Current issue: 56(2)
Models that predict forest development are essential for sustainable forest management. Constructing growth models via regression analysis or fitting a family of sigmoid equations to construct compatible growth and yield models are two ways these models can be developed. In this study, four species-specific models were developed and compared. A compatible growth and yield stand basal area model and a five-year stand basal area growth model were developed for Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies (L.) Karst.). The models were developed using data from permanent inventory plots from the Swedish national forest inventory and long-term experiments. The species-specific models were compared, using independent data from long-term experiments, with a stand basal area growth model currently used in the Swedish forest planning system Heureka (Elfving model). All new models had a good, relatively unbiased fit. There were no apparent differences between the models in their ability to predict basal area development, except for the slightly worse predictions for the Norway spruce growth model. The lack of difference in the model comparison showed that despite the simplicity of the compatible growth and yield models, these models could be recommended, especially when data availability is limited. Also, despite using more and newer data for model development in this study, the currently used Elfving model was equally good at predicting basal area. The lack of model difference indicate that future studies should instead focus on model development for heterogeneous forests which are common but lack in growth and yield modelling research.
Forest management inventories assisted by airborne laser scanner data rely on predictive models traditionally constructed and applied based on data from the same area of interest. However, forest attributes can also be predicted using models constructed with data external to where the model is applied, both temporal and geographically. When external models are used, many factors influence the predictions’ accuracy and may cause systematic errors. In this study, volume, stem number, and dominant height were estimated using external model predictions calibrated using a reduced number of up-to-date local field plots or using predictions from reparametrized models. We assessed and compared the performance of three different calibration approaches for both temporally and spatially external models. Each of the three approaches was applied with different numbers of calibration plots in a simulation, and the accuracy was assessed using independent validation data. The primary findings were that local calibration reduced the relative mean difference in 89% of the cases, and the relative root mean squared error in 56% of the cases. Differences between application of temporally or spatially external models were minor, and when the number of local plots was small, calibration approaches based on the observed prediction errors on the up-to-date local field plots were better than using the reparametrized models. The results showed that the estimates resulting from calibrating external models with 20 plots were at the same level of accuracy as those resulting from a new inventory.
Because today’s tree planting machines do a good job silviculturally, the Nordic forest sector is interested in finding ways to increase the planting machines’ productivity. Faster seedling reloading increases machine productivity, but that solution might require investments in specially designed seedling packaging. The objective of our study was to compare the cost-efficiency of cardboard box concepts that increase the productivity of tree planting machines with that of today’s two most common seedling packaging systems in southern Sweden. We modelled the total cost of these five different seedling packaging systems using data from numerous sources including manufacturers, nurseries, contractors, and forest companies. Under these southern Swedish conditions, the total cost of cardboard box concepts that increase the productivity of intermittently advancing tree planting machines was higher than the cost of the cultivation tray system (5–49% in the basic scenario). However, the conceptual packaging system named ManBox_fast did show promise, especially with increasing primary transport distances and increased planting machine productivities and hourly costs. Thus, our results show that high seedling packing density is of fundamental importance for cost-efficiency of cardboard box systems designed for mechanized tree planting. Our results also illustrate how different factors in the seedling supply chain affect the cost-efficiency of tree planting machines. Consequently, our results underscore that the key development factor for mechanized tree planting in the Nordic countries is the development of cost-efficient seedling handling systems between nurseries and planting machines.
