- Open Access
Above-ground woody biomass allocation and within tree carbon and nutrient distribution of wild cherry (Prunus avium L.) – a case study
© Morhart et al. 2016
- Received: 27 August 2015
- Accepted: 8 February 2016
- Published: 11 February 2016
The global search for new ways to sequester carbon has already reached agricultural lands. Such land constitutes a major potential carbon sink. The production of high value timber within agroforestry systems can facilitate an in-situ carbon storage function. This is followed by a potential long term ex- situ carbon sinkwithin long lasting products such as veneer and furniture. For this purpose wild cherry (Prunus avium L.) is an interesting option for middle Europe, yielding high prices on the timber market.
A total number of 39 wild cherry were sampled in 2012 and 2013 to assess the leafless above ground biomass. The complete trees including stem and branches were separated into 1 cm diameter classes. Wood and bark from sub-samples were analysed separately and nutrient content was derived. Models for biomass estimation were constructed for all tree compartments.
The smallest diameter classes possess the highest proportion of bark due to smaller cross sectional area. Tree boles with a greater amount of stem wood above 10 cm in diameter will have a more constant bark proportion. Total branch bark proportion also remains relatively constant above d 1.3m measurements of 8 cm. A balance is evident between the production of new branches with a low diameter and high bark proportion offset by the thickening and a relative reduction in bark proportion in larger branches. The results show that a single tree with an age of 17 and 18 years can store up to 85 kg of carbon within the aboveground biomass portion, an amount that will increase as the tree matures. Branches display greater nutrient content than stem sections per volume unit which can be attributed to a greater bark proportion.
Using the derived models the carbon and the nutrient content of above-ground woody biomass of whole trees can be calculated. Suggested values for carbon with other major and minor nutrients held within relatively immature trees strongly supports the idea of the inclusion of wild cherry within agroforestry systems as an option for carbon sequestration.
- Carbon sequestration
- Nutrient content
The period from 1983 to 2012 has been the warmest 30-year period of the last 1400 years in the Northern Hemisphere (IPCC 2014). The increase of anthropogenic greenhouse gas (GHG) emissions has especially occurred in the last decades, approximately half of the anthropogenic CO2 emissions contributing to the increase between 1750 and 2011 have occurred in the last 40 years (IPCC 2014). Consequently, to counteract this rise there are three major forest management activities that can help reduce atmospheric carbon, namely; carbon sequestration, carbon conservation and carbon substitution (Montagnini and Nair 2004). The global search for new ways to sequester carbon has already reached agricultural lands. Such land constitutes a major potential sink and could sequester large quantities of carbon. Aside from suggested options for adapted cultures and management systems the high potential of agroforestry systems (AFS) has recently been raised (Albrecht and Kandji 2003; Masera et al. 2003; Makundi and Sathaye 2004; Sharrow and Ismail 2004; Montagnini and Nair 2004; Peichl et al. 2006; Nair et al. 2009; Nair et al. 2010). These systems facilitate the production of trees under short rotations as well as the production of high valuable timber on agricultural land. By harvesting this timber, in-situ carbon storage is followed by a long term storage potential as ex-situ carbon storage. Such carbon storage consists of long lasting products such as veneer and furniture.
