(2)
The size of Finnish wood harvesting enterprises has grown, and entrepreneurs have become responsible for various additional tasks, resulting in networking with other harvesting enterprises of various sizes and suppliers of supporting services, but the profitability of the wood harvesting sector has remained low. In the present study, the financial performance of 83 wood harvesting companies in Eastern and Northern Finland was evaluated, based on public final account data from a five-year period between 2013 and 2017. The factors underlying economic success were identified based on 19 semi-structured entrepreneur interviews. The Business Model Canvas framework was applied in the analyses. In particular, the smallest companies (with an annual turnover of less than 600 000 €) struggled with profitability. They showed increasing indebtedness, suffered from poor power in negotiations, had typically short-term contracts, and faced difficulties in retaining skilled operators. Most of the small companies were subcontractors of larger wood-harvesting companies. The better economic success of larger companies was likely based on their capacity to provide wood harvesting services in large volumes and supply versatile services, power in negotiations, and more cost-effective operations. The future development of wood harvesting seems to be polarised: larger enterprises are likely to continue growing, while the size of smaller enterprises has stabilised. Enhancing business management skills and practices is required in enterprises of all size groups.
In wintertime, the payload capacity of a timber truck is reduced by snow that accumulates on the structures of the truck. The aim of this study was to quantify the potential payload loss due to snow and winter accessories and to predict the loss with weather variables. Tare weights of eight timber trucks were collected at mill receptions in Finland over a one-year period. Monthly and annual loss of potential payload was estimated using the tare measurements in summer months as a reference. Each load was also connected with weather data at the location and time of delivery and payload loss explained by the weather data with the aid of regression models. The maximum loss of payload varied between 1560 kg and 3100 kg. On a monthly basis, the highest losses occurred in January, when the median values varied between 760 kg and 2180 kg. Over the year, the payload loss ranged between the trucks from 0.5% to 1.5% (from 1.9% and 5.1% in January) of the total number of loads in the study. Payload loss was found to increase with decreasing temperature, increasing relative humidity and increasing precipitation. Although the average payload loss was not very high, the biggest losses occur just during the season of highest capacity utilization. Big differences were also found in the tare weights between the trucks. The results of the study give incentive to develop truck and trailer structures that reduce the adherence of snow.
Scarification is a mechanical site preparation technique designed to create microsites that will favor the growth of planted tree seedlings after clearcutting. However, the positive growth response of black spruce (Picea mariana (Mill.) Britton, Sterns & Poggenb.) to scarification varies across different sites. We hypothesized that this was due to different forms of physiological stress induced by different climates or by the severity of competition from ericaceous shrubs. We thus compared the effects of scarification on black spruce needle gas exchange and other foliar properties, as well as on indices of soil water and nitrogen availability, in relatively warm-dry (Abitibi) vs. cool-humid (Côte-Nord) climates in the province of Québec (Canada). We found a similar positive effect of scarification on tree height in Abitibi and Côte-Nord. Scarification reduced soil moisture in both climatic regions, but increased soil N mineralization in Côte-Nord only. Accordingly, scarification increased the instantaneous water use efficiency in both climate regions, but decreased photosynthetic N use efficiency in Côte-Nord only. In both regions, we found a positive relationship between foliar δ18O and δ13C on scarified plots, providing further evidence that increased growth due to scarification depends on a decrease in stomatal conductance. We conclude that scarification increases total evapotranspiration of trees evenly across the east-to-west moisture gradient in the province of Québec, but also improves long-term soil nutritional quality in a cooler-humid climate.
Ingrowth is an important element of stand dynamics in several silvicultural systems, especially in continuous cover forestry. Earlier predictive models for ingrowth in Finnish forests are few and not based on up-to-date statistical methods. Ingrowth is here defined as the number of trees over 1.3 m entering a plot. This study developed new ingrowth models for Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies (L.) H. Karst.) and birch (Betula pendula Roth and B. pubescens Ehrh.) using data from the permanent sample plots of the Finnish national forest inventory. The data were over-dispersed compared to a Poisson process and had many zeros. Therefore, a zero-inflated negative binomial model was used. The total and species-specific stand basal areas, temperature sum and fertility class were used as predictors in the ingrowth models. Both fixed-effects and mixed-effects models were fitted. The mixed-effects model versions included random plot effects. The mixed-effects models had larger likelihoods but provided biased predictions. Also censored prediction was considered where only a certain maximum number of ingrowth trees were accepted for a plot. The models predicted most pine ingrowth in pine-dominated stands on sub-xeric and xeric sites where stand basal area was low. The predicted amount of spruce ingrowth was maximized when the basal area of spruce was 13 m2 ha–1. Increasing temperature sum increased spruce ingrowth. Predicted birch ingrowth decreased with increasing stand basal area and towards low fertility classes. An admixture of pine increased the predicted amount of spruce ingrowth.