The natural northern distribution limit for pedunculate oak (Quercus robur L.) is in southern Finland. We hypothesized that the maximum frost hardiness (FHmax) in the winter limited the cultivation of oaks in northern latitudes. We tested the hypothesis with controlled freezing tests in midwinter. The acorns for the experiment were collected from the four main oak populations in southernmost Finland. The seedlings were raised in the nursery, frost hardened in field conditions, and then moved to a growth chamber at –2 °C on two occasions in winter and tested for FHmax in controlled freezing tests. Frost hardiness was assessed by differential thermal analysis (DTA) based on the low temperature exotherm (LTE) and relative electrolyte leakage (REL) of the stem, and visual damage scoring (VD) of the buds and stem. The initiation and peak of the LTE took place at an average of –41 °C and –43 °C respectively, without differences among the populations. The variation in the initiation and peak of the LTE was high, ranging from –34.6 °C to –45.5 °C and from –37.1 °C to –46.9 °C respectively. According to the REL method, the frost hardiness of the populations ranged from –44.0 °C to –46.4 °C in February and from –40.6 °C to –41.6 °C in March, without significant differences among the populations. According to VD, the bud was the least frost hardy organ, with FH between –19 °C and –33 °C, depending on population and assessment time. We conclude that the maximum hardiness may set the limit for the distribution of pedunculate oak northwards, but the high within-population variation offers potential to breed more frost hardy genotypes.
Currently, tools to predict the aboveground and belowground biomass (AGB and BGB) of woody species in Guinean savannas (and the data to calibrate them) are still lacking. Multispecies allometric equations calibrated from direct measurements can provide accurate estimates of plant biomass in local ecosystems and can be used to extrapolate local estimates of carbon stocks to the biome scale. We developed multispecies models to estimate AGB and BGB of trees and multi-stemmed shrubs in a Guinean savanna of Côte d’Ivoire. The five dominant species of the area were included in the study. We sampled a total of 100 trees and 90 shrubs destructively by harvesting their biometric data (basal stem diameter Db, total stem height H, stump area SS, as well as total number of stems n for shrubs), and then measured their dry AGB and BGB. We fitted log-log linear models to predict AGB and BGB from the biometric measurements. The most relevant model for predicting AGB in trees was fitted as follows: AGB = 0.0471 (ρDb2H)0.915 (with AGB in kg and ρDb2H in g cm–1 m). This model had a bias of 19%, while a reference model for comparison (fitted from tree measurements in a similar savanna ecosystem, Ifo et al. 2018) overestimated the AGB of trees of our test savannas by 132%. The BGB of trees was also better predicted from ρDb2H as follows: BGB = 0.0125 (ρDb2H)0.6899 (BGB in kg and ρDb2H in g cm–1 m), with 6% bias, while the reference model had about 3% bias. In shrubs, AGB and BGB were better predicted from ρDb2H together with the total number of stems (n). The best fitted allometric equation for predicting AGB in shrubs was as follows: AGB = 0.0191 (ρDb2H)0.6227 n0.9271. This model had about 1.5% bias, while the reference model overestimated the AGB of shrubs of Lamto savannas by about 79%. The equation for predicting BGB of shrubs is: BGB = 0.0228 (ρDb2H)0.7205 n0.992 that overestimated the BGB of the shrubs of Lamto savannas with about 3% bias, while the reference model underestimated the BGB by about 14%. The reference model misses an important feature of fire-prone savannas, namely the strong imbalance of the BGB/AGB ratio between trees and multi-stemmed shrubs, which our models predict. The allometric equations we developed here are therefore relevant for C stocks inventories in trees and shrubs communities of Guinean savannas.
Pathogenic wood decay fungi such as species of Heterobasidion are some of the most serious forest pathogens in Europe, causing rot of tree boles and loss of growth, with estimated economic losses of eight hundred million euros per year. In conifers with low resinous heartwood such as species of Picea and Abies, these fungi are commonly confined to heartwood and thus external infection signs on the bark or foliage of trees are normally absent. Consequently, determining the extent of disease presence in a forest stand with field surveys is not practical for guiding forest management decisions such as optimal rotation time. Remote sensing technologies such as airborne laser scanning and aerial imagery are already used to reduce the reliance on fieldwork in forest inventories. This study aimed to use remote sensing to detect rot in spruce (Picea abies L. Karst.) forests in Norway. An airborne hyperspectral imager provided information for classifying the presence or absence of rot in a single-tree-based framework. Ground reference data showing the presence of rot were collected by harvest machine operators during the harvest of forest stands. Random forest and support vector machine algorithms were used to classify the presence and absence of rot. Results indicate a 64% overall classification accuracy for presence-absence classification of rot, although additional work remains to make the classifications usable for practical forest management.
