Variation in wood density
The values of Chinese fir’s wood physical properties varied considerably among different geographic sources and Tukey-HSD testing showed that some of these differences were statistically significant (Fig. 1). The maximum value (HNYX-T) of wood all-dry density (WDD) was 62.70% higher than the minimum (FJYK-P). The WDD of each source was consistent with the classification and performance indexes of conifer trees in the timber strength grade for structural use, a standard in China’s forestry industry39: FJYK-P was at level S10 (< 0.30 g/cm3) and HNYX-T was at level S36 (< 0.50 g/cm3).
There kinds of wood density in different Chinese fir geographic sources, (I) is the wood air-dry density (WAD g/cm3); (II) is the wood all-dried density (WDD g/cm3); (III) is the wood basic density (WAD g/cm3). Different letters (a, b, c, d, e) mean significant difference at 0.05 level.
The wood air-dried density (WAD) results matched the WDD, in that significant differences were found among all the geographic sources, with maximum value (HNYX-T) 60.85% higher than the minimum (FJYK-P) (Fig. 1a,b). The maximum value (HNYX-T) was 60.85% higher than the minimum value (FJYK-P). According to the classification regulations of wood quality in China12, the WAD of fast-growing Chinese fir was at the lowest level (≤ 0.35 g/cm3). Likewise, according to the classification standard of physical properties indicators: FJYK-P was at level I (0.35 g/cm3), while the other four geographic sources were at level II (0.35–0.55 g/cm3). Through many experimental studies19, the Chinese Academy of Forestry concluded the WAD of Chinese fir in various regions ranged from 0.32 to 0.42 g/cm3. But here we found the HNYX-T (0.54 g/cm3) and JXCS-R (0.49 g/cm3) values exceeded 0.45 g/cm3.
The wood basic density (WBD) of HNYX-T (0.46 g/cm3) and JXCS-R (0.42 g/cm3) were highest among the five geographic sources, being lowest for FJYK-P (0.29 g/cm3), though HNYX-P (0.37 g/cm3) was similar HNZJJ-P (0.34 g/cm3) (Fig. 1c). The maximum value of HNYX-T was 63% higher than the FJYK-P (0.25 g/cm3). In terms of classification standards for physical mechanical indexes40, FJYK-P belonged to level I (≤ 0.30 g/cm3), HNYX-T belonged to level III (0.46–0.60 g/cm3), and the rest of geographic sources belonged to level II (0.31–0.45 g/cm3).
Variation in shrinkage
Among the five geographic sources from four sampled sites, the most represented in shrinkage was that of black-heart Chinese fir. According to Table 1, the tangential shrinkage rate of air-dry (TSR.RD) of HNYX-T’s wood was 3.41% and it was lowest in FJYK-P (1.06%). Radial shrinkage rate of air-dry (RSR.RD) of JXCS-R (1.20%) and HNYX-T (1.60%) was higher in JXCS-R (1.20%) and HNYX-T (1.60%) than in FJYK-P (0.08%).
The volume shrinkage rate of air-dry (VSR.RD) was ranked as follows: FJYK-P < HNZJJ-P < HNYX-P < JXCS-R < HNYX-T. The rank ordering of DDS.RD (air-dry) differed slightly: HNZJJ-P < HNYX-T < JXCS-R < HNYX-P < FJYK-P. Among the five sources, the maximum difference dry shrinkage of air-dry (DDS.RD) value (HNYX-T) was 62.10% higher than the lowest (FJYK-P). According to China’s timber classification regulations: the drying shrinkage rate of FJYK-P was at the lowest level (≤ 2.5%), while the rest of the Chinese fir sources were at the moderate level (2.6–4.0%). VSR.RD of HNYX-T was intermediate level (4.6–5.5%), the rest were low-grade (≤ 4.5%).
Wood dry shrinkage was also an important indicator for evaluating its physical properties. The tangential shrinkage rate of all-dry (TSR.LD) of HNYX-P was the highest among the five geographic sources. The radial shrinkage rate of all-dry (RSR.LD) of HNYX-T was 67.70% higher than that of FJYK-P (2.10%). Among the five sources, the highest volume shrinkage rate of all-dry (VSR.LD) was obtained for HNYX-P, whereas the DDS.LD was the greatest in FJYK-P (2.85%), and was the lowest in JXCS-R (1.97%) (Table 2).
