Variation in the value recovery when bucking to alternative timber assortments and log dimensions

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1 Forestry Studies Metsanduslikud Uurimused 45, 89, 2006 Variation in the value recovery when bucking to alternative timber assortments and log dimensions Jukka Malinen *, Harri Kilpeläinen, Tapio Wall and Erkki Verkasalo Malinen, J., Kilpeläinen, H., Wall, T., Verkasalo, E Variation in the value recovery when bucking to alternative timber assortments and log dimensions. Forestry Studies Metsanduslikud Uurimused 45, 89. ISSN Abstract. In this study, the objective was to investigate the variation in the commercial value of timber stands of Scots pine and Norway spruce, both from the forest owners and wood buyers points of view, when targeting for alternative end-products in timber harvesting. The study material comprised a total of 124 stands from final-cuttings in southern Finland. The field data was collected by measurements from circular sample plots, 1 6 of them per stand. Every stem from each plot was measured and evaluated for the dimensions and quality. The stems were bucked using the bucking-to-value simulator which is capable to consider the effective lengths of the measured stem defects, and to buck the stems according to the given bucking objectives, stem dimensions and quality requirements. The results of the study depict the differences between sale value and processing value, both per unit area and value per unit volume (o.b.). Maximizing saw log percentage and including special assortments meets the interests of forest owners and those of wood processing industries when there is a shortage of wood in the market. However, when the users of wood have no raw material shortage, the value recovery from the sawable timber can be increased by increasing minimum allowable log diameter and length, thus, increasing the average size of the logs. Key words: Scots pine, Norway spruce, harvesting, assortments, bucking, value Authors address: Finnish Forest Research Institute, Joensuu Research Unit, P.O. Box 68, FIN Joensuu, Finland, * Jukka.Malinen@metla.fi Introduction Timber procurement using the shortwood (cut-to-length) harvesting method can be described as a series of operations where wood raw material is converted from standing tree stock to timber assortments for various production plants. Within this process, wood is bought from forest owner, cut to pieces in the forest and transported to the mills for processing to different wood-based products. Furthermore, timber procurement can be viewed from three different viewpoints: 1) forest owner, 2) timber procurement company, and 3) user of wood raw material. In practice, timber procurement companies are usually owned by wood processing companies or mills, although they are run as independent business and operative units. However, there are also timber procurement companies which are owned by forest owners, or by their collaborative associations. In the context of timber procurement, the forest owner s interest is to get as high net income as possible from the stands to be sold. When applying the so-called 89

2 J. Malinen et al. assortment pricing, where all timber assortments cut from the trees have their own unit prices, the sale value recovery for forest owner is dependent on the volume and value of different assortments. In practice, this means that wood should be sold to the buyer who cuts and/or uses the most valuable assortments, pays best for them, and maximizes the sale value recovery by bucking maximum amount of the most valuable products. Nowadays, sawmilling and timber procurement are governed by the customeroriented philosophy. This means that timber has to be procured according to the demands of markets, customers and end-products, i.e., supplying the right materials to the right mills, following the just-in-time principle. Furthermore, the interest of the user is to get as good raw material as possible with as low costs as possible. This means that the quality and the length-diameter distribution of the logs should meet the requirements of the products. In practice, raw material should come from stands which produce the demanded quality, and trees should be cut into pieces according to the demands. Thus, the objective of the user is to maximize the net revenue achieved from the wood based products by maximizing the added value of the bought wood raw material. Furthermore, from the viewpoint of production, the volume recovery of certain assortments from the stand is not the limiting factor unless there is shortage of wood raw material. In order to maximize the net revenue from each stand, the bucking-to-value approach has been developed. This approach aims to the most valuable combination of assortments based on the predicted stem tapering and given prices for different assortments and log