ASSESSMENT OF PLOT SIZE EFFECT ON STEM BIOMASS AND CARBON STOCK AT THE AKURE STRICT NATURE RESERVE, ONDO STATE, NIGERIA

ASSESSMENT OF PLOT SIZE EFFECT ON STEM BIOMASS AND CARBON STOCK AT THE AKURE STRICT NATURE RESERVE, ONDO STATE, NIGERIA. A PROJECT REPORT BY OLAKUNLE HENRY OGUNBIYI AND MOHAMMHED OLAKUNLE ORUNSOLU FORESTRY RESEARCH INSTITUTE OF NIGERIA JERICHO HILLS, IBADAN, OYO STATE FEBRUARY, 2025 ii Abstract Accurate estimation of forest biomass and carbon stock depends largely on appropriate sampling strategies, including plot size selection. This study evaluated the influence of sampling plot size on biomass and carbon stock estimates within the Strict Nature Reserve of the Akure Forest Reserve, Ondo State, Nigeria. Eight 50 m x 50 m plots were established, each containing three nested subplots of 20 m x 20 m, 25 m x 25 m, and 35 m x 35 m. Tree diameters at breast height, base, middle, and top were measured and used to compute basal area and volume. Biomass was estimated by multiplying tree volume by species specific wood density from published sources, and carbon stock was derived using a conversion factor of 0.5. Analysis of Variance (ANOVA) was conducted to test for differences in biomass and carbon estimates among the plot sizes. Results showed no significant effect of plot size on biomass and carbon stock estimation (P> 0.05). The 25 m x 25 m subplot recorded the highest biomass (900,289.05 kg/ha) and carbon stock (450.14 t/ha), followed by the 35 m x 35 m, 20 m x 20 m, and 50 m x 50 m plots. Despite these differences in magnitude, estimates were statistically similar across all plot sizes. Findings suggest that any of the four plot sizes can provide reliable biomass and carbon assessments. Considering the labour-intensive nature of field measurements, the 20 m x 20 m plot is recommended as the most efficient option without compromising accuracy 1 1.0 INTRODUCTION 1.1 General Background Climate Change is a global issue and countries all over the world are concerned on ways to mitigate its effect. The United Nations Framework Convention on Climate Change (UNFCCC) defines climate change as “A change of climate which is attributed directly or indirectly to human activity that alters the composition of the global atmosphere and which is in addition to natural climate variability observed over comparable time periods” (Assefa et al., 2013). Given zero-emission targets by 2050, forests have been identified to play an increasingly important role in the mitigation of climate change (UNFCCC 2021). Therefore, recognizing the significant contribution of the forest sector to contribute towards net-zero emission targets requires robust national forest monitoring systems. At both country and global levels, there is a growing demand for reliable information on forest carbon stock which implies that monitoring the state and changes that occurs in forests carbon pools is an important element for climate change mitigation. Therefore, measuring and estimating carbon stocks and changes in carbon stocks in various pools are very important to carbon trading and marketing. Biomass consists of approximately 50% carbon and sample plot size could have an effect on the accuracy of carbon stock estimated. This has affected the accuracy in carbon emissions assessment as much as uncertainties in deforestation rates. Information on Above Ground Biomass is significant Forest biomass estimation at local or global scale is very crucial and served as an important indicator for monitoring and estimating the forest carbon ecosystem especially in the context of climate change (Nor Farika Zani et al., 2017). Plot sizes have a significant effect on forest biomass 2 estimation and large plot size can be time consuming with regards to complications to measure which may affect the accuracy of the collected field data and result precision. Small plot sizes may also not capture the entire available forest biomass and carbon stock due to sparse distribution of tree species. This makes it challenging to select the optimal plot size that balances the tradeoffs between plot size and precision of above-ground biomass in different forest types. Thus, this study aimed to examine the effect of sample plot size on stem biomass and carbon stock estimation and also to check the variabilities. 