EDP Sciences logo
Submit your paper
Search
Menu
All issues Volume 8 (2023) Renew. Energy Environ. Sustain., 8 (2023) 3 Full HTML
Open Access
Issue
Renew. Energy Environ. Sustain.
Volume 8, 2023
Article Number 3
Number of page(s) 12
DOI https://doi.org/10.1051/rees/2023001
Published online 26 April 2023

© A. Adu-Poku et al., Published by EDP Sciences, 2023

Licence Creative CommonsThis is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

1 Introduction

The carbon cycle has recently become an important global issue due to climate change, and plants serve a major function in carbon storage. This has led to an awareness of estimating the current carbon stocks within the global forests and the stock changes. Forest covers approximately one-third of the terrestrial ecosystem and serves as the most important carbon sink [1]. Forests store vast amounts of carbon, which is transformed into carbon dioxide and released into the atmosphere when destroyed; hence, a change in forest biomass is considered a vital component of climate change [2]. Global warming and climate change are influenced by carbon dioxide (CO2) and other greenhouse gases, including methane, sulfate fluoride, and water vapour [3]. Anthropogenic activities, like burning fossil fuels for energy and deforestation, have impacted this phenomenon [4,5].

Biomass fuels, on the other hand, reduce reliance on fossil fuels and improve energy sustainability. In developing countries, tree biomass is primarily used as a source of energy for cooking, heating, and electricity generation. Monitoring and reporting forest biomass inventories for bioenergy applications is necessary to ensure sustainable usage of biomass resources. As a result, much attention has been given to biomass estimation in recent years because vegetative biomass grows through photosynthesis [6].

Although estimates of forests' carbon sequestration potential and stocks play an essential role in formulating strategies for reducing CO2 emissions, most studies of woody species neglect bamboo in assessing the capability of trees to sequester carbon. This neglect had been attributed to bamboo being an invasive giant grass growing sporadically on degraded lands, with a few other studies arguing that bamboo is a net carbon emitter [7–9]. There has since been enough data implying that bamboo land covers can absorb enormous amounts of carbon, helping to alleviate the consequences of climate change, due to their broad geographic spread (persists in tropical and subtropical areas between latitude 46 °N and 47 °S) compared to other plant species and their fast growth rates, which favours the storage of organic carbon through photosynthesis [7,10]. Bamboo was officially considered in the same bracket as trees for Clean Development Mechanism (CDM) projects in 2008 during the 39 th meeting of the United Nations Framework Convention on Climate Change (UNFCCC) CDM executive board [11].

Limited information about Ghana's forest biomass makes it essential to accurately inventory of the biomass establish an and carbon stocks of important plant species such as bamboo. Ghana has an estimated 400,000 hectares of bamboo, representing 4.28% of the total forest vegetation, which makes it the leading country in West Africa in terms of bamboo sector development and second to only Ethiopia on the continent as a whole [12]. Bambusa vulgaris forms part of the eight (8) indigenous bamboo species and is the most widespread in Ghana, accounting for about 90 to 95% of the bamboo resource [13,14].

Previous studies on carbon assessment for trees in Ghana have only focused on the carbon stored in aboveground biomass, ignoring other carbon pools such as litter and woody debris [6,15], which can contain significant amounts of carbon (around 5% and 8% of bamboo ecosystem carbon, respectively [16]). Comprehensive estimates of forest carbon stocks are required to improve the accuracy and reduce uncertainties in carbon and climate change forecasts at regional and global levels. Therefore, this study aims to estimate the biomass carbon storage of Bambusa vulgaris natural stands in the Bobiri Forest Reserve, including aboveground and belowground biomass, coarse woody debris, and litter. To the best of our knowledge from existing literature, no prior research has been conducted on assessing the amount of carbon stored in the biomass of Bambusa vulgaris, taking into account litter and coarse woody debris [15,17–23]. This study will be the first of its kind, and the outcomes of this research will be instrumental in quantifying the quantity of CO2 that Bambusa vulgaris can capture from the atmosphere. These findings will also aid in developing future carbon policies and climate change mitigation strategies.

2 Materials and methods

2.1 Study site

The study was carried out in the Bobiri Forest Reserve (BFR) and Butterfly Sanctuary, located in the Moist Semi-deciduous South-East sub-type ecological zone of Ghana, and covers an area of 5445 ha [24]. BFR boasts of a one-and-half-hectare arboretum and a hectare Bambusetum with fifteen provenances, besides several other clumps of Bambusa vulgaris at locations scattered throughout the reserve. BFR is interspersed with rivers such as Abobiri, Wurapon, Anwiasu, Abofre and Juabenika [24]. The Bobiri forest, according to the Forestry Commission of Ghana, is divided into 73 compartments categorised into Production and Conservation/Research zones. To assess the carbon sequestration potential of Bambusa vulgaris, the study was conducted in four (4) randomly selected compartments representing the four major bamboo zones in the BFR, which are the production (18A), conservation (7), reserve border (70), and the bambusetum (69) (Fig. 1). This grouping of the bamboo was done with the help of local forest rangers familiar with the BFR terrain and knowledgeable of the bamboo stands in the forest reserve.

thumbnail Fig. 1

Map of Bobiri forest reserve showing the compartments understudy.