dimensions. Although this bucking approach optimizes the sale value within the given cutting patterns, large differences occur in the sale value if the length-diameter patterns of the logs vary. E.g., according to Piira et al. (2006), increasing the minimum log diameter from 14 cm to 16 cm and using the lengths dm (3 dm modules) instead of the lengths of dm may decrease the net sale value of a Scots pine timber stand by 8%. For the forest owner, maximizing the net revenue of timber usually includes the decision of when and to whom to sell the timber. However, comparisons between the bids from different timber procurement companies are difficult to make. It is easy to compare unit values of different assortments, but varying constraints for the dimensions and the quality and the possible cutting of special assortments impair comparisons. In general, the volumes and value recovery of timber assortments can be predicted for the stands by models which predict the volumes of timber assortments using stand-level information, or by models which predict the assortments for single stems from predicted tapering of the stems followed by summing the stem-level results into stand-level results (Laasasenaho & Sevola, 1971; Nyyssönen & Ojansuu, 1982; Päivinen, 1983). In order to predict the reduction in the volumes of saw and veneer log assortments, and, then, in the value recovery that are attributable to stem or stand defects, different deterministic models have been created (Vähäsaari, 1988; Mehtätalo, 2002). However, these models are incapable to show the differences which are the consequence from applying different bucking regimes. The value loss compared to theoretical optimal value recovery due to the bucking practiced has been studied in the southern hemisphere (Marshall, 2005; Carey & Murphy, 2005; Marshall et al., 2006), northern America (Conradie et al., 2004) and Scandinavia (Sondell et al., 2002). The effects of bucking regimes to the volume of the assortments and the value recovery have been studied by using bucking simulators for which the study data have been collected by manual forest measurements or harvester-measured data (Kerkelä,

3 Variation in the value recovery when bucking to alternative timber assortments and log dimensions 1997; Haring, 1997; Puumalainen, 1998; Gobakken, 2000), or by statistical approach (Buehlmann, 1998). However, the problem with bucking simulators has usually been their incapability to consider the technical quality of stems and stands. For the management of wood procurement, methods for predicting and controlling the log length-diameter distribution have been developed. Methods for predicting log length-diameter distribution include non-parametric methods (Tommola et al., 1999; Malinen et al., 2001; Malinen, 2003), neural computing (Räsänen et al., 2000; Lappalainen, 2005), and airborne laser scanning (Peuhkurinen et al., 2006). For adjusting the log length-diameter distribution to the demanded distribution, different optimization methods, such as mixed linear programming and heuristics (Arce et al., 2002), fuzzy logic (Kivinen & Uusitalo, 2002), adaptive control heuristic (Murphy et al., 2004) and genetic algorithm approach (Kivinen, 2004), and similarity measures (Kirkkala et al., 2000; Malinen & Palander, 2004; Kivinen et al., 2005; Nummi et al., 2005; Sinha et al., 2005), have been studied. The aim of this study was to investigate the variation in the commercial value of different final-cutting timber stands of Scots pine and Norway spruce, both from the forest owners (sale value) and wood buyers (processing value) points of view, when targeting for alternative products in timber harvesting. The study was based on empirical stand and tree data, where particular attention was paid to the occurrence and effective lengths of the stem defects (sweep, crooks, branchiness, scars, etc.). The stems in the data were bucked using bucking-to-value simulator which is capable to consider the defects, and bucks the stems according to the given bucking objectives for different assortments, stem dimensions and stem defects. Based on the achieved bucking outcomes, the sale values of the stands were calculated at the stump using typical prices for different wood assortments in southern Finland. Processing value recoveries were calculated using typical unit values achieved in Finnish wood processing enterprises for each assortment and dimension, thus, relating to the value of logs at the mill. Materials and methods For the study, the empirical data was collected from 124 final-cutting stands dominated by Scots pine or Norway spruce from southern and central Finland during (Figure 1). The data was collected by measurements on sample trees from circular sample plots (1 6 plots/stand, each of them m 2 ). Every tree from each plot was measured and evaluated for the dimensions and quality, and particular attention was paid to the occurrence and effective lengths of the technical defects in the tree (sweep, crooks, branchiness, scars, etc.). In the 32 stands located in southeastern and western Finland, which included saw timber size pines, the length of the grade-one butt log, i.e. straight and branchless butt log section, was measured as well (Figure 1). A total of 4598 trees of Scots pine (Pinus Sylvestris) and Norway spruce (Picea Abies) were measured (Table 1). Table 1. Number of sample trees and average characteristics of stands. Stems Average diameter (cm, o.b.) Average height (m) Average volume (m 3, o.b.) Scots pine Norway spruce

