<p>Forests are crucial for climate change due to their significant capacity to act as carbon sinks. This study aimed to evaluate the carbon storage capacity of the Gantamie Natural Forest, which is in the Bure Zuria woreda in Northwestern Ethiopia. The study were employed systematic random sampling to collect field data across the study site. The main 20&#xa0;m × 20&#xa0;m plot was used for aboveground biomass (AGB) and belowground biomass (BGB) measurements, while smaller 1&#xa0;m × 1&#xa0;m subplots provided soil and litter samples. Additionally, we utilized allometric models to estimate carbon stock across different carbon pools. The results showed that the mean carbon stock of AGC, BGC, LC, and SOC was 320.3t/ha, 64.1t/ha, and 1.50t/ha and 40.4 t/ha, respectively. The study underscores the significance of precise carbon stock estimates and the role of allometric models in achieving this objective. Gantamie Natural Forest had 426.3 t/ha of total carbon and was sequestering 1563.3t/ha of CO2-equivalent. Using the linear regression equation y = a + bx, the regression study between NDVI readings and carbon stock produced a model equation of Y = 1048.6x + 43.192 with R2 = 0.7958. After a month, the index's correctness was confirmed, and a model equation of Y = 1071x + 32.844 with an R2 of 0.7863 was found. Gantamie Natural Forest has the potential to store significant amounts of carbon, with above-ground and soil organic carbon pools having the greatest potential for building up carbon stocks. These results offer valuable insights into carbon distribution across different pools, aiding the development of strategies for carbon stock management to mitigate climate change. Sustainable forest management and use will be crucial in minimizing the temperature rise, and further research should focus on quantifying dead wood, shrubs, and herbaceous carbon stock of Gantamie Natural Forest to have more comprehensive information and strengthen management systems.</p>

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Evaluating carbon stock in Gantamie Natural Forest, Bure Zuria district, north-western Ethiopia: a climate change mitigation approach

  • Getasew Fisseha,
  • Binega Derebe,
  • Adamsew Marelign Melese,
  • Kiros Getachew

摘要

Forests are crucial for climate change due to their significant capacity to act as carbon sinks. This study aimed to evaluate the carbon storage capacity of the Gantamie Natural Forest, which is in the Bure Zuria woreda in Northwestern Ethiopia. The study were employed systematic random sampling to collect field data across the study site. The main 20 m × 20 m plot was used for aboveground biomass (AGB) and belowground biomass (BGB) measurements, while smaller 1 m × 1 m subplots provided soil and litter samples. Additionally, we utilized allometric models to estimate carbon stock across different carbon pools. The results showed that the mean carbon stock of AGC, BGC, LC, and SOC was 320.3t/ha, 64.1t/ha, and 1.50t/ha and 40.4 t/ha, respectively. The study underscores the significance of precise carbon stock estimates and the role of allometric models in achieving this objective. Gantamie Natural Forest had 426.3 t/ha of total carbon and was sequestering 1563.3t/ha of CO2-equivalent. Using the linear regression equation y = a + bx, the regression study between NDVI readings and carbon stock produced a model equation of Y = 1048.6x + 43.192 with R2 = 0.7958. After a month, the index's correctness was confirmed, and a model equation of Y = 1071x + 32.844 with an R2 of 0.7863 was found. Gantamie Natural Forest has the potential to store significant amounts of carbon, with above-ground and soil organic carbon pools having the greatest potential for building up carbon stocks. These results offer valuable insights into carbon distribution across different pools, aiding the development of strategies for carbon stock management to mitigate climate change. Sustainable forest management and use will be crucial in minimizing the temperature rise, and further research should focus on quantifying dead wood, shrubs, and herbaceous carbon stock of Gantamie Natural Forest to have more comprehensive information and strengthen management systems.