Preliminary evaluation for quality of dimension lumber in four common softwoods in Mongolia

The quality of dimension lumber (2 by 4 lumber) was preliminarily investigated in four common Mongolian soft‐ woods: Pinus sylvestris L., Pinus sibirica Du Tour, Picea obovata Ledeb., and Larix sibirica Ledeb. to produce high quality dimension lumber for structural use. In total 61, 39, 67, and 37 pieces of lumber were prepared for Pinus sylvestris, Pinus sibirica, Picea obovata, and L. sibirica, respectively. The lumber was visually graded and then tested in static bending to obtain the 5% lower tolerance limits at 75% confidence level (f0.05) of the modulus of elasticity (MOE) and the modu‐ lus of rupture (MOR). In addition, the effects of sawing patterns on bending properties were also analyzed. The f0.05 of the MOE and MOR were 4.75 GPa and 15.6 MPa, 3.39 GPa and 11.0 MPa, 3.78 GPa and 11.7 MPa, and 6.07 GPa and 22.3 MPa for Pinus sylvestris, Pinus sibirica, Picea obovata, and L. sibirica, respectively. These results suggested that with a few exceptions, characteristic values of MOR in the four common Mongolian softwoods resembled those in similar commercial species already used. In visual grading, over 80% of total lumber was assigned to select structural and No. 1 grades in Pinus sylvestris and Pinus sibirica, whereas approximately 40% of total lumber in L. sibirica was No. 3 and out of grades. Sawing patterns affected bending properties in Pinus sylvestris and L. sibirica, but did not affect Pinus sibirica and Picea obovata. Dynamic Young’s modulus was significantly correlated with bending properties of dimension lumber for the four species. Based on the results, it was concluded that dimension lumber for structural use can be produced from the four common Mongolian softwoods.


Introduction
In Mongolia, coniferous tree species occupy over 70% in total forest area of 11.5 million hectares [1]. The amounts of log production were around 162,000 m 3 from the coniferous forest at 2018 [2]. For approximately 70% of total forests, Larix sibirica Ledeb. 62.5%, and the other species, including Pinus sylvestris L., Pinus sibirica Du Tour, and Picea obovata Ledeb., cover 9.8% [1]. Thus, wood from these four species are considered as common softwoods for production of structural lumber in Mongolia.
Mechanical properties of structural lumber, such as dimension lumber, have been investigated for softwood species by many researchers [3][4][5][6][7][8][9][10][11][12][13]. In terms of mechanical properties, the 5% lower tolerance limit with a 75% confidence level (f 0.05 ) in modulus of elasticity (MOE) and modulus of rupture (MOR) is important information for utilization of wood as structural material [14][15][16][17]. Dahlen et al. [16] reported that the f 0.05 values for Southern pine (Pinus spp.) were 5.8 to 8.2 GPa and 13.0 to 23.0 MPa for MOE and MOR, respectively, in dimension lumber (No. 2 grade in visual grading) for 2 by 6, 2 by 8, 2 by 10, and 2 by 12 cross sections. Recently, Tumenjargal et al. [13] reported that f 0.05 values of MOE and MOR in 2 by 4 (38 × 89 mm) lumber of L. sibirica grown in Mongolia were 5.70 GPa and 15.1 MPa, respectively. However, available information on f 0.05 for the MOE and MOR of dimension lumber for structural use remain limited for common softwoods in Mongolia.
Visual grading is one of the grading methods for lumber [8,12,14,15,[18][19][20][21][22]. Erikson et al. [19] reported that the highest MOE and MOR values in 2 by 4 lumber of Pinus contorta Douglas ex Loudon were recorded in select structural (SS) grade, whereas lumber of No. 3 grade demonstrated the lowest MOE and MOR values. Similar results were obtained in 2 by 4 (40 × 90 mm) lumber for Larix gmelinii (Rupr.) Kuzen.: the mean MOR value at 15% moisture content was 65.1 MPa for SS, and the value was higher than those of other visual grades [22]. These results suggest that high bending properties can be obtained in lumber classified into higher visual grading classes. On the other hand, Tumenjargal et al. [13] reported that no significant differences in either the MOE or MOR of 2 by 4 (38 × 89 mm) lumber in L. sibirica were found between SS, No. 1, No. 2, and No. 3 grade lumber, whereas the MOE and MOR in out of grade lumber showed notably lower values. Thus, further research is needed to clarify the relationship between visual grading results and bending properties of dimension lumber in each softwood species.
Effects of sawing pattern on mechanical properties of softwood lumber have been investigated for efficient lumber production [12,13,16,23,24]. In general, lower mechanical properties and instability of structural lumber are caused by the presence of juvenile wood with high microfibril angle [11,16,23]. In Pinus taeda L., significantly lower MOE and MOR were found in 2 by 4 lumber with pith rather than that without pith, due to the large percentage of juvenile wood contained in lumber with pith [24]. Thus, considering the sawing patterns in relation to the presence of juvenile wood is important for producing lumber with high mechanical properties.
To produce high quality dimension lumber for structural use in Mongolia, the lumber quality of four common softwoods, L. sibirica, Pinus sylvestris, Pinus sibirica, and Picea obovata, was preliminarily investigated in this study. Based on the results, the effects of visual grading and sawing patterns were evaluated in terms of the lumber's mechanical properties. In addition, f 0.05 of MOR in each species was compared to characteristic values of MOR (F b ) in Japanese Agricultural Standard (JAS) for structural lumber and finger jointed structural lumber for wood frame construction [25]. Furthermore, correlations among the different markers of lumber quality in each species were discussed. This is the first report on the evaluation of quality of dimension lumber in four common softwoods naturally grown in Mongolia, except for L. sibirica [12,13].

