# Determining the quality of aspen (Populus tremula) logs for mechanical wood processing in Finland.

AbstractThe quality of aspen logs (Populus tremula) was studied by sawing sample logs. For grading of aspen logs and sawn timber, the proposed system of Keinanen et al. (1995) plus the reject-grade was used. The length of the knot-free section at the base of an aspen stem is short. Green knots are concentrated in the top logs and red knots at the top of the butt logs and first middle logs. Most of the decayed knots are located in the second middle logs and top logs. The most common reasons for decreasing quality grade were rot and knots. The yield from aspen timber is best when relatively short components of sawn wood can be utilized. Regression models are presented for predicting the value of aspen logs. Lumber cutting length in the lumber grading criteria had the largest effect on grade yields and monetary values of the aspen logs.

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The possibility of increasing the use of Finnish hardwood has been a public interest for several years. The paper and composite board industry in Finland conducted aspen and birch utilization projects, based on the utilization of aspen. Furthermore, the Finnish sawmill industry has also expressed an interest in the use of Finnish hardwood, because this seems to be one possibility for improving the profitability of small sawmills. Although the process of sawing hardwood is slow and the yield is low compared to softwoods, the price of quality products is relatively high.

The opportunities to increase the use of aspen wood in Finland are good. According to the 8th National Forest Inventory (VMI8), the amount of aspen timber in southern Finland is 15,659,000 [m.sup.3], of which 2,352,000 [m.sup.3] are logs and 13,307,000 [m.sup.3] are pulpwood (Karki 1997). At the same time, the use of aspen wood per year is about 10,000 to 15,000 [m.sup.3] logs and 150,000 to 200,000 [m.sup.3] pulpwood (Louna et al. 1995).

The aspen log and lumber grading system suggested by Keinanen et al. (1995) (Tables 1 and 2) was used to evaluate the log and timber values and to test the suitability of this classification.

Several methods are used to study the internal quality of logs. The methods and classifications based on external defects have been presented in studies by Orver (1970), Hanks (1976), Weslien (1983), Flinkman (1985), Blomqvist et al. (1988), Klinkhachorn et al. (1988), Harless et al. (1991), and Grace (1993). In study by Uusitalo (1994), the prediction of sawn-wood quality in pine stands was studied. The technical properties and use of aspen wood have been presented in studies by Eklund et al. (1925), Tikka (1954, 1955), Salmi (1978), Karkkainen (1978, 1981), Ekstrom (1989), Verkasalo (1990), and Karki (1999).

The overall objective was to investigate the possibility of predicting the value of aspen logs by using their external, visual properties. Specific objectives included:

1. Investigate the reasons for variation in the quality of logs and sawn timber;

2. Evaluate the quality distribution of sawn timber in logs of different sizes and grades;

3. Test the suitability of the quality classification presented by Keinanen et al. (1995);

4. Measure the differences in the amount of sawn wood in different quality grades when the minimum acceptable length of pieces of sawn wood changes.

Material and methods

The research material comprised 139 aspen logs from 3 stands, all situated in Kukkola instruction forest, located in Joensuu, Northern Karelia, Finland. All the stands were eutrophic Myrtillus site type. One stand was a pure aspen stand and two were mixed forest stands. The trees were randomly selected with the limitation that the logs had to be sawable (stem form). Adjacent trees were also avoided. Average diameter of the trees (40 stems) at breast height was 28 cm, and average height was 20 m.

The total log volume was 19.1 [m.sup.3]. The logs were sawn during January and February. For grading aspen logs and sawn timber, the proposed log and lumber grading system (Keinanen et al. 1995) was used, plus the Reject grade was added. In grading, all criteria were followed except that length was taken into account.

From the 139 aspen logs, 37 were butt logs, 29 were first middle logs, 19 were second middle logs, and 37 were top logs. Top diameter (with bark) of the individual logs varied from 70 to 308 mm. The average values for sawlogs are presented in Tables 3 and 4.

