Issue 3 (203)/2025

ALSE and ACS Style
Awe, D.V.; Dangaï, Y.; Korji, D.; Kondasso, T.L. Carbon sequestration and storage in biomass and soil of Gardenia aqualla stands in Cameroon. Journal of Applied Life Sciences and Environment 2025, 58 (3), 437-450.
https://doi.org/10.46909/alse-583184

AMA Style
Awe DV, Dangaï Y, Korji D, Kondasso TL. Carbon sequestration and storage in biomass and soil of Gardenia aqualla stands in Cameroon. Journal of Applied Life Sciences and Environment. 2025; 58 (3): 437-450.
https://doi.org/10.46909/alse-583184

Chicago/Turabian Style
Awe, Djongmo Victor, Youhana Dangaï, and Taiga Léa Kondasso. 2025. “Carbon sequestration and storage in biomass and soil of Gardenia aqualla stands in Cameroon.” Journal of Applied Life Sciences and Environment 58, no. 3: 437-450. 
https://doi.org/10.46909/alse-583184

Carbon sequestration and storage in biomass and soil of Gardenia aqualla stands in Cameroon

Djongmo Victor AWE^1*, Youhana DANGAϹ and Taiga Léa KONDASSO²

¹Department of Plant Production, Higher Institute of Agriculture, Wood, Water Resources and Environment of Belabo, University of Bertoua, Cameroon; e-mail: dangaiyouhana@gmail.com

²Department of Environmental Sciences, Higher Institute of Agriculture, Wood, Water Resources and Environment of Belabo, University of Bertoua, Cameroon; e-mail: kondassotaiga@gmail.com

*Correspondence: awevictor20@yahoo.fr 

Received: Jul. 01, 2025. Revised: Sep. 13, 2025. Accepted: Sep. 22, 2025. Published online: Oct. 07, 2025

ABSTRACT. This study evaluates the carbon sequestration and storage in biomass and soil of Gardenia aqualla stands in Cameroon, emphasising the importance of ecosystems in mitigating climate change effects. Data on vegetation, dead wood, herbs, litter, soils, and fine roots were collected from 50 m × 50 m quadrats using systematic sampling methods. The aboveground, belowground, litter, dead wood, and soil organic carbon contents were higher in G. aqualla stands in Bénoué (50.28 ± 1.60, 11.05 ± 0.09, 1.98 ± 0.01, 8.98 ± 0.11, and 47.50 ± 0.19 Mg C ha^-1, respectively). In Mayo-Loutii, the carbon storage capacity of herbaceous and fine roots of G. aqualla stands was 2.94 ± 0.06 and 8.01 ± 0.05 Mg C ha^-1, respectively. In Bénoué, the total carbon stock and CO₂ equivalents of G. aqualla stands was 127.28 ± 10.72 Mg C ha^-1 and 467.11 ± 39.34 Mg CO₂eq ha^-1, respectively. The findings support the significant role of G. aqualla stands to store carbon and to mitigate climate change in Cameroon.

Keywords: biomass; carbon capacity; climate change; Gardenia aqualla stands; soil carbon storage.

 

INTRODUCTION

It is now widely recognised that ecosystems play a critical role in reducing and responding to the effects of climate change (Koffi et al., 2021). Due to their capacity to absorb and store carbon, ecosystems naturally contribute to climate change mitigation (Awé et al., 2021b). In addition to helping people and ecosystems adapt to climate change and other natural disasters, ecosystems are essential for reducing the risks and vulnerabilities associated with climate change (Awé et al., 2019). Consistently, international climate change agreements now place a strong emphasis on the ability of plants to mitigate climate change (Awé et al., 2021a). A promising strategy for forest-based climate change mitigation in developing countries is reducing emissions from deforestation, forest degradation, sustainable forest management, conservation, and enhancement of forest carbon stocks (REDD+) (Noiha et al., 2018a). Developed countries are urging lower-income countries to protect their forests to reduce greenhouse gas emissions through the REDD+ programme (Noiha et al., 2017). Measuring, reporting, and verifying forest carbon stocks are essential elements of carbon-based forest management (Noiha et al., 2018b). Increasing the terrestrial carbon dioxide (CO₂) sink is a mitigation technique aimed at reducing the amount of greenhouse gases in the atmosphere, in accordance with the Kyoto Protocol (FAO, 2019). Gross primary production is used to describe how plants absorb CO₂ (Krishnan et al., 2025).

