2.5.2. Carbon stock and value analysis
Data analysis of various carbon pools measured in the forests was performed in R version 4.0.1. The AGB of trees was calculated using a previously published allometric equation in which the independent variables were trunk diameter (D, cm), height (H, m), and wood density (p, g cm− 3) (predictors) (Chave et al. 2014).The Global Wood Density database was used to determine the wood density of different species (Zanne et al. 2009). The following formula (Chave et al. 2014) was employed to calculate the above-ground biomass with the BIOMASS package in R (Réjou-Méchain et al. 2017):
\(\mathbf{A}\mathbf{G}\mathbf{B} \left(\mathbf{k}\mathbf{g}\right)=0.0673\mathbf{*} {({p}{D}}^{2}{{H})}^{0.976}\) 1
Where: AGB is the above-ground biomass of trees (kg), p is the specific wood density (g cm− 3), D is the trunk diameter at breast height (cm), and H is the total height of trees (m). The total AGB carbon for each quadrat was calculated as aggregate AGB carbon for all trees. Carbon stocks were determined for each quadrat and then extrapolated to tonnes per hectare. The carbon content in AGB is calculated by multiplying the default carbon fraction by 50% (Hiraishi et al. 2014).
Below-ground biomass was estimated with the equation developed by (MacDicken 1997)
\(\mathbf{B}\mathbf{G}\mathbf{B}=\mathbf{A}\mathbf{G}\mathbf{B}\mathbf{*}0.2\) 2
Where: BGB is below-ground biomass, AGB is above-ground biomass, 0.2 is the conversion factor (or 20% of AGB).
For standing deadwood (SDW) which has branches, the biomass was estimated using the allometric equation for the estimation of above-ground biomass (Pearson et al. 2005).
For the remaining standing deadwood, the biomass was estimated using wood density and volume calculated from the truncated cone (Pearson et al. 2005).
\(\mathbf{V}\mathbf{o}\mathbf{l}\mathbf{u}\mathbf{m}\mathbf{e} {\left(\mathbf{m}\right)}^{3}\) = \(\frac{1}{3}{\pi }\mathbf{h} {\mathbf{r}}_{1}^{2}+{\mathbf{r}}_{2}^{2}\) + \({\mathbf{r}}_{1 }\)* \({\mathbf{r}}_{2}\) 3
Where: h is the height in meters, r1 is the radius at the base of the tree, andr2 is the radius at the top of the tree.
\(\mathbf{B}\mathbf{i}\mathbf{o}\mathbf{m}\mathbf{a}\mathbf{s}\mathbf{s} = \mathbf{V}\mathbf{o}\mathbf{l}\mathbf{u}\mathbf{m}\mathbf{e} \mathbf{x} \mathbf{W}\mathbf{o}\mathbf{o}\mathbf{d} \mathbf{d}\mathbf{e}\mathbf{n}\mathbf{s}\mathbf{i}\mathbf{t}\mathbf{y} \left(\mathbf{f}\mathbf{r}\mathbf{o}\mathbf{m} \mathbf{s}\mathbf{a}\mathbf{m}\mathbf{p}\mathbf{l}\mathbf{e}\mathbf{s}\right)\) 4
The biomass of lying deadwood was estimated by the equation given below (Pearson et al. 2005).
\(\mathbf{L}\mathbf{D}\mathbf{W} =\sum _{{i}=1}^{{n}}\mathbf{V}\mathbf{*}\mathbf{S}\) 5
Where: LDW is lying dead wood, V is volume, and s is the specific density of each density class.
The lying deadwood volume per unit area is estimated with:
\(\mathbf{V}= {{\pi }}^{2} \left(\frac{{\mathbf{D}}^{2}}{8\mathbf{L}}\right)\) 6
Where: V is the volume in m3/ha; L is the length of the line transect, and D is the diameter of the deadwood tree. The carbon content in AGB is calculated by multiplying the default carbon fraction by 50% (Hiraishi et al. 2014).
The biomass in the pool of leaf litter, grass, and herbs was estimated using destructive sampling. Forest floor litter material (dead leaves, twigs, fruit, and flowers) was collected from a 1 m2 area. The living components, primarily grass and herbs, were harvested and weighed as well. Dry weight was determined in laboratory samples of the material. To estimate the biomass carbon stock of the litter, 100 g of fresh litter subsample was taken for laboratory use, and each sample was then dried in an oven at 105oC for 24 hours to obtain the dry weight (Pearson et al. 2005).
