Agroforest Syst (2013) 87:287–294 DOI 10.1007/s10457-012-9547-z

Bioenergy production and soil conservation from Norway spruce (Picea abies L. Karst) plantations in Denmark Rasoul Yousefpour

Received: 25 November 2011 / Accepted: 9 July 2012 / Published online: 19 July 2012 Ó Springer Science+Business Media B.V. 2012

Abstract This study analyses the trade-off between bioenergy production and soil conservation through thinning operations in Norway spruce (Picea abies L. Karst) plantations in Denmark. Thinning operations were evaluated under different regimes and intensities for a complete rotation period of sixty years and for different site qualities (site-classes I–VI). Applying a dynamic forest growth modeling tool, evolution of forest structure was predicted to observe the potentials for biomass production and inevitable soil degradation. Results showed thinning from below, with a higher utilization (maintenance of a minimum basal area of 25 m2 ha-1) could produce more bioenergy. However, these operations require simultaneous severe forest soil degradation. Therefore, the optimum thinning for bioenergy production under preservation constraints was thinning from above with a lower intensity (maintenance of a minimum basal area of 45 m2 ha-1). The ratio of bioenergy win (kWh) to soil-loss (m3 ha-1) was calculated for this regime varying between 74,894 kWh m-3 in a high quality site (site-class I) and 6,516 kWh m-3 in a low quality site (site-class VI) with an average of 44,282 kWh m-3. However, this could not always preserve the highest amount of growing stock essential for natural

R. Yousefpour (&) Department of Forest and Landscape, University of Copenhagen, Rolighedsvej 23, 1958 Frederiksberg, Denmark e-mail: [email protected]

dynamics of forest ecosystem with an exception of the low quality sites (site-class VI). Thus, when aiming at bioenergy production through thinning operations, trade-offs with soil conservation and growing stock preservation should be regarded to prevent environmental degradation. Keywords Bioenergy  Soil conservation  Thinning  Site-class  Norway spruce

Introduction Climate change and increasing oil prices have resulted in an ever-increasing need for alternative energy solutions. Therefore, the European Commission proposed a target of doubling the contribution of renewable energy sources to the EUs total primary energy needs to 12 % by 2010 (Schroeter et al. 2005). Renewable energies attract great attention from policy makers and moreover, bioenergy production, as a green-energy, provided by terrestrial ecosystems has already been put into practice for the management of natural and agricultural resources. World forest ecosystems provide the bulk of current biofuel recourses, and therefore, wood fuel production is currently one of the mostly prioritized objectives in strategic forest management at national, regional and local levels (Johansson 1999; Nord-Larsen and Talbot 2004;

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Bjørnstad 2005; Eriksson 2006; Polagye et al. 2007; Ince et al. 2008; Susaeta et al. 2009; Callesen et al. 2010; Heikkila et al. 2007). Nevertheless, this implies a variety of environmental impacts on forest ecosystems from which soil modification (i.e. erosion and change in organic matter, nutrients and, carbon stores) jeopardizes the whole ecosystem functioning (Kelty et al. 2008; Page-Dumroese et al. 2010; Walmsley and Godbold 2010). Fast growing trees play a major role in bioenergy (fuel wood) production, especially the species having a wide range distribution. Norway spruce (Picea abies L. Karst), one of the most widespread tree species in Europe, meets all of these criteria and covers 28 % of the total forests area in Denmark (Danish Nature Agency 2003), which accounts for 19 % of total forest basal area (Nord-Larsen et al. 2008). Norway spruce has been planted since the early nineteenth century for the traditional wood production additionally contributing social and ecological services. To fulfill the visions of the Danish National Forest Program, Danish forests are multi-purpose, supplying not only wood but also offering diverse opportunities for outdoor recreation, conservation of biological diversity and climate change mitigation. Therefore, any biomass utilization must guarantee forest sustainability and the continuity of forest growth. Moreover, future work is required to develop techniques for estimating the quantity of bioenergy resources available under different management strategies and to elucidate the environmental impacts of producing wood for energy from forestry systems (Sabourin et al. 1992; Mead and Pimentel 2006; Gue et al. 2010). The main forestry operation to manipulate the forest structure and provide forestry products like bioenergy is thinning (Gamborg 1997; Johansson 1999; Nord-Larsen 2002; Nord-Larsen and Talbot 2004; Bjørnstad 2005; Eriksson 2006; Bergstro¨m 2009; Heikkila et al. 2007; Yousefpour et al. 2010). Thinning, as a management strategy, has the potential to provide higher and more frequent economic returns, reduce the costs of combating fires and assure forest health, while achieving conservation goals (Jacobsen and Thorsen 2003; Vogt et al. 2005; Polagye et al. 2007; Kelty et al. 2008; Page-Dumroese et al. 2010; Yousefpour et al. 2010). Recently, thinning operations for bioenergy production, especially in Norway spruce plantations, have been a focus of many forestry studies (Bjørnstad 2005; Eriksson 2006; Callesen et al. 2010;