Additionally, forests release carbon into the atmosphere through respiration and other activities, so they represent a ‘carbon source’. Half of the gross primary productivity is respired during respiration, with the remaining part being called net primary productivity, which corresponds to the total amount of biomass produced (Awé et al., 2021a; Koffi et al., 2021; Noiha et al., 2018a). Forests are valued under the Kyoto Protocol for their particular function as carbon sinks – that is, the ability to absorb and retain CO₂ from the atmosphere (Subba and Honnurappa, 2024).

Moreover, this function can be enhanced by increasing the aboveground biomass, which enhances forest cover and the soil organic carbon content (Awé et al., 2019; Noiha et al., 2018b; Zapfack et al., 2016). The species Gardenia aqualla Stapf and Hutch. is a member of the Rubiaceae family, one of the largest families of flowering plants, with over 200 genera. Gardenia aqualla is present from Senegal to Sudan, including Mali, the Republic of Guinea, Guinea-Bissau, Ghana, Togo, Côte d’Ivoire, Benin, Niger, Nigeria, and Cameroon (Arbonnier, 2002). It grows on compact, sandy clay soils with temporarily flooded ferruginous crusts in the Guinean and Sahelo-Sudanese savannas (Arbonnier, 2019). Given the current climate change problems that the world is facing, it is necessary to develop conservation and management strategies that involve G. aqualla. Hence, this study was conducted to evaluate the carbon sequestration and storage in the biomass and soil of G. aqualla stands in Cameroon.

 

MATERIALS AND METHODS

Study area

This research took place in northern Cameroon, specifically within four subdivisions: Bénoué, Mayo-Louti, Mayo-Rey, and Faro. The centre region of the Bénoué valley rises to a height of 176 m at Garoua. The zone is located at 8°-10°N and 12°-16°E (Bourou et al., 2019) (Figure 1).

There is a sizable periplain between the Adamawa Plateau in the south and the Mandara Mountains (1442 m) in the north (Nguemhe et al., 2014). There are two distinct seasons in the Sudano-Sahelian climate: a six-month dry season (November-May) and a six-month rainy season (June-October) (Nguemhe et al., 2014). The average monthly temperature is 26°C in August and 40°C in March, with a temperature range of 17-40°C (Nguemhe et al., 2014). The ferruginous soil is characterised by its low cation exchange capacity and acidity (pH = 5.5-6) (Nguemhe et al., 2014). The landscape around the communities is a clear and degraded Sudanian savannah, with shrubby vegetation (Letouzey, 1985). The primary occupation of the inhabitants in the area is agriculture.

Data collection

The factors that led to the selection of G. aqualla populations included their prevalence in the region, relief, socio-economic importance, and prominent role in traditional medicine in northern Cameroon. A total 130 quadrats, each 50 m × 50 m (covering 48 ha) (Figure 2), were sampled to assess G. aqualla stands in the Bénoué (n = 40 quadrats), Faro (n = 27 quadrats), the Mayo-Loutii (n = 34 quadrats), and the Mayo-Rey (n = 29 quadrats) subdivisions. The quadrats were plotted over a ground distance of 2500 m², using a GPS device and a compass. Along the quadrats, G. aqualla with a diameter at breast height ≥ 5 cm were counted in both areas.

This inventory system applies to the model used by Dossa et al. (2019). The circumference of the trees, dead wood, and shrubs was measured with a measuring tape. The dendrometric data included the diameter at breast height on the bark, measured using a measuring tape, and the height, measured using a clinometer. The circumference of four agroforestry parklands was measured using a tape measure at 1.30 m from the ground.

The circumference was converted to the diameter at breast height using Equation 1:

 

where 𝜋 is 3.14.

 

 

 

Estimation of carbon storage

Aboveground and belowground biomass

The aboveground biomass of G. aqualla stands was determined with Equation 2:

Carbon typically makes up 50% of a forest’s woody biomass (Awé et al., 2021a). Consequently, the aboveground biomass divided by 2 to obtain the aboveground carbon content. It is presented as megagrams of C per hectare (Mg C ha^-1), where 1 Mg is equivalent to 1 tonne or 1000 kg.

Equation 3 was used to calculate the belowground biomass (Ananthi et al., 2016; Chavan and Rasal, 2012):

The belowground carbon content was determined by dividing the belowground biomass by 2 (Ananthi et al., 2016; Chavan and Rasal, 2012). Similarly, to the aboveground carbon content, it is presented as Mg C ha^-1.