Variation in mechanical properties
As Table 3 shows, the modulus of rupture (MOR) of the five geographic sources was ranked as follows: JXCS-R > HNYX-T > HNYX-P > HNZJJ-P > FJYK-P. The flexural strength index was determined according to the China’s classification standard of physical properties indexes. Both HNYX-T (110.70 MPa) and JXCS-R (95.60 MPa) were categorized as level III (88.10–118.00 Mpa); all other fir sources were designated level II (54.10–88.10 Mpa).
The modulus of elasticity (MOE) of HNYX-P was highest among the five geographic sources. Their ranking for tensile strength parallel to grain (TSG) was HNYX-T > JXCS-R > HNYX-P > HNZJJ-P > FJYK-P, for which the maximum was 47.60% higher than the minimum value. According to the grading standard of mechanical properties, HNYX-T, JXCS-R, and HNYX-P qualified for level III (10.4–13.2 GPa), while the other two sources were at level II (7.5–10.3 GPa).
The compression strength parallel to the grain (CSG) had this ranking: HNYX-T > JXCS-R, HNYX-P > HNZJJ-P > FJYK-P, for which the maximum 58.0% higher than the minimum value. According to the wood grading standards in the grain compression index, HNZJJ-P and FJYK-P were at level II (29.1–44.0 MPa) and the rest of geographic sources were at level III (44.1–59.0 MPa) (Table 3).
The compression strength perpendicular to the grain of total tensile (CPG.TT) among geographic sources was ranked as follows: HNYX-T > JXCS-R > HNYX-P > HNZJJ-P > FJYK-P (Table 4). Its maximum value (HNYX-T) was 29.3% higher than the minimum (FJYK-P). The ranking for compression strength perpendicular to the grain of total radial (CPG.TR) was slightly different: HNYX-T > JXCS-R > HNYX-P > HNZJJ-P > FJYK-P, for which the maximum was 42.1% higher than the minimum value. Compression strength perpendicular to the grain of part radial (CPG.PR) had the same rank order as CPG.TT, with a maximum value (HNYX-T) 35.0% higher than the minimum (FJYK-P). Finally, compression strength perpendicular to the grain of part tensile (CPG.PT) was ranked as HNYX-T > JXCS-R > HNZJJ-P > HNYX-P > FJYK-P for the five geographic sources of Chinese fir.
Factors influencing wood physical properties
Climate factors effect on wood physical properties
The influence of precipitation on the three kinds of density was consistent. Pre in January, October, November, and December was positively related to wood density, while it was negatively correlated with density in others months, especially in May (r = − 0.39), June (r = − 0.59), and August (r = − 0.64). On a seasonal scale, Pre in summer was negatively correlated with density (r = − 0.77), but it was positively correlated with autumn (r = 0.22). MaxT was positively correlated with density during the whole year, except in May (r = − 0.34), and likewise with wood density but most strongly in summer (r = 0.75). MinT was positively correlated with density, especially in Jan (r > 0.7), though it was not significantly so in February and October (r < − 0.01). AveT was positively correlated with density except in January, February, and March, reaching statistical significance in June (r = 0.42), July (r = 0.55), and August (r = 0.64). AveT was positively correlated with density in all seasons except winter (r = − 0.12) (Fig. 2a).
The correlation between climate and wood physical properties, and (a) is the wood density; (b) is the mechanical properties and (c) is the shrinkage. Pre is sum precipitation of every month. AveT is average daily mean temperature of each month. MinT is average daily min temperature of each month. MaxT is average daily max temperature of each month. Spr is the value of Mar, Apr, May. Sum is the value of Jun, Jul, Aug. Aut is the value of Sep, Oct, Nov. Win is the value of Dec, Jan, Feb.