Companies operate in a nested and complex system where global challenges shape their environments and put pressure on business activities. Systemic understanding of the past and ongoing changes within a national industry help to analyze the global influences and identify phenomena that reshape business collaborations. To address this issue in the case of a forest sector, this study constructs a systemic picture of the historical development of the Finnish pulp and paper industry’s business network and analyzes it qualitatively through the Actors-Resources-Activities framework. Books discussing the history of the Finnish forest industry were used as secondary data, which were analyzed with a theory-based content analysis method. The analysis revealed four development phases during which the network has evolved from rather simple one emphasizing cooperation organizations (1st) to a more complex one with stronger roles of the state and individual influencers (2nd), and then emphasizing export and advocacy associations (3rd), before returning to be rather simple, based around three large multinationals and the EU playing an important role (4th). The industry is concerned about securing its key resources, with varying foci. Research and technological innovation activities play an important role together with cooperative interactions. Overall, actors favor a business-as-usual strategy, which is overruled only by a radical change in the operating environment, leading to notable changes in the network. Thus, a suggestion for all actors within the forest sector is that actively detecting and interpreting change signals in the whole environment can help actors in pursuing sustainable activities.
Management of Scots pine (Pinus sylvestris L.) in Norway requires a forest growth and yield model suitable for describing stand dynamics of even-aged forests under contemporary climatic conditions with and without the effects of silvicultural thinning. A system of equations forming such a stand-level growth and yield model fitted to long-term experimental data is presented here. The growth and yield model consists of component equations for (i) dominant height, (ii) stem density (number of stems per hectare), (iii) total basal area, (iv) and total stem volume fitted simultaneously using seemingly unrelated regression. The component equations for stem density, basal area, and volume include a thinning modifier to forecast stand dynamics in thinned stands. It was shown that thinning significantly increased basal area and volume growth while reducing competition related mortality. No significant effect of thinning was found on dominant height. Model examination by means of various fit statistics indicated no obvious bias and improvement in prediction accuracy in comparison to existing models in general. An application of the developed stand-level model comparing different management scenarios exhibited plausible long-term behavior and we propose this is therefore suitable for national deployment.
Newly developed positioning systems in cut-to-length harvesters enable georeferencing of individual trees with submeter accuracy. Together with detailed tree measurements recorded during processing of the tree, georeferenced harvester data are emerging as a valuable tool for forest inventory. Previous studies have shown that harvester data can be linked to airborne laser scanner (ALS) data to estimate a range of forest attributes. However, there is little empirical evidence of the benefits of improved positioning accuracy of harvester data. The two objectives of this study were to (1) assess the accuracy of timber volume estimation using harvester data and ALS data acquired with different scanners over multiple years and (2) assess how harvester positioning errors affect merchantable timber volume predicted and estimated from ALS data. We used harvester data from 33 commercial logging operations, comprising 93 731 harvested stems georeferenced with sub-meter accuracy, as plot-level training data in an enhanced area-based inventory approach. By randomly altering the tree positions in Monte Carlo simulations, we assessed how prediction and estimation errors were influenced by different combinations of simulated positioning errors and grid cell sizes. We simulated positioning errors of 1, 2, …, 15 m and used grid cells of 100, 200, 300 and 400 m2. Values of root mean square errors obtained for cell-level predictions of timber volume differed significantly for the different grid cell sizes. The use of larger grid cells resulted in a greater accuracy of timber volume predictions, which were also less affected by positioning errors. Accuracies of timber volume estimates at logging operation level decreased significantly with increasing levels of positioning error. The results highlight the benefit of accurate positioning of harvester data in forest inventory applications. Further, the results indicate that when estimating timber volume from ALS data and inaccurately positioned harvester data, larger grid cells are beneficial.
