Bark content variations and the production of Populus deltoides as a source of bioenergy under temperate climate conditions

Jamshid Eslamdoust 1

Abstract Bark biomass as an energy source has a high economic value. Bark content variations and production helps recognize the potential of this bioenergy source spatially before harvesting. The percentage of fresh and dry bark in Populus deltoides grown under a monoculture system was examined in the temperate region of northern Iran. Diameter at breast height (DBH) and total height data were analyzed based on an initial inventory. Ten sample trees were felled,separated into 2 m-segments, and weighted in the f ield. A 5-cm-thick disc from each segment was extracted for determining fresh and dry bark percentages. These were statistically signif icantly diff erent in disc diameter classes and decreased with increasing disc diameters. Bark percentage of the disc classes ranged from 21.8 to 24.4% in small-sized diameters to 8.1-9.3% in large-sized diameters. The diff erences between fresh and dry bark percentages depended on water content variations. Allometric power equations were f itted to data of fresh and dry bark percentages and disc diameters as well as DBH. The values of R 2 ranged from 0.89 to 0.90. In addition, allometric power equations provided the best f its for relationships between total stem dry biomass, dry bark biomass, and DBH, R 2 = 0.986 and 0.979 for the total stem dry biomass and stem dry bark biomass,respectively. The allometric models can be used to estimate bark percentage and bark production of P. deltoides in segments and for the whole stem for a wide range of segment diameters (8-44 cm) and DBH (15-45 cm).

Keywords Biomass · Energy source · Forest plantation ·Eastern cottonwood · Northern Iran

Introduction

Bioenergy is energy retrieved from organic material such as crops, organic wastes, and forest residues (IEA 2021).It is a renewable energy option attractive to countries in all stages of economic development due to its high f lexibility and potential for integration into wide-ranging energy systems (Cross et al. 2021). It is considered an eff ective source of renewable energy and a suitable alternative to reduce the use of fossil fuels. The biomass production of forests is one of the primary sources of energy to help countries meet their long-term renewable energy targets (Ghaff ariyan et al.2017; Routa et al. 2020). Based on the IEA ( 2008) report,biomass represents over 10% of the world’s energy. The World Bioenergy Association, the Renewable Energy Policy Network for the 21stCentury, and the International Energy Agency emphasizes the importance of promoting the use of sustainable biomass from the perspective of global climate change, and provide insight into global energy, bioenergy,and renewables statistics. The energy derived from biomass has a signif icant role in current international strategies to mitigate climate change, reduce CO2emissions and enhance energy security (Wu et al. 2020).

Forest biomass as bioenergy is one of the key options to mitigate greenhouse gas emissions and to substitute fossil fuels. Forest bioenergy is typically a co-product of wood material production (?oban and Eker 2014; Matthews et al.2014). Currently, 3% of the world’s forests are plantations,comprised of 60 million ha in developed countries and 55 million ha in developing countries (REN21 2011; Eslamdoust and Sohrabi 2016). The total current carbon storage in forest plantations is approximately 11.8 Gt, increasing at a rate of 0.2 Gt a -1 (FAO 2010), and off ers an opportunity to reduce the reliance on burning fossil fuels for energy production. Some countries like the United Kingdom, the United States, China, India, and Brazil (Sang and Zhu 2011; Singh et al. 2014; Smith et al. 2016; Baik et al. 2018; Tagomori et al. 2019), have evaluated the potential of forest biomass.However, most research has focused on stem wood, wood chips, and forest residues, but one of the sizable feedstocks for bioenergy is bark. Technically, three main products obtained from extracted round wood after processing are the bark, sawn timber, and wood residues. The amount of bark produced in forest plantations is considerable and provides a major source for bioenergy.