2.2 Data collection

2.2.1 Aboveground measurements

Five (5) bamboo clumps were randomly sampled for allometric measurements in each compartment. The dendrometric variables were clump basal dimensions, culm diameter, culm age, and the number of culms per clump. Tapes were used to measure the basal dimensions for each clump in meters. Culms were measured for diameter at breast height (1.3 m) using diameter tapes and Vernier callipers. Culms were tagged and categorised into four age classes (1, 2, 3, and ≥4). Culm ages were recorded by observing the morphological features as culms appear light green with sheaths around the base for age one and below, green with sub-branches for two years, dark green with white surfaces due to the presence of lichen for three years, and dense lichen with yellowish colouration for culms that are four years and above [25].

2.2.2 Coarse woody debris (downed dead bamboo) measurements

For each compartment, a 100 × 1-meter transect was established to measure downed dead bamboo culms in various stages of decomposition. Dead culms that fell within the transect were measured for length, diameters at both ends (inner and outer for samples), and the stage of decomposition. The stages of decomposition of dead culms were categorised as sound (the blunt side of a machete bounces back when struck against a culm), intermediate (blunt side of a machete sticks inside a bamboo culm when struck against it), and rotten (blunt side of a machete crumples bamboo culm when struck against it) [26]. Ten culm discs of length 10 cm were sampled in each compartment for the various decomposition categories for wood density determination in the laboratory.

2.2.3 Litter measurements

Sample plots were set up to collect data on ground litter within the bamboo sites covered by the canopy. The size of each sample plot was 30 × 30 m. Within the sample plots, five 50 × 50 centimetres (0.25 m2) quadrats were laid close to the four corners and one around the centre of the plot to assess ground litter. The litter in each quadrat was collected, and the total fresh mass was immediately determined with an electronic weighing balance. Subsamples were taken, and fresh mass was measured on-site.

2.3 Laboratory analyses

Laboratory analysis was carried out to determine the bamboo components' dry-to-fresh mass ratios, volume, density, and carbon content. The dry mass of the samples was determined by oven-drying the leaf litter at 60 °C and the dead culm samples at 105 °C for 48 h to ensure that the samples were bone-dried [27]. The volume of the culm disk samples was determined by the water displacement method. To determine the mass and carbon stock of dead bamboo culms in the transects, average densities and carbon contents were estimated and analysed in the laboratory. Vernier callipers were used to re-measure the dimensions of the culm disc samples to check for field errors due to cutting. The culm disk dry mass and volume were used to determine wood density, whereas the dry mass of the culm disks and leaf litter were used for the dry-to-fresh mass ratio determination. The dry-to-fresh mass and culm disk density analyses were conducted at the Biodiversity, Conservation and Ecosystem Services Division (BCESD) laboratory of the Forestry Research Institute of Ghana (FORIG), while the carbon content analysis of the various plant components was done in the Soil Research Institute (SRI) laboratory in Kumasi.

3 Calculation

3.1 Aboveground mass (AGM)

The aboveground mass of bamboo was estimated using the most efficient model from the allometric models [28] for Bambusa vulgaris in the moist-semi deciduous ecozone in Ghana, where the Bobiri forest lies. The allometric equation is presented as;

(1)

where AGM = culm aboveground mass, ρ = bamboo culm density, DBH = culm diameter at breast height.

The predictive variables of the allometric equation are diameter at breast height (DBH) and density (ρ).

3.2 Belowground biomass (BGM)

The below-to-aboveground ratio or root-shoot ratio of 0.20, as prescribed by the Intergovernmental Panel on Climate Change [29,30], was used to estimate the belowground biomass and carbon stock of bamboo for the study since there is no belowground biomass allometric equation for B. vulgaris from literature.

(2)

3.3 Bamboo distribution density

The number of clumps and culms was calculated using equations (3) and (4) [31].

(3)

(4)

where Nclumpsha−1 = number of clumps per hectare, Nclump = number of clumps in the study area, Nculmsha−1 = number of culms per hectare, Nculm = average number of culms per clump.

3.4 Biomass carbon stock estimation

The litter biomass carbon stock (Lb, Mg C ha−1) per plot was estimated by considering the number of quadrats, n, area of quadrat (A, m2), sample fresh (Ssw, g) and dry (Sdw, g) mass, total fresh mass (Tfw, kg) in the quadrat and carbon fraction or content (CFr) as:

(5)

The coarse woody debris (CWD) of the culm was estimated based on the diameter and length measurements as well as the wood density determination. The diameter and length measurements were used to calculate the inner and outer volumes of the dead culm. Together with the wood density of the decomposition class (ρc), the mass of the CWD was estimated. The biomass carbon of the dead culm (DCb, Mg C ha−1) was thus calculated as;

(6)

where n is the number of samples, d1 and d2 (cm) are the diameters at both ends of the CWD having taken cognisance of the hole in the culm, L (cm) is length, A (m2) is the area of the plot, and CFr is the carbon fraction.

Above or below-ground biomass carbon stock per stand (BBC, Mg C ha−1) was estimated from the sum of the aboveground mass of the culm (Eq. (1)) or the belowground (Eq. (2)) as:

(7)

where GM (kg) is the above or below-ground mass of a culm, A (m2) is the area of the bamboo stand, and CFr is carbon content.

The total carbon stock comprising the woody components of a bamboo ecosystem was estimated by adding the carbon stocks in the various components of the bamboo ecosystem, namely the aboveground bamboo biomass (ABBc), belowground bamboo biomass (BBBC), litter biomass (LBC) and dead culm (DCC). Biomass carbon dioxide equivalent is the long-term storage of CO2 and other carbon forms in the bamboo ecosystem's biomass components [31]. This was calculated by multiplying the carbon stock by the conversion factor from atomic C to molecular CO2.