Pre was positively correlated with TSR.RD, RSR.RD, DDS.RD, and VSR.RD in October, November, and December. Pre was significant negatively correlated with TSR.RD, RSR.RD, VSR.RD in June, August, and summer (r > 0.45). Pre showed no significant correlation with TSR.LD, RSR.LD, DDS.LD, and DDS.RD, whose correlation coefficients were 0.1–0.3. But Pre was negatively correlated with VSR.LD most of the year (except July, October). AveT was negatively correlation with TSR.RD, RSR.RD, and VSR.RD in January, February, March, and winter; however, AveT showed no significant correlation with DDS.RD. AveT was negatively correlated with TSR.LD, RSR.LD, DDS.LD, and VSR.LD during the whole year. In general, MinT had a significant positive relationship to TSR.RD (r = 0.47), RSR.RD (r = 0.48), and VSR.RD (r = 0.52), except in October, and it was negatively correlated with DDS.RD. MinT was positively related to RSR.LD, VSR.LD, yet negative related to DDS.LD. MaxT was negatively correlated with TSR.RD, RSR.RD, VSR.RD in January, February, May, and December, and winter. MaxT showed no significant correlation with DDS.RD, RSR.LD, DDS.LD or VSR.LD (Fig. 2c).
Pre had significant negative correlations with all of the mechanical properties in May, June, August, and summer, as evince by Fig. 2b, which also showed positive correlations in October. As we can seen, the effects of Pre on wood density and mechanical properties have the same tendency. Pre in all other months was not significantly correlated with mechanical properties (r < 0.3). AveT in January, February, March, and winter was negatively correlated with mechanical properties, but was positively correlated with mechanical properties in June, July, and summer, when the correlation coefficient reached its maximum, in August (r = 0.67). MinT was significantly correlated with mechanical properties, which was strongest in January (r > 0.75), while it was showed no significant correlation in Feb and Oct (r < 0.2). On a seasonal scale, MinT in winter was showed no significant correlation with mechanical properties (r < 0.3). As for MaxT, which was positively correlation with mechanical properties in August, October, and summer, while it was negatively correlation with mechanical properties in May, which was a interesting result we got from Fig. 2b. MinT showed a significant correlation with CSG, whose coefficient was higher 0.75 in summer.
PCA analysis of physical properties
Although the physical properties of wood can be affected by all 19 variables (including WBD WDD WAD MOE MOR TSG CSG CPG.TT CPG.TR CPG.PT CPG.PR TSR.RD RSR.RD DDS.RD VSR.RD TSR.LD RSR.LD DDS.LD and VSR.LD) considered, it was not necessary to include all these variables in our research. Variance inflation factor was used to judge whether collinearity exists among the variables. We calculated the VIF values of all 19 variables. Among them, WAD (83.63), WDD (6196.39), WBD (6015.66), TSR.RD (13.93), RSR.RD (11.36), VSR.RD (22.57), VSR.RD (22.57), TSR.LD (38.35), RSR.LD (44.30), DDS.LD (16.49), VSR.LD (123.78) had VIF values > 10. Those of WDD and WBD were > 1000. Through stepwise regression modeling, 14 variables without multicollinearity were retained (i.e., MOE, MOR, TSG, CSG, CPG.TT, CPG.TR, CPG.PT, CPG.PR, DDS.RD, WDD, DDS.LD, TSR.RD, RSR.RD, VSR.LD).
PCA was applied to the above 14 selected physical variables. These results showed that the physical properties of wood loaded strongly on the first axis of the PCA, explaining 51.8% of variation in the 14 tested properties, while the second axis explained 11.0% of it. MOE, MOR, TSR.RD, RSR.RD, and VSR.LD loaded on the positive axis of PC1 and PC2. Both DDS.LD and DDS.RD loaded on the negative axis of PC1 and PC2, while TSG, CSG, CPG.TT, CPG.TR, CPG.PT, CPG.PR, and WDD loaded on the positive axis of PC1 and the negative axis of PC2 (Fig. 3). For a comprehensive evaluation of Chinese fir’s wood physical properties, we calculated the comprehensive scores of five geographic sources via the PCA. In this respect, significant differences were detected among the five geographic sources. Among them, the comprehensive score of HNYX-T was the highest whereas that of FJYK-P was the lowest (Fig. 4).
Sequence diagram plot of PCA analysis showing the relationship among physical properties of wood.
Mean comprehensive score of PCA plot with 95% CI. Different letters (a, b, c, d, e) mean significant difference at 0.05 level.
Source: Ecology - nature.com