Materials
The following four Mongolian common softwoods were used in the present study: Pinus sylvestris L., Pinus sibirica Du Tour, Picea obovata Ledeb., and Larix sibirica Ledeb. These trees were obtained from natural forests in Selenge, Mongolia (48°49′ N, 106°53′ E and 48°41′ N, 106°38′ E). In total, 20 trees (five trees in each species) were harvested. They had 20 to 30 cm of stem diameter at 1.3 m above the ground, with good stem shape without any severe damage. The stem diameter of trees in each species was similar to mean stem diameter in each species in each natural forest. Table 1 shows the number of annual rings, the stem diameter 1.3 m above the ground, and the tree height of the sampled trees. Annual ring numbers were 72, 62, 60, and 50 for Pinus sylvestris, Pinus sibirica, Picea obovata, and L. sibirica, respectively, suggesting that the age of the sample trees ranged from approximately 50-70 years. Logs ca. 2 m in length were collected 1.3 m above the ground to the tree top until the top diameter of the log was less than 14 cm. In all, 27, 16, 22, and 21 logs for Pinus sylvestris, Pinus sibirica, Picea obovata, and L. sibirica were collected, respectively. Lumber production Figure 1 shows lumber production procedures. Approximately 2 m-length logs were sawn into lumber with cross section (50 × 100 mm), maximizing the yield of lumber as much as possible. Sawing patterns were recorded for each log. In all, 61, 39, 67, and 37 pieces of lumber were obtained for Pinus sylvestris, Pinus sibirica, Picea obovata, and L. sibirica, respectively. After sawing, the lumber was air-dried in a laboratory at ambient conditions in Ulaanbaatar, Mongolia from September 2018 to September 2019. During air drying, monthly minimum, maximum, and mean temperature and relative outdoor humidity in Ulaanbaatar were-20.3 °C (December 2018), 19.3 °C (July 2019), and 1.6 °C, and 35% (May 2019), 67% (August 2019), and 49%, respectively. After air-drying, the lumber was planed into 38 × 89 mm cross sections, and then the lumber length was cut to 1820 mm using a cross cut saw.
To evaluate the effect of sawing patterns on lumber quality, the lumber was categorized into three types: lumber including pith at almost the center position in the cross section (I), lumber obtained near the pith (sometimes including pith on the surface area of the lumber) (II), and lumber obtained from the outer part of the log (without pith) (III) (Fig. 1).