The volumes of sawlogs were calculated according to the following formula:

V = 1/3 L ([R.sup.2] + Rr + [r.sup.2])

where:

V = volume of the sawlog

L = length

R = radius at the butt end

r = radius at the top end

Prior to sawing, the dimensions, shape, branchiness, and other visible defects were measured at given intervals: 0 to 0.5 m, 0.5 to 1.5 m, 1.5 to 2.5 m, 2.5 to 3.5 m, and 3.5+ m. It was necessary to measure these factors to be able to classify the logs into different quality classes (Table 1). After this, the logs were sawn unedged. This "saw-dry-rip" method is commonly used for hardwood logs. The logs are first sawn unedged, and the sawn timber (with bark) is then dried. Sawn wood is edged after drying or when necessary ripped into narrower widths. The test logs were sawn into two thicknesses: 19 and 32 mm. After sawing, the sawn wood was graded according to the requirements listed in Table 2.

The volume and quality of the sawn pieces and logs were calculated as follows:

1. The sawn pieces were graded according to classifications in Table 2.

2. The volume for each sawn piece was calculated in [m.sup.3].

3. Based on quality and volume, the value in FIM/[m.sup.3] for each sawn piece was calculated. The values used for different quality classes are shown in Table 5. The value for other wood material (by-products, including bark), that could be used as firewood or material for fiberboards or chipboards, was 150 FIM ($1 = 7.01 FIM).

4. The values of individual sawn pieces and by-products in the log were used to calculate the value for the whole log. These log values were used in the models.

The effect of the minimum acceptable length in a grade was tested. Each piece of lumber was graded three times, using allowable length of actual lumber length, then component length of 40 and 110 cm. In all, 308 pieces of sawn timber (2.87 [m.sup.3]) were graded.

The data were analyzed with SPSS software. Linear regression analysis was used to derive the relationship between the log value and the external properties of the log.

Results

Quality of logs

There were considerably more green knots in top logs. The base (0 to 0.5 m) of butt logs was the only section that was totally free of green knots. In all log types, the number of green knots increased from base to top (Fig. 1).

Red knots were concentrated at the top (2.5 to 3.5 m) of the butt logs. Furthermore, the first middle logs had many red knots, which were situated evenly along the logs. In contrast, the top logs had few red knots (Fig. 2).

The second middle logs had many decayed knots, as did the top logs. The only sections that were totally free of decayed knots were sections from 0 to 0.5 m and 0.5 to 1.5 m in butt logs (Fig. 3).

As expected, butt logs had fewer knots than other logs. This is consistent with the process of self-pruning. The number of knots in middle logs and top logs was the same, but first middle logs had proportionally more red knots and top logs had proportionally more green knots than other types of logs did (Fig. 4).

One aim of this study was to investigate the suitability of the quality classifications listed in Tables 1 and 2. The results of sawlog grading are shown in Table 6. According to the log grade classification in Table 1, none of the logs could be classified into grade A or B. Only 6.4 percent of the logs could be classified as grade C, and the remaining 93.6 percent of the logs were Rejects, below grade.

Quality of sawn wood

Table 7 shows the results of the lumber grading. According to the lumber grade classification (Table 2), 28.6 percent of the sawn wood could be classified as A, B, or C grades; and 71.4 percent of the sawn wood was classified as Rejects. The amount of sawn-wood grade A was only 0.6 percent, and the amount of grade B was 11.6 percent, and C was 16.4 percent.

When the results in Table 7 (lumber grades) are compared to the log grades in Table 6, it can be seen that the amount of the Reject grade is smaller (22.2%) than could be predicted from the log grading. In other words, the amount of lumber in grades A, B, and C is larger than predicted from log grades using the Keinanen et al. (1995) log-grading rules.

Figure 5 shows the lumber quality obtained from log grades C and Reject and the average distribution of lumber grades.