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Gue et al. 2010). However, optimal thinning should not only provide sustainable use of forest products but also prevent site exploitations and guarantee forest health and regeneration (Polagye et al. 2007; Skovsgaard 2009; Ernsting 2010; Walmsley and Godbold 2010). So far, very few studies have analyzed the environmental impacts of thinning operations for bioenergy production (Kelty et al. 2008; Karahalil et al. 2009). Thus, in the present study, both thinning operations and site preservation of soil and growing stock were analyzed during a planning period of sixty years to generate the thinning recipe for bioenergy production from Norway spruce forests within sites of different qualities (site-classes) in Denmark. Decision parameters of thinning operations, namely thinning modes and intensities were modeled in the present study to determine the optimal thinning regime for Norway spruce plantations. This was conducted for all site qualities present in Denmark (site-classes I–VI) since according to recent research (Skovsgaard 2009), thinning response is clearly site dependent. An evaluation of the combinations of silvicultural treatments (e. g. thinning, pruning, regeneration cut) was not conducted because combined treatments are not as useful for estimating energy input to output ratios as separate evaluations (Mead and Pimentel 2006). Potential energy production through different thinning operations and the imposed soil-loss as growing stock decline were evaluated to determine the most desirable thinning regime for each site. Accordingly, a win-toloss ratio was calculated to determine the ratio of bioenergy production (kWh m-3) to soil-loss (m3 ha-1). Furthermore, basal area in a stand may decrease as a consequence of increasing disturbance pressure, and additionally, it appears to be one of the most useful forest structural measures e.g. productivity and biodiversity (Ingram et al. 2005; Yousefpour et al. 2010). Therefore and in addition, the level of remaining growing stock (basal area) was monitored to avoid site degradation in the determination of the most desirable thinning operation.

Materials and methods Recent advances in forest growth modelling have facilitated the development of models that include the effect of varying silvicultural practices. In the present study, annual harvesting volumes of Norway spruce

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(Picea abies L. Karst) were estimated using a dynamic growth model ‘‘Vidar’’ (Nord-Larsen and Talbot 2004). The dynamic growth model ‘‘Vidar’’ consists of a series of simultaneous differential equations, which allows for the application of alternative treatment models (initial spacing, thinning practice, etc.). In this study, the treatment model was designed according to a study of actual forest practices in Denmark (Nord-Larsen and Talbot 2004). Site-class varied from I to VI for Norway spruce plantations in different regions according to Danish National Forest Research Department (SFF 1990). Modeling of thinning operations was conducted under different modes and intensities. For Norway spruce, the mean growing stock is calculated directly from the dynamic growth model at the midpoint of each period and replaces the first parenthesis in Eq. (1):    Pi;jþ1  Pi;j si;jþ1 Hi;j ¼ Ai;j Vi;j þ ð1Þ 1 2 si;j where H = harvest volume from final felling; A = forest area; i = species (i.e. Norway spruce); j = ageclass; V = growing stock per hectare; P = total production; s = probability that the area has not yet been felled based on the two successive forest inventories in 1990 and 2000. Calculation of the thinning volume (T) in ‘‘Vidar’’ is based on the forest area in each age-class and the total production and growing stock in the beginning and at the end of each period. For Norway spruce the thinning volume is calculated as annual thinning yield accumulated for each period as follows Eq. (2).  

si;jþ1 Ti;j ¼ Ai;j Vi;j þ Pi;jþ1  Pi;j  Vi;jþ1 si;j ð2Þ In the present study, three thinning modes, namely thinning from below (B), thinning from the middle (M) and thinning from above (A), i.e. removing overtopped, intermediate and dominant trees, corresponding, were considered for three different intensities (high, moderate and low) maintaining, respectively, 25, 35 and 45 m2 ha-1 of basal area. Multiplication of three thinning regimes and three thinning intensities creates nine scenarios for each site quality Tj,j = {A25–A45, M25–M45,B25–B45}. Applying above motioned Eqs. (1) and (2) and existing height increment models of the dynamic growth program (Nord-Larsen and Talbot