4 J. Malinen et al. Figure 1. Location of the study stands. The sub-data for the Scots pine grade-one butt log analysis was limited to south-eastern and western Finland (bounded regions). The sample trees were bucked with the bucking simulator developed in the Finnish Forest Research Institute for the research purposes. The simulator uses bucking-tovalue approach, and is capable to consider the technical quality of the stems. The bucking regimes were selected to cover the typical assortments, and, for saw and veneer log assortments, the typical ranges of dimensions (Table 2). The trees were bucked to the fixed lengths in 3 dm modules, except pulpwood which could be cut to variable lengths between minimum and maximum and pine poles which were cut to the fixed lengths of 91, 101 and 111 dm. The unit prices used in the price matrix for each timber assortment were calculated according to the relative unit processing value of each dimensions (diameter class, length), scaling to the average Finnish stumpage prices during (Table 2). The sale and processing values of the sample trees were calculated according to the achieved bucking results. The sale values were based on before mentioned unit prices (Table 2), and processing values were calculated according to the actual average unit processing values in the Finnish wood industries. In the analysis on grade-one pine butt logs, the processing values of saw logs were calculated in two groups: grade one butt-logs and other logs. However, due to the confidential nature of the processing value data, they are not reported as themselves but presented as utilities for sale and processing values. The utility value for each variable (sale value per unit area, sale value per unit volume (o.b.), processing value per unit area and processing value per unit volume (o.b.)) was set to be on average. 92

5 Variation in the value recovery when bucking to alternative timber assortments and log dimensions Table 2. Different bucking regimes used in the bucking simulations. Available log lengths were defined in 3 dm modules between the minimum and maximum lengths. Assortment Unit price ( /m3, o.b) Min. diameter (cm, o.b.) Min. length (dm) Max. length (dm) Pine Saw logs 45 14, 16, 18, Grade-one butt logs Small-diameter logs Poles Pulpwood Spruce Saw logs 46 14, 16, 18, Small-diameter logs Veneer logs Pulpwood The sale values were calculated for the total stem volumes (o.b.), depicting net income including all assortments which were bucked. Total volumes included all assortments and waste wood. On the contrary, the processing values were calculated only for the assortments of mechanical wood industries, i.e. for pine for saw logs, poles and small-diameter logs, and for spruce, for saw logs, small-diameter logs and veneer logs. Thus, pulpwood and waste wood were excluded from the calculation of net income and the total stem volume. Results The average sale and processing utilities of the timber stands varied a lot depending on the average size of the stems (Tables 3 and 4). Average utilities per unit area varied a lot depending on the size of the basal-area median tree; at its lowest the sale utility per unit area was for pine only about 14% and the processing utility for was about 11% compared to the respective average (Table 3). Utilities of the stands with the smallest trees were also low for spruce (Table 4). However, the sale utilities of spruce stands were considerable higher per unit area than for pine stands in small and large categories of tree sizes. Utilities per unit volume did not vary as much as utilities per unit area. The average processing utility of the stand per unit volume increased by increasing size of the trees. That was the result also for spruce stands for the sale utility per unit volume, whereas the pine stands with the largest trees seemed to have proportionally lower sale utility, probably due to the large amount of defects decreasing the saw log percentage. The sale and processing utilities in a stand can be increased by bucking special assortments in addition to conventional saw logs and pulpwood (Table 3 and 4). Bucking pine poles in addition to saw logs and pulpwood produced the best utilities in pine stands. The added value when bucking poles was the largest in the stands where the average trees were moderately large; however, the largest trees were prone to stem defects which diminished the pole recovery (Table 3). Bucking veneer logs in spruce stands produced an increment in the processing values of large spruce trees, which, however, did not increase as much the sale value (Table 4). For both species, bucking small-diameter logs produced more added value in the stands where the average trees were the smallest (Table 3 and 4). However, the processing utilities 93