From the grade C sawlogs, 11.7 percent was grade B lumber and 58.3 percent grade C lumber. The proportion of Reject lumber was 29.6 percent. The amount of lumber grade A was very small, only 0.4 percent. As expected, the lumber quality in Reject grade logs was worse than in log grade C. The proportion of Reject sawn wood was 74.1 percent and the amount of sawn wood grade C was 13.6 percent. From the grade Reject sawlogs, there was almost as much grade B sawn wood (11.6%) as from the grade C sawlogs, and the amount of grade A sawn wood was at the same level (0.7%). The average amount of grade A lumber in the sawlogs was 0.6 percent, grade B was 11.6 percent, grade C was 16.4 percent, the Reject class was 71.4 percent.

Table 8 shows the reasons for reductions in grade of individual pieces of sawn wood. In all, the most common reason for reduction in sawn timber grade was rot. Rot was also the most common reason for reduction in the quality grade Reject. Other important reasons for reduction in sawn timber grade were number of green knots, size of red knots, number of black knots, and number of decayed knots.

[FIGURE 1 OMITTED]

[FIGURE 2 OMITTED]

[FIGURE 3 OMITTED]

The effect of the minimum length of one grade in a piece of sawn wood is presented in Figure 6, where it can be seen that when the estimated component length of 110 cm was used instead of the full length of lumber, the amount of grade A lumber increased from 0.3 to 10.6 percent and the amount of grade B increased from 43.4 to 49.1 percent. At the same time, the proportion of grade C decreased from 29.7 to 15.9 percent and grade Reject decreased from 26.6 to 24.4 percent.

When the minimum length of one grade in a piece of sawn wood was 40 cm, the amount of sawn wood in grade A was as high as 28.1 percent. The proportion of class B was 44.2 percent and class C was 15.2 percent. Only 12.5 percent of the sawn wood was classified as Reject.

Models for predicting the value of logs

The correlation matrix between the main predictors is shown in Table 9. These 10 factors were used when the final models were constructed. The other factors, such as cracks and crook, were not significant, so the correlation matrix for them is not presented.

The highest correlation was between GK2 and GK3. In addition, high correlations were found between GK2 and GK4, between DK1 and DK3, and between TD and Bark. These correlations were taken into consideration when the regression models were constructed. The correlations between the predictors used in the models are smaller than [+ or -] 0.355 (between GK2 and Bark). The regression models used to predict the value for aspen logs based on external quality are shown in Table 10.

In Table 10, separate regression models are presented for butt logs, for top logs, and for all logs combined. The models were made in order to predict the value of different kinds of logs. In the explanation of the models, however, there are differences in interpretations.

In model 1, there were no significant external signs of quality that could be used for prediction. For the knot-free base section of a log, the only external signs that can be used are dimensional (e.g., top diameter and length).

[FIGURE 4 OMITTED]

[FIGURE 5 OMITTED]

Model 2 gives the value for top logs based on the type of the bark on the log. The coefficient is positive, which indicates that the rougher the bark of the log, the more valuable the log is. The explanation for this model is logical, because the bark of the log usually becomes rougher and rougher as the diameter of the log increases.

In model 3, the top diameter of a log was included, and at the same time all the other main predictors became insignificant. However, the coefficient of determination is better in model 3 than in model 2. In addition, the standard error is smaller in model 3 than in model 2.

In model 4, the type of bark on the log and the number of different kinds of knots were included. The coefficients for the number of knots are negative, which means knots decrease the value of the stem. This is a direct result of preferring knot-free timber as the most valuable. Decayed knots decrease the value more than green knots do. The positive coefficient for the bark type is logical, as shown in model 2.

In model 5, the top diameter was included, which increased the significance of the model. Here the coefficient of determination is 92 percent, and the standard error is also smaller than in model 4. After the top diameter of a log, the most significant predictor is the number of decayed knots at the section from the base to 0.5 m. The explanations for the coefficients in this model are logical; the top diameter increases the value of a log, and knots decrease its value.

