Linear Regression Model to Identify the Factors Associated with Carbon Stock in Chure Forest of Nepal

Use of woody plants for greenhouse gas mitigation has led to the demand for rapid cost-effective estimation of forest carbon stock and related factors. This study aims to assess the factors associated with carbon stock in Chure forest of Nepal. The data were obtained from Department of Forest Research and Survey (DFRS) of Nepal. A multiple linear regression model and then sum contrasts were used to observe the association between variables such as stem volume, diameter at breast height, altitude, districts, number of trees per plot, and ownership of the forest. 95% confidence interval (CI) plots were drawn for comparing the adjusted carbon stocks with each of the factors and with the overall carbon stock. The linear regression showed a good fit of the model (adjusted R² = 83.75%) with the results that the stem volume (sv), diameter at breast height (dbh), and the number of trees per plot showed statistically significant (p value ≤ 0.05) positive association with carbon stock. The highest carbon stock was associated with sv more than 199 m³/ha, average dbh more than 43.3 cm/plot, and number of trees more than 20/plot, whereas the altitude, geographical location, and ownership had no statistical associations at all. The results can be of use to the government for enhancing carbon stock in Chure that supports both natural resource conservation and United Nations-Reducing Emission from Deforestation and Forest Degradation program to mitigate carbon emission issues.