6 J. Malinen et al. Table 3. Average relative (the utility value for each variable was set to be on average) sale and processing utilities of Scots pine stands in different bucking regimes. The results are grouped by the basal-area median tree volume (VgM) in the stand. VgM Sale utility Processing utility Sale utility per Processing utility per per unit area per unit area unit volume (o.b.) unit volume (o.b.) Saw logs & pulpwood < > Saw logs, poles & pulpwood < > Saw logs, small-diameter logs & pulpwood < > Table 4. Average relative (the utility value for each variable was set to be on average) sale and processing utilities of Norway spruce stands in different bucking regimes. The results are grouped by the basal-area median tree volume (VgM) in the stand. VgM Sale utility Processing utility Sale utility per Processing utility per per unit area per unit area unit volume (o.b.) unit volume (o.b.) Saw logs & pulpwood < > Saw logs, veneer logs & pulpwood < > Saw logs, small-diameter logs & pulpwood < >

7 Variation in the value recovery when bucking to alternative timber assortments and log dimensions Table 5. Average relative (the utility value for each variable was set to be on average) sale and processing utilities of Scots pine stands when grade-one butt logs and other logs were bucked separately. The results are grouped by the basal-area median tree volume (VgM) in the stand. VgM Sale utility Processing utility Sale utility per Processing utility per per unit area per unit area unit volume (o.b.) unit volume (o.b.) Saw logs & pulpwood < > Butt logs, saw logs & pulpwood < > Saw logs, small-diameter logs & pulpwood < > Butt logs, saw logs, small-diameter logs & pulpwood < > per unit volume were smaller when bucking small-diameter logs in addition to the conventional-size saw logs owing to the smaller average log size. The sale and processing utilities for pine stands when grade-one butt logs were considered as an individual timber assortments are presented in Table 5. Since grade-one butt logs were identified in a sub-region data only, the sale and processing utilities are presented here also for option where grade-one butt logs were not considered separately. The sale utilities were higher when grade-one butt logs were considered due to their higher unit value and slightly higher total saw log recovery. Moreover, the saw log recovery was greater when grade-one butt logs were bucked since it was allowable to use shorter log lengths. The processing utilities were higher for the large tree size, but lower for the stands with small compared to the bucking where grade-one butt logs were not considered separately. The processing utility per unit area increased more than processing utility per unit volume due to the increasing saw log recovery. The effect of small-diameter logs on the utilities was constant regardless whether the grade-one butt logs were bucked or not. In pine stands, increasing minimum allowable saw log diameter did diminish the sale utilities per unit area and unit volume in all cases (Figure 2). At the most, sale

8 J. Malinen et al. utility per unit area diminished roughly 21% when the minimum saw log diameter grew from 14 cm to 20 cm. Bucking poles lead to the best utilities in all cases, although it did not seem to reduce the crucial importance of minimum saw log diameter. The processing utility per unit volume had in most cases contrary relationships with the affecting factors compared to the other utilities, the increase in minimum saw log diameter or length leading to the increase. However, bucking small-diameter logs in addition to conventional saw logs did reverse this effect, and diminish the impact of minimum saw log diameter. Relative value Relative value Relative value Minimum allowable log top diameter (cm) Minimum allowable log length (dm) 96 Sale value per unit area Processing value per unit area Sale value per unit volume Processing value per unit volume Figure 2. The impacts of minimum log diameter (14 cm, 16 cm, 18 cm and 20 cm) and minimum saw log length (31 dm, 34 dm, 37 dm, 40 dm, 43 dm and 46 dm) on the average sale and processing utilities in Scots pine stands with different bucking regimes (top row = saw log and pulpwood, middle row = saw logs, poles and pulpwood, lower row = saw logs, smalldiameter logs and pulpwood).