Visual grading of lumber
Each lumber piece was graded according to JAS for structural lumber and finger jointed structural lumber for wood frame construction [25]. For grading, the typical visual sorting criteria, such as annual ring width, knot size, existence and size of holes, slope of grain, deformation (bow, crook, and twist), wane, and crack were measured on surfaces of all lumber as described in JAS [25]. Bow and crook were measured as the proportion of maximum deflection to the length of lumber. To measure twist, the lumber was set on the flat surface of a steel plate. After fixing three edges of lumber, distance was measured between the remaining one edge and the flat surface. The twist of the lumber was also evaluated as the angle calculated of the arcsine of the ratio of the lumber's measured distance to width. Based on the results, lumber was graded according to the following five visual grades: SS, No. 1, No. 2, No. 3, and out of grade.

Dynamic Young's modulus
The dynamic Young's modulus of the lumber was measured after visual grading using vibrational analysis [26]. The natural frequency of longitudinal vibration due to sound emitted by hitting the cross-section of lumber with a hammer was measured using a handheld fast Fourier transform analyzer (AD-3527, A&D), with an accelerometer (PV-85, RION) set on the other cross end of each lumber. The dynamic Young's modulus of the lumber was calculated using the same methods described in Tumenjargal et al. [12].

Static bending test
Four-point static bending tests were conducted using a material testing machine (WDW-20E, Jinan Kason Testing Equipment) according to the methods described in Japan Housing and Wood Technology Center [27]. Crosshead speed, whole span, and distance between load points were 14 mm/min, 1602 mm, and 534 mm, respectively. The load and deflection were measured by a load cell (LC-5TV, Kyowa) and a displacement transductor (DTH-A-100, Kyowa), respectively. After bending tests, static bending properties, MOE, and MOR were calculated by the following equations: where ΔP (N) is difference of load between 10 and 40% values of maximum load (P max ), Δy is the difference in deflection corresponding to ΔP, l (mm) is the span, l′ (mm) is the difference between load points, b (mm) is the width of the specimen, and h (mm) is the height of specimen.
After static bending tests, 2.5 cm-thickness (longitudinal direction) small-clear specimens without defects were collected from each lumber to measure the annual ring width, moisture content at testing, and air-dry density at testing. The moisture content of lumber was measured using the oven-dry method. Results revealed that mean and standard deviation of moisture content were 11.7 ± 0.4% for all pieces of lumber. Thus, MOE and MOR values were adjusted to those at 15% moisture content using the following formula described in Japan Housing and Wood Technology Center [27]: where P 2 is the MOE or MOR value at a moisture content of 15%, P 1 is the MOE or MOR value at testing, M is the moisture content at testing, α is 1.44 and 1.75 for MOE and MOR, respectively, and β is 0.0200 and 0.0333 for MOE and MOR, respectively.