2004), evolution of forest structure e.g. diameter and basal area was predicted through thinning operations and during the rotation period. Both forest structural parameters were used to calculate demanded measurements i.e. bioenergy production, soil-loss, and growing stock preservation potentials for different thinning scenarios. The following equations reveal the calculation of demanded measurements from forest inventory data. Biomass production of each tree (Eq. 3) was calculated from a Richard’s function (Johansson 1999). B ¼ 21988:7574ð1  eð0:0006DÞÞ2:4400  ðR2 ¼ 0:975Þ

ð3Þ

where B = biomass production (kg dry weight tree-1); D = diameter at breast height, mm, generated from. The energy content per mass unit (kg) is quite constant across species, but it varies due to substantial differences in wood density. Therefore, the basic density for Norway spruce trees of different ageclasses was used to derive the biomass volume of thinning woods. According to Johansson (Johansson 1999), the following densities were applied to calculate biomass volume; 371 kg m-3 (-25 years), 368 kg m-3 (26–50 years), 383 kg m-3 (51–100 years) and 401 kg m-3 (101– years). Applying these, the energy content of 1,900 kWh m-3 in spruce wood was calculated (measured as kWh m-3). A logarithmic function was used to calculate soil-loss (Karahalil et al. 2009) through different thinning operations (Eq. 4). ln SL ¼ 2:553079  0:065BA ðR2 ¼ 0:67Þ

ð4Þ

where BA = basal area (m2 ha-1); SL = approximate soil-loss (m3 ha1 year-1); ln = natural logarithm. Providing measurements for the performance of alternative thinning scenarios (1–9) for different sites, the trade-offs between bioenergy production and soil-loss were analyzed and a ‘‘win-to-loss ratio’’ was calculated to determine the win of bioenergy production potential to soil-loss. Moreover, the data was statistically analyzed to examine their variances and to find differences between the thinning operations scenarios. Univariate Tukey test of SPSS 15.0 for windows evaluations was used including two dependent variable win-to-loss and growing stock and two fixed factor thinning scenario and site-class. Difference significances were evaluated at the 0.05 level.

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Results

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Applying thinning operations (scenarios 1–9) to different site qualities for sixty years, the bioenergy production, and soil-loss were calculated. Total bioenergy production varied for different thinning operations, as shown in Fig. 1a–f, but the fluctuations within each scenario followed, more or less, the same pattern. Nevertheless, an exception was for site-class VI, where an unexpected increase in bioenergy

production occurred with thinning from above under a low intensity (maintaining 45 m2 ha-1 of the total basal area), which also led to a less soil-loss. Otherwise, any increase of bioenergy production would impose greater soil-loss under a thinning operation. In all sites, B25, or thinning from below under a high intensity (maintaining 25 m2 ha-1 of the total basal area) produced more bioenergy wood. In fact, this resulted in a heavy soil-loss but, surprisingly, not always the greatest soil-loss, as observed in sites

a

b

c

d

e

f

Fig. 1 Bioenergy production and soil-loss calculations for alternative thinning operations (scenarios) and different site qualities (siteclasses I–VI). a Site-class I. b Site-class II. c Site-class III. d Site-class IV. e Site-inex V. f Site-class VI

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III, IV and VI. Maximum soil-loss occurred under thinning regimes M25 (sites III and IV) and A25 (site VI). Optimal thinning scenario for bioenergy production As mentioned above, B25 offers the highest bioenergy production from Norway spruce plantations through thinning. Nevertheless, this might result in a very heavy soil-loss. Table 1 shows the ratio of the bioenergy win to soil-loss (win-to-loss ratio) and the remaining growing stock at the end of the thinning period for different sites. With no exception, the A45 regime of thinning from above under a low intensity (maintenance of 45 m2 ha-1 of basal area) provides more bioenergy per soil-loss than other thinning operations. However, this could not coincide with the maximum level of remaining growing stock. For example, maximum preservation of remaining growing stock might occur with M45 in productive sites (I, II and IV) and A35 for the moderate site (site-class III). Low quality sites V and VI could preserve the maximum remaining growing stock through thinning operations B45 and A45, respectively. Only for the

lowest quality site (VI), the ratio of win-to-loss and preservation of the remaining growing stock could be reached with an identical thinning operation A45. The maximum energy production ratio (win-to-loss for A45, Table 1) was found for the most productive site (site-class I) with 74,894 kWh m-3 and a minimum of 6, 516 kWh m-3 for the lowest site quality (site-class VI) which counts for 11.5 times more bioenergy production on high quality rather than low quality. However, low bioenergy production of siteclass VI was an exceptional case where there was a huge collapse of bioenergy production between siteclass V and VI. If the potential of bioenergy production between sites V and I was considered, the ratio would come to only 3.5 (74,894/21,508). On average, the win-to-loss ratio including and excluding site-class VI would come to 44,282 and 46,780 kWh m-3, respectively. Site-class VI could preserve, however, a very high growing stock (56 m2 ha-1) which was already the highest remaining growing stock after thinning operations in different sites. Results of Tukey’s honestly significant difference test showed that thinning scenarios were significantly different considering growing stock as the dependent variable. However, the thinning scenarios were not