Farmers and land managers are often reluctant to implement AFS due to additional shade cast and subsequent nutrient export brought about by the culture of trees above annual crops. To date, beneficial or disadvantageous effects of AFS with a focus on the production of valuable wood in middle Europe to carbon and nutrient cycles have not been fully understood. Also different management measures such as pruning or harvesting exert an influence on carbon and nutrient storage and export within AFS, meanwhile, exact figures are still missing. Our idea is that AFS could be planned and managed in a more customised way towards valuable wood production, and carbon storage or nutrient recirculation, if appropriate models would be available. One of the most important valuable tree species, which can be used for the composition of AFS in middle Europe is wild cherry (Prunus avium L.) as it displays a rather fast growth and its timber can be of high value (Spiecker and Spiecker 1988; Balandier and Dupraz 1999; Morhart et al. 2014). Besides, its fruits, flowers and leaves are a food source for numerous animals and microorganisms. Wild cherry is neither a large nor long lived tree, mean dimensions are often quoted as 20 to 30 m in height (Evans 1984; Joyce 1998; Ducci et al. 2013), while diameters at breast height of between 50 and 90 cm are achievable within a 70 or 80 year rotation (Otter 1954; Evans 1984; Spiecker 2006; Ducci et al. 2013). Within published volume tables for wild cherry merchantable stem volumes are suggested to reach over 2 m3 within a full rotation (Pryor 1988; Röös 1994), or more if radial increment is greater on high productivity sites (Spiecker 1994). Carbon is widely accepted to encompass up to 50 % of total woody biomass (e.g., Fang et al. 2001; Kurz et al. 2009; Pretzsch 2010). Applying such volume tables as a basis, the carbon content can be calculated utilising the density and a general conversion factor as suggested by Pretzsch (2010). This efficient approach has some limitations due to the fact that the density is acknowledged not only to differ within tree species (Hamilton 1975) and between sites (Maniatis et al. 2011), but is also recognised not to be homogenous throughout a tree (Wassenberg et al. 2015). Additionally, the carbon content is neither the same between tree species nor among tissue types (Thomas and Martin 2012). Furthermore, volume functions usually are not prepared for the prediction of wood with diameters below merchantable timber size.
Zianis et al. (2005) wrote that the estimation of tree biomass is necessary for both the sustainable planning of forest resources and for the study of energy and nutrient flows within given situations. Whole tree estimations and approaches separating only stem and branches seem not to be adequate for the detailed estimation of bark biomass, further partitioning, for example into diameter classes increase the accuracy (Adler et al. 2005; Guidi et al. 2008; Morhart et al. 2013). The nutrient content for various tree species has been quantified by a number of authors. Regarding leafless biomass, consensus maintains that bark contains more nutrients than wood tissues, in both broadleaved and coniferous species. This has been presented for various species including: Paper birch (Betula papyrifera Marshall) and subalpine fir (Abies lasiocarpa [Hooker] Nuttall) (Wang et al. 1996; Wang et al. 2000), grey alder (Alnus incana [L.] Moench) (Uri et al. 2002), silver birch (B. Pendula Roth) (Uri et al. 2007), sessile oak (Quercus petraea [Mattuschka] Liebl.), European beech (Fagus sylvatica L.) and European hornbeam (Carpinus betulus L.) (André and Ponette 2003; André et al. 2010), and with hybrid poplar (Populus spp.) (Morhart et al. 2013). The heartwood of certain species is suggested to contain low proportions of nutrients such as phosphorus and potassium while the sapwood is generally high in all primary and secondary nutrients (Wright and Will 1958). It has also been suggested that the nutrient concentrations can be two to three times greater in branches than in stem wood (Alriksson and Eriksson 1998). This can possibly be attributed to lower cross sectional diameter, thus a higher bark proportion. Branches represent the sites of active growth thus requiring a greater availability of nutrient for associated growth processes. Pretzsch (2010) calculated that 0.9 t/ha N is stored within European beech. Of this total, 63 % are stored in leaves, bark and branch wood (equating to 25 % of total stand biomass). He notes that a further 37 % of stand biomass is held within the merchantable timber fraction (diameter over 7 cm). Furthermore, Pretzsch (2010) estimates that the harvest of the merchantable timber results in the removal of one third of N, P, K, Ca and Mg from the site. The removal of crown portions will further exacerbate the removal of nutrients from the site. The effect of nutrient removal from forest stands through whole tree harvesting mechanisms is thoroughly reviewed by Kimmins (1977).
Models predicting the volume production potential of valuable timber of wild cherry within AFS already exist (Dagnelie et al. 1999; Alberti et al. 2006; Hackenberg et al. 2014); models predicting its carbon and nutrient storage within AFS are still missing. A combination of all models would be beneficial to customise AFS to individual targets. This study aims to provide models to predict the exact amount of biomass within different compartments of wild cherry in AFS. The allometric biomass functions for wild cherry can serve as a basis for the calculation of the stored carbon as well as the nutrient content between tree compartments and between wood and bark fractions.