Since fire frequency is expected to increase globally due to climate change, it is important to understand its effects on forest ecosystems. We studied the long-term patterns in species diversity, cover and composition of vascular plants and bryophytes after forest fire and the site-related factors behind them. Research was carried out in northwestern Estonia, using a chronosequence of Scots pine (Pinus sylvestris L.) stands, located on nutrient poor sandy soils, where fires had occurred 12, 23, 38, 69, 80 and 183 years ago. In every stand three 100 m2 vegetation plots were established to collect floristic and environmental information. The effects on floristic characteristics of time since fire, light, and soil variables were evaluated with linear mixed models, followed by backward variable selection. Compositional variation was analysed with non-metric multidimensional scaling, Multi-response Permutation Procedures, and Indicator Species Analysis. Altogether, 31 vascular plant and 39 bryophyte species were found in vegetation plots. The cover of the vascular plant and bryophyte layers increased with a longer time since fire. Soil and light variables impacted the richness of several vascular plant and bryophyte groups, whereas only the richness of liverworts and dwarf-shrubs correlated with time since fire. Considerable compositional differences were observed in vascular plant and bryophyte assemblages between recently vs. long-time ago burned stands. To conclude, time since fire significantly impacted compositional patterns of vascular plants and bryophytes in pine forests on nutrient poor soils, although time-related trends in species richness were less evident.
Terrestrial laser scanning (TLS) has been applied to estimate forest wood volume based on detailed 3D tree reconstructions from point cloud data. However, sources of uncertainties in the point cloud data (alignment and scattering errors, occlusion, foliage...) and the reconstruction algorithm type and parameterisation are known to affect the reconstruction, especially around finer branches. To better understand the impacts of these uncertainties on the accuracy of TLS-derived woody volume, high-quality TLS scans were collected in leaf-off conditions prior to destructive harvesting of two forest-grown common ash trees (Fraxinus excelsior L.; diameter at breast height ~28 cm, woody volume of 732 and 868 L). We manually measured branch diameters at 265 locations in these trees. Estimates of branch diameters and tree volume from Quantitative Structure Models (QSM) were compared with these manual measurements. The accuracy of QSM branch diameter estimates decreased with smaller branch diameters. Tree woody volume was overestimated (+336 L and +392 L) in both trees. Branches measuring < 5 cm in diameter accounted for 80% and 83% of this overestimation respectively. Filtering for scattering errors or improved coregistration approximately halved the overestimation. Range filtering and modified scanning layouts had mixed effects. The small branch overestimations originated primarily in limitations in scanner characteristics and coregistration errors rather than suboptimal QSM parameterisation. For TLS-derived estimates of tree volume, a higher quality point cloud allows smaller branches to be accurately reconstructed. Additional experiments need to elucidate if these results can be generalised beyond the setup of this study.
For constructing growth and yield models the concept of site index as measure of productivity is crucial. Here, we use nonlinear mixed-effects models (NLME) with random individual effects and nonlinear models with dummy variables as fixed individual effects (NLFE) to fit mechanistic growth functions to stem analysis data of the economically most important tree species in Zhongtiaoshan forest region, China. The Richards and Lundqvist function are formulated into five dynamic equations (R1, R2, L1, L2 and L3) applying the generalized algebraic difference approach (GADA), which inherit polymorphism, varying asymptotes and base-age invariance. According to Akaike information criterion the R1 model as NLFE fits height growth data of Pinus tabuliformis Carrière, Pinus armandii Franch., Quercus liaotungensis Koidz., Quercus aliena Blume and Betula platyphylla Sukaczev best, while for Quercus variabilis Blume R2 as NLFE fits height growth data best. For Larix principis-rupprechtii Mayr L1 as NLME has been selected as best model, as R1 and R2 both as NLFE and NLME are not extrapolating the comparably short length of height growth data well enough. However, according to the root mean square error and bias differences between model fits of both the selected equation and the chosen model fitting approach are not so clear. Presented families of height growth curves serve as planning tools to identify site index and therefore assess productivity of forest stands in the studied region. A direct comparison of the productivity of forest stands of the same tree species is possible due to base-age invariance of the selected models.