Table 1 Win-to-loss ratio and remaining growing stock of thinning scenarios for different site qualities (site-classes I–VI) in planning period of sixty years Site-class

Measurement

Unit

Win-to-loss ratioa

I II III IV V

kWh m-3 2

-1

Growing stock

m ha

Win-to-loss ratioa

kWh m-3 2

-1

Growing stock

m ha

Win-to-loss ratioa

kWh m-3 2

-1

A25

A35

A45

62,695

58,684

74,894b

43,075

52,018

60,838

48,203

56,652

67,551

41.1

44.5

46.5

28.4

36.2

46.3

33.4

43.7

47.2b

39,355

51,193

67,993b

39,370

44,102

50,135

42,071

44,239

57,651

34.7

43.1

46.2

27.9

36.1

46.5

29.2

35.0

46.4b

30,937

48,696

51,744b

34,838

35,953

39,921

29,903

38,284

44,572 47.7

b

B25

B35

B45

M25

M35

M45

Growing stock

m ha

29.0

49.5

47.9

26.1

38.7

47.2

29.5

39.8

Win-to-loss ratioa

kWh m-3

26,309

33,297

43,035b

30,502

29,745

30,350

26,127

30,951

35,927

Growing stock

m2 ha-1

28.9

38.4

47.0

26.6

37.5

46.9

26.5

36.2

48.2b

Win-to-loss ratioa

kWh m-3

15,082

18,474

21,508b

20,523

14,861

13,626

15,052

16,107

17,312

27.1

39.5

46.4

29.0

35.9

46.5b

30.1

39.0

48.9

2

Growing stock VI

Scenario

Win-to-loss ratio Growing stock

m ha a

-1

kWh m

-3

m2 ha-1

b

6,317

6,156

6,516

3,630

3,121

3,069

4,493

4,591

4,679

39.3

40.6

56.3b

38.2

36.7

56.6

39.8

38.8

56.3

B thinning from below, M thinning from the middle, A thinning from above plus three different intensities (high, moderate and low) maintaining, respectively, 25, 35 and 45 m2 ha-1 of basal area, creating nine scenarios for each site quality {A25–A45, M25–M45, B25–B45} a

The ratio of bioenergy production (kWh m-3) to soil-loss (m3 ha-1, see details in Fig. 1a–f)

b

Max. of measurements (win-to-loss ratio and growing stock) for each site quality (site-class)

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significantly different applying win-to-loss ratio as the dependent variable. Results of the test was constant and significantly different for the fixed factor site-class under both evaluation variables win-to-loss and growing stock.

Discussion Bioenergy production versus soil-loss Replacement of fossil fuels by renewable energy sources has recently been encouraged to mitigate climate change. Among renewable energies, bioenergy production from plant biomass is thought to provide the most viable alternative in Europe (NordLarsen and Talbot 2004). This introduces a new challenge for forestry practices where wood fuel production can notably contribute to bioenergy production. However, this can not carried out without regard to environmental considerations and sustainable use of forest products, especially the preservation of forest growing stock and soil quality (Walmsley and Godbold 2010). In the present study, these two considerations were observed along with bioenergy production to evaluate the impacts of different forestry intensities (i.e. thinning operations) of Norway spruce in Denmark. The results showed that site quality plays a major role in the level of bioenergy production, but thinning operation had to maximize bioenergy production without any environmental constraints would be similar for all sites, i.e. thinning from below with a high intensity of maintaining only 25 m2 ha-1 of basal area. However, this could not be an optimal thinning regime as it imposes a very high potential for soil-loss in forest plantations of Norway spruce. The only exception was the lowest quality site (site-class VI), which had a lower growing stock (basal area) and could maintain this amount of growing stock although operated by B25. This is in line with the study by Eriksson (2006), where ‘‘low thinning’’ (thinning aiming at maintaining a basal rea of 25 m2 ha-1) could provide the maximum bioenergy (0.63 tons d.w. ha-1 year-1) in Norway spruce stands in Sweden, however, preserving less standing biomass (109.56 ton d.w. ha-1). Similarly, Kelty et al. (2008) concluded that average biomass levels harvested in crown thinning (thinning from above) ? low thinning treatment (thinning from below) were 2.5–3.0 times