Herbaceous, litter, and fine root biomass

A destructive sampling method was used to quantify the herbaceous and litter biomass. Herbaceous plants were sampled in 1 m × 1 m sub-quadrats (Figure 2B). All emergent herbaceous vegetation in the quadrats was cut above the ground, weighed, and a pooled sample was taken from each sub-quadrat to determine oven dry weight in the laboratory. Litter was taken from sub-quadrats (0.5 m × 0.5 m) (Figure 2C), and the combined waste was collected. Using a trowel, the fine roots were removed from the soil in a 0.25 × 0.25 x 3 m² container. They were then cleaned, identified, and placed in envelopes to dry in the laboratory.

The samples were promptly brought back to the lab for reweighing, dried at 65°C to a consistent weight, and then reweighed to determine the moisture content. To determine the amount of organic carbon, the herbaceous plant, litter, and fine root samples were dried in an oven at 65°C for 72 h until they reached a constant weight. Then, they were reweighed and powdered using the calcination method. The dry matter content was determined with Equation 4:

where DW is the sample’s dry weight (g) following drying in an oven set at 65°C for 72 h, and WW is the sample’s wet weight (g) as determined in the field (Awé et al., 2021a).

The dry matter calculated from Equation 4 was used to calculate the biomass (Equation 5):

where DM is the percentage of dry matter (%) and TWW is the total wet weight as determined in the field (g) (Awé et al., 2021b).

Dead wood biomass

To harvest dead wood, 50 m × 50 m strips were placed on each elementary quadrat (2500 m²). Only the internal dead wood was measured in these bands. Dead wood that fell outside the plot but that was from trees that grew inside the plot was not measured, whereas dead wood that fell inside the plot but that was from trees that grew outside the plot was measured. Equation 6 was used to calculate dead wood biomass (Awé et al., 2021a):

where di is the diameter of each piece of samples wood debris sampled (m), and L is the length of the quadrat (m) – 50 m in our study.

By adjusting the wood density to 0.47 kg MS m^-3, the bulk volume was converted to the dead wood carbon stock using Equation 7 (Ifo, 2010):

where DM is the quantity of dry matter (%) and FC is a conversion factor, specifically 0.5 (Awé et al., 2019, 2020, 2021b; Ifo et al., 2015; Ndiaye et al., 2020).

Soil sampling and laboratory analysis

In every 50 m × 50 m quadrat, earth specimens were gathered from five 0.2 m × 0.2 m sub-quadrats (Figure 2D). They were cleaned and put in labelled envelopes to dry in the laboratory. Samples were taken from the four fundamental quadrants at a depth ranging from 0 to 30 cm. Soil from each depth was gathered using a cutlass and a dipper and then promptly placed into an airtight container and kept cool to avoid any moisture loss. The organic carbon content was assessed using the Walkley and Black analytical method, which involves oxidation with potassium bicarbonate (K₂Cr₂O₇) in an acidic solution (H₂SO₄), in line with ISO 14235 (Awé et al., 2020). The organic matter content was calculated by multiplying the organic carbon percentage by the Sprengel factor (1.724 for soils that are cultivated). The soil organic carbon stock per area was determined with Equation 8:

where OC is the organic carbon content (%), d is the soil depth (cm), and BD is the bulk density (g cm⁻³). The cylinder method was implemented to determine the bulk density. This method involves obtaining a soil sample using a hollow metal cylinder with a volume of 100 cm³. The soil taken out with the cylinder is evenly levelled at both ends. The volume of the extracted soil corresponds to that of the cylinder. Afterward, the soil is dried at 105°C for 48 h before being weighed again. Then, bulk density is calculated with Equation 9 (Awé et al., 2020):

where W is the weight of the dried soil (g) and V is the volume of the core (cm³).

Total carbon stock

The total carbon stock (Mg C ha^-1) was determined by adding the aboveground, belowground, litter, herbaceous plant, fine root, dead wood, and soil organic carbon contents, which were measured as described above.

CO₂ equivalent stock in the atmosphere

The total carbon stock was converted into the CO2 equivalent stock based on Equation 10 (Awé et al., 2021a):

where TC is the total carbon stock (Mg C ha^-1) and CFC is the conversion factor based on the CO₂/C ratio, which is 44/12.

Statistical analysis

The data were entered into Excel and analysed with STATGRAPHICS and the R Studio software. Analysis of variance (ANOVA) followed by Duncan’s multiple range test was used to determine whether there was a significant difference between the different parameters. A p-value < 0.05 was considered to indicate a statistically significant difference.