1.2 Statement of Problem Above Ground Biomass information is useful for the understanding how forest contributes to global carbon cycle. Despite the multifunctional roles of forest biomass, there is a limit to the research done with regards to the effect of sample plot size on stem biomass and carbon stock estimation. There is a need to examine the variability of biomass and carbon stock available using different plot sizes and also determine the optimal plot size that provide precision which will allow easy monitoring and evaluation of forest management activities and also of use to forest inventory. 1.3 Research Objectives The general objective of this study is to assess the plot size effect on stem biomass and carbon stock at the Akure Strict Nature Reserve, Ondo State, Nigeria. The specific objectives are to: • Carry out tree measurements (diameter and height) in samples plots of different sizes (50m x 50m, 35m x 35m, 25m x 25m and 20m x 20m) in the study area. • Estimate stand volume, biomass and carbon stock in the study area. 3 • Compare the effect of different sample plot sizes (50m x 50m, 35m x 35m, 25m x 25m and 20m x 20m) on stand volume, biomass and carbon stock in the study area. 1.4 Justification The global demands for reliable information on forest monitoring, management, and forest carbon estimation to contribute towards climate change mitigation and the achievement of zero-emission targets by 2050 is dependent on forest carbon stock assessment. Forest inventory on large plot size can be time consuming and it is important to examine if using a smaller plot size will yield the same result as large plot size with regards to forest biomass and carbon estimation. The information from this study is expected to determine the optimal plot size for estimating forest carbon and biomass to allow easy monitoring and evaluation of forest carbon ecosystem. 1.5 Scope of Study The scope of this study is to compare some selected sample plot sizes for estimating forest carbon stock in Akure Strict Nature Reserve, Akure, Ondo State. The research will also focus on aboveground biomass (AGB) because it contributes larger biomass from the total tree biomass (TTB) while below-ground biomass (BGB) only contributes a small portion. The selected sample plot sizes that will be adopted are: 50m x 50m plot size, 35m x 35m plot size, 25m x 25m plot size, and 20m x 20m plot size. 4 2.0 METHODOLOGY 2. 1 Study Area This study was carried out at The Strict Nature Reserve at the Akure Forest Reserve which is situated between Aponmu and Obada communities in Akure South Local Government Area, Ondo State, Nigeria. Akure Forest Reserve covers an area of 6993 ha and it is under the management of the Department of Forestry, Ondo State, Nigeria. The Strict Nature Reserve (Queen’s plot) is a part of the Akure Forest Reserve and was instituted in 1948, covering an area of about 32 hectares (Adeduntan, 2009): established with a view to preserving the genetic diversity of the forest ecosystem. The Akure Forest Reserve is located on Latitude 7◦ 18 N and Longitude 5◦ 02 E. The area is gently undulating and lies on a general altitude of 229 m above sea level. The reserve is well drained due to the presence of Owena River, which flows North to South across the forest reserve into the Atlantic Ocean, about 160 km away. 5 Figure 1: Map of Akure Forest Reserve Figure 2: Map Showing Queen's Plot 6 The underlying rock is crystalline mainly gneissose and referable to the basement complex. As a result of continuous weathering, the ferric luvisol soils, which feature abundantly in the typical upland soils in many parts of South-Western Nigeria is also present in this forest reserve. Akure Forest Reserve has a tropical climate with prominent wet and dry seasons (rainy season and dry season). The mean annual rainfall is about 4000 mm generally occurs between March and October while the dry season occurs between November and February yearly. The monthly mean temperature is about 26◦C (minimum 19◦C and maximum 34◦C) (Adekunle et al., 2013b). The forest cover of the vegetation has been un-modified for several decades (Ola-Adams & Hall 1987). Jones (1948) description of the vegetation stresses four strata of woody plants; emergent (height of trees above 40 m), upper storey (20–40 m), lower storey (3–20 m) and shrubs. The dominant plant species are the indigenous tropical rainforest hardwood species such as Khaya species, Mansonia altissima, Cordia millenii and Milisia excelsa. 