(8)

where 44 and 12 are the relative molecular masses of carbon dioxide and carbon, respectively.

3.5 Data analysis

All computations were done using statistical spreadsheets. Tables for analysis were drawn using pivot tables. The data from forest measurements and laboratory analysis were analysed using descriptive statistics, ANOVA, and covariance of the statistical toolpack for data analysis.

4 Results and discussion

4.1 Aboveground biomass and carbon

A summary of bamboo mass results, including bamboo stand areas for the four zones, the number of clumps in stands, mean (± SD) of culm diameter, age, and culm density, have been presented in Table 1. In total, 2114 culms were sampled for allometric measurements.

Uncertainty analysis was performed by calculating the mean, standard deviation, and confidence interval. The mean is the average value of the dataset, while the standard deviation measures the spread of the data around the mean. The confidence interval is a range of values likely to contain the true population mean with a certain level of confidence.

Aboveground mass uncertainty analysis was conducted by considering two main sources of uncertainty: measurement error and sampling error. Measurement error refers to the error that arises from the measurement process itself. For example, suppose the culm diameter (DBH) is measured using a tape that is not accurate. In that case, the diameter value will be incorrect, and this will introduce measurement error into the dataset. Sampling error, on the other hand, refers to the error that arises from taking a sample from a larger population. It was impossible to measure the properties of every single item in the population, so we took a sample to make inferences about the entire population. However, the sample was not perfectly representative of the population, and this introduces sampling error into the dataset.

Table 1

Summary of dendrometric parameters in the selected compartments.

4.1.1 B. vulgaris distribution density

The highest mass of B. vulgaris was recorded in compartment 70, representing the forest reserve's border areas. Since borders are naturally demarcated with drainage systems that are natural habitats of bamboo, the border areas in the forest reserve were the most bamboo-populated zone recording a clump density of 145 ± 58 culms per clump and an aboveground mass of 103.32 Mg. This was followed by compartment 7 (128 ± 38 culms per clump), compartment 18 (86 ± 42 culms per clump), and compartment 69 (65 ± 26 culms per clump), respectively (Tab. 1). The total stand density of B. vulgaris in the selected zones in the Bobiri forest was 3710 culms ha−1 (35 clumps ha−1) (Tab. 2).

Bamboo harvesting for commercial reasons except for research purposes [32] is prohibited in all the forest reserves in Ghana. Therefore, almost all the bamboo stands in the forest are primary stands except for the bamboo species in the bambusetum, which CSIR-FORIG intentionally established for bamboo research. The forest zone with the least bamboo density of 65 culms per clump was recorded in compartment 69, which is the closest natural stand of B. vulgaris in the Bambusetum. The bamboo clumps in compartment 69 had notable truncated shoots showing evidence of destructive sampling of culms from past research. Comparatively, the culm density of 3710 culms ha−1 for this study is higher than culm densities of 2296 and 2933 culms ha−1 reported for B. vulgaris from Cameroon [17] and Bangladesh [18] respectively, but lower than 4800 culms ha−1 reported in North-East India [33]. The variations in culm densities could emanate from the different silvicultural management practices in other countries.

Table 2

Aboveground bamboo biomass and carbon stock of B. vulgaris in the study area.

4.1.2 Aboveground biomass

The study area's estimated aboveground mass of B. vulgaris was 194.51 Mg. The highest mass was recorded in compartments 70, 7, 18, and 69, with 103.32 Mg, 45.24 Mg, 26.59 Mg, and 19.36 Mg, respectively, in decreasing order (Tab. 1). The measured overall mass was extrapolated per hectare to give aboveground biomass of 90.36 Mg ha−1, slightly higher than the biomass storage of 81.74 Mg ha−1 reported in North-East India [34]. The value recorded in this study somewhat varies from the values of 97.8 Mg ha−1, and 115 Mg ha−1 recorded for B. vulgaris in a degraded tropical forest in Bangladesh [18] and a moist semi-deciduous ecological zone in Ghana [15]. However, [15] conducted their study using 15 culms in a single stand, while this study used 2,114 culms. It is important to note that accuracy of biomass estimation improves as the number of field samples increases [35]. The aboveground biomass recorded in this study greatly differs from aboveground biomass values of 53.42 Mg ha−1 [36] and 63.02 t ha−1 [17] recorded for B. vulgaris in a school research farm in India and a closed tropical broad-leaved rainforest in Cameroon, respectively. The wide variations may be attributed to the ecological zone, the method of inventory, and the allometric models used for biomass estimation.

4.1.3 Aboveground mass uncertainty analysis

The aboveground mass uncertainty analysis was performed by determining the descriptive statistics of the culm diameter and mass datasets. The results are presented in Table 3.

The sampling error was estimated by calculating the standard error of the mean (SEM). The SEM for diameter indicates that the sample mean diameter is expected to be within 0.167 units of the true population mean diameter, with 95% confidence. The SEM for mass suggests that the mean sample mass is expected to be within 1.126 units of the true population mean mass, with 95% confidence. Finally, the uncertainty was visualised in the data using error bars on a scatter plot in Figure 2. The error bars were calculated using the SEM and measurement error estimates for each point in the dataset.

