National Academy of Agricultural Sciences (NAAS)
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PRINT ISSN : 2319-7692
Online ISSN : 2319-7706 Issues : 12 per year Publisher : Excellent Publishers Email : editorijcmas@gmail.com submit@ijcmas.com Editor-in-chief: Dr.M.Prakash Index Copernicus ICV 2018: 95.39 NAAS RATING 2020: 5.38 |
The study investigates linear and non-linear regression models for estimating the volume of Tectona Grandis in the Thithimathi forest, Kodagu district, India. With increasing demand for teak plantations under national afforestation programs, accurate volume estimation is essential for productivity and sustainable forest management. Data were collected from three sample plots, each with 30 trees, measuring diameter at breast height (Dbh) and height to calculate tree volume. Karl Pearson’s correlation analysis revealed a strong positive correlation between volume and Dbh, while the relationship between volume and height was weak across plots. Regression models- linear, quadratic and logarithmic were fitted to the data and evaluated using R2 and RMSE values. Results showed that quadratic models consistently provided the best fit, with R2 value 0.9999 and minimal error, outperforming linear and logarithmic models. Multiple linear regression yields high predictive accuracy (R2 = 0.9821). The findings confirm that quadratic regression models are most suitable for teak volume estimation in the Thithimathi forest, offering reliable tools for forest productivity and management.
Chaturvedi, A. N., and Khanna, L. S., 1982. Forest Mensuration. International Book Distributors, Dehradun. Foundation text on tree volume estimation methods in Indian forestry.
Giri, K., Pandey, R., Jayaraj, R. S. C., Nainamalai, R., and Ashutosh, S., 2019. Regression equations for estimating tree volume and biomass of important timber species in Meghalaya, India. Current Science, 116(1), 25-31.
Gul, A., Nazir, N., and Pukhta, M. S., 2024. Regression models for tree volume prediction in Pinus wallichiana stands of South Western Himalayan Region of Kashmir. Journal of the Indian Society of Agricultural Statistics, 78(2). https://doi.org/10.56093/JISAS.V78I2.8
Husch, B., Beers, T. W., and Kershaw, J. A., 2003. Forest Mensuration. John Wiley and Sons. Global reference for regression models and statistical approaches in volume estimation.
Kangas, A., Pitkanen, T. P., Mehtatalo, L., and Heikkinen, J., 2023. Mixed linear and non-linear tree volume models with regional parameters to main tree species in Finland. Forestry: An International Journal of Forest research, 96(2), 188-206. https://doi.org/10.1093/forestry/cpac038
Oyebade, B. A., Aigbe, H. I., Eguakun, F. S., and Edem, M. A., 2020. Non-linear Regression models for volume estimation of Gmelina arborea (Roxb.) in Uyo Ravine Plantation, Akwa lbom State, Nigeria. World Scientific News, An International Scientific Journal, 145, 46-61.
Magaji, K., Saka, M. G., Jatau, D. F., and Yakubu, M., 2025. Regression equations for estimating tree plot volume in Eucalyptus camaldulensis plantations of the Sudan Savanna, Nigeria. International Journal of Applied Environmental Sciences, 11(7), 215-224.
Saikia, P., Khan, M. L., and Tripathi, O, P., 2016. Regression equations for estimating tree volume and biomass of important timber species in Meghalaya, India. Tropical Ecology, 57(4), 617-628.
Singh, S., and Ghosh, S., 2023. Non-destructive allometric modelling for tree volume estimation in tropical dry deciduous forests of Betul, Madhya Pradesh, India. Environmental Monitoring and Assessment, 195(6), 512. https://doi.org/10.1007/s10661-023-11192-0
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