Based on FAO ( 2006), Iran was ranked 10th globally among other countries in terms of forest plantation area.Currently, Iran has 1,001,000 ha planted forest (FAO 2020)of which about 230,000 ha are in temperate regions based on the offi cial report of Forests, Rangelands, and Watershed Management Organization of Iran. The temperate regions,located between the Caspian Sea and the Alborz Mountains,provide suitable temperatures and ample rainfall for forest production, reforestation, and aff orestation. Therefore, these regions are substantial components to commercial forestry mainly because of Hyrcanian natural forests.Populus deltoidesBartr. ex Marsh. is one of the most planted species in this region (FAO 2020) and has signif icant importance,especially for wood production.

As a fast-growing species,P. deltoideshas considerable importance in diff erent planting systems due to its deciduous nature, easy regeneration and establishment, rapid growth, straight cylindrical bole, high volume returns, early maturation, and soil enrichment qualities (Puri et al. 2002).P. deltoidesrapidly produces large quantities of biomass per unit area and provides a valuable raw material for various products (e.g., plywood, paper pulp, furniture, f iberboard,veneer, sports goods, newsprint, f ine and packing paper,and match industries).P. deltoidesis also a key species for degraded land restoration (de Mello et al. 2012). However,the production rates vary widely, depending on genotype,plantation density, age, location, and silvicultural practices(Lodhiyal and Lodhiyal 1997; Zabek and Prescott 2006).Comprehensive reports on biomass and productivity ofP.deltoidsplantations in temperate regions of Iran are available (Parsapour et al. 2014; Eslamdoust and Sohrabi 2018;Yousofvand Mofrad et al. 2018; Farhad et al. 2019). However, information on its bark production as a potential source of bioenergy is unclear. This study aimed to: (1) estimate bark percentage and variation throughout the stem of fastgrowingP. deltoides; (2) derive equations as a tool for estimating bark percentage and bark production; and, (3) apply the equations to mature stands ofP. deltoidsto estimate ranges of stand bark production.

Materials and methods

Experimental site description

Fig. 1 Location of the study area

Fig. 2 Embrothermic curve of the study area

The study was conducted in the temperate regions of Northern Iran (southern coast of the Caspian Sea), 36°35′N, 52°10′ E (Fig. 1). The topography is characterized by f latlands to low hills at elevations of 5 to 100 m a.s.l. The region has a temperate climate (Fig. 2), with a mean annual temperature of 16.9 °C, mean annual precipitation range is 802 - 823.5 mm. The soil is silt-loam with poor drainage.Soil characteristics are shown in Table 1 based on Kooch et al. ( 2016). Historically, the study sites were occupied by a temperate deciduous forest dominated byQuercus castaneifolia(C.A.Mey.),Gleditschia caspica(Desf.),Carpinus betulus(L), andParrotia persica(DC.) C.A. Mey.P. deltoidswas planted in three separate areas at an initial density of 625 trees ha -1 (4 m × 4 m) after clear-cutting the original temperate deciduous forest. No thinning operations were performed in the plantations. Table 2 provides the description of the plantations.

Plot design and harvesting

Twelve sampling plots (16 m × 16 m) in thereP. deltoidesplantations were established based on systematic random design. To minimize edge eff ects, surrounding rows were not considered during sampling. The age of the stands was 18 - 20 years old. In each sampling plot, the DBH (diameter at breast height 1.3 m above the ground) of the individual trees was measured with a caliper in two perpendicular directions and the mean DBH determined. Tree height was measured by Hagl?f-Vertex IV hypsometer. Based on the DBH and height measurements, 10 DBH classes from 15 to 42 cm (3 cm intervals) were established. The value of each DBH class represented the central value (i.e., class 15included all DBH from 12.5 to 17.5 cm). In each DBH class,one representative tree was selected and harvested for a total of 10P. deltoidestrees.