9 Variation in the value recovery when bucking to alternative timber assortments and log dimensions The impact of minimum saw log length was similar to the impact of minimum saw log diameter. Bucking poles increased all utilities, and small-diameter logs diminished the impact of the reduced log lengths. Longer minimum log lengths increased the processing utility per unit volume, although bucking small-diameter logs reversed that effect. In spruce stands, the effects of minimum saw log diameter and length were similar to those for pine stands (Figure 3). As the minimum diameter grew the sale utilities and the processing utility per unit area diminished. However, the larger minimum Relative value Relative value Relative value Minimum allowable log top diameter (cm) Minimum allowable log length (dm) Sale value per unit area Processing value per unit area Sale value per unit volume Processing value per unit volume Figure 3. The impacts of minimum log diameter (14 cm, 16 cm, 18 cm and 20 cm) and minimum saw log length (31 dm, 34 dm, 37 dm, 40 dm, 43 dm and 46 dm) on the average sale and processing utilities in Norway spruce stands with different bucking regimes (top row = saw log and pulpwood, middle row = saw logs, veneer logs and pulpwood, lower row = saw logs, small-diameter logs and pulpwood). 97

10 J. Malinen et al. diameter enhanced the processing utility per unit volume. The impact of minimum saw log length was straightforward for all utilities, and the sale utility per unit area diminished when the minimum length grew. On the contrary, the processing utility per unit area increased when the minimum saw log length grew. Bucking veneer logs considerably increased the processing utilities, although the effects of minimum allowable saw log diameter and length remained significant. Discussion In this paper the commercial value of timber stands was considered from four viewpoints: 1) sale value per unit area, in which the forest owners are usually interested, 2) sale value per unit volume, where the effect of stand density is insignificant, 3) processing value per unit area, which bears importance during the shortage of wood for wood processing industries, 4) processing value per unit volume which bears importance during the unlimited supply of wood and in which the wood processing industries are primarily interested in. Maximizing saw log percentage, including special assortments, meets the interests of forest owners, and the interests of wood processing industries when there is shortage of wood raw material. It should be noted that the actual profitability when bucking small-diameter logs is considered low and partly involves commercial losses for wood industries. Moreover, the values of all assortments vary between different enterprises, production units and years. However, when there is no shortage for raw materials in the market, the value recovery from the sawable timber can be increased by increasing minimum allowable log diameter and length, and by reallocating unwanted share to pulping industry. Although this would lead to a higher overall raw material value from the viewpoint of wood processing industries, the value losses for forest owner would not be compensated unless the unit prices of assortments were higher or different pricing systems were used. The minimum allowable saw log diameter was more important than the minimum allowable saw log length for the sale value recovery. Puumalainen s (1997) results were contrary. However, the minimum log lengths were there shorter (grade-one butt logs dm, and other logs dm) and the variation in the minimum allowable log diameters was smaller (grade-one butt logs cm, and other logs cm). Especially the reduction in the grade-one butt logs minimum length from 37 dm to 22 dm added considerably to the sale vale recovery; in some stands the grade-one butt log recovery was tripled. On overall, the relative values of Norway spruce trees were greater those of Scots pine trees in a given tree sizes. The smaller average size of spruce trees compared pine trees also lead to the smaller relative value for spruce stands, on average. Moreover, this is not necessarily consistent with the true value recovery between pine and spruce stands. Furthermore, the processing values were defined at the mill, thus, not including the effects of loggings or transportation. These additional costs vary between different production units and stands, and the profitability of logging and use differs between different assortments and log sizes. In practice, the bucking decisions include numerous sources of error, which could not be included in the analysis. The starting point of bucking result is in the correct measurement of diameters and length the stem (measured part and predicted part including measurement and prediction errors), technical quality of the stem and then unit values in the price matrix, the potential and restrictions set with the allowable dimensions for different assortments, and finally, the visual observations of the stem 98