Studies of the spatial patterns of dominant plant species may provide significant insights into processes and mechanisms that maintain stand stability. This study was performed in a permanent 1 ha plot in evergreen and deciduous broad-leaved mixed forests on Tianmu Mountain. Based on two surveys (1996 and 2012), the dynamics of the spatial distribution pattern of the dominant population (Cyclobalanopsis myrsinifolia (Blume) Oersted) and the intra- and interspecific relationships between C. myrsinifolia and other dominant species populations were analyzed using Ripley’s K(r) function. We identified the importance value of a species in a community, which is the sum of the relative density, relative frequency, and relative dominance. The drivers of spatial distribution variation and the maintenance mechanisms of the forest were discussed. The results showed that the importance value of C. myrsinifolia within the community decreased over the past 16 years. The C. myrsinifolia population exhibited a significantly aggregated distribution within a spatial scale of 0–25 m in 1996 whereas it changed to a random distribution at scales larger than 5.5 m in 2012. From 1996 to 2012, the spatial distribution patterns between C. myrsinifolia and Cyclocarya paliurus (Batal.) Iljinsk. and between C. myrsinifolia and Cunninghamia lanceolata (Lamb.) Hook did not change significantly. In 1996, C. myrsinifolia and Daphniphyllum macropodum Miq. were positively associated at the scale of 0–25 m; this relationship was strongly significant at the scale of 6–10 m. However, there was no association between the populations of two species in terms of the spatial distribution at the scale of 0–25 m in 2012. Our findings indicate that the drivers of variation in the spatial distribution of the C. myrsinifolia population were intra- and interspecific mutual relationships as well the seed-spreading mechanism of this species.
Investing in planting genetically improved silver birch (Betula pendula Roth) in Swedish plantations requires understanding how birch stands will develop over their entire rotation. Previous studies have indicated relatively low production of birch compared to Norway spruce (Picea abies (L.) Karst.) and Scots pine (Pinus sylvestris L.). This could result from using unrepresentative basic data, collected from unimproved, naturally-regenerated birch (Betula spp.) growing on inventory plots often located in coniferous stands. The objective of this study was to develop a basal area development function of improved silver birch and evaluate production over a full rotation period. We used data from 52 experiments including planted silver birch of different genetic breeding levels in southern and central Sweden. The experimental plots were established on fertile forest sites and on former agricultural lands, and were managed with different numbers of thinnings and basal area removal regimes. The model best describing total stand basal area development was a dynamic equation derived from the Korf base model. The analysis of the realized gain trial for birch showed a good stability of the early calculated relative differences in basal area between tested genotypes over time. Thus, the relative difference in basal area might be with cautious used as representation of the realized genetic gain. On average forest sites in southern Sweden, improved and planted silver birch could produce between 6–10.5 m3 ha–1 year–1, while on fertile agriculture land the average productivity might be higher, especially with material coming from the improvement program. The performed analysis provided a first step toward predicting the effects of genetic improvement on total volume production and profitability of silver birch. However, more experiments are needed to set up the relative differences between different improved material.
Forestry and forest industries are important for regional income and employment in Norway as well as in most North European countries, but few studies exist about factors affecting the timber supply at regional level. The main objective of this study is to estimate aggregated regional timber supply elasticities for six regions in Norway. Thereby we also test for regional differences, focusing on wood prices, standing stock volume and interest rate as explanatory variables. We have used three different statistical models (fixed and random effects panel models and first difference models) on regional data from the Norwegian forest inventory on standing volume and official statistics on harvested volumes, interest rate and prices of sawlogs and pulpwood for the period 1996–2016. Statistically significant different price elasticities are found in 12 out of total 15 pairs of regions. The price elasticity was lower and the volume elasticity higher in the western region compared to the other regions. The first difference models are best with respect to specification tests. The use of region specific price elasticities gives slightly better fit for the panel data models than using a uniform price parameter. The results show that the econometric specification influence the parameter values, and it is thus complicated to directly compare results in different timber supply studies. Regional differences in timber supply are important to consider.