Data analysis
Data analyses were conducted using Excel 2016 (Microsoft) and R software [28]. The MOE and MOR values for the f 0.05 were calculated using the following formula [17,27]: where μ is the mean value, K is a coefficient depending on the sample size (The K values for Pinus sylvestris, Pinus sibirica, Picea obovata, and L. sibirica were 1.7932 (n = 61), 1.8365 (n = 39), 1.7856 (n = 67), and 1.8425 (n = 37), respectively), and σ is the standard deviation. When the data was not fitted to normal distribution by Shapiro-Wilk test (p < 0.05), μ and σ were obtained by equivalent transformation from logarithmic normal distribution to normal distribution.
To evaluate the effects of visual grading on bending properties, data on MOE and MOR were again classified into three groups: (1) SS and No. 1, (2) No. 2 and No. 3, and (3) out of grade. Significant differences among visual grading classes and wood type (I, II, and III) were detected using the Tukey-HSD test at a 5% significance level. The relationships between the measured lumber properties were determined using Pearson's correlation analysis.  (3) lumber from L. sibirica demonstrated the highest bending properties but also higher twist. Table 3 shows F b for 2 by 4 lumber in softwood species groups [25] that are similar to the four species used in the present study and the quantity of lumber in each grade. In Pinus sylvestris, over 90% of total lumber were SS and No. 1 grades in the Spruce-Pine-Fir (S-P-F) species group. Similar results were obtained in Pinus sibirica and L. sibirica. Approximately 85% of total lumber for Pinus sibirica belonged to SS and No. 1 grades in the W Cedar species group, which includes similar species, Pinus koraiensis Siebold & Zucc. Furthermore, over 90% of total lumber of L. sibirica classified into SS and No. 1 grades in D. fir-L, Hem-Tam, and JS-III species groups, which include Larix species. In terms of MOR value for Picea obovata, approximately 70% of total lumber fitted to SS and No. 1 grades in the S-P-F species group. In addition, the f 0.05 of the MOR in Picea obovata (11.7 MPa) was lower than the F b of No. 3 grade (12.6 MPa) for the S-P-F species group, suggesting that Picea obovata naturally grown in Mongolia does not belong to the S-P-F species group that includes many Picea species. On the other hand, the value was higher than the F b in No. 3 grade in the W Cedar species group (9.6 MPa). When the results obtained from Picea obovata were compared with those of the W Cedar species group, approximately 85% of lumber fitted to SS and No. 1 grades, and the f 0.05 exceeded No. 3 grade of the species group. Thus, Picea obovata lumber naturally grown in Mongolia should be regarded as lumber of the W Cedar species group rather than that of the S-P-F species group. However, further research is needed to obtain data on bending properties using a larger number of samples. Table 4 shows the results of the visual grading of lumber in each species expressed as percentage of each grade. The percentage of lumber assigned into the SS grade were 91.8%, 82.1%, 35.8%, and 32.4% in Pinus sylvestris, Pinus sibirica, Picea obovata, and L. sibirica, respectively. Among the four species, the lowest and the highest percentage of out of grade lumber were found in Pinus sylvestris (6.6%) and in L. sibirica (29.7%). Table 5 shows downgrading factors in the visual grading of lumber in each species. For lumber produced from Picea obovata, main downgrading factors from SS or SS + No. 1 grades to lower grades were knot (37.3%) or twist (25.4%). For L. sibirica lumber, wane (48.6%) and twist (32.4%) were the main downgrade factors from SS + No.1 grades. In Picea species, whorled branches occurred along the stems with short intervals [29]. This characteristic may be related to the large number of knots in Picea obovata used in the present study. In addition, lumber twist is induced by the presence of spiral grain in Picea and Larix species [20,30,31]. Thus, twist and wane observed in lumber from Picea obovata and L. sibirica are considered to be caused by the presence of spiral grain. Tumenjargal et al. [12] examined the visual grading for 2 by 4 (38 × 89 mm) lumber of L. sibirica produced in Mongolia, and they reported that the wane, twist, and knot were the main factors for the downgrading of lumber. Our results obtained in L.

Table 2 Statistical values of lumber properties
n, number of lumber; ARW, annual ring width; AD, air-dry density at testing; DMOE, dynamic Young's modulus; MOE and MOR, modulus of elasticity and modulus of rupture adjusted to the values at 15% moisture content by the methods described in Japan Housing and Wood Technology Center [27]; f 0.05 , 5% lower tolerance limit with 75% confidence level. The value indicates mean ± standard deviation. The values in parentheses indicate minimum and maximum values. Mean and standard deviation of moisture content was 11.7 ± 0.4% for all pieces of lumber sibirica were similar to those reported by Tumenjargal et al. [12]. Based on the results, it was concluded that (1) Pinus sylvestris and Pinus sibirica trees could produce lumber with fewer knots, wane, and warp, and such lumber was mainly assigned into the SS grade; and (2) the presence of knot, wane, and twist should be considered for producing lumber with higher grade in Picea obovata and L. sibirica.