 

RESULTS AND DISCUSSION

Aboveground and belowground carbon contents

The aboveground carbon content ranged from 17.98 ± 0.05 to 50.28 ± 1.60 Mg C ha^-1, while the belowground carbon content ranged from 4.45 ± 0.01 to 11.05 ± 0.09 Mg C ha^-1 (Table 1). ANOVA revealed a significant difference in the aboveground carbon content (F = 5.84, p = 0.04) and the belowground carbon content (F = 5.99, p = 0.038) between the four subdivisions.

Bénoué showed the highest aboveground and belowground carbon contents, perhaps because G. aqualla in this subdivision has a large diameter and ability to form deep litter. These factors contribute to increase the accumulation of organic matter in the soil and the aboveground biomass, thus increasing carbon storage. The aboveground carbon content in this study is similar to what has been reported in Costa Rican agroforestry systems (10-60 Mg C ha^-1) (Valentini, 2007), but different from two other studies (Bello et al., 2017; Ouedraogo et al., 2019).

These discrepancies are likely because the species, area, the sampling methodology are different, and the fact that the carbon storage capacity varies from one species to another depending on the studied environment.

Table 1
The aboveground and belowground carbon contents of the Gardenia aqualla stands within the four subdivisions

Parameter	Subdivision
	Bénoué	Faro	Mayo-Loutii	Mayo-Rey
Aboveground carbon content (Mg C ha-1)	50.28 ± 1.60d	28.74 ± 0.55b	39.94 ± 0.16c	17.98 ± 0.05a
Belowground carbon content (Mg C ha-1)	11.05 ± 0.09d	6.74 ± 0.04b	9.02 ± 0.06c	4.45 ± 0.01a

The same letter in the same row indicates the lack of a significant difference 
(Duncan’s multiple range test, p > 0.05)

The herbaceous plant, litter, and dead wood carbon contents

As shown in Table 2, the carbon content ranged from 2.08 ± 0.02 to 2.94 ± 0.02 Mg C ha^-1 in herbaceous plants; from 1.01 ± 0.01 to 1.98 ± 0.01 Mg C ha^-1 in litter; and from 0.51 ± 0.01 to 8.98 ± 0.11 Mg C ha^-1 in dead wood. ANOVA indicated no significant difference in the carbon content of herbaceous plants (F = 0.84, p = 0.06) and litter (F = 0.14, p = 0.077) among the four subdivisions, but the carbon content of dead wood did show a significant difference (F = 4.54, p = 0.038).

The Mayo-Loutii subdivision showed the highest herbaceous plant carbon content (2.94 ± 0.02 Mg C ha^-1). This could be due to ecological conditions conducive to the development of herbaceous vegetation, such as high rainfall, nutrient-rich soils, and/or intense photosynthesis in this region. In addition, certain targeted agricultural methods or natural ecological elements (such as the robustness of the herbaceous system) could also contribute to the accumulation of carbon in herbaceous biomass.

These findings do not correspond with the outcomes reported by Awé et al. (2021). Variation in inventory systems and canopy cover may explain the discrepancy between our results and that previous study.

The Bénoué subdivision had the highest litter carbon content (1.98 ± 0.01 Mg C ha^-1). This could be attributed to a combination of factors, including weather conditions conducive to intense plant growth, high inputs of organic matter from dense aboveground biomass, and relatively slow decomposition rates that promote the accumulation of litter. Subsequently, litter decomposes into organic matter in the soil, which helps increase carbon reserves. Our results differ from those of Awé et al. (2019) and Masens et al. (2024), likely due to differences in vegetation type, climatic conditions, soil composition, decomposition methods, microbial activity, and soil management practices, all of which influence carbon inputs and outputs.

The Bénoué subdivision also had the highest dead wood carbon content (8.98 ± 0.11 Mg C ha^-1). This could be explained by a mixture of environmental factors and forest dynamics, including less marked forest degradation, later decomposition processes, and high forest productivity that favours the accumulation of dead wood. This region also has weaker human pressures (deforestation and fires), favouring the accumulation of biomass, including dead wood. Variation in the sampling technique may explain the discrepancy between our results and a previous study (Awé et al., 2019). Other studies conducted in tropical regions (Carlson, 2013; Ifo, 2010) used transect lines of 160 and 800 m per study plot in Congo and Gabon, respectively, while the present study used a quadrat line of only 50 m per study site. Based on the published findings, the deadwood carbon content increases as the study transect line lengthens.

