Estimation of Carbon Stock Monetary Value of a Tropical Rainforest in Nigeria

The study estimated the carbon stock contents of a lowland rainforest in Nigeria, Okomu National Park. Four major Carbon pools were estimated, they were: above-ground biomass; below-ground biomass: dead wood; and litter. Nested plot design was used for sample plot demarcation. All trees (living and dead) with Dbh ≥ 10cm were enumerated within the 50 m X 50 m and 20 m X 20 m plots, while the 1 m X 1 m quadrant was for litter samples collection. Core samples and sub-samples were collected from live tree and dead wood respectively. The total carbon stock estimated for the study area was 177.58 tons/ha, of which above-ground biomass accounted for 134.01 tons/ha and below-ground biomass was 33.50 tons/ha. Dead wood was 6.05 tons/ha while litter was 4.02 tons/ha.  The Carbondioxide equivalent was estimated to be 651.14 tons/ha.  The monetary value for the carbon stock estimated at $ 4/tCO2 was $ 52,674,679. Thus, carbon sequestration is one of the significant ecosystem services provided by mature rainforests. Keywords : Carbon pool, Rainforest, Ecosystem Service, Climate Change, Forest Management DOI: 10.7176/JRDM/63-06 Publication date: March 31 st 2020

between longitude 5.187 °E and 5.431 °E and latitude 6.278 °N and 6.435 °N as shown in Figure 1. The park has four range namely: Igowan range, Arakhuan range, Julius creek range and Babui creek range (Ijeomah et al., 2015).

Data Collection
Nested plot design was adopted for the study and fourteen temporary plot sizes of 50 m X 50 m were laid within which sub-plots sizes of 20 m X 20 m and 1 m X 1 m were marked out, using line transect method. Trees with dbh ≥ 20 cm were enumerated within the 50 m X 50 m plots and core samples were collected from those trees using increment borer. Core samples collected were weighed on field and labelled appropriately. In addition, all dead trees/wood encountered were also enumerated within these plots. The dead wood included both standing and lying dead wood. Standing dead trees were classified into three categories based on the presence or absence of leaves, twigs and big branches as recommended by Genene et al. (2013). On the hand, lying dead wood were classified based on their healthiness as either sound, intermediate or rotten using the machete test. Samples obtained from the dead wood were measured using water displacement method and labelled appropriately Trees with dbh between 10 cm and 19.9 cm were enumerated with the 20 m X 20 m sub-plots while the 1 m X 1 m sub-plots were adopted for the collection of litter. The litter was weighed and sub-samples were collected. A total of 531 live trees, 33 standing dead wood and 62 lying dead wood were enumerated within the 50 m X 50 m plots while 199 live trees were measured within the 20 m X 20 m. All samples collected were later dried in the laboratory and values obtained were used for further analysis.

Data Analysis
Live tree volume, core volume and core density were estimated using Equations 1,2 and 3 respectively.
Where V is Volume (m 3 ), h is height (m), Db is Diameter at the base (m), Dm is Diameter at the middle (m), Dt is Diameter at the top (m), and π is 3.142. = ………………………………………………………… Equation 2 Where d is diameter of the core sample (diameter of the increment borer used) cm 2 , and L is mean length of the two core samples that were obtained from each tree (cm).
Above ground biomass was calculated using Equation 4 while below ground biomass was estimated using simple default value of 25% (for hardwood species) of the total above ground biomass as recommended by IPCC (2006). The carbon contents of AGB and BGB were estimated by halving their biomass and converting them to tonnes per plot (tons/plot) by dividing it by 1000.
= × × ………………………………………… Equation 4 Where B is Biomass (kg), V is Stem Wood Volume (m 3 ), WCD is Wood Core Density (kg/m 3 ) and BEF is Biomass Expansion Factor (2.292). The mean BEF value of 2.292 was used for this study as prescribed by Nigeria R-PP (2013) for lowland Rainforest National Parks. Biomass of dead standing tree in categories 1 and 2 were discounted by 3% to correct for absence of leave. Smalian volume equation given in Equation 5 was used for category 3 of dead standing tree and lying dead wood. The biomass was then estimated using Equation 6. The result obtained was divided by 2 to obtain the amount of carbon in the litter, this was then expressed in tonnes per plot by dividing it by 1000. The resulting value was multiplied by 10,000 (the number of 1m X 1m in a hectare) to obtain the amount in tonnes per hectare.
The total carbon stock in tonnes per hectare for the study area was estimated by summing up the carbon in tonnes per hectare of all the pools estimated, the formula is given in Equation 8.
. Carbon in below-ground biomass (tons/ha), Csdw is Estimated Carbon in standing dead wood (tons/ha), Cldw is Estimated Carbon in Lying dead wood (tons/ha), and Cl is Estimated Carbon in litter (tons/ha). The estimated total carbon in tons/ha obtained was used to estimate the Carbondioxide equivalent in tons/ha using Equation 9 as stipulated by IPCC (2006). . P Q , = . FRS/T< × 44 12 X …………………………………………… Equation 9 Where tCO2e is Total Carbondioxide equivalent in tons/ha, and Cttons/ha is Total carbon (tons/ha). The total carbon and Carbondioxide equivalent for the entire stand were then estimated using Equations 10 and 11 respectively.
'4M%-Z4$[, = P Q , 8F<S × $4 …………………………………… Equation 12 3  Nigeria R-PP (2013) and the estimated 115 tons/ha reported by Pandey (2012) for World Heritage Sites in Tropical Forests. It was also higher than 77.64 tons/ha estimated by Pragasan (2015) for a forest in India, 120 tons/ha reported by Lai (2005)  When compared with values for other National Parks and World Heritage sites as estimated by Pandey (2012), total carbon stock of the study area was higher than that obtained for Sundarbans National Park (56.3 tons/ha) in India and Sangay National Park (67.5 tons/ha) in Ecuador. It was also higher than those estimated for Tikal National Park (85 tons/ha) in Guatemala, Canaima National Park (92 tons/ha) in Venezuela, and Lorentz National Park (100 tons/ha) in Indonesia. It was higher than the estimated values for Manu National Park (134.4 tons/ha), Peru, Chitwan National Park (140.7 tons/ha), Nepal and Gunung Mulu National Park (167 tons/ha), Malaysia. It was however in the range reported for Sangha Trinational (163 tons/ha), (bordering Cameroon, Central African Republic and Republic of Congo) and Taï National Park (188 tons/ha), Cote d'Ivoire. The Carbondioxide equivalent of carbon estimated followed the same trend since it was a derivate of estimated carbon. The carbon stock of the Park also falls within the high carbon density area (158-408 tons/ha) as classified by Ravilious et al. (2010). Given that the forest is a regenerating forest, it has a high capacity to sequester even more carbon with improved management.

. RESULTS AND DISCUSSION
The result obtained for the financial estimate is presented in Table 2, the total monetary value for the carbon stock estimated at $ 4/tCO2 was $ 52,674,679. The estimated monetary value of carbon stock obtained for the Park was greater than US$ 40,709,088 estimated by Assaye (2014) for Awash National Park but lesser than US$ 62,184,364 reported for Simien Mountains National Park by Assaye (2015). These values are subjective depending on estimated carbon stock per hectare, size of area being assessed and the price per tonne adopted. However, the value obtained for the Park has the potential of providing adequate funds through programmes such as REDD+ if carbon stock is incorporated as a management objective. This will help in providing additional funds for the management of the Park and also make available financial incentives for forest dependent communities, thereby reducing the pressure on the forest.