Since the 1990’s, forest resource maps and small area estimates have been produced by combining national forest inventory (NFI) field plot data, optical satellite images and numerical map data using a non-parametric k-nearest neighbour method. In Finland, thematic maps of forest variables have been produced by the means of multi-source NFI (MS-NFI) for eight to ten times depending on the geographical area, but the resulting time series have not been systematically utilized. The objective of this study was to explore the possibilities of the time series for monitoring the key ecosystem condition indicators for forests. To this end, a contextual Mann-Kendall (CMK) test was applied to detect trends in time-series of two decades of thematic maps. The usefulness of the observed trends may depend both on the scale of the phenomena themselves and the uncertainties involved in the maps. Thus, several spatial scales were tested: the MS-NFI maps at 16 × 16 m2 pixel size and units of 240 × 240 m2, 1200 × 1200 m2 and 12 000 × 12 000 m2 aggregated from the MS-NFI map data. The CMK test detected areas of significant increasing trends of mean volume on both study sites and at various unit sizes except for the original thematic map pixel size. For other variables such as the mean volume of tree species groups, the proportion of broadleaved tree species and the stand age, significant trends were mostly found only for the largest unit size, 12 000 × 12 000 m2. The multiple testing corrections decreased the amount of significant p-values from the CMK test strongly. The study showed that significant trends can be detected enabling indicators of ecosystem services to be monitored from a time-series of satellite image-based thematic forest maps.
Despite the importance of spectral properties of woody tree structures, they are seldom represented in research related to forests, remote sensing, and reflectance modeling. This study presents a novel imaging multiangular measurement set-up that utilizes a mobile handheld hyperspectral camera (Specim IQ, 400–1000 nm), and can measure stem bark spectra in a controlled laboratory setting. We measured multiangular reflectance spectra of silver birch (Betula pendula Roth), Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies (L.) Karst.) stem bark, and demonstrated the potential of using bark spectra in identifying tree species using a Support Vector Machine (SVM) based approach. Intraspecific reflectance variability was the lowest in visible (400–700 nm), and the highest in near-infrared (700–1000 nm) wavelength regions. Interspecific variation was the largest in the red, red-edge and near-infrared spectral bands. Spatial variation of reflectance along the tree height and different sides of the stem (north and south) were found. Both birch and pine had increased reflectance in the forward-scattering directions for visible to near-infrared wavelength regions, whilst spruce displayed the same only for the visible wavelength region. In addition, spruce had increased reflectance in the backward-scattering directions. In spite of the intraspecific variations, SVM could identify tree species with 88.8% overall accuracy when using pixel-specific spectra, and with 97.2% overall accuracy when using mean spectra per image. Based on our results it is possible to identify common boreal tree species based on their stem bark spectra using images from mobile hyperspectral cameras.
Five Scots pine (Pinus sylvestris L.) progeny field trials, each established in different Lithuanian regions of provenance in 1983, were studied. Each progeny field trial consists of 140 half-sib families from seven populations (20 families from each population). The evaluation was carried out in 2012 and 2018 to assess the families resistance to Heterobasidion annosum (Fr.) Bref. An index of resistance in the infected plots was calculated. To verify the accuracy of the method, total phenolic compounds (TPC) was chosen as key parameter to compare with the plant resistance index. During the six years between the two assessments, the percentage of living Scots pine trees in the progeny field trials decreased up to 20 percentage points (range: 4 p.p. to 20 p.p.). In 2018 the area of H. annosum damaged plots (in percentage from total field trial area) varied from 17 to 27%. Tree mortality in the trial correlates with site soil fertility – more fertile soils were distinguished by higher tree percentage loss and vice versa. Using analysis from combined data of all progeny trials, the family variance component reached 13.3 ± 2.2% and family heritability was 0.81. Family heritability estimates for root rot resistance show possibilities of high breeding effectiveness. The correlations between the trials in family resistance estimates were negligible (ranging from 0 to 0.28). The significant high correlation coefficient was determined between the resistance index and TPC concentration (r = 0.77, p = 0.0003). This allows us to assume that plant resistance is directly linked on TPC synthesis. The results indicate that the chosen methods of chemical resistance for identification of root rot-resistant genotypes are applicable for the selection of Scots pine half-sib families in the field trials with higher resistance to pathogens.
The tree stem volume models of Norway spruce, Scots pine and silver and downy birch currently used in Finland are based on data collected during 1968–1972. These models include four different formulations of a volume model, with three different combinations of independent variables: 1) diameter at height of 1.3 m above ground (dbh), 2) dbh and tree height (h) and 3) dbh, h and upper diameter at height of 6 m (d6). In recent National Forest Inventories of Finland, a difference in the mean volume prediction between the models with and without the upper diameter as predictor has been observed. To analyze the causes of this difference, terrestrial laser scanning (TLS) was used to acquire a large dataset in Finland during 2017–2018. Field-measured predictors and volumes predicted using spline functions fitted to the TLS data were used to re-calibrate the current volume models. The trunk form is different in these two datasets. The form height is larger in the new data for all diameter classes, which indicates that the tree trunks are more slender than they used to be. One probable reason for this change is the increase in stand densities, which is at least partly due to changed forest management. In models with both dbh and h as predictors, the volume is smaller a given h class in the data new data than in the old data, and vice versa for the diameter classes. The differences between the old and new models were largest with pine and smallest with birch.