Residual plots for the three most useful models (1, 3, and 5) are shown in Figure 7. The residual plot for model 1 has a relatively large standard error, but the residual is still relatively symmetrical. Model 3 has the smallest standard error (9.55). Of these five models, model 5 has the best coefficient of determination.

Discussion

In general, aspen logs have knots from the base to the top. The knot-free section at the base of butt logs is usually very short, although, all in all, butt logs have fewer knots than other kind of logs. The difference between a red-knot section and a green-knot section is more obvious than, for example, in grey alder (Karki 1999). According to this study, knottiness in aspen is comparable to that in birch (Karkkainen 1986).

Green knots are clearly concentrated in the top logs, which have twice as many green knots as middle logs. As expected, butt logs have only a few green knots. Both butt logs and middle logs have many red knots, most of which are situated in the top of butt logs (sections from 1.5 to 2.5 m and from 2.5 to 3.5 m) and in first middle logs. The second middle logs also have many red knots. Decayed knots are very strongly concentrated in the second middle logs and the top logs, possibly due to damage caused by the weight of snow or harvesting. On the whole, the most valuable part of an aspen stem is the short knot-free section at the base of the butt log.

[FIGURE 6 OMITTED]

The proposed log and lumber quality classification used in this study (Keinanen et al. 1995) found that none of the logs could be classified as grade A or B. The main reason for this was the number of knots, and another important reason for reduction in quality grade was decay. When sawn wood was classified, there were again the same two main reasons for reduction in quality grade: knots and rot. When we compare the number of logs in different quality grades and the amount of sawn wood in the same grades, we find that the classifications are fairly comparable to each other. The criteria for better quality grades might still be too strict, because such a small number of logs and sawn wood belonged to grades A, B, or C, and most of the logs and pieces of sawn wood were classified as Rejects. Therefore, the criteria in the proposed log and lumber grades should be re-evaluated.

Different component lengths (40 and 110 cm) were used in grading the sawn wood, and it was noted that when shorter lengths of components were used, the proportion of higher lumber grades increased considerably. Therefore, according to this study, the best yield from aspen timber can be achieved when industry can utilize relatively short components of sawn wood.

When the models for predicting the value of aspen logs are considered, three models seem to be valid: models predicting the value for butt logs (model 1), for top logs including top diameter (model 3), and for all logs including top diameter (model 5). Model 4, which predicts the value for all logs based only on the external appearance of the log, is also relatively valid, although the standard error is higher. Model 2 is also logical, but the coefficient of determination is relatively low and the standard error is higher, because the top diameter was not included.

The logs on which this study was based were from three forests, which are situated geographically close to each other. Because of the small amount of the material and the geographical concentration, there are only limited possibilities to apply the results elsewhere in Finland. However, the material gives an indication of the quality of aspen logs in the North Karelia region.

[FIGURE 7 OMITTED]