11 Variation in the value recovery when bucking to alternative timber assortments and log dimensions quality made by harvester operator. The results of the simulation study of Marshall et al. (2006) revealed that in the bucking operations 3 23% of the potential value was lost because of measurement errors. In Nordic countries, Norway spruce is usually cut by harvesters using automatic bucking where the stem is bucked according to the decisions from the optimization system unless there are any significant abnormal quality deviations. Instead, Scots pine is usually bucked using manual control, according to the visible quality of the stems. In the quality-bucking the role of the operator can be enormous. Murphy (2002) reported that 39 value recovery studies and confidential audits from Australian and New Zealand harvesting companies revealed the average value loss of 20%, the maximum being 68%. However, these studies did not concern Nordic countries. Here Sondel et al. (2002) showed that the loss in the value recovery was smaller than five percent from the optimal for five of the most popular harvesters. The variation of the commercial value of different final-cutting timber stands of Scots pine and Norway spruce were described in this paper. The results revealed dependencies on bucking regimes and commercial value on general level; the results can not be generalized on a single stand. For the prediction of the timber assortment and commercial value recovery for a stand by different bucking regimes, separate prediction models have to be constructed. Acknowledgements. This work was supported financially by the Ministry of Agriculture and Forestry in Finland through the Finnish-Swedish Wood Material Science and Engineering Research Programme ( ). The advisory group of the project assisted in planning and focusing the study and evaluating the results. The authors are grateful to Juha Metros, Eeva Nurmela, Erkki Salo and Tapio Ylimartimo for their assistance in the field data collection and Jouko Hassinen, Jorma Mononen, Antti Päivinen and Jussi Taipale for assisting them. We are also grateful for the Finnish Forest and Park Service, and several sawmill enterprises and their staff in sampling the timber stands, organizing the practical measurements and harvester data collection and finding the unit processing values for the timber assortments as well as different diameter classes and log lengths. References Arce, J.E., Carnieri, C., Sanquetta, C.R., Filho, A.F A Forest-Level Bucking Optimization System That Considers Customer s Demand and Transportation Costs. Forest Science, 48(3), Buehlmann, U Understanding the relationship of lumber yield and cutting bill requirements: a statistical approach. Ph.D. thesis. Virginia Polytechnic Institute and State University. 209 pp. Carey, P.B., Murphy, G.E Mechanised Versus Manual Log-Making in Two Chilean Pinus Radiata Stands. New Zealand Journal of Forestry Science, 35(1), Conradie, I.P., Greene, W.D., Murphy, G.E Value recovery with harvesters in southeastern U.S. pine stands. Forest Product Journal, 54(12), Gobakken, T The effect of two different price systems on the value and cross-cutting patterns of Norway spruce logs. Scandinavian Journal of Forest Research, 15, Haring, M Arvoapteeraus tukkijakauman ohjauksen apuvälineenä. B.Sc. thesis. North Carelian Polytechnic. 27 pp. (In Finnish). Kerkelä, M Jakauma- ja arvoapteerauksen vaikutus leimikon tukkisaantoon, hakkuukoneen tuottavuuteen sekä tukkien laatuun ja keskipituuteen. M.Sc. thesis. University of Joensuu, Faculty of Forestry. 51 pp. (In Finnish). Kirkkala, A., Sikanen, L., Harstela, P., Ruha, T., Tarnanen, T Penalty segmental demand distribution for evaluating bucking outcome and calculating distribution level. (Sakkosegmentoitu tavoitejakauma apteeraustuloksen arvioinnissa ja jakauma-asteen laskennassa). Metsätieteen aikakauskirja, 1(2000), (In Finnish). 99