Sawing pattern
To examine the effects of sawing patterns (type I, II, and III, Fig. 1) on the bending properties of dimension lumber, mean values of MOE and MOR in each wood type were calculated. The results are shown in Table 6. In Pinus sylvestris and L. sibirica, the MOE and MOR of lumber in type III were significantly higher than those in types I and II. It has been reported that MOE and MOR values in 2 by 4 lumber consisting of mature wood were significantly higher than those consisting of juvenile wood in Southern pine (Pinus spp., [16,23]), Pinus taeda [11,24], and L. sibirica [13]. In the present study, lumber classified into types I and II mainly contained juvenile wood, whereas lumber classified into type III included mature wood. Thus, the results obtained in Pinus sylvestris and L. sibirica were similar to those obtained in Pinus taeda and Larix species [11,13,24]. On the other hand, no significant differences were found in Pinus sibirica and Picea obovata lumber among the three sawing patterns, suggesting that wood properties of juvenile wood were similar to those in mature wood for Pinus sibirica and Picea obovata. However, further detailed research is needed for clarifying wood properties in juvenile wood and mature wood in these two species. The obtained results indicated that sawing pattern should be considered for producing lumber with higher mechanical properties in Pinus sylvestris and L. sibirica. However, for Pinus sibirica and Picea obovata, lumber with similar mechanical properties can be obtained regardless of sawing pattern. Table 7 shows correlation coefficients between annual ring width and measured properties of lumber produced from the four species. Significant negative correlations with air-dry density were found in type III lumber and total lumber (without classification of lumber types) in Pinus sylvestris and Picea obovata, whereas a positive correlation was found in type III lumber in L. sibirica. For mechanical properties, negative correlations were found in types I (except for MOR) and II and in total lumber in Pinus sylvestris and for total lumber in Picea obovata (except for MOR). In the present study, lumber classified into type I and II might consist of juvenile

Table 5 Downgrading factors in the visual grading of lumber in each species
n, number of lumber; ARW, annual ring width; SS, select structural. The value was calculated from the ratio of the lumber downgraded to total lumber number in each species. When the threshold values of the JAS for structural lumber and finger jointed structural lumber for wood frame construction [25] are the same in several visual grading classes in a factor, the classes were combined  wood, and type III lumber mainly had mature wood. Juvenile wood is characterized by larger ring width and lower wood density and mechanical properties [11,13,32,33]. Thus, differences in the relationships between annual ring width and lumber properties among the four species might be affected by wood type, that is, juvenile wood versus mature wood. Particularly, in Pinus sylvestris, the mechanical properties of lumber in types I and II were negatively affected by radial growth, whereas these lumber properties from type III were not affected by radial growth. The results suggested that mechanical properties of this species are not directly correlated with radial growth rate, but were closely related with the presence of juvenile wood. Air-dry density was significantly correlated with dynamic Young's modulus and/or MOE in Pinus sylvestris, L. sibirica, and Picea obovata, whereas it was not significantly correlated with MOR in all species (Fig. 3). Table 7 Correlation coefficients between annual ring width and lumber properties in each wood type n, number of lumber; AD, air-dry density at testing; DMOE, dynamic Young's modulus; MOE and MOR, modulus of elasticity and modulus of rupture adjusted to the values at 15% moisture content by the methods described in Japan Housing and Wood Technology Center [27]; I, lumber including pith at almost-center position in cross section; II, lumber obtained near the pith (sometimes including pith on the surface area of the lumber); III, lumber obtained from outer part of the logs (without pith). Values in parentheses followed by correlation coefficients are p-values obtained by a test of no correlation  Fig. 3 Correlation coefficients among lumber properties in the four softwood species. n, number of lumber; AD, air-dry density at testing; DMOE, dynamic Young's modulus; MOE and MOR, modulus of elasticity and modulus of rupture adjusted to the values at 15% moisture content by the methods described in Japan Housing and Wood Technology Center [27]; r, correlation coefficients. Values in parentheses followed by correlation coefficients are p-values obtained by a test of no correlation