The error bars show the range of values each point in the dataset could take, given the uncertainty in the measurements and sampling. Although there are a few outliers in the scatter plot, the correlation between diameter and mass is 0.961, indicating a strong positive linear relationship between the two variables. The histogram of the mass suggests that the distribution is approximately normal (Fig. 3). However, the Shapiro-Wilk test suggests that the aboveground mass is not normally distributed at a confidence level of 0.05.

Table 3

Descriptive statistics of culm diameter and mass.

thumbnail Fig. 2

Scatter plot of aboveground culm mass and diameter.
thumbnail Fig. 3

Aboveground mass normal distribution curve with histogram.

4.1.4 Aboveground carbon stock (ACS)

The carbon content of the various culm components was found to be 49.23% for the culm base, 48.48% for the middle, 48.22% for the top, 47.75% for branches, and 45.78 % for foliage, thus showing a decreasing tendency from the bottom to the top of the culm. Therefore, the weighted average of culm carbon content was 47.42% (Tab. 2), which corresponds to the bamboo carbon content of 47%, as reported by [31], in conformity with IPCC recommendations for tropical forests [31]. The bamboo carbon content was also comparable to the carbon content of 45.38% recorded for the same species in the Bobiri Forest Reserve [15].

4.1.5 Comparison of ACS across various species of bamboo

The carbon stock of B. vulgaris found in the study (42.85 ± 9.32 Mg C ha−1) showed a wide variation with carbon stock values reported for the same species in Nigeria, 138.7 Mg C ha−1 [23] and Cameroon, 29.62 t C ha−1 [17]. However, it compares favourably with values of 38.42 Mg C ha−1 in India [33], 50.44 Mg C ha−1 in Bangladesh [18], and 50.76 Mg C ha−1 in Ghana [15] for the same species. Table 3 thoroughly compares numerous bamboo species from diverse climatic and geographic regions. In this study, B. vulgaris was found to have biomass-carbon storage in a manner nearly equivalent to that of other tropical and subtropical bamboo species. For example, B. vulgaris, which is a sympodial bamboo, was found to have comparable biomass and carbon storage values with monopodial bamboo species, P. heterocycla [37] and P. pubescens [38], from Taiwan and China (Tab. 4).

Table 4

Comparison of biomass and carbon stock of various bamboo species worldwide.

4.1.6 Comparison of ACS with other land-use systems in Ghana

The primary determinants of carbon emissions from tropical forests are land-use change rates and carbon stock changes due to degradation and deforestation [2]. Carbon stock changes in other carbon pools largely depend on the vegetation cover, which is the aboveground biomass [42]; hence, many carbon studies report on the aboveground carbon stock for which inter-plant comparisons can be made. The biomass carbon stock of B. vulgaris from the study was compared with other land-use systems in varied ecological zones in Ghana (Tab. 5). Natural forests, teak plantations, fallow land, and cultivated land were four diverse land-use systems considered across Ghana's various ecological zones. It was found that bamboo stands (from the study) accumulated more biomass carbon than both fallow and cultivated land usage across the ecological zones in Ghana. For example, the carbon stock value of 42.85 ± 9.32 Mg C ha−1 for bamboo in this study is higher than cocoa agroforestry, 22.9 ± 2.60 Mg C ha− 1 [43]. The bamboo stands in the study also demonstrated higher biomass carbon accumulation than teak stands in the Savannah and Dry Semi-Deciduous Forest (DSDF) and natural forests in the Savannah zone. This indicates that Bambusa vulgaris's fast growth and rapid carbon accumulation can play a vital role in the “Green Ghana Initiative” launched by the Ministry of Lands and Natural Resources in 2021 to plant five million trees annually in the fight against climate change and also contribute to the national development agenda. This is also crucial in targeting plant species for carbon farming in Ghana's attempt to expedite the transition to a low-carbon economy to promote a sustainable environment as well as other ecological benefits.

Table 5

Comparison with other land-use systems in varied ecological zones in Ghana.

4.2 Belowground biomass and carbon stock

Below to above-ground biomass ratio of 20% was used per the Intergovernmental Panel on Climate Change (IPCC) recommendation to estimate the belowground biomass of B. vulgaris [29,30] The total belowground or rhizome biomass and carbon stocks were 18.07 Mg ha−1 and 8.57 Mg C ha−1, respectively. The value of 51.42 Mg C ha−1 estimated for the vegetative component consisting of the aboveground biomass carbon stock and belowground biomass carbon stock (ABBc + BBBc) in this study favourably corresponds to plausible ranges of 24 to 192 Mg C ha−1 estimated for ABBc + BBBc in 70 bamboo species from 184 case studies globally [46].

4.3 Coarse woody debris (downed dead bamboo) carbon stock

Three hundred and eleven dead bamboo culms were measured within the transects across all four compartments, with 139, 72, and 100 culms representing sound, intermediate, and rotten categories, respectively. Compartment 70 recorded the highest dead culm (DC) carbon stock of 4.71 Mg C ha−1, almost twice the DC carbon stock of the other compartments (Tab. 6). This is probably due to the relationship between DC and carbon stock [47]. Overall mean carbon stock across all four compartments was estimated to be 3.02 ± 1.19 Mg C ha−1 (Tab. 5). The sound category recorded the highest carbon stock value of 6.26 Mg C ha−1, roughly double the carbon stocks in the other categories. Densities estimated based on the decomposition category showed a consistent decline trend from sound, intermediate, to rotten, respectively. However, the carbon content showed inverse results to density, with the “rotten” category recording the maximum carbon content of 48.67% organic carbon, followed by intermediate and sound with 48.58% and 48.28%, respectively. This trend of variations could be attributed to the loss of organic matter and carbon build-up as DC transitions from sound to rotten [47].