2.2 Data Collection 2.2.1 Plot layout and Measurement of Tree Growth variables The entire work was done based on field data which was collected via physical measurement by the field crew. The tasks carried out on the field are listed below. • Forest survey which includes demarcations of boundary, marking out of 50m x 50m plot and three sub plots of 20m x 20m, 25m x 25m and 35m x 35m then lining. • Tree labelling. 7 • Forest mensuration which includes tree stem diameter (DBH) at 1.3m above ground level excepts for stems with abnormalities using girth tape and big buttress using Spiegel relaskop. • Forest mensuration which includes tree diameter at the top (Dt), middle (Dm) and base (Db) using Spiegel relaskop. • Perpendicular distance measurement to the baselines. • Data recording. The following collection procedures were also adopted for effective and accurate field work. 1. 20m was measured from the edge of the forest inward to avoid edge effect before laying the first 50m x 50m plot. 2. GPS and compass reading were taken at the corners of the plot. 3. Marking and pegging was done at 20m, 25m and 35m along the 50m boundary line. 4. The opposite points were connected together parallel to the boundary line to have a demarcation of 50m x 50m plot and three subplots of 20m x 20m, 25m x25m, and 35m x 35m. 5. Starting with the 20m x 20m subplot, all tree species with dbh of 2cm and above were numbered, identified and then the dbh and perpendicular distance to the baselines were measured and recorded. 6. For trees with dbh greater or equal to 10cm, they were numbered, identified and then the dbh, Dt, Dt, Dm, stem height (height from ground to crown point) and total height were measured and recorded. 14 Table 3: Summation of No of Trees per hectare Plot Type 20X20 25X25 35X35 50X50 TOTAL 1 1075 1216 750.72 552 3593.72 2 1600 1488 1044.48 588 4720.48 3 575 480 416.16 432 1903.16 4 950 1312 1003.68 656 3921.68 5 2200 1952 1387.2 1036 6575.2 6 1650 1600 1566.72 1032 5848.72 7 650 736 563.04 496 2445.04 8 350 448 554.88 536 1888.88 Total 9050 9232 7287 5328 30897 0.00 200.00 400.00 600.00 800.00 1000.00 1200.00 1400.00 20x20 25x25 35x35 50x50 Mean of No of Trees per hectare Figure 8: Bar chart showing mean of No of Trees per hectare 15 Table 4: Mean of Mean Dbh per hectare in each sample plot size Plot size Mean 20x20 301.38 25x25 384.07 35x35 307.25 50x50 210.19 Figure 9: Bar chart showing mean of mean dbh per hectare Table 5: Summation of Volumes in each plot per hectare Plot Type 20X20 25X25 35X35 50X50 TOTAL 1 166.74 296.78 243.97 171.52 879.02 2 212.36 331.88 300.82 207.80 1052.85 3 513.37 519.19 411.47 287.09 1731.12 4 237.08 326.77 232.90 181.28 978.02 5 205.91 390.38 346.05 213.93 1156.27 6 206.12 276.29 230.75 151.03 864.19 7 318.85 390.86 317.34 232.94 1259.99 8 550.61 540.39 374.72 235.94 1701.66 Total 2411.03 3072.53 2458.03 1681.53 9623.11 0.00 50.00 100.00 150.00 200.00 250.00 300.00 350.00 400.00 450.00 20x20 25x25 35x35 50x50 Mean of Mean Dbh per hectare 16 Table 6: Mean of Volume per hectare in each sample plot size Plot size Mean 20x20 162.53 25x25 197.43 35x35 182.23 50x50 151.13 Figure 10: Bar chart showing Mean of Volume per hectare 0.00 50.00 100.00 150.00 200.00 250.00 20x20 25x25 35x35 50x50 Mean of Volume per hectare 17 Table 7: Summary of the volume data in each plot and sample plot size This table shows the summary of the volume estimation per hectare in each plot type and plot sample sizes. The lowest (692864.55kg/ha) and highest (900289.05kg/ha) biomass was recorded for plot sizes of 50mx50m and 25mx25m respectively. Table 7: Summary of the volume data in each