Description of the experimental site
Research plot soil types and nutrient composition
Organic matter (%)
Sandy clay silt
Tree population and sampling
The research site was planted in 1997 with 1 + 1 stock. The site layout was planted in a randomised block design. The initial aim of the research plot was to investigate the growth of different tree species within a widely spaced planting design. Beside wild cherry, other broadleaf species such as European ash (Fraxinus excelsior L.), pedunculate oak (Q. robur L.), sycamore (Acer pseudoplatanus L.), small-leaved lime (Tilia cordata Mill.) and European hornbeam are grown in the mixture. The initial spacing of all trees on the research plot was 1.5 m × 7.5 m and 1.5 m × 15.0 m. Trees were sampled during regular thinning treatments at the end of the 2012 (n = 20) and 2013 (n = 19) growing seasons, the total number of sampled trees (n = 39) is in the range of that suggested by Roxburgh et al. (2015). Since the trees were felled during the period of winter dormancy, biomass refers to leafless above ground biomass. The sample trees are representative of all social classes (Kraft 1884) ranging from dominant and co-dominant individuals to dominated and suppressed individuals. The dominated and suppressed trees were additionally sampled to augment the allometric curve at the lower end, as is common practice to cover the entire spread of tree sizes on the plot (Cifuentes Jara et al. 2015). Up to the point of sampling no management operations such as thinning or pruning were applied to the sampled trees.
d 1.3m (cm)
Tree length (m)
To analyse the wood/bark- proportion of the different DC, bark thickness was measured in four radii at every ascending metre (n = 175) for the 20 trees sampled in 2012, these data were used to calculate a regression line between stem diameter and double bark thickness (Pryor 1988; Dagnelie et al. 1999; Toader 2009). Furthermore, the samples taken for the fresh/dry- weight conversion factor were debarked and both compartments (wood and bark), were separately weighed (using the scale described above). These samples (n = 99) were also obtained from the 20 sample trees from 2012 intended to separately attain biomass data for wood and bark. After taking the fresh weight, all stem discs and branch samples including wood and bark were oven-dried at 105 °C until reaching a constant weight.
Analysis for the determination of carbon (C), nitrogen (N), phosphorus (P), potassium (K), magnesium (Mg) and calcium (Ca) content was carried out according to standardised procedure (VDLUFA 1976, 2011). Wood and bark analyses were based on homogenised samples taken from three sampled trees. Bark was removed from the sample pieces prior to analysis. Wood and bark was analysed separately. All samples used for the nutrient analyses were air-dried to avoid the excessive loss of volatile compounds which may occur under forced drying conditions. The results presented reflect a mean of these values. In order to quantify the biomass production of single branches, the branch diameter at origin (bd origin) and the fresh biomass was recorded for branches with a diameter of approximately 3 cm upwards (3 cm represents the diameter at which a branch would be pruned (Springmann et al. 2011)). Data were sourced in 2012 (n = 200) and in 2013 (n = 130). For the calculation of dry biomass, the same method was used as described above.
Calculated dry weights (kg) for stem and branch sections by diameter class (DC)
n = 276
n = 99
Bark of wild cherry
Modelling the biomass of widely spaced wild cherry
Model parameters derived from regression analysis and statistical parameters
Number of observations
Correction factor (CF)
r 2 adj
Whole tree biomass
d 1.3m a
d 1.3m a
d 1.3m a
Individual branch biomass
Total bark biomass
d 1.3m a
Stem bark biomass
d 1.3m a
Branch bark biomass
d 1.3m a
Individual branch bark biomass
Bark percentage by sectional diameter
Bark thickness by diameter class
The visualisation of the whole tree regression model (Fig. 5) clearly shows the good fit of the allometric model, spread of points across the range of diameter measurements and between the two sample years. Included in Table 4 are the parameters for the biomass equations for total tree, stem and branch. All parameter estimates were found to have a p-value of less than 0.001 providing high significance. The displayed r 2 adj of more than 0.96, in combination with low values for the normalised root mean square error (NRMSE) show an exceptionally high model fit. As d 1.3m of the sample trees increased, the number of observations decreased, thus providing a subsequent decrease in predictive precision. An increase in variance on a logarithmic scale with increasing diameter can be attributed to the deviation of residuals around the line of best fit (Roxburgh et al. 2015). Working also with juvenile wild cherries (n = 18) with a maximum age of 23 years Alberti et al. (2006) published one allometric equation (following the form of Eq. 1; coefficients: a = 0.12, b = 2.33) for whole tree biomass based on data from Italy. In comparison, such results show corresponding biomass values for small d 1.3m but suggest an increasing divergence with increasing d 1.3m. The work by Alberti et al. (2006) represents the only previous biomass equation for wild cherry, other published equations have been constructed for the estimation of volume. Dagnelie et al. (1999) sampled 334 trees in Belgium with an age of 15 to 80 years in order to predict stem volume. Their resulting model utilised circumference at different heights and total tree height as predictor variables in the form of a polynomial function. Furthermore, in recent research Hackenberg et al. (2014) utilised terrestrial laser scanning methodology for the analysis and prediction of total volume and merchantable timber volume (diameter over 7 cm) of wild cherry on the same study site, allometric models were constructed as a method of ground truthing the point cloud data. Individual branch models (Models 4 and 8) using the bd origin as a predictor variable reveal that branches with a diameter of 3 cm present a total dry biomass of 0.7 kg (including 0.2 kg of dry bark biomass), likewise, branches with a diameter of 10 cm can be suggested to contain 21.4 kg of dry biomass (4.7 kg dry bark).