Table 6

Summary of results for coarse woody debris carbon.

4.4 Litter pool carbon

Litter biomass (LB) carbon stock in the study area was estimated as 4.25 ± 1.3 Mg C ha−1 (Tab. 7). The order of decrease in carbon stocks observed in the forest zones was the bambusetum (5.30 ± 1.6), followed closely by the production zone (4.89 ± 1.0), conservation (4.06 ± 0.9), and border zone (3.45 ± 0.9). An aggregated carbon content of 38.66% organic carbon was recorded for litter from laboratory analysis across all compartments.

Table 7

Summary of litter mass and carbon analysis.

4.5 Biomass carbon stock and CO2 sequestration

Bamboo is known to be an effective method for prolonged carbon storage, and recent literature reviews have investigated its potential to reduce carbon dioxide in the atmosphere [48]. One study suggests that assembled bamboo components have a 37% chance of reducing CO2 from the atmosphere, and one tonne of laminated bamboo lumber stores 140 kg more carbon than timber [48]. The total biomass carbon stock of Bambusa vulgaris in the Bobiri Forest Reserve was determined to be 58.69 Mg C ha−1. This value compares well with the carbon stock range of 40–75 Mg C ha−1 for trees in general [49]. The aboveground carbon pool recorded the highest wood carbon stock, with 42.85 Mg C ha−1 representing 73% of the total biomass carbon stock. Carbon stock values of 8.57 Mg C ha−1, 3.02 Mg C ha−1, and 4.25 Mg C ha−1 were recorded for belowground, coarse wood, and litter carbon pools, respectively. The carbon dioxide equivalent of B. vulgaris in the various forest zones was estimated separately to understand better the carbon sequestration potential of bamboo stands in a tropical forest like Bobiri (Fig. 4).

The research findings showed that bamboo plantations distributed along the edge (border) of the forest reserve sequester more carbon dioxide (238.48 Mg CO2e ha−1) from the atmosphere than bamboo stands located deep inside the production (218.77 Mg CO2e ha−1) and conservation zones (217.63 Mg CO2e ha−1). The border zone recorded the highest aboveground carbon stock, an indication of the potential of this zone to sequester higher CO2. Therefore, it was not surprising that the highest CO2 equivalent was recorded in this zone. Furthermore, this variation was ascribed to the “edge effect”, where there is a greater diversity of life in boundary or overlapping regions of two ecosystems [50]. This effect may also account for the high population density of B. vulgaris in the border zone. The natural B. vulgaris stand closest to the bambusetum recorded the least carbon dioxide sequestration potential of 147.90 Mg CO2e ha−1, probably due to anthropogenic disturbance due to its ease of accessibility.

The total CO2 equivalent for the study area was 215.39 Mg CO2e ha−1. Regarding the percentage of CO2 equivalent for the biomass carbon pools, the contribution was 73, 15, 5, and 7% for aboveground, belowground, dead culm, and litter, respectively.

thumbnail Fig. 4

Biomass CO2 sequestration of B. vulgaris in the study compartments.

5 Conclusions and future directions in the field

This study comprehensively assessed the biomass and carbon stock for Bambusa vulgaris, a fast-growing bamboo species commonly found in Ghana. The study was carried out to contribute to understanding carbon sequestration potential and the role of bamboo in mitigating climate change.

The results showed that the aboveground biomass and carbon stock of B. vulgaris were 5-fold that of belowground, with a value of 90.36 ± 19.65 Mg ha−1 and 42.85 ± 9.32 Mg C ha−1, respectively. The aboveground carbon stock was 73% of the total biomass carbon stock, indicating the importance of the aboveground components in carbon sequestration. Carbon stocks for belowground, coarse wood and litter in B. Vulgaris stands were also found to be 8.57, 3.02, and 4.25 Mg C ha−1, respectively. These carbon pools are often overlooked in carbon accounting studies, but their inclusion in forest carbon estimates is crucial for accurately assessing the plant carbon cycle. In addition, the study found that the carbon dioxide sequestration potential of B. vulgaris was significantly high compared to trees in general, with a value of 215.39 Mg CO2e ha−1.

The study's findings demonstrate that bamboo has considerable potential to contribute to Ghana's transition to a low-carbon economy through carbon stock monitoring, reporting and policy development. Also, it could be a valuable resource for climate change mitigation strategies, and future research should investigate the potential of other bamboo species in this regard. Moreover, the inclusion of relevant carbon pools, including coarse wood and litter, in forest carbon estimates should be encouraged to provide a comprehensive understanding of the plant carbon cycle.

Data availability

The raw and processed data required to reproduce these findings are available to download from https://data.mendeley.com/datasets/2mkx8ht3k8/1 [51].

Conflict of Interest

The authors declare that there is no conflict of interest.

Acknowledgments

Acknowledgments. This work was funded by the International Bamboo and Rattan (INBAR) with support from the KNUST Engineering Education Project (KEEP). The authors acknowledge the Forestry Research Institute of Ghana (FORIG) staff for their enormous support in the data collection and laboratory assistance.