Table 1. -- Quality classification of aspen logs (Keinanen et al. 1995). Factor of dimension or quality Grade A Grade B Top diameter Min. 23 cm Min. 15 cm Length (a) 3.1 to 5.5 m 3.1 to 5.5 m Accuracy of length + 3 cm + 3 cm Growth Even No special requests Crook 2 cm/m 2 cm/m No. knots per m/max. knot size All knots 0 pieces Max. 4 pieces Green knots Not allowed Max. 2 pieces/max. 4 cm Red knots Not allowed Max. 2 pieces/max. 2 cm Decayed knots Not allowed Not allowed Cracks Not allowed Not allowed Discoloration Not allowed Slightly allowed Decay, centered Max. 1 cm Max. 5 cm Factor of dimension or quality Grade C Top diameter Min. 15 cm Length (a) 3.1 to 5.5 m Accuracy of length + 3 cm Growth No special requests Crook 3 cm/m No. knots per m/max. knot size All knots Max. 6 pieces Green knots Max. 3 pieces/max. 8 cm Red knots Max. 3 pieces/max. 4 cm Decayed knots Max. 2 pieces/max. 3 cm Cracks Not restricted Discoloration Max. 1/2 diam. Decay, centered Max. 1/2 diam. (a) Lengths in intervals of 1 dm. Table 2. -- Grading system for aspen sawn wood, proposed in study by Keinanen et al. (1995). Factor of dimension or quality Grade A Grade B Min. width of the board 200 mm 150 mm Max. no. of knots, on the worst side and worst meter 1 piece 3 pieces Allowable knots (pieces/mm) Green knots 1 piece/10 mm 3 pieces/40 mm Red knots Not allowed 2 pieces/30 mm Decayed knots Not allowed Not allowed Black knots Not allowed Not allowed Cracks over 100 mm length Not allowed Max. length 300 mm Growth Even Not restricted Tension wood (% percent of the length) Not allowed Max. 10% Discoloration (% percent of the length) Not allowed Max. 10% Rot centered, width Not allowed Max. 20 mm Factor of dimension or quality Grade C Min. width of the board 150 mm Max. no. of knots, on the worst side and worst meter 5 pieces Allowable knots (pieces/mm) Green knots 3 pieces/70 mm Red knots 3 pieces/40 mm Decayed knots 1 piece/30 mm Black knots 1 piece/30 mm Cracks over 100 mm length Not restricted Growth No meaning Tension wood (% percent of the length) Max. 20% Discoloration (% percent of the length) Max. 15% Rot centered, width Max. 50 mm Table 3. -- Test logs divided into 1 cm top diameter classes. Top diameter class Pieces 7 1 8 3 9 1 10 2 11 2 12 3 13 4 14 4 15 4 16 5 17 13 18 4 19 5 20 13 21 11 22 8 23 10 24 9 25 12 26 5 27 8 28 3 29 8 30 -- 31 1 Total 139 Table 4. -- Minimum, maximum, and average sizes and grades of sawlogs. Variable Minimum Maximum Average Length (cm) 248 485 314.34 Top diameter with black (mm) 70 308 205.78 Volume with black (d[m.sup.3]) 21 399 137.18 Quality 3 (C) 4 (Reject) 3.95 Saw timber (%) 21 72 50.34 Table 5. -- Prices of aspen sawn timber. Lumber grade Price (FIM/[m.sup.3] A 1,200 B 1,000 C 800 Reject 600 Table 6. -- Log grade distribution. Sawlog quality No. of logs Volume with bark Proportion ([m.sup.3]) (%) A 0 0 0.0 B 0 0 0.0 C 7 1.22 6.4 Reject 132 17.85 93.6 Total 139 19.07 100.0 Table 7. -- Lumber grade distribution. Lumber quality Volume of lumber Proportion ([m.sup.3]) (%) A 0.06 0.6 B 1.11 11.6 C 1.58 16.4 Reject 6.85 71.4 Total 9.60 100.0 Table 8. -- Summary of defects causing down-grading (percentage). Reason for Class B Class C reduction in grade (A[right arrow]B) (A,B[right arrow]C) Green knot Number of knots 13.9 32.3 