Current issue: 53(3)
Under compilation: 53(4)
Korean pine (Pinus koraiensis Siebold & Zucc.) is economically the most important tree species in northeast China. Korean pine plantations are established and managed for the production of timber and seeds. Despite the importance of the species, few models have been developed for the comparison of alternative management schedules. Model development is affected by the fact that permanent sample plots and thinning experiments have not been designed and managed for modeling purposes. The permanent sample plots include few non-thinned plots, and weak trees are removed in thinning treatments, leading to low mortality rate. Moreover, the measurement interval is irregular. This study used optimization-based modeling approach in tree-level diameter increment and survival modeling to deal with the above problems. Models for self-thinning limit were developed to alleviate the problem of underestimated mortality arising from the features of the data. In addition, improved site index and individual-tree height models were developed. The model of Lundqvist and Korf was used as the site index model and the model proposed by Schumacher as the height model. Quantile regression was used to model the maximum stand basal area and maximum number of trees as a function of mean tree diameter and site index. Tree diameter, stand basal area, basal area in larger trees and site index were used as the predictors of diameter increment and tree survival. The models developed in this study constitute a model set that is suitable for simulation and optimization studies. The models produced simulation results that correspond to measured stand development.
The Green River precommercial thinning (PCT) trial was established between 1959–1961 in New Brunswick (Canada) within natural balsam fir (Abies balsamea (L.) Mill.)-dominated stands. Three silviculture scenarios differing only by the increasing nominal spacings of PCT treatments (1.2 m, 1.8 m, 2.4 m) were compared to an unthinned control within randomized replicates that were clearcut harvested in 2008 and treated with herbicide in 2011. During the fourth post-harvest growing season, we assessed regeneration, competing vegetation and coarse woody debris (CWD; differentiated between large woody debris and slash) to assess the legacy effects of PCT on regeneration of next rotation stands. Our results confirmed that silviculture scenarios including PCT significantly increased conifer stocking in treated plots compared to control conditions, but only in the 1.8 m nominal spacing. Considering that treated and untreated stands were fully stocked, we conclude that PCT using the spacing gradient tested has no legacy effect on the regeneration of next rotation natural balsam fir stands. Given the known sensitivity of balsam fir to future climate conditions in this region, we suggest that future treatments should promote tree species diversity to support ecosystem resilience to climate change by favouring more warm-adapted species, such as some hardwoods.
The aim in the study was to compare alternatives for the prediction of factual sawlog volumes using airborne laser scanning (ALS) data in Scots pine (Pinus sylvestris L.) dominated forests in eastern Finland. Accurate estimates of factual sawlog volume are desirable to ease the planning of harvesting operations. The factual sawlog volume of pines was derived from visual bucking, i.e. a procedure where the defects were located on each stem during sample plot measurements. For other species, the theoretical sawlog volume was considered also as the factual sawlog volume due to data restrictions. We predicted factual sawlog volume with eight alternatives that were based on either linear mixed-effects models or k-nearest neighbour imputations. An existing sawlog reduction model, commonly used in Finland, was also tested individually and combined with a number of the alternatives, and site type information was also utilised. Model fitting and prediction was implemented at the 15 × 15 m level, but accuracy was assessed at the 30 × 30 m level. The relative root mean squared error (RMSE%) values for the factual sawlog volume predictions varied between 20.9% and 33.5%, and the best accuracy was obtained with a linear mixed-effects model. These results indicate that factual sawlog volumes in Scots pine dominated forests can be predicted with reasonable accuracy with ALS data.
Simulation and modeling have become more common in forest biomass studies. Dynamic simulation has been used to study the supply chain of forest biomass with numerous different models. A robust predictive multi-year model requires biomass availability data, where annual variation is included spatially and temporally. This can be done by using data from enterprises, but in some cases relevant data is not accessible. Another option is to use forest inventory data to estimate biomass availability, but this data must be processed in the correct form to be utilized in the model. This study developed a method for preparing forest inventory data for a multi-year simulation supply model using the theoretical availability of feedstock. Methods for estimating quality changes during roadside storage are also presented, including a possible parameter estimation to decrease the amount of data needed. The methods were tested case by case using the inventory database “Biomass Atlas” and weather data from a weather station in Mikkeli, Finland. The data processing method for biomass allocation produced a reasonable quantity of stands and feedstock, having a realistic annual supply with variation for the demand point. The results of the study indicate that it is possible to estimate moisture content changes using weather data. The estimations decreased the accuracy of the model and, therefore, estimations should be kept minimal. The presented data preparation method can generate a supply of forest biomass for the simulation model, but the validity of the data must be ensured for correct model behavior.
Considering the increasing use of wood biomass for energy and the related intensification of forest management, the impacts of different intensities of biomass harvesting on nutrient leaching risks must be better understood. Different nitrogen forms in the soil solution were monitored for 3 to 6 years after harvesting in hemiboreal forests in Latvia to evaluate the impacts of different biomass harvesting regimes on local nitrogen leaching risks, which potentially increase eutrophication in surface waters. In forestland dominated by Scots pine Pinus sylvestris L. or Norway spruce Picea abies L. (Karst.), the soil solution was sampled in: (i) stem-only harvesting (SOH), (ii) whole‐tree harvesting, with only slash removed (WTH), and (iii) whole‐tree harvesting, with both slash and stumps harvested (WTH + SB), subplots. In agricultural land, sampling was performed in an initially fertilised hybrid aspen (Populus tremula L.× P. tremuloides Michx.) short-rotation coppice (SRC), where above-ground biomass was harvested. In forestland, soil solution N (nitrogen) concentrations were highest in the second and third year after harvesting. Mean annual values in WTH subplots of medium to high fertility sites exceeded the mean values in SOH subplots and control subplots (mature stand where no harvesting was performed) for the entire study period; the opposite trend was observed for the low-fertility site. Biomass harvesting in the hybrid aspen SRC only slightly affected NO3–-N (nitrate nitrogen) and NH4+-N (ammonium nitrogen) concentrations in the soil solution within 3 years after harvesting, but a significant decrease in the TN (total nitrogen) concentration in the soil solution was found in plots with additional N fertilisation performed once initially.
Dead wood profile of a forest is a useful tool for describing forest characteristics and assessing forest disturbance history. Nevertheless, there are few studies on dead wood profiles, including both coarse and fine dead wood, and on the effect of sampling intensity on the dead wood estimates. In a semi-natural boreal forest, we measured every dead wood item over 2 cm in diameter from 80 study plots. From eight plots, we further recorded dead wood items below 2 cm in diameter. Based on these data we constructed the full dead wood profile, i.e. the overall number of dead wood items and their distribution among different tree species, volumes of different size and decay stage categories. We discovered that while the number of small dead wood items was immense, their number dropped drastically from the diameter below 1 cm to diameters 2–3 cm. Different tree species had notably different abundance-diameter distribution patterns: spruce dead wood comprised most strikingly the smallest diameter fractions, whereas aspen dead wood comprised a larger share of large-diameter items. Most of the dead wood volume constituted of large pieces (>10 cm in diameter), and 62% of volume was birch. The variation in the dead wood estimates was small for the numerically dominant tree species and smallest diameter categories, but high for the sub-dominant tree species and larger size categories. In conclusion, the more the focus is on rare tree species and large dead wood items, the more comprehensive should the sampling be.