Table 2 Stand characteristics of P. deltoides

Measurements of bark percentages

The stems of harvested trees were marked and cut into 2 m-segments. The mid-length diameter of each segment was measured outside the bark in two perpendicular directions with a caliper to determine the mean diameter. A 5 cmthick disc was cut from the middle of each segment. A total of 123 discs were obtained and brought to the laboratory.All the discs were arranged into 2-cm wide diameter classes.The value of each disc class represents the central value(i.e., class 20 included all discs whose diameters ranged from 19.5 to 20.5 cm). Bark was separated from the wood using a peeler knife for each disc. Fresh bark and wood were weighted separately, oven-dried at 80 °C until constant weight, and the oven-dry weight measured. The bark percentage of each disc was considered as bark percentage of a 2 m-segment for fresh and dry weight. Finally, the barkpercentage of the whole stem in each DBH class was calculated by adding the 2 m-segments.

Table 1 Soil characteristics (Mean ± SE) of the study area (Kooch et al. 2016)

Data analysis

The normality of distributions was tested with the Shapiro-Wilk test. Paired-samples T-test was conducted to determine the mean diff erence between the fresh and dry bark percentages in each diameter class. One-way ANOVAs were used to assess the diff erences in bark percentage of disc samples. Least signif icant diff erences test (LSD,p＜ 0.05)was used to determine signif icant diff erences between class sections. All allometric relationships were developed by the power law function, widely used to describe tree biomass(Forrester et al. 2017). The simple power law is:

whereXis the predictor variable (section’s diameter,DBH),Ythe response variables (bark percentage, stem dry mass), andαand?are allometric function parameters. To evaluate the regression equations, the proportion of variance explained by the model (R 2 ) and the standard error of estimate (SSE) were used. All statistical analyses were performed using SPSS ver. 19.0 software (SPSS Inc., Chicago, Ill., USA).

Results

The percentages of fresh and dry bark varied signif icantly(p＜ 0.001) according to diameter class (Table 3). As diameters increased, fresh and dry bark percentages decreased signif icantly. Less than 10% of both fresh and dry bark percentages were in the higher diameter classes (i.e., ＞ 34 cm).The highest bark percentage was in the smallest disc diameter class (i.e., 8 cm, 24.4% for fresh bark and 21.8% for dry bark, represents the lowest wood content (75.6% and 78.2%,respectively). Mean fresh and dry bark percentages were 15.7% and 13.8%, respectively.

The results show signif icant diff erences between fresh and dry bark percentages of class 8 up to 36 cm (Fig. 3). The largest diff erences of approximately 2.5% occurred within the 8-14 cm classes. However, no diff erences occurred among the higher classes (38-44 cm).

A reliable trend of power equations (highly signif icant withP＜ 0.001) for the relation between disc diameters with fresh bark percentage (R 2 = 0.91) and dry bark percentage(R 2 = 0.90) was detected (Table 4).

Table 5 represents the fresh and dry bark content of each 2 m-segments of stems as determined by sample discs. The highest percentages of fresh and dry bark were in the smallest DBH class (i.e., 15 cm). However, the larger 39 and41-cm DBH classes were relatively stable for both fresh and dry bark percentages. The bark percentage of whole stems in each DBH class decreased with increasing DBH, but the amount of fresh and dry bark increased.

Table 3 Mean (± SE, total n = 123) fresh and dry bark percentage of diff erent disc classes

Allometric power equations between DBH and whole stem bark percentages were established based on stem analysis (Table 6). The values of R 2 ranged from 0.89 to 0.90 to provide satisfying goodness-of-f it.

The relationships between DBH and stem dry mass(Fig. 4 a) and DBH and stem dry bark (Fig. 4 b) were established following power-law functions indicating the variations with increasing diameter. The estimated parameters are shown in Table 7. The equations were applied to mature stands ofP. deltoides(12 sampling plots and 171 trees).Total stem dry mass production was 161.6 ± 24.8 Mg ha -1 and total dry bark production was 22.3 ± 3.6 Mg ha -1 .