Size of knots 2.5 1.9 Red knot Number of knots 1.6 13.9 Size of knots 77.1 26.6 Black knot Number of knots -- 1.3 Size of knots -- 5.7 Decayed knot Number of knots -- 1.3 Size of knots -- 12.7 Cracks -- -- Tension wood 0.8 0.6 Discoloration 1.6 -- Decay 2.5 3.8 Bark inside wood -- -- Total 100 100 Reason for Reject reduction in grade (A,B,C[right arrow]Rej.) Total Green knot Number of knots 8.3 12.8 Size of knots -- 0.6 Red knot Number of knots 1.6 3.6 Size of knots 2.7 15.8 Black knot Number of knots 13.6 9.9 Size of knots 0.9 1.5 Decayed knot Number of knots 23.8 17.2 Size of knots 2.3 3.7 Cracks 0.3 0.2 Tension wood 1.0 0.9 Discoloration 7.4 5.5 Decay 37.2 27.5 Bark inside wood 1.0 0.7 Total 100 100 Table 9. -- Partial correlation coefficients between the main predictors. (a) GK1 GK2 GK3 GK4 DK1 DK2 DK3 DK4 Bark GK1 1.00 GK2 .457 1.00 GK3 .445 .718 1.00 GK4 .235 .509 .433 1.00 DK1 .025 -.033 -.016 -.080 1.00 DK2 -.096 -.196 -.197 -.181 .349 1.00 DK3 -.084 -.221 -.174 -.161 .508 .465 1.00 DK4 -.193 -.243 -.227 -.232 .211 .368 .396 1.00 Bark -.243 -.355 -.302 -.145 -.215 -.309 -.139 -.075 1.00 TD -.193 -.239 -.266 -.144 -.212 -.278 -.328 -.037 .580 GK1 GK2 GK3 GK4 DK1 DK2 DK3 DK4 Bark TD 1.00 (a) Number of green knots: GK1 = from base to 0.5 m; GK2 = from 0.5 m to 1.5 m; GK3 = from 1.5 m to 2.5 m; GK4 = from 2.5 m to 3.5 m. Number of decayed knots: DK1 = from base to 0.5 m; DK2 = from 0.5 m to 1.5 m; DK3 = from 1.5 m to 2.5 m; DK4 = from 2.5 m to 3.5 m. Bark type of the log: 1 = smooth, 2 = medium, 3 = rough; TD = top diameter of the log. Table 10. -- Regression models for predicting the value of aspen logs based on full length of a board. (a) X-variable B SE B Beta t p > Model 1: Value of butt logs incl. top diameter (FIM), [r.sup.2] = 0.77, SEE = 18.61, n = 37 Intercept 28.66 TD 3.80E-06 0.03 0.77 7.13 0.000 Model 2: Value of top logs (FIM), [r.sup.2] = 0.40, SEE = 19.85, n = 37 Intercept 7.90 Bark 18.81 7.39 0.40 2.55 0.016 Model 3: Value of top logs incl. top diameter (FIM), [r.sup.2] = 0.89, SEE = 9.55, n = 37 Intercept 11.84 TD 3.80E-06 0.02 0.89 12.00 0.000 Model 4: Value of all logs (FIM), [r.sup.2] = 0.67, SEE = 24.78, n = 139 Intercept 23.27 Bark 25.36 3.50 0.51 7.25 0.000 DK3 -7.72 1.80 -0.29 -4.30 0.000 GK2 -3.90 1.80 -0.16 -2.17 0.032 Model 5: Value of all logs incl. top diameter (FIM), [r.sup.2] = 0.92, SEE = 13.44, n = 139 Intercept 13.37 TD 4.40E-06 0.01 0.90 25.25 0.000 DK1 -2.93 1.52 -0.07 -1.93 0.056 (a) B = coefficient of regression; SE B = standard deviation; of regression coefficient; Beta = standard coefficient of regression; t = value of t-test; p = level of risk.

[c]Forest Products Society 2004.

Forest Prod. J. 54(7/8):64-71.

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The authors are Research Scientists, Lappeenranta Univ. of Technology, Dept. of Mechanical Engineering, P.O. Box 20, FIN-53851 Lappeenranta, Finland. This paper was received for publication in July 2001. Article No. 9333.

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Author: | Karki, Timo; Vainikainen, Vesa |
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Publication: | Forest Products Journal |

Geographic Code: | 4EUFI |

Date: | Jul 1, 2004 |

Words: | 5063 |

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