Current issue: 56(4)
Under compilation: 57(1)
To enhance the utilization of the wood, the sawmills are forced to place more emphasis on planning to master the whole production chain from the forest to the end product. One significant obstacle to integrating the forest-sawmill-market production chain is the lack of appropriate information about forest stands. Since the wood procurement point of view in forest planning systems has been almost totally disregarded there has been a great need to develop an easy and efficient pre-harvest measurement method, allowing separate measurement of stands prior to harvesting. The main purpose of this study was to develop a measurement method for Scots pine (Pinus sylvestris L.) stands which forest managers could use in describing the properties of the standing trees for sawing production planning.
Study materials were collected from ten Scots pine stands located in North Häme and South Pohjanmaa, in Southern Finland. The data comprise test sawing data on 314 pine stems, diameter at breast height (dbh) and height measures of all trees and measures of the quality parameters of pine sawlog stems in all ten study stands as well as the locations of all trees in six stands. The study was divided into four sub-studies which deal with pine quality prediction, construction of diameter and dead branch height distributions, sampling designs and applying height and crown height models. The final proposal for the pre-harvest measurement method is a synthesis of the individual sub-studies.
Quality analysis resulted in choosing dbh, distance from stump height to the first dead branch, crown height and tree height as the most appropriate quality characteristics of Scots pine. Dbh and dead branch height are measured from each pine sample tree while height and crown height are derived from dbh measures by aid of mixed height and crown height models. Pine and spruce diameter distribution as well as dead branch height distribution are most effectively predicted by the kernel function. Roughly 25 sample trees seem to be appropriate in pure pine stands. In mixed stands the number of sample trees needs to be increased in proportion to the intensity of pines in order to attain the same level of accuracy.
Factors affecting soil disturbance caused by harvester and forwarder were studied on mid-grained soils in Finland. Sample plots were harvested using a one-grip harvester. The harvester operator processed the trees outside the strip roads, and the remaining residues were removed to exclude the covering effect of residues. Thereafter, a loaded forwarder made up to 5 passes over the sample plots. The average rut depth after four machine passes was positively correlated to the volumetric water content at a depth of 0–10 cm in mineral soil, as well as the thickness of the organic layer and the harvester rut depth, and negatively correlated with penetration resistance at depths of both 0–20 cm and 5–40 cm. We present 5 models to predict forwarder rut depth. Four include the cumulative mass driven over a measurement point and combinations of penetration resistance, water content and the depth of organic layer. The fifth model includes harvester rut depth and the cumulative overpassed mass and provided the best fit. Changes in the penetration resistance (PR) were highest at depths of 20–40 cm. Increase in BD and VWC decreased PR, which increased with total overdriven mass. After four to five machine passes PR values started to stabilize.
The aim of this study was to determine the effect of leaching of heavy metals (Cr, As, Cd, Cu, Ni, Pb, Zn, Co, Mo) and earth-alkaline metal, barium (Ba), on the percolation and ditch water quality from the forest roads that contained ash in the road structures. Water quality was studied in the immediate vicinity below the ash layers as well as deeper in the road structure. Water quality was also determined in the drainage water in ditches that crossed the forest roads. A mixture of wood and peat based fly ash was used in the road structures. The treatments were: 1) no ash, 2) a 15 cm layer of ash/gravel mixture, 3) a 20 cm layer of ash/gravel mixture, 4) a 25 cm layer of ash, and 5) a 50 cm layer of ash. Large variation in the concentrations of Cr, As, Cu, Ni, Pb, Mo and Ba in the percolation water, even within the same treatment, caused difficulties to generalize the results. The concentrations of Cr, As, Ni, Pb, Mo and Ba in water samples were high in some treatment plot lysimeters containing ash compared to the control (no ash). On the other hand, many lysimeters had low and similar concentrations in water samples in the treatment plots containing ash compared to concentrations in the control plots. The ash in the roads did not affect the concentrations in the ditches. The leaching is uneven and seems to take place only from some parts of the ash layer. Risk for leaching is minimal if such parts are not widely spread.
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.
Physical soil properties have a marked influence on the quality of forest sites and on the preconditions for forest growth and management. In this study, water retention characteristics (WRC) and related physical soil properties in addition to vegetation coverage and tree stand data were studied at upland forest sites in Finland. Fixed and mixed models between soil and site characteristics were formed to estimate physical and hydrologic soil characteristics and the site quality with indirect co-varying variables. In the present data, the site quality index (H100) shows a high coefficient of determination in respect to the temperature sum. It is also related to soil fine fraction content, topsoil pH and water retention at field capacity. The thickness of the humus layer is predictable from the pH and cover of xeric and mesic plant species. The soil fine fraction content (clay + silt) is closely related to water retention at field capacity, the soil layer and site type, and without WRC to the temperature sum and site index and type, as well as the slope angle. The soil bulk density is related to organic matter, depth (layer) or alternatively to organic matter, slope and field estimated textural class (fine, medium, coarse). Water retention characteristics were found to be best determinable by the fine fraction content, depth and bulk density. Water content and air-filled porosity at field capacity are closely related to the fine fraction. This study provides novel models for further investigations that aim at improved prediction models for forest growth, hydrology and trafficability.
The amount of water in peat soil is one factor affecting its bearing capacity, which is a crucial aspect in planning peatland timber harvesting operations. We studied the influence of weather variables on the variation of drained peatland growing season water conditions, here the ground water table depth (WTD). WTD was manually monitored four times in 2014 and three times in 2015 in 10–30 sample plots located in four drained peatland forests in south-western Finland. For each peatland, precipitation and evapotranspiration were calculated from the records of the nearest Finnish Meteorological Institute field stations covering periods from one day to four weeks preceding the WTD monitoring date. A mixed linear model was constructed to investigate the impact of the weather parameters on WTD. Precipitation of the previous four–week period was the most important explanatory variable. The four-week evapotranspiration amount was interacting with the Julian day showing a greater effect in late summer. Other variables influencing WTD were stand volume within the three-metre radius sample plot and distance from nearest ditch. Our results show the potential of weather parameters, specifically that of the previous four-week precipitation and evapotranspiration, for predicting drained peatland water table depth variation and subsequently, the possibility to develop a more general empirical model to assist planning of harvesting operations on drained peatlands.
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.