Current issue: 56(1)
Under compilation: 56(2)
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.
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.