References

  1. B. Waring, M. Neumann, I.C. Prentice, M. Adams, P. Smith, M. Siegert, Forests and decarbonization − roles of natural and planted forests, Front. Forests Glob. Change. 3, 6 (2020) [Google Scholar]
  2. A. Androff, Trees Are Climate Change, Carbon Storage Heroes, USDA Forest Service, Forest Products Laboratory—Lab Notes. (2021) [Google Scholar]
  3. V. Pechanec, F. Stržínek, J. Purkyt, L. Šterbová, P. Cudlín, Carbon stock in forest aboveground biomass − Comparison based on Landsat data, Central Eur. Forestry J. 63, 126–132 (2017) [CrossRef] [Google Scholar]
  4. K. Abbass, M.Z. Qasim, H. Song, M. Murshed, H. Mahmood, I. Younis, A review of the global climate change impacts, adaptation, and sustainable mitigation measures, Environ. Sci. Pollut. Res. 29, 42539–42559 (2022) [CrossRef] [Google Scholar]
  5. IPCC, Global Warming of 1.5 °C: IPCC Special Report on Impacts of Global Warming of 1.5 °C above Pre-industrial Levels in Context of Strengthening Response to Climate Change, Sustainable Development, and Efforts to Eradicate Poverty (2022) [Google Scholar]
  6. R. Aabeyir, S. Adu-Bredu, W.A. Agyare, M.J.C. Weir, Allometric models for estimating aboveground biomass in the tropical woodlands of Ghana, West Africa, J. Forest Ecosyst. 7, 1–23 (2020) [Google Scholar]
  7. A.J. Nath, R. Lal, A.K. Das, Managing woody bamboos for carbon farming and carbon trading, Glob. Ecol. Conserv. 3, 654–663 (2015) [CrossRef] [Google Scholar]
  8. E.J. Zachariah, B. Sabulal, D.N.K. Nair, A.J. Johnson, C.S.P. Kumar, Carbon dioxide emission from bamboo culms, Plant Biol. 18, 400–405 (2016) [CrossRef] [Google Scholar]
  9. B. Wang, W.J. Wei, C.J. Liu, W.Z. You, X. Niu, R.Z. Man, Biomass and carbon stock in moso bamboo forests in subtropical China: characteristics and implications, J. Trop. Forest Sci. 25, 137–148 (2013) [Google Scholar]
  10. X. Song, G. Zhou, H. Jiang, S. Yu, J. Fu, W. Li, W. Wang, Z. Ma, C. Peng, Carbon sequestration by Chinese bamboo forests and their ecological benefits: assessment of potential, problems, and future challenges, Environ. Rev. 19, 418–428 (2011) [CrossRef] [Google Scholar]
  11. Y. Kühl, G. Henley, L. Yiping, The Climate Change Challenge and Bamboo: Mitigation and Adaptation − INBAR Working Paper (2013). Available at https://resource.inbar.int/upload/file/1489546834.pdf (accessed 31.12.2018) [Google Scholar]
  12. International Network for Bamboo and Rattan (INBAR), Bamboo for Africa: A strategic resource to drive the continent's Green Economy, Beijing (2015). Available at https://www.inbar.int/resources/inbar_publications/policy-synthesis-report-2-bamboo-for-africa-a-strategic-resource-to-drive-the-continents-green-economy/ [Google Scholar]
  13. E. Ebanyenle, A.A. Oteng-Amoako, Site differences in morphological and physical properties of Bambusa vulgaris grown in Ghana, Discov. Innov. 19, 222–225 (2007) [Google Scholar]
  14. T. Bahru, Y. Ding, A review on bamboo resource in the african region: a call for special focus and action, Int. J. Forestry Res. 2021, 23 (2021) [CrossRef] [Google Scholar]
  15. M. Amoah, F. Assan, P.K. Dadzie, Aboveground biomass, carbon storage and fuel values of Bambusa vulgaris, Oxynanteria abbyssinica Bobiri forest reserve of Ghana, J. Sustain. Forestry 39, 113–136 (2019) [Google Scholar]
  16. Y. Pan, R.A. Birdsey, J. Fang, R. Houghton, P.E. Kauppi, W.A. Kurz, O.L. Phillips, A. Shvidenko, S.L. Lewis, J.G. Canadell, P. Ciais, R.B. Jackson, S.W. Pacala, A.D. McGuire, S. Piao, A. Rautiainen, S. Sitch, D. Hayes, A large and persistent carbon sink in the world's forests, Science (1979) 333, 988–993 (2011) [Google Scholar]
  17. N.N. Barnabas, R. Kaam, L. Zapfack, M. Tchamba, D.C. Chimi, Bamboo diversity and carbon stocks of dominant species in different agro-ecological zones in Cameroon, Afr. J. Environ. Sci. Technol. 14, 290–300 (2020) [CrossRef] [Google Scholar]
  18. M.S.I. Sohel, M. Alamgir, S. Akhter, M. Rahman, Carbon storage in a bamboo (Bambusa vulgaris) plantation in the degraded tropical forests: Implications for policy development, Land Use Policy 49, 142–151 (2015) [CrossRef] [Google Scholar]
  19. A. Ndiaye, M.S. Diallo, D. Niang, Y.K. Gassama-Dia, In vitro regeneration of adult trees of Bambusa vulgaris, Afr. J. Biotechnol. 5, 1245–1248 (2006) [Google Scholar]
  20. B.N. Nfornkah, R. Kaam, T. Martin, Z. Louis, C.D. Cedric, G.W. Forje, T. Armand Delanot, T. Mélanie Rosine, A. Jean Baurel, T. Loic, Z.T. Guy Herman, K. Yves, D.S. Vartent, Culm allometry and carbon storage capacity of Bambusa vulgaris Schrad. ex J.C. Wend L. in the tropical evergreen rain forest of Cameroon, J. Sustain. Forestry 40, 622–638 (2021) [CrossRef] [Google Scholar]
  21. E. Ebanyenle, A.A. Oteng-Amoako, Site differences in morphological and physical properties of Bambusa vulgaris grown in Ghana, Discov. Innov. 19, 222–225 (2007) [Google Scholar]
  22. S. Islam, M. Alamgir, S. Akhter, Land use policy carbon storage in a bamboo (Bambusa vulgaris) plantation in the degraded tropical forests: implications for policy development, Land Use Policy 49, 142–151 (2015) [CrossRef] [Google Scholar]
  23. T.V. Borisade, N.O. Uwalaka, A.B. Rufai, A.I. Odiwe, G.A.D. Junior, Carbon stock assessment of Bambusa vulgaris stands in a regenerating secondary rainforest, thirty-four years after ground fire in Ile-Ife, Nigeria, J. Bamboo Rattan. 17, 11–25 (2018) [Google Scholar]
  24. C. Opoku-Kwarteng, E.N.A. Tagoe, The Bobiri Forest Reserve and Butterfly Sanctuary − A home of colour and beauty, J. Indigen. Shamanic Stud. 2, 46–52 (2021) [Google Scholar]
  25. P. Singnar, M.C. Das, G.W. Sileshi, B. Brahma, A.J. Nath, A.K. Das, Allometric scaling, biomass accumulation and carbon stocks in different aged stands of thin-walled bamboos Schizostachyum dullooa, Pseudostachyum polymorphum and Melocanna baccifera, For. Ecol. Manag. 395, 81–91 (2017) [CrossRef] [Google Scholar]