Organic carbon content, soil bulk density, soil organic carbon content, and fine roots

As shown in Table 3, the organic carbon content ranged from 1.32% ± 0.02% to 2.03% ± 0.02%, the bulk density of the soil ranged from 0.53 ± 0.01 to 0.91 ± 0.01 g cm^-3, the soil organic carbon content ranged from 26.92 ± 0.11 to 47.50 ± 0.19 Mg C ha^-1, and the fine root carbon content ranged from 1.03 ± 0.01 to 8.01 ± 0.05 Mg C ha^-1. Based on ANOVA, the organic carbon content (F = 0.18, p = 0.08), soil bulk density (F = 0.15, p = 0.077), and fine root carbon content (F = 0.11, p = 0.077) did not differ among the four subdivisions. However, the soil carbon organic content did differ significantly (F = 5.08, p = 0.031).

The Bénoué subdivision had the highest organic carbon content (2.03% ± 0.02%). The amount of humus that has collected in the soil is sufficient to explain this outcome. This would also be due to a favourable balance between the input of organic matter and a low decomposition rate. This slowdown in decomposition is often linked to specific climatic and soil conditions, such as abundant vegetation that provides a lot of plant and root residues.

The findings differ from several other published studies (Agboadoh, 2011; Bessah et al., 2016; Jiao et al., 2010, 2012; Yenondan et al., 2020), likely due to varying textures and biological contents of the soils evaluated in those studies.

The Mayo-Loutii subdivision presented the highest soil bulk density (0.91 ± 0.01 g cm^-3). This result is the combination of intrinsically denser soil types and agricultural techniques leading to increased compaction that justifies the high soil bulk density noted in the Mayo-Loutii subdivision.

Bulk density was < 1.65 g cm^-3 for cultivated fields in Ghana’s Upper East Region (Dawidson and Nilsson, 2000) and was 1.34 g cm^-3 in the Bechem District (Agboadoh, 2011).

The Bénoué subdivision had the highest soil organic carbon content (47.50 ± 0.19 Mg C ha^-1).

This subdivision has a higher soil organic carbon content due to its topography, which facilitates the accumulation of organic matter at the bottom of the slope. In addition, agricultural pressure is low, and organic residues are largely provided by dense natural vegetation which, despite mineralisation, continues to accumulate to stabilise carbon.

This outcome can be specifically ascribed to the litter supply, which includes root renewal and leaf fall; soil chemistry; root exudates; and microclimate, all of which contribute to increase the soil carbon content. The findings of this study do not support the findings of numerous other studies (Awé et al., 2021a; Kooke et al., 2019).

Table 2
The herbaceous plant, litter, and dead wood carbon contents within the four subdivisions

Parameter	Subdivision
	Bénoué	Faro	Mayo-Loutii	Mayo-Rey
Herbaceous plants carbon content (Mg C ha-1)	2.41 ± 0.02a	2.92 ± 0.02a	2.94 ± 0.02a	2.08 ± 0.02a
Litter carbon content (Mg C ha-1)	1.98 ± 0.01a	1.03 ± 0.01a	1.01 ± 0.01a	1.72 ± 0.01a
Dead wood carbon content (Mg C ha-1)	8.98 ± 0.11d	2.03 ± 0.02b	0.51 ± 0.01a	5.52 ± 0.04c

The same letter in the same row indicates the lack of a significant difference
(Duncan’s multiple range test, p > 0.05)

Table 3
The organic carbon content, soil bulk density, soil organic carbon stock (SOCs) and fine roots within the four subdivisions

Parameter	Subdivision
	Bénoué	Faro	Mayo-Loutii	Mayo-Rey
Organic carbon content (%)	2.03 ± 0.02a	2.02 ± 0.02a	1.44 ± 0.02a	1.32 ± 0.02a
Bulk density (g cm-3)	0.78 ± 0.01a	0.53 ± 0.01a	0.91 ± 0.01a	0.68 ± 0.01a
Soil organic carbon content (Mg C ha-1)	47.50 ± 0.19d	32.11 ± 0.13b	39.31 ± 0.17c	26.92 ± 0.11a
Fine root carbon content (Mg C ha-1)	5.08 ± 0.03c	1.03 ± 0.01a	8.01 ± 0.05d	3.72 ± 0.02b

The same letter in the same row indicates the lack of a significant difference
(Duncan’s multiple range test, p > 0.05)

Anthropogenic and biophysical variables that degrade and diminish organic returns from the environment to the soil, as well as the varying textures and biochemical compositions of soils, may contribute to these discrepancies.