The leaf litter, grass, and herbs (LGH) biomass per hectare was computed using the following formula:
\(\mathbf{L}\mathbf{H}\mathbf{G} =\frac{\mathbf{W}{f}{i}{e}{l}{d}}{{A}}\) x \(\frac{\mathbf{W}{s}{u}{b}{s}{a}{m}{p}{l}{e},{d}{r}{y}}{\mathbf{W}{s}{u}{b}{s}{a}{m}{p}{l}{e}, {w}{e}{t}}\)x \(\frac{1}{10000}\) 7
Where: LHG is the leaf litter, herbs, and grass biomass (tonne ha–1), Wfield is the weight of fresh leaf litter, herbs, and grass sampled destructively within area A (g), A is the size of the area where leaf litter, herbs, and grass were collected (ha), Wsubsample, dry is the weight of oven-dried sub-sample of leaf litter, herbs, and grass taken to the laboratory for moisture content determination (g), Wsubsample, wet is the weight of fresh sub-sample of leaf litter, herbs, and grass taken to the laboratory for moisture content determination (g).
Carbon stocks in litter biomass were calculated using the following formula:
\(\mathbf{C}\mathbf{L} = \mathbf{L}\mathbf{H}\mathbf{G} \mathbf{*} \mathbf{\%}\mathbf{C}\) 8
Where: CL is the total carbon stocks in litter in tonne ha–1, and % C is the carbon fraction determined in the laboratory (Pearson et al. 2005).
The soil carbon stock was assessed in this study using the fine soil fraction to a depth of 30 cm. The following equation was used to calculate the bulk density (BD):
\(\mathbf{B}\mathbf{D} =\frac{\mathbf{M}\mathbf{S}}{\mathbf{V}\mathbf{C}}\) 9
Where: BD is the bulk density (g cm–3), MS is the mass of the oven-dry soil (g)
The amount of carbon stored per hectare was calculated using the following formula, taking into account soil depth (cm), bulk density (g cm–3), the percentage of soil organic carbon content (SOC), and the Total Nitrogen (TN) in the recommended method (Pearson et al. 2005).
\(\mathbf{S}\mathbf{O}\mathbf{C} = \mathbf{B}\mathbf{D} \mathbf{x} \mathbf{d} \mathbf{x} \mathbf{\%}\mathbf{C}\) 10
where SOC stock is the soil organic carbon stock per unit area (tonne ha–1), TN stock is the total nitrogen stock (tonne ha–1), BD is the bulk density (g cm–3), d is the total depth of the sample (cm), percent SOC is the soil organic carbon concentration, and VC is the volume of the core sampler (cm3).
The carbon stock density of each stratum was calculated by aggregating the carbon stock densities of each stratum's carbon pools using the formula in the following equation.
\(\mathbf{C} \left(\mathbf{L}\mathbf{U}\right) = \mathbf{C} \left(\mathbf{A}\mathbf{G}\mathbf{B}\right) +\mathbf{C}\left(\mathbf{B}\mathbf{B}\right)+\mathbf{C} \left(\mathbf{D}\mathbf{W}\mathbf{B}\right) + \mathbf{C}\left(\mathbf{L}\mathbf{H}\mathbf{G}\right)+\mathbf{S}\mathbf{O}\mathbf{C}\) 11
Where:
C (LU) is the carbon stock density for a land-use category (C t ha–1)
C (AGB) is the carbon in above-ground tree components (C t ha–1)
C (BB) is the carbon in below-ground components (C t ha–1)
C (DWB)is the carbon in deadwood tree components (C t ha–1)
C (LHG) is the carbon in the litter, herbs, and grass (C t ha–1)
SOC is the soil organic carbon (C t ha–1)
Carbon was summed, and the total was then multiplied by 44/12 (3.67) to convert it into the carbon dioxide equivalent.
A chronological carbon storage change investigation was conducted at MFBR for the reference years 1987, 2002, and 2017 according to the method proposed by (Niquisse et al. 2017). After calculating the carbon stock and value based on the previous, baseline year in the MFBR, change was analysed using the below equation.
∆C = \(\frac{{C}{F}{i}{n}{a}{l}{y}{e}{a}{r}-{C}{i}{n}{i}{t}{i}{a}{l}{y}{e}{a}{r}}{{C}{i}{n}{i}{t}{i}{a}{l}{y}{e}{a}{r}}{*}100\) 12
Where: ∆C is the percentage change in carbon, C final year is the carbon stock in the final (recent) year, and C initial year is the carbon stock in the initial years.