plot and sample plot size Plot Typess 20X20 25X25 35X35 50X50 TOTAL Plot1 71.54 140.56 91.84 83.36 387.29 Plot 2 163.93 186.90 173.83 100.77 625.43 Plot 3 215.19 153.16 142.80 207.17 718.32 Plot 4 117.52 239.40 148.51 126.94 632.37 Plot 5 276.01 419.13 400.49 260.34 1355.97 Plot 6 197.24 198.14 254.61 163.28 813.27 Plot 7 110.68 112.32 113.89 138.34 475.23 Plot 8 148.16 129.82 131.87 128.86 538.71 Total 1300.27 1579.41 1457.84 1209.06 5546.58 Mean of Biomass in each sample plot size Table 8: Mean of Biomass in each sample plot size Plot size Mean 20x20 92712.80 25x25 112536.13 35x35 104101.60 50x50 86608.07 18 Figure 11: Mean of Biomass in each sample plot Table 9 shows the summary of the biomass estimation per hectare in each plot type and plot sample sizes. The lowest (692864.55kg/ha) and highest (900289.05kg/ha) biomass was recorded for plot sizes of 50mx50m and 25mx25m respectively. Table 9: Summation of the available biomass in each plot and sample plot size Plot Type 20X20 25X25 35X35 50X50 Plot1 38688 77516.9 51151.2462 46095.616 Plot 2 94219.08 107689.9 98148.297 57621.649 Plot 3 121208.8 86424.31 82239.2546 119432.35 Plot 4 67130.1 142340.6 87772.5777 73762.252 Plot 5 162655.3 241937.8 224735.971 146449.19 Plot 6 112027 107971 146388.5 94125.118 Plot 7 58602.32 60613.15 63767.8001 78466.982 Plot 8 87171.83 75795.47 78609.123 76911.389 Total 741702.41 900289.05 832812.77 692864.55 0.00 20000.00 40000.00 60000.00 80000.00 100000.00 120000.00 20x20 25x25 35x35 50x50 Mean of Biomass per hectare 19 Table 10: Mean of Carbon per hectare Plot size Mean 20x20 46356.40 25x25 56268.07 35x35 52050.80 50x50 43304.03 Figure 12: Bar chart showing mean of Carbon Table 11: Summary of the carbon stock data in each plot and sample plot size Table 11 shows the summary of the carbon estimation per hectare in each plot type and plot sample sizes. The lowest (346432.28kg/ha) and highest (450144.52kg/ha) carbon was recorded for plot sizes of 50mx50m and 25mx25m respectively. Table 11: Summary of the carbon stock data in each plot and sample plot size Plot Type 20X20 25X25 35X35 50X50 Plot1 19344 38758.45 25575.6231 23047.808 Plot 2 47109.54 53844.93 49074.1485 28810.824 Plot 3 60604.41 43212.16 41119.6273 59716.177 0.00 10000.00 20000.00 30000.00 40000.00 50000.00 60000.00 20x20 25x25 35x35 50x50 Mean of Carbon 20 Plot 4 33565.05 71170.29 43886.2888 36881.126 Plot 5 81327.65 120968.9 112367.985 73224.596 Plot 6 56013.48 53985.49 73194.2501 47062.559 Plot 7 29301.16 30306.58 31883.9001 39233.491 Plot 8 43585.92 37897.73 39304.5615 38455.694 Total 370851.21 450144.52 416406.38 346432.28 3.2 Discussion Estimating volume of tree species from one or two variables has no standard model or formula. However, the need to generate volume for tree species with incomplete variables was done using square of the tree Dbh and two constants (b1 = 0.00212 and b2 = 0.00055). Wood densities of each tree species were obtained from Prof. S.O Akindele (Akindele, 2023, pers.comm.) on Tropical wood density and the mean of the obtained species was used for the tree species not obtained. Several researches have been carried out on the optimal plot size for biomass and carbon estimation and authors like Hamdan et al (2013) identified 25x25m, 40x40m and 50 x50m as optimal plot size for natural forest. Although, 40 x 40m is difficult to establish due to some forest conditions and then recommended 20x20 m as a smaller plot size while 50x50m is recognized as the best sample plot size for estimation. The data presented in Tables 1 provides the summary of the growth variables and carbon estimation. The lowest (1681.53cm) and highest (3072.53cm) dbh was recorded for plot sizes of 50mx50m and 25mx25m respectively. Tree volumes ranged from 1209.06.56m3 to 1579.41m3. It was found to be lowest for 50mx50m and highest for 25mx25m plot size. Similarly, plot size of 50mx50m accumulated the lowest biomass (692864.55kg/ha) and carbon (346.43 metric tons) in the study area while that of 25mx25m had the highest values – 900289.05kg/ha of biomass and 450.14 metric tons of carbon. The mean volume per plot size presented in Table 6 confirms this 21 