Estimating the Potential of Carbon Sequestration in Tree Species of Chintapalle Forest Range, Narsipatnam Division, Visakhapatnam, Andhra Pradesh, India

The potential of carbon sequestration of tree species in the Chintapalle forest range, of Narsipatnam Division, was estimated by using a non-destructive method. The sequestration of 6033 trees belonging to 22 species was investigated; the approximate height of tree species and the diameter at breast height (DBH) were measured for the estimation of CO2 sequestration. The maximum weight of carbon was observed in Pongamia pinnata (L.) Pierre species i.e (37987.06 kg) and the minimum weight of carbon was noted in Phyllanthus emblica L. species i.e is (61.8kg). The total carbon sequestrated by the entire tree species was (2370614.0 kg), The average carbon sequestered was (39865.81 kg). The highest sequestration was noted in the species P. pinnata (L.) Pierre i.e. (139271.95 kg) and the lowest (226.79 kg) was noted in the species P. emblica L. The maximum average DBH with maximum carbon sequestration potential was observed in Ficus benghalensis L. species, with higher total green (AGW) observed in all sites, whereas minimum average DBH with minimum carbon sequestration potential was noted in Bambusa vulgaris species. The regression analysis tests the relationship between two variables. The height of trees has no significant impact on the amount of CO2 sequestered F (32085087175.84, 12946607900) = 2.478262; P ? 0.05, which indicates that the tree height plays an insignificant role in CO2 sequestration (? = 2713.28 P ? 0.05). The dependent variable CO2 sequestered was also regressed on the predictor variable soil organic carbon (SOC) to test the relationship. SOC insignificantly predicted CO2 sequestrated F (5.83, 2.62) = 0.2236; P ? 0.25, indicating that the SOC has an insignificant role in CO2 sequestration (? = 102780.3 P ? 0.05). Insignificant relation was observed between the parameters SOC and height of tree species to the rate of carbon dioxide sequestered, and gave a regression equation of y = 10278x + 50863 with R2 = 0.100; y=2713.285803x-209800.8762 with R2 = 0.55