  26. M.B. Araújo, R.G. Pearson, W. Thuiller, M. Erhard, Validation of species-climate impact models under climate change, Glob. Chang Biol. 11, 1504–1513 (2005) [CrossRef] [Google Scholar]
  27. G.B. Williamson, M.C. Wiemann, Measuring wood specific gravity… correctly, Am. J. Bot. 97, 519–524 (2010) [CrossRef] [Google Scholar]
  28. S. Adu-Bredu, A. Duah-gyamfi, J.K. Govina, F. Ibrahim, Biomass Allometric Models for Bambusa vulgaris in Ghana (2020) [Google Scholar]
  29. M. Meragiaw, B.R. Singh, Z. Woldu, V. Martinsen, Carbon stocks of above − And belowground tree biomass in Kibate Forest around Wonchi Crater Lake, Central Highland of Ethiopia, PLoS One 16, 15 (2021) [Google Scholar]
  30. J. Penman, M. Gytarsky, T. Hiraishi, W. Irving, T. Krug, 2006 IPCC − Guidelines for National Greenhouse Gas Inventories, 2006. http://www.ipcc-nggip.iges.or.jp/public/2006gl/index.html [Google Scholar]
  31. INBAR, A Manual for Bamboo Forest Biomass and Carbon Assessment (2019) 146 [Google Scholar]
  32. IUCN/PACO, Parks and reserves of Ghana: Management Effectiveness Assessment of Protected Areas, 2010. https://portals.iucn.org/library/sites/library/files/documents/2010-073.pdf [Google Scholar]
  33. A.J. Nath, G.W. Sileshi, A.K. Das, Bamboo based family forests offer opportunities for biomass production and carbon farming in North East India, Land Use Policy 75, 191–200 (2018) [CrossRef] [Google Scholar]
  34. A.C. Waikhom, A.J. Nath, P.S. Yadava, Aboveground biomass and carbon stock in the largest sacred grove of Manipur, Northeast India, J. For. Res. (Harbin) 29, 425–428 (2018) [CrossRef] [Google Scholar]
  35. T.G. Morais, R.F.M. Teixeira, M. Figueiredo, T. Domingos, The use of machine learning methods to estimate aboveground biomass of grasslands: A review, Ecol. Indic. 130, 108081 (2021) [CrossRef] [Google Scholar]
  36. P.K. Pathak, H. Kumar, G. Kumari, H. Bilyaminu, Biomass production potential in different species of, The Ecoscan. 10, 41–43 (2016) [Google Scholar]
  37. T.-M. Yen, J.-S. Lee, Comparing aboveground carbon sequestration between moso bamboo (Phyllostachys heterocycla) and China fir (Cunninghamia lanceolata) forests based on the allometric model, For Ecol Manag. 261, 995–1002 (2011) [CrossRef] [Google Scholar]
  38. H. Zhang, S. Zhuang, B. Sun, H. Ji, C. Li, S. Zhou, Estimation of biomass and carbon storage of moso bamboo (Phyllostachys pubescens Mazel ex Houz.) in southern China using a diameter-age bivariate distribution model, Forestry 87, 674–682 (2014) [CrossRef] [Google Scholar]
  39. L. Puangchit, S.M. Hnin, S. Sungkaew, Allometric equations for estimating the aboveground biomass of a 14-year-old bamboo plantation at Moeswe Research Station, Myanmar, J. Trop. Forest Sci. 3, 1–19 (2019) [Google Scholar]
  40. M. Amoah, F. Assan, P.K. Dadzie, Aboveground biomass, carbon storage and fuel values of Bambusa vulgaris, Oxynanteria abbyssinica and Bambusa vulgaris var. vitata plantations in the Bobiri forest reserve of Ghana, J. Sustain. For. 39, 113–136 (2020) [CrossRef] [Google Scholar]
  41. T.-M. Yen, Y.-J. Ji, J.-S. Lee, Estimating biomass production and carbon storage for a fast-growing makino bamboo (Phyllostachys makinoi) plant based on the diameter distribution model, For. Ecol. Manag. 260, 339–344 (2010) [CrossRef] [Google Scholar]
  42. D.W. Sintayehu, A. Belayneh, N. Dechassa, Aboveground carbon stock is related to land cover and woody species diversity in tropical ecosystems of Eastern Ethiopia, Ecol. Process. 9, 1–10 (2020) [CrossRef] [Google Scholar]
  43. M. Asigbaase, E. Dawoe, B.H. Lomax, S. Sjogersten, Biomass and carbon stocks of organic and conventional cocoa agroforests, Ghana, Agric. Ecosyst. Environ. 306, 11 (2021) [Google Scholar]
  44. S. Adu-Bredu, M.K. Abekoe, E. Tachie-Obeng, P. Tshakert, Carbon Stock under Four Land-use Systems in three varied ecological Zones in Ghana, Afr. Carbon Cycle. 104, 105–113 (2008) [Google Scholar]
  45. S. Moore, S. Adu-Bredu, A. Duah-Gyamfi, S.D. Addo-Danso, F. Ibrahim, A.T. Mbou, A. de Grandcourt, R. Valentini, G. Nicolini, G. Djagbletey, K. Owusu-Afriyie, A. Gvozdevaite, I. Oliveras, M.C. Ruiz-Jaen, Y. Malhi, Forest biomass, productivity and carbon cycling along a rainfall gradient in West Africa, Glob. Chang Biol. 24, e496–e510 (2018) [CrossRef] [Google Scholar]
  46. J.Q. Yuen, T. Fung, A.D. Ziegler, Carbon stocks in bamboo ecosystems worldwide: estimates and uncertainties, For. Ecol. Manag. 393, 113–138 (2017) [CrossRef] [Google Scholar]
  47. M. Pfeifer, V. Lefebvre, E. Turner, J. Cusack, M.S. Khoo, V.K. Chey, M. Peni, R.M. Ewers, Deadwood biomass: an underestimated carbon stock in degraded tropical forests? Environ. Res. Lett. 10, 11 (2015) [Google Scholar]
  48. X. Xu, P. Xu, J. Zhu, H. Li, Z. Xiong, Bamboo construction materials: Carbon storage and potential to reduce associated CO2 emissions, Sci. Total Environ. 814, 152697 (2022) [CrossRef] [Google Scholar]
  49. C.M. Hoover, J.E. Smith, Current aboveground live tree carbon stocks and annual net change in forests of conterminous United States, Carbon Balance Manag. 16, 1–12 (2021) [CrossRef] [Google Scholar]
  50. S. Saeed, S. Yujun, M. Beckline, L. Chen, B. Zhang, A. Ahmad, A. Mannan, A. Khan, A. Iqbal, Forest edge effect on biomass carbon along altitudinal gradients in Chinese Fir (Cunninghamia lanceolata): a study from Southeastern China, Carbon Manag. 10, 11–22 (2019) [CrossRef] [Google Scholar]
  51. A. Adu-Poku, G.Y. Obeng, E. Mensah, S. Adu-Bredu, Dataset for the aboveground biomass and vegetative carbon dioxide stocks of Bambusa vulgaris in Ghana's Bobiri Forest, Mendeley Data. 1, 1 (2022) [Google Scholar]