trend, with the 25x25m plot size having the highest mean volume (197.43 m3/ha) and the 50x50m plot size having the lowest mean volume (151.13 m3/ha). The mean biomass per plot size presented in Table 8 confirms this trend, with the 25x25m plot size having the highest mean biomass (112,536.13 kg) and the 50x50m plot size having the lowest mean biomass (86,608.07 kg). The mean carbon stock per plot size presented in Table 10 also reflects this trend, with the 25x25m plot size having the highest mean carbon stock (56,268.07kg/ha) and the 50x50m plot size having the lowest mean carbon stock (43,304.03 kg/ha). ANOVA tests conducted for number of trees, mean dbh, volume, biomass, and carbon stock estimation as shown in Figures 3, 4, 5, 6, and 7 indicates that there is no significant difference among the plot sizes for both biomass (p-value = 0.707) and carbon stock (p-value = 0.707). These findings suggest that plot size has no significant impact on biomass and carbon stock estimation in the natural forest studied. However, it does affect volume estimation, indicating that larger plot sizes tend to have higher volumes. Further research and analysis are needed to understand the underlying factors contributing to these patterns and to validate the results with a larger sample size. 22 4. 0 CONCLUSION AND RECOMMENDATION 4.1 Conclusion In conclusion, this study assessed the effect of sample plot size on biomass and carbon stock estimation at the Akure Strict Nature Reserve, Ondo State, Nigeria. The results of this study demonstrated that there are no significant differences between the 20m x 20m, 25m x 25m, 35m x 35m, and 50m x 50m sample plot size. The findings showed that the plot size of 25x25m consistently had the highest biomass and carbon stock, followed by 35x35m, 20x20m, and 50x50m. However, the differences in biomass and carbon stock among the plot sizes were not statistically significant unlike volume estimation. Therefore, any of the four (4) sample plot sizes can be used to obtain precise biomass and carbon stock estimation. 4.2 Recommendations Based on the result, it is recommended that forest inventory and monitoring activities should consider using smaller sample plot sizes such as 20m x 20m, 25m x 25m or 35m x 35m for forest biomass and carbon stock estimation when labour requirement and financial cost for forest inventory is limited since larger plot size will not result to significantly different results in biomass and carbon stock estimation. Further research is needed to compare other smaller plot sizes such as 15 m x15 m, 10 m x10 m, 10 m x 20 m to determine their effect on forest biomass and carbon stock estimation. Overall, the findings of this study contribute to improved reliability of biomass, carbon stock, and volume estimates in Strict Nature Reserve, Akure Forest Reserve, Ondo state, Nigeria. 23 REFERENCES Adeduntan, S. A. (2009). Diversity and abundance of soil macro-fauna in the Strict Nature Reserve of Akure Forest Reserve, Nigeria. Research Journal of Soil and Water Management, 1(1), 14-18. Adekunle, V.A.J., Olagoke, A.O., And Ogundipe, L.F. (2013). Timber species diversity and volume distribution in natural forest ecosystems in Southwestern Nigeria: Implication for forest ecosystem conservation. Journal of Forest Reserve, 24(4), 763-770N. Akindele, S. O. (2023, personal communication). Data on tropical wood density of Nigeria tree species provided via correspondence. Assefa, M., Beyene, A., & Tesfay, B. (2013). Climate change and its impact on the environment: a review. Journal of Environmental Science and Technology, 4(1), 1-10. Jones, E.W. (1948). The forest types of Nigeria and their significance to man. Journal of Ecology, 36(2),219-252. Nor Farika Zani, W., Nor Azila, A., & Mohd Azmi, M. (2017). Effect of sample plot size on aboveground biomass estimation in a tropical rainforest. Forest Ecology and Management, 393, 24-31 Ola-Adams, B.A., Hall, J.B. (1987). Composition and structure of a tropical lowland rainforest in South Western Nigeria. Vegetation, 70(1), 23-32. United Nations Framework Convention on Climate Change (UNFCCC). (2021). The Paris Agreement.