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greater than the crown thinning treatment alone. Gue et al. (2010) mentioned that the thinning from below (low thinning) to a target residual basal area of 70 % would be an optimal thinning mode for bioenergy production in loblolly pine (Pinus teada) forests in Mississippi, and concluded that woody biomass availability for biofuels would increase under intensive management practices, such as thinning with higher intensity and/or frequency, intensive site preparation, closer initial tree spacing, earlier thinning and shorter rotation age. Bioenergy production under environmental constraints Although wood fuel provides the largest percentage of bioenergy generation in terms of energy output, it is associated with certain negative consequences, including much greater pressures on forests and other ecosystems, on soils and freshwater (Ernsting 2010). Extensive forestry impacts inherent soil productivity (Grigal 2000) and therefore, efforts must be made to preserve soil productivity during thinning operations (Page-Dumroese et al. 2010). Moreover, the structure of forest responses to disturbances especially humanimpacted damages e.g. decrease of mean basal area (Ingram et al. 2005). In this study, thinning operations which maximize bioenergy production were evaluated subject to soil-loss prevention and growing stock preservation. As discussed above, the B25 regime, or thinning from below under a high intensity could impose a very large soil-loss to the forest. The electricity gain due to soil-loss was also among the lowest ratios. Moreover, this would not properly preserve the growing stock. Table 1 reveals that the A45 regime (thinning from above under a low intensity) always produced the highest ratio (win-toloss) and simultaneously imposes a much lower exploitation to remaining growing stock. To this point, it might become an optimum thinning regime for the lowest quality site, where the energy production (win-to-loss) and growing stock preservation were both at a maximum. For the productive sites (I, II and IV), however, A45 did not achieve maximum preservation, but a near maximum preservation, where the M45 could preserve the growing stock maximally. This was also true for the moderate sites (III and V), where A35 and B45 represented the thinning regimes with highest preservation capacities.

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Bioenergy production and site quality Site quality, and consequently forest growth rate, directly influences the potential of forest ecosystems for bioenergy production (Eriksson 2006; Gue et al. 2010). This is clearly observed in the present study and was approved by Tukey’s test showing significant differences among site-classes. Moreover, according to Fig. 1a–f and Table 1, the higher the site quality, the more bioenergy that would be produced through thinning operations. The very low quality site (siteclass VI) failed to produce a comparable amount of bioenergy through defined thinning operations. Nevertheless, it performed very well in preserving a high growing stock at the end of thinning periods. This was possibly due to the fact that, as a result of this site being poor in growing stock at the beginning of the thinning operations, not enough wood could be provided for the defined intensities (minimum maintenance of 25 m2 ha-1) so, the growing stock was just preserved during the thinning period. Moreover, the bioenergy productions pattern of site-class VI was different from all other sites. While thinning from below was the most productive regime in the other sites (Fig. 1a–e), thinning from above was the most productive regime in this site (Fig. 1f). Thus, if bioenergy production is the first or only goal in productive forestry, high quality sites might be utilized and thinning from above under a low intensity would be the most desirable operation.

Conclusion Maximizing thinning operations for forest products must take into account environmental considerations (Jacobsen and Thorsen 2003; Vogt et al. 2005; Kelty et al. 2008; Walmsley and Godbold 2010). Aiming at the maintenance of soil productivity, for example, requires a change in thinning regimes (Grigal 2000; Page-Dumroese et al. 2010). In this study, the most desirable thinning operation to maximize bioenergy production while preserving soil and growing stock in Norway spruce plantations in Denmark was determined to be thinning from above under a low density, although, this should be avoided on very low quality sites. In addition, when managing for bioenergy production through thinning operations, it is very useful to consider the ratio of energy production to

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soil-loss (or any other damage to the forest ecosystem). Future research should focus on management interventions including final cuttings, especially in commercial plantations, to assess long term environmental impacts. More studies are required to monitor other services from forest ecosystems like water production and balance, carbon storage and biodiversity, and evaluate these considerations throughout management interventions for bioenergy production. Acknowledgments The author would like to expresses his gratitude to the forest growth modeling tool working group at the Department of Forest and Landscape, University of Copenhagen. This study was a part of an ongoing project entitled ‘‘MOdels for adapTIVE forest management’’ (MOTIVE), which is supported by the European Commission under the 7th Framework Programme for Research and Technological Development (FP7/2007-2013) under grant agreement no. 226544.

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