Cite this article as: Akwasi Adu-Poku, George Yaw Obeng, Ebenezer Mensah, Michael Kwaku, Ernest Nti Acheampong, Akwasi Duah-Gyamfi, Stephen Adu-Bredu, Assessment of aboveground, belowground, and total biomass carbon storage potential of Bambusa vulgaris in a tropical moist forest in Ghana, West Africa, Renew. Energy Environ. Sustain. 8, 3 (2023)

All Tables

Table 1

Summary of dendrometric parameters in the selected compartments.

In the text
Table 2

Aboveground bamboo biomass and carbon stock of B. vulgaris in the study area.

In the text
Table 3

Descriptive statistics of culm diameter and mass.

In the text
Table 4

Comparison of biomass and carbon stock of various bamboo species worldwide.

In the text
Table 5

Comparison with other land-use systems in varied ecological zones in Ghana.

In the text
Table 6

Summary of results for coarse woody debris carbon.

In the text
Table 7

Summary of litter mass and carbon analysis.

In the text

All Figures

thumbnail Fig. 1

Map of Bobiri forest reserve showing the compartments understudy.
In the text
thumbnail Fig. 2

Scatter plot of aboveground culm mass and diameter.
In the text
thumbnail Fig. 3

Aboveground mass normal distribution curve with histogram.
In the text
thumbnail Fig. 4

Biomass CO2 sequestration of B. vulgaris in the study compartments.
In the text

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.