The Mayo-Loutii subdivision had the highest fine root carbon content (8.01 ± 0.05 Mg C ha^-1). This high carbon content is due to a combination of factors specific to this area, including its vegetation type, soil conditions, and decomposition rates that promote the accumulation of root organic matter. Suitable agricultural or forestry practices and stable water conditions may also play a key role in this phenomenon.

The findings of this study differ from what has been reported in the literature (Marschner, 2012; Rich and Watt, 2013). The variable textures and biological contents of the soils likely contribute to this discrepancy.

Total carbon stock and atmospheric CO₂ equivalents

The total carbon stock ranged from 162.39 ± 3.95 to 127.28 ± 10.72 Mg C ha^-1, while the atmospheric CO₂ equivalents ranged from 228.97 ± 14.49 to 467.11 ± 39.34 Mg C ha^-1 (Table 4).

Based on ANOVA, the total carbon stock (F = 8.84, p = 0.032) and atmospheric CO₂ equivalents (F = 9.89, p = 0.025) differed significantly among the four subdivisions. The Bénoué subdivision had the highest total carbon stock (127.28 ± 10.72 Mg C ha^-1). This can be explained by the combination of dense plant biomass and pedoclimatic conditions favourable to carbon sequestration in the soil.

Table 4
Total carbon stock and atmospheric CO₂ equivalents within the four subdivisions

Parameter	Subdivision
	Bénoué	Faro	Mayo-Loutii	Mayo-Rey
Total carbon stock (Mg C ha-1)	127.28 ± 10.72d	74.60 ± 5.41ab	100.74 ± 8.33c	62.39 ± 3.95a
CO2 equivalents (Mg CO2eq ha-1)	467.11 ± 39.34d	273.78 ± 19.85ab	369.71 ± 30.57c	228.97 ± 14.49a

The same letter in the same row indicates the lack of a significant difference
(Duncan’s multiple range test, p > 0.05)

These factors probably include a climate conducive to photosynthesis, the strong presence of these tree species in the savannah, and soil management techniques that favour the accumulation of organic matter.

This result only confirms that the differences between the total carbon stock of the four subdivisions would be due to the difference in the diameter at breast height, the basal area, and the density of individuals which influence the biomass produced by the species.

These findings do not correspond with the outcomes reported in several previous studies (Ananthi et al., 2016; Awé et al., 2019; Koffi et al., 2021; Krishnan et al., 2025; Siregar et al., 2025; Siswanto et al., 2025; Subba and Honnurappa, 2024).

This difference between our study and those previous reports is likely related to the different sampling methods, allometric model, geographical areas, and populations studied.

The Bénoué subdivision presented the highest total CO₂ equivalents (467.11 ± 39.34 Mg CO₂eq ha^-1). Gardenia aqualla stands can compensate for human-caused CO₂ emissions. According to similar research (Noiha et al., 2017, 2018a), other species have the following CO₂ sequestration capacity: 327.47 ± 2.07 Mg CO₂eq ha^-1 for cashew trees, 859.33 ± 10.01 Mg CO₂eq ha^-1 for eucalyptus, 296.70 ± 1.98 Mg CO₂eq ha^-1 for neem, and 539.87 ± CO₂eq ha^-1 for cocoa farms.

 

CONCLUSIONS

The results show that G. aqualla stands in Cameroon can serve as carbon sinks to mitigate climate change, providing a solution to reduce greenhouse gas emissions. Due to the carbon sequestration potential, G. aqualla stands could be used in carbon credit markets through small CDM and REDD+ projects. To strengthen this role, the government should promote sustainable management methods and policies, including intensive reforestation.

 

Author contributions: Conceptualization of the manuscript and development of the methodology: DVA, YD and TLK; Data collection and curation: DVA, YD and TLK; Data analysis and interpretation: DVA, YD and TLK; Writing of the orginal manuscript: DVA, YD and TLK; Writing, review and editing: DVA, YD and TLK. All authors declare that they have read and approved the publication of the manuscript in this present form.

Acknowledgments: Authors thank the entire reviewer whose contributions have been very significant for the improvement of this study.

Data availability statement: The data presented in this study are available on request from the corresponding authors.

Funding: There was no external funding for this study.

Conflicts of interest: Authors declare no conflict of interest.

 

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