Climate change sets high pressures on the construction industry to decrease greenhouse gas emissions. Due to the carbon storage properties and potential to use renewable resources efficiently, wooden multi-storey construction (WMC) is an interesting alternative for the construction industry to enhance sustainable development combined with the aesthetic and well-being benefits of wood perceived among many consumers. For forest industry firms, industrial wood construction is a possibility to seek for business opportunities and bring socio-economic benefits for local economies. Despite positive drivers, WMC still remains a niche even in the forest-rich countries.The purpose of our study is to add understanding on the WMC market development by conducting a systematic literature analysis on international peer-reviewed studies from the past 20 years. Our special focus is on the role of WMC in the housing markets studied from the perspectives of the demand, supply and local governance factors. As specific aims, we 1) synthesize the key barriers and enabling factors for the WMC market growth; 2) identify the actors addressed in the existing studies connected to the WMC market development, and 3) summarize research methods and analytical approaches used in the previous studies. As a systematic method to make literature searches in Web of Science and Scopus for years 2000–2020, we employed PRISMA guidelines. By using pre-determined keywords, our searches resulted in a sample of 696 articles, of which 42 full articles were after selection procedure included in-depth content analysis. Our results showed cost-efficiency gains from industrialized prefabrication and perceived sustainability benefits by consumers and architects enabled a WMC market diffusion. The lack of experiences on the WMC, and path dependencies to use concrete and steel continue to be key barriers for increased WMC. Although our research scope was the global WMC market development, most of the literature concerned the Nordic region. The key actors covered in the literature were businesses (e.g., contractors, manufacturers and architects) involved in the wood construction value-chains, while residents and actors in the local governance were seldomly addressed. Currently, case studies, the use of qualitative data sets and focus on the Nordic region dominate the literature. This hinders the generalizability of findings in different regional contexts. In the future, more research is needed on how sustainability-driven wood construction value-chains are successfully shaping up in different geographical regions, and how they could challenge the dominant concrete-based construction regime.
Foliage spectra form an important input to physically-based forest reflectance models. However, little is known about geographical variability of coniferous needle spectra. In this research note, we present an assessment of the geographical variability of Norway spruce (Picea abies (L.) H. Karst.) needle albedo, reflectance, and transmittance spectra across three study sites covering latitudes of 49–62°N in Europe. All spectra were measured and processed using exactly the same methodology and parameters, which guarantees reliable conclusions about geographical variability. Small geographical variability in Norway spruce needle spectra was observed, when compared to variability observed between previous measurement campaigns (employing slightly varying measurement and processing parameters), or to variability between plant functional types (broadleaved vs. coniferous). Our results suggest that variability of needle spectra is not a major factor introducing geographical variability to forest reflectance. The results also highlight the importance of harmonizing measurement protocols when collecting needle spectral libraries. Furthermore, the data collected for this study can be useful in studies where accurate information on spectral differences between broadleaved and coniferous tree foliage is needed.
The sap yield of birches (Betula pendula Roth and B. pubescens Ehrh.) was modelled as a function of tree diameter (girth) at breast height, as well as site and stand characteristics measured and reported by citizen scientists representing mainly non-industrial private forest owners in the South Savo, North Karelia, and Northern Ostrobothnia regions in Finland. Birches (tree species not recorded) growing on both mineral and peatland sites were tapped during the springs of 2019 and 2020. Citizen scientists were mainly voluntary forest owners who received the instructions and equipment (spouts, drop lines and buckets) for collecting sap from three birches of different diameters in the same birch stand. Citizen scientists were instructed to measure and report the sap yield and girth of the trees, as well as stand characteristics from the forest resource data, if available. Based on the linear mixed model fitted to the data, the sap yield increased with the increasing tree diameter and mean stand height, and varied between years, stands, and trees; between-region variation was not significant. In a birch stand, the simulated total sap yield ha–1 was depended on the average tree size and the stem number ha–1 and was at its highest just before the first commercial thinning and again before the second thinning. The sap model can be used to predict the necessary sap yield in profitability analyses for sap tapping.