Discussion

The current study assessed the variations ofP. deltoidesbark production.P. deltoidesis a fast-growing species and bark production is a signif icant source of bioenergy. As fastgrowing species,P. deltoidescould be a f inancial and ecological solution to address energy demands. Also, it can beconsidered a high potential for the mitigation of greenhouse gases and for carbon sequestration (Baishya and Barik 2011;Eslamdoust and Sohrabi 2018). This study emphasized that:(1) the percentage of fresh and dry bark varied signif icantly according to the diameter of the segments; (2) the highest bark percentage was in the smallest diameter class (24.4%for fresh bark and 21.8% for dry bark); (3) a reliable trend of power equations was detected between segment diameter and fresh and dry bark percentage; and, 4) a mature stand ofP. deltoides.(625 trees per ha, 18 - 20-years old) can provide 22.3 ± 3.6 Mg ha -1 of dry bark production.

Fig. 3 Mean fresh and dry bark percentages in diff erent diameter classes of P. deltoides. Different letters indicate signif icant diff erences between fresh and dry bark percentages in each disc class at P ＜ 0.05

Table 4 Estimated parameters for allometric relationship between disc diameter classes and bark percentage (n = 123)

Table 5 Fresh and dry bark content (kg) of segments in diff erent DBH classes

Table 6 Estimated parameters for allometric relationships between DBH and bark percentage (n = 10)

The results demonstrate that fresh and dry bark percentages in smaller disc classes (8 cm up to 36 cm) are signif icantly diff erent. These diff erences could be due to higher bark water content in smaller diameter classes (Vusi? et al.2019; Routa et al. 2020). Guidi et al. ( 2008) found a signif icant diff erence between fresh and dry bark percentages(12.5%-15.1% diff erences) in small diameter classes primarily due to high water content. Vusi? et al. ( 2014) described a similar f inding in a poplar experiment. Generally, bark thickness increases with stem diameter, but water content decreases and the bark contain relatively more dry matter (Williams et al. 2007; Lawes et al. 2011; Pausas 2015;Shearman and Varner 2021). However, growing conditions and management regimes may inf luence bark thickness, its specif ic gravity, and tree architecture. Microclimate, site conditions, and silvicultural treatments may signif icantly change the diff erences in bark thickness inP. deltoidestrees(Subedi and Sharma 2012; Routa et al. 2020).

The percentage of fresh and dry bark varied signif icantly in disc diameter classes, with a mean of 15.7% and 13.8%fresh and dry bark percentages, respectively. These f indings are consistent with Fang et al. ( 2010) who indicated bark percentages of 13.4-15.4% in diff erent conf igurations of poplar planting. Results also show that, as the diameter increased, the fresh and dry bark percentage decreased signif icantly. Observing a higher bark percentage in smaller diameter classes is expected. Vusi? et al. ( 2014) reported an average bark content of 17.3% to 20.7% for two diff erent clones ofP. deltoidesin a short rotation coppice (5-7 years).The bark percentage of whole stems in each DBH class decreased with increasing DBH, whereas fresh and dry bark increased. This trend is like observations made in other studies with diff erent plantation systems and diff erent species(Guidi et al. 2008; Subedi and Sharma 2012; Suchomel et al.2012; Eslamdoust and Sohrabi 2018), resulting to increasing proportions of wood to bark ratios within the stem (Li et al. 2011; Subedi and Sharma 2012; Matthews et al. 2014).Results were similar to those of Vusi? et al. ( 2019) who found an average of 18.4% bark content and a range from 15.4% to 21.1%. Guidi et al. ( 2008) reported bark content ranges from 33.9% to 15.1% for 1 cm to 9 cm diameters,respectively. These variations may depend on forest management, stand-specif ic canopies, and soil conditions, which aff ect light, water, and nutrient availability for stem growth(Li et al. 2011). However, Rance et al. ( 2012) indicated that for trees more than 6 cm in diameter, the wood to bark ratio only incrementally changed.