12 J. Malinen et al. Kivinen, V.-P A Genetic Algorithm Approach to Tree Bucking Optimization. Forest Science, 50(5), Kivinen, V.-P., Uusitalo, J Applying Fuzzy Logic to Tree Bucking Control. Forest Science, 48(4), Kivinen, V.-P., Uusitalo, J., Nummi, T Comparison of four measures designed for assessing the fit between the demand and output distributions of logs. Canadian Journal of Forest Research, 35, Laasasenaho, J., Sevola, Y Timber assortment relationships and stumpage value of Scots pine and Norway spruce. Communicationes Instituti Forestalis Fenniae, 74(3), 87 pp. (In Finnish with English summary). Lappalainen, J Leimikon tukkijakauman ennustaminen itseohjautuvan kartan tuottamaa luokittelua käyttäen. M.Sc. thesis. University of Joensuu, Faculty of Forestry. 39 p. (In Finnish). Marshall, H., Murphy, G.E., Boston, K Evaluation of the economic impacts of length and diameter measurement error on mechanical harvesters and processors operating in pine stands. Canadian Journal of Forest Research, 36, Marshall, H An investigation of factors affecting the optimal output log distribution from mechanical harvesting and processing systems. Ph.D. thesis. Oregon State University. 211 p. Malinen, J Locally Adaptable Non-parametric Methods for Estimating Stand Characteristics for Wood Procurement Planning. Silva Fennica, 37(1), Malinen, J., Palander, T Metrics for distribution similarity applied to the bucking to demand procedure. International Journal of Forest Engineering, 15(1), Malinen, J., Maltamo, M., Harstela, P Application of Most Similar Neighbor Inference for Estimating Marked Stand Characteristics Using Harvester and Inventory Generated Stem Databases. International Journal of Forest Engineering, 12(2), Mehtätalo, L Log reduction models for the stems of pine, spruce, birches and aspen in Finland. (Valtakunnalliset puukohtaiset tukkivähennysmallit männylle, kuuselle, koivuille ja haavalle). Metsätieteen aikakauskirja, 4(2002), (In Finnish). Murphy, G.E Worldwide experiences with mechanization and value recovery. Kellogg, L., Spong, B., Licht, P. (eds.). Proceedings of the Wood for Africa 2002 Conference, Pietermaritzburg, South Africa, July 2 3, Oregon State University, Corvallis, Oregon Murphy, G., Marshall, H., Bolding, M.C Adaptive control of bucking on harvesters to meet order book constraints. Forest Product Journal, 54(12), Nummi, T., Sinha, B., Koskela, L Statistical properties of the apportionment degree and alternative measures in bucking outcome. Investigacion operational, 26(3), 1 7. Nyyssönen, A., Ojansuu, R Assessment of timber assortments, value and value increment of tree stands. Acta Forestalia Fennica, 179, 52 p. (In Finnish with English summary). Peuhkurinen, J., Maltamo, M., Malinen, J., Pitkänen, J., Packalén, P Pre-harvest measurement of marked stand using airborne laser scanning. Manuscript. Piira, T., Kilpeläinen, H., Malinen, J., Wall, T., Verkasalo, E Variation in timber recover and sale value in marked stands by using different bucking models. (Leimikon puutavaralajikertymän ja myyntiarvon vaihtelu erilaisilla katkontaohjeilla). Metsätieteen aikakauskirja. (Accepted). (In Finnish). Puumalainen, J Optimal cross-cutting and sensitivity analysis for various log dimension constrains by using dynamic programming approach. Scandinavian Journal of Forest Research, 13, Päivinen, R A Method for estimating the sawlog percentage in Scots pine and Norway spruce stands. Folia Forestalia, p. (In Finnish with English summary). Räsänen, T., Imponen, V., Lindroos, J., Malinen, J., Sorsa, J.-A Runkopankki puunhankinnan ohjauksen välineenä. Metsätehon raportti pp. (In Finnish). Sinha, B., Koskela, L., Nummi, T On some statistical properties of the apportionment index. Revista Investigacion Operational, 26(2), Sondell, J., Möller, J.J., Arlinger, J Third-generation merchandising computers. Skogforsk Results, 2. 6p. Tommola, M., Tynkkynen, M., Lemmetty, J., Harstela, P., Sikanen, L Estimating the characteristics of a marked stand using k-nearest-neighbor regression. Journal of Forest Engineering, 10(2), Vähäsaari, H Puutavaralajirakenteen arvioiminen eri mittausmenetelmillä. M.Sc. thesis. University of Joensuu, Faculty of Forestry. 96 p. (In Finnish). Received September 19, 2006, revised November 6, 2006, accepted November 13, 2006