We consider that the ten biomass models introduced in Table 4 create a solid foundation for future modelling activities where the biomass and yield of wild cherry needs to be quantified. The presented models are both site and age specific, predictions gained through the application of these models should be treated cautiously when applied to other stand conditions.
Based on the detailed results of biomass per DC (Table 3) and the related bark proportion by sectional diameter (see also Fig. 3) the share of bark and wood can be determined. Using the models given in Table 4 we are able to precisely determine the proportion of bark and wood tissues within specified DC and at a whole tree level, between different tree compartments. Whole tree predictions can be made per d 1.3m class to demonstrate the change in biomass distribution between tree parts with increasing diameter. The presented models can be used as a basis for further biomass and nutrient assessments on a single tree basis as well as at the plot scale. With the exception of models 4 and 8–10, all presented models are based on d 1.3m as independent predictor variable, as such a variable that is quick and easy to measure in the field. Models 4 and 8 was constructed in order to estimate the amount of biomass and nutrients that are removed by pruning operations and is therefore based on the bd origin a measurement that can be derived from pruned branches on the ground derived during normal pruning treatments. The presented models utilise trees of a similar age (±1 year) but of varied size (diameter and height), assumptions should not overlook the reasons for differences in tree size. Such differences observed within this study may amount to differences in competition (both inter- and intra-specific), plant quality (genetics) and variation in microsite conditions.
Carbon and nutrient content
Carbon and nutrient content within different tree compartments
C (% Dry Mass)
Wood from branches
Bark from branches
Wood from stem
Bark from stem
On a general level, larger proportions of nutrients are contained within the bark of smaller trees. Nutrient content becomes proportionally more constant with increased d 1.3m while the nutrient content held within the branch fraction increases. Growth of a plant is generally proportional to the amount of available nitrogen (Wang et al. 2000). The largest proportion of nitrogen, a nutrient which is essential for photosynthesis, rapid growth and biomass production is stored within stem bark tissues in early developmental stages. Alongside an increased d 1.3m the share of nitrogen stored in branch bark increases, leading to a share of around 35 % at a d 1.3m of 25 cm, a value that is only slightly less than the 40 % contributed by stem wood and stem bark together. Phosphorus, used within energy transfer processes remains in small concentrations within whole tree biomass (remaining below 0.5 % of total tree biomass at all analysed diameters). Nevertheless, a clear trend towards an increase of the share of the branch bark can be observed. It should be noted that soil pH is higher than that suggested for optimal phosphorus solubility and therefore availability to the plant is reduced. The final of the three major nutrients, potassium, shows a comparable distribution within the tree fractions to phosphorus although at four times the magnitude. Magnesium displays comparable absolute values to that given by phosphorus, however, a greater share of the element is held within the wood tissues. Calcium concentrations in the bark fraction were observed to be up to 16 times greater than that in corresponding wood tissues. For a given d 1.3m of 25 cm bark tissues including stem and branch fractions contribute approximately 70 % of calcium of the total tree biomass. Calcium has previously been noted to be present in high concentrations within bark tissues due to its function within cell wall lignification processes (André and Ponette 2003), particularly within broadleaved trees (Jacobsen et al. 2003). Calcium is freely available within the soils of this study site and is not limited by external factors.
The allocation of carbon within a tree may vary dependent on species, age, and influencing environmental factors. Wang et al. (2000) showed that at the expense of root growth more carbon was allocated to the leaves/ needles in response to increased shading (Wang et al. 2000). Likewise, a reduction in belowground competition for primary nutrients such as N and P may increase the proportion of carbon partitioning above ground (Forrester et al. 2006) thus altering the shoot:root ratio of individual trees. Pretzsch (2010) describes differences in nutrient accumulation between Norway spruce (Picea abies [L.] H. Karst.) and European beech, the former as it senesces, loses accumulated minerals through needle and branch wood, the latter continues to accumulate biomass as it matures, a strategy that may be employed by the broadleaved wild cherry. The nutrient cycling potential of leaf fall is an important aspect to be considered within agroforestry systems. Annual and intra-annual leaf fall may contribute significantly to the soil nutrient status of the site. However, the assessment of leaf biomass as for example carried out by Axelsson et al. (1972), Clough & Scott (1989) and Alberti et al. (2005) and consequently nutrient cycling through leaf fall is beyond the scope of this paper. Full shade cast by trees within an AFS during the vegetation period may suggest that the use of wild cherry may suit the co-production of winter crops, further research is needed towards the shade cast potential of such a system.
In order to obtain high quality timber, artificial pruning is a necessary silvicultural procedure (Otter 1954; Balandier and Dupraz 1999; Springmann et al. 2011). The sample trees employed within this study were unpruned in order to quantify the biomass production potential between tree components. Utilising the derived model for individual branch biomass (Model 4) it can be suggested that approximately 41.9 kg of branch biomass is pruned per tree during one rotation of 60–80 years (three pruning treatments). This estimation assumes a standard branch diameter of 3 cm, that there are five branches per whorl and at each pruning treatment five whorls are pruned from each tree (in years 10, 15 and 20). This calculation suggests that removed branches, which are often left on-site, may liberate 4.2 kg of calcium, 1.7 kg of nitrogen and 1.0 kg, of potassium (Table 5), also 300 g and 200 g of phosphorus and magnesium respectively. At the stand level where 50 to 80 mature crop trees per hectare (Balandier and Dupraz 1999) can be cultivated, this therefore constitutes a large potential recycling of nutrient when left on site. It is likely that the application of pruning treatments needed for the production of high value timber would artificially reduce the proportion of branches as well as reducing the overall diameter growth of the stem (Springmann et al. 2011) and overall biomass production as a consequence of increasing wood quality. Conversely, it can be suggested that thinning operations might increase the rate of growth of released trees. Hence, Fig. 6 could be modified to reflect an increased stem wood proportion. Nevertheless, for the long-term, a high value timber goal must be reached, production of a high value log requires large d 1.3m of above 40 cm (Thies et al. 2009) this undoubtedly benefits the long term on-site carbon storage ultimately followed by long term storage within ex-situ products.
The presented study details the amount of biomass within different compartments of wild cherry accounting not only stem and branches, but also the wood and bark content of whole trees. It was demonstrated that larger unpruned trees show a higher proportion of branch biomass, but due to increased stem diameter this counteracts the proportional increase in bark biomass within the whole tree. As a result, a reduction of whole tree bark biomass is observed. Using the derived models the carbon and the nutrient content of whole trees can be calculated. The results show that a single tree at an age of 20 years can store a total amount of 85 kg of carbon within the aboveground biomass portion, an amount that will increase as the tree matures. This strongly supports the idea of the inclusion of wild cherry within AFS as an option for carbon sequestration e.g., in combination with winter crops. Analysed nutrient content supports previous research outlining nutrient concentration between tree parts. For minimised nutrient export, branches and bark fraction should not be removed at the time of thinning or at eventual harvest whenever possible.
The authors would like to thank, Cristina Prado Rubio, Benjamin Goebel, Greta Ehrhart, Brian Shaw, George Ciubotaru and Nicoleta Cristea for assistance with data collection and processing. The authors would also like to thank Felix Baab for his valuable support. This research was supported by the EU FP7 project StarTree (Grant Agreement Number 311919), the Federal Ministry of Education and Research (BMBF) within the AGROCOP project (support code 033L051B) and the German Federal Ministry of Food and Agriculture (BMEL) within the project Agro-Wertholz (support code 22031112).
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