Table 7 Estimated parameters for allometric relationship between DBH, stem dry mass and stem dry bark (n = 10)

Fig. 4 Relationship between DBH (cm) as a predictor variable, stem dry mass ( a) and stem dry bark ( b) of P. deltoides(n = 10)

In this study, allometric models established power-law functions, and the values of R square ranged from 0.89 to 0.91 to provide satisfying goodness-of-f it. Power functions are used to estimate the biomass of whole trees or tree components as the most common form of biomass functions (Zianis and Mencuccini 2004; Suchomel et al. 2012).Power functions can provide a good balance of accurate predictions and low data requirements by using DBH,the most common and easily measured variable used to estimate the biomass of diff erent tree components (Guidi et al. 2008; Rance et al. 2012; Arora et al. 2014; Eslamdoust and Sohrabi 2018). In this study, DBH was used for predicting total stem dry biomass and stem dry bark.The goodness-of-f it of the equations was good because the power functions using DBH could explain more than 98% of the variability in the observed stem bark and total stem biomass. The results of this study are consistent with other studies onP. deltoideswhich developed allometric equations for predicting biomass production (Rance et al.2012; Subedi and Sharma 2012; Eslamdoust and Sohrabi 2018; Truax et al. 2021). Arora et al. ( 2014) developed allometric equations to estimate biomass of diff erent tree components ofP. deltoides, which had adjusted R squares greater than 94%. The lack of independent data in this study did not allow us to validate the models. However,data from a relatively small number of sample trees would be suffi cient for modeling biomass if representing all possible sizes, sites, and stand conditions (Zianis et al. 2005;Subedi and Sharma 2012). However, validation, verif ication, and re-calibration of the proposed allometric models with new data from a wide range of sites, sizes and stand conditions ofP. deltoidestrees are recommended.

The equations were applied to mature stands ofP. deltoides(12 sampling plots and 171 trees). Total stem dry mass production was 161.6 ± 24.8 Mg ha-1and total dry bark production 22.3 ± 3.6 Mg ha -1 . For other species, Peichl and Arain ( 2006) reported 2.0 Mg ha-1 and 5.4 Mg ha -1 bark production of 15 and 30 year-old stands ofPinus strobusL. Li et al. ( 2011) studied an age-sequence plantation ofP. koraiensisSiebold & Zucc and reported 5.7 and 9.3 Mg ha -1 bark production for 30 and 35 year-old stands,respectively. He et al. ( 2013) found bark production forCastanopsis hystrixA.DC. (6.2 Mg ha -1 ) andPinus massonianaLamb. (8.8 Mg ha -1 ) in subtropical China. The results indicated a considerable amount of bark production inP.deltoides, compared to other species in diff erent studies,which can potentially provide a high source of bioenergy.However, biomass partitioning is aff ected by pedoclimate conditions, tree species, origin, and stand age. Also, among the woody component biomass, bark is the most heterogeneous and chemically complex (Routa et al. 2020). Therefore,to determine bark production in diff erent climate conditions,more parallel experiments should be performed.

Conclusions

This study determined bark percentage and bark storage variation ofP. deltoides, is one of the most planted species in the temperate regions of Iran and has a signif icant demand market. The allometric power equations provide high precision predictions of individual fresh and dry bark percentages in diff erent diameter classes. Also, the equations, based on available and easily measured DBH are helpful to estimate bark production as fuel before harvesting. However, diff erent site conditions may inf luence prediction precision. The results highlight that the amount of bark production ofP.deltoidesis considerable and it can provide a high-value source of bioenergy.

Acknowledgements The trees sampled were provided by Klodeh plantations in northern Hyrcanian forests. I would like to thank Dr. Bahram Naseri and Masoud Khaledi for their valuable help in f ieldwork. Also, I greatly appreciate anonymous reviewers and journal editors for the helpful comments and suggestions.

Funding This research did not receive any specif ic grant from funding agencies in the